From 6af1ba72f101c95e713df9c775c4e2cf8ada5e29 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Thu, 11 Jul 2024 13:23:21 -0700 Subject: [PATCH 01/96] integrate custom docs with new UI --- custom_dc/custom_data.md | 6 ++++- custom_dc/custom_ui.md | 6 ++++- custom_dc/data_cloud.md | 6 ++++- custom_dc/deploy_cloud.md | 4 +++- custom_dc/faq.md | 2 +- custom_dc/index.md | 45 +++++++++++++++--------------------- custom_dc/launch_cloud.md | 2 +- custom_dc/manage_repo.md | 2 +- custom_dc/quickstart.md | 9 ++++---- custom_dc/troubleshooting.md | 2 +- 10 files changed, 45 insertions(+), 39 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index ee28e4312..ae5ff720f 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -2,7 +2,7 @@ layout: default title: Work with custom data nav_order: 3 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} @@ -11,6 +11,10 @@ parent: Custom Data Commons * TOC {:toc} +This page shows you how to format and load your own custom data into your local instance. This is step 2 of the [recommended workflow](/custom_dc/index.html#workflow). + +## Overview + Custom Data Commons provides a simple mechanism to import your own data, but it requires that the data be provided in a specific format and file structure. - All data must be in CSV format, using the schema described below. diff --git a/custom_dc/custom_ui.md b/custom_dc/custom_ui.md index 3149ca6a0..94aec3646 100644 --- a/custom_dc/custom_ui.md +++ b/custom_dc/custom_ui.md @@ -2,15 +2,19 @@ layout: default title: Customize the site nav_order: 4 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} # Customize the site +This page shows you how to customize the UI of your local instance. This is step 3 of the [recommended workflow](/custom_dc/index.html#workflow). + * TOC {:toc} +## Overview + The default custom Data Commons image provides a bare-bones UI that you will undoubtedly want to customize to your liking. Data Commons uses the Python [Flask](https://flask.palletsprojects.com/en/3.0.x/#) web framework and [Jinja](https://jinja.palletsprojects.com/en/3.1.x/templates/) HTML templates. If you're not familiar with these, the following documents are good starting points: - [Flask Templates](https://flask.palletsprojects.com/en/3.0.x/tutorial/templates/) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 614697ee1..1a3eb25d9 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -2,7 +2,7 @@ layout: default title: Test data in Google Cloud nav_order: 6 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} @@ -11,6 +11,10 @@ parent: Custom Data Commons * TOC {:toc} +This page shows you how to store your custom data in Google Cloud database and load it into a local instance. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). + +## Overview + Once you have tested locally, you need to get your data into Google Cloud so you can test it remotely. You can continue to run the custom Data Commons instance locally, but retrieve data from the Cloud. In this scenario, the system is set up like this: ![setup3](/assets/images/custom_dc/customdc_setup3.png) diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index bc7ebb9e5..2b7191777 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -2,7 +2,7 @@ layout: default title: Deploy a custom instance to Google Cloud nav_order: 7 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} @@ -11,6 +11,8 @@ parent: Custom Data Commons * TOC {:toc} +This page shows you how to create an artifact and run it in Google Cloud Run. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow). + When you are ready to launch your custom Data Commons site, we recommend hosting your site in [Google Cloud Run](https://cloud.google.com/run/), which is a serverless solution that is by far the simplest and least expensive option, providing auto-scaling. This is the production setup: ![setup4](/assets/images/custom_dc/customdc_setup4.png) diff --git a/custom_dc/faq.md b/custom_dc/faq.md index e09edc386..b755210f9 100644 --- a/custom_dc/faq.md +++ b/custom_dc/faq.md @@ -2,7 +2,7 @@ layout: default title: Frequently asked questions nav_order: 9 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} diff --git a/custom_dc/index.md b/custom_dc/index.md index 26752fbc0..a23ac2c80 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -1,12 +1,12 @@ --- layout: default -title: Custom Data Commons +title: Build and run your own Data Commons nav_order: 90 has_children: true --- {:.no_toc} -# Custom Data Commons +# Build and run your own Data Commons * TOC {:toc} @@ -19,20 +19,6 @@ A custom instance natively combines the base Data Commons data (from datacommons A custom Data Commons site is deployed in Google Cloud Platform (GCP). The owner has full control over data, computing resources and access. The site can be accessible by the general public or can be controlled to limited principals. When base data is joined with the custom instance data, it is pulled in from the base Data Commons site; custom data is never pushed to the base data store. -## Case studies - -### Feeding America Data Commons - -[Feeding America Data Commons](https://datacommons.feedingamerica.org/) provides access to data from [Map the Meal Gap](https://map.feedingamerica.org/), overlaid with data from a wide range of additional sources into a single portal under a common scheme. Combining datasets from the CDC and Map the Meal gap, the relationship between heart health and food insecurity can be retrieved with a few clicks. - -![fa](/assets/images/custom_dc/home-heart-food.png){: height="450" } - -### India Data Commons - -[India Data Commons](https://datacommons.iitm.ac.in/) is an effort by Robert Bosch Center for Data Science and Artificial Intelligence, IIT Madras, to highlight India-specific data. India Data Commons features datasets published by Indian Ministries and governmental organizations and provides it through the Data Commons knowledge graph. - -![iitm](/assets/images/custom_dc/iitm.png){: height="450" } - ## Why use a custom Data Commons instance? If you have the resources to develop and maintain a custom Data Commons instance, this is a good option for the following use cases: @@ -49,19 +35,23 @@ For the following use cases, a custom Data Commons instance is not necessary: - You only want to make your own data available to the base public Data Commons site and don't need to test it. In this case, see the procedures in [Data imports](/import_dataset/index.html). - You want to make the base public data or visualizations available in your own site. For this purpose, you can call the Data Commons APIs from your site; see [Data Commons web components](/api/web_components/index.html) for more details. -## Supported features - -A custom Data Commons instance supports the following features: - -- All of the same interactive tools as the base site, including the natural language query interface -- REST APIs --- no additional setup neeeded -- Python and Pandas API wrappers, and/or Spreadsheets --- requires additional setup and maintenance. If you would like to support these facilities, please contact us. -- Access controls to the site, using any supported Google Cloud Run mechanisms, such as Virtual Private Cloud, Cloud IAM, and so on. Please see the GCP [Restricting ingress for Cloud Run](https://cloud.google.com/run/docs/securing/ingress) for more information on these options. +## Comparison between base and custom Data Commons -The following are not supported: +| Feature | Base Data Commons | Custom Data Commons | +|--------------------------------------------------------------|--------------------|---------------------| +| Interactive tools (Exploration tools, Statistical Variable Explorer, etc.) | yes | yes | +| Natural language query interface | yes, using open-source models only | yes, using Google AI technologies and models | +| REST APIs | yes | yes, no additional setup needed | +| Python and Pandas API wappers | yes | yes, but requires additional setup[^1](#bullets) | +| Bigquery interface | yes | no +| Google Spreadsheets | yes | yes, but requires additional setup[^1](#bullets) | +| Site ccess controls | yes, using any supported Cloud Run mechanisms[^2](#bullets) | n/a | +| Fine-grained data access controls[^3](#bullets) | no | n/a | -- Fine-grained data access controls; you cannot set access controls on specific data, only the entire custom site. -- Bigquery APIs + {: #bullets} +1. If you would like to support these facilities, please contact us. +2. For example, Virtual Private Cloud, Cloud IAM, and so on. Please see the GCP [Restricting ingress for Cloud Run](https://cloud.google.com/run/docs/securing/ingress) for more information on these options. +3. You cannot set access controls on specific data, only the entire custom site. ## System overview @@ -92,6 +82,7 @@ In terms of development time and effort, to launch a site with custom data in co The cost of running a site on Google Cloud Platform depends on the size of your data, the traffic you expect to receive, and the amount of geographical replication you want. For a small dataset, we have found the cost comes out to roughly $100 per year. You can get more precise information and cost estimation tools at [Google Cloud pricing](https://cloud.google.com/pricing). +{: #workflow} ## Recommended workflow 1. Work through the [Quickstart](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. diff --git a/custom_dc/launch_cloud.md b/custom_dc/launch_cloud.md index 863fdc410..50e2d364f 100644 --- a/custom_dc/launch_cloud.md +++ b/custom_dc/launch_cloud.md @@ -2,7 +2,7 @@ layout: default title: Launch a custom site nav_order: 8 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} diff --git a/custom_dc/manage_repo.md b/custom_dc/manage_repo.md index 12fc58df7..859ef63a7 100644 --- a/custom_dc/manage_repo.md +++ b/custom_dc/manage_repo.md @@ -2,7 +2,7 @@ layout: default title: Build and run a custom image nav_order: 5 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 62990144a..9c00a47aa 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -2,7 +2,7 @@ layout: default title: Quickstart nav_order: 2 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} @@ -11,11 +11,12 @@ parent: Custom Data Commons * TOC {:toc} -To start developing a custom Data Commons instance, we recommend that you develop your site and host your data locally. This uses a SQLite database to store custom data. +This page shows you how to run a local custom Data Commons instance inside a Docker container and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/). + +This is step 1 of the [recommended workflow](/custom_dc/index.html#workflow). -![setup2](/assets/images/custom_dc/customdc_setup2.png) -This page shows you how to run a local custom Data Commons instance inside a Docker container, load sample custom data, and enable natural querying. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/). +![setup2](/assets/images/custom_dc/customdc_setup2.png) ## Prerequisites diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index 52f681b03..f96225815 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -2,7 +2,7 @@ layout: default title: Troubleshooting nav_order: 8 -parent: Custom Data Commons +parent: Build and run your own Data Commons --- {:.no_toc} From cd0fe76f759b1682ddcc1585024f0b0defdc473b Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Thu, 11 Jul 2024 13:32:02 -0700 Subject: [PATCH 02/96] more edits --- custom_dc/index.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/custom_dc/index.md b/custom_dc/index.md index a23ac2c80..5a2282635 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -40,18 +40,18 @@ For the following use cases, a custom Data Commons instance is not necessary: | Feature | Base Data Commons | Custom Data Commons | |--------------------------------------------------------------|--------------------|---------------------| | Interactive tools (Exploration tools, Statistical Variable Explorer, etc.) | yes | yes | -| Natural language query interface | yes, using open-source models only | yes, using Google AI technologies and models | +| Natural language query interface | yes, using open-source models only2 | yes, using Google AI technologies and models | | REST APIs | yes | yes, no additional setup needed | -| Python and Pandas API wappers | yes | yes, but requires additional setup[^1](#bullets) | +| Python and Pandas API wappers | yes | yes, but requires additional setup2 | | Bigquery interface | yes | no -| Google Spreadsheets | yes | yes, but requires additional setup[^1](#bullets) | -| Site ccess controls | yes, using any supported Cloud Run mechanisms[^2](#bullets) | n/a | -| Fine-grained data access controls[^3](#bullets) | no | n/a | +| Google Spreadsheets | yes | yes, but requires additional setup2 | +| Site ccess controls | yes, using any supported Cloud Run mechanisms2 | n/a | +| Fine-grained data access controls4 | no | n/a | - {: #bullets} +1. Open-source Python ML library, Sentence Transformers model, from [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). 1. If you would like to support these facilities, please contact us. -2. For example, Virtual Private Cloud, Cloud IAM, and so on. Please see the GCP [Restricting ingress for Cloud Run](https://cloud.google.com/run/docs/securing/ingress) for more information on these options. -3. You cannot set access controls on specific data, only the entire custom site. +1. For example, Virtual Private Cloud, Cloud IAM, and so on. Please see the GCP [Restricting ingress for Cloud Run](https://cloud.google.com/run/docs/securing/ingress) for more information on these options. +1. You cannot set access controls on specific data, only the entire custom site. ## System overview From c45d61b766cf7d1f912be42b53968b5ca5c4c2e2 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 15 Jul 2024 10:14:58 -0700 Subject: [PATCH 03/96] use website wording for intro --- custom_dc/index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/custom_dc/index.md b/custom_dc/index.md index 5a2282635..722609cc3 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -13,11 +13,11 @@ has_children: true ## Overview -Data Commons is an open source platform. Any organization can create a custom Data Commons instance with its own data, customized user interface and visualization tools. +A custom instance natively joins your data and the base Data Commons data (from datacommons.org) in a unified fashion. Your users can visualize and analyze the data seamlessly without the need for further data preparation. -A custom instance natively combines the base Data Commons data (from datacommons.org) and the custom data in a unified fashion. Users can generate visualizations and perform data analyses across base and custom datasets seamlessly. +You have full control over your own data, computing resources, and access, with the ability to limit access to specific individuals or open it to the general public. -A custom Data Commons site is deployed in Google Cloud Platform (GCP). The owner has full control over data, computing resources and access. The site can be accessible by the general public or can be controlled to limited principals. When base data is joined with the custom instance data, it is pulled in from the base Data Commons site; custom data is never pushed to the base data store. +Note that each new Data Commons is deployed using the Google Cloud Platform (GCP). ## Why use a custom Data Commons instance? From f5cbbcabb0c1a6f664636875868b9f0a5916b821 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 15 Jul 2024 10:24:47 -0700 Subject: [PATCH 04/96] fix numbering in table --- custom_dc/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/custom_dc/index.md b/custom_dc/index.md index 722609cc3..aed706e16 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -40,12 +40,12 @@ For the following use cases, a custom Data Commons instance is not necessary: | Feature | Base Data Commons | Custom Data Commons | |--------------------------------------------------------------|--------------------|---------------------| | Interactive tools (Exploration tools, Statistical Variable Explorer, etc.) | yes | yes | -| Natural language query interface | yes, using open-source models only2 | yes, using Google AI technologies and models | +| Natural language query interface | yes, using open-source models only1 | yes, using Google AI technologies and models | | REST APIs | yes | yes, no additional setup needed | | Python and Pandas API wappers | yes | yes, but requires additional setup2 | | Bigquery interface | yes | no | Google Spreadsheets | yes | yes, but requires additional setup2 | -| Site ccess controls | yes, using any supported Cloud Run mechanisms2 | n/a | +| Site ccess controls | yes, using any supported Cloud Run mechanisms3 | n/a | | Fine-grained data access controls4 | no | n/a | 1. Open-source Python ML library, Sentence Transformers model, from [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). From 57ca62f2be84160a6dcc0f392b05410d0311966a Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 15 Jul 2024 12:59:21 -0700 Subject: [PATCH 05/96] rename and some edits --- custom_dc/custom_data.md | 6 ++++-- custom_dc/custom_ui.md | 2 +- custom_dc/data_cloud.md | 6 +++--- custom_dc/deploy_cloud.md | 6 ++++-- custom_dc/faq.md | 2 +- custom_dc/index.md | 8 ++++---- custom_dc/launch_cloud.md | 4 +++- custom_dc/manage_repo.md | 2 +- custom_dc/quickstart.md | 15 +++++++++------ custom_dc/troubleshooting.md | 2 +- 10 files changed, 31 insertions(+), 22 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index ae5ff720f..092e5d992 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -2,16 +2,18 @@ layout: default title: Work with custom data nav_order: 3 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} # Work with custom data +This page shows you how to format and load your own custom data into your local instance. This is step 2 of the [recommended workflow](/custom_dc/index.html#workflow). + + * TOC {:toc} -This page shows you how to format and load your own custom data into your local instance. This is step 2 of the [recommended workflow](/custom_dc/index.html#workflow). ## Overview diff --git a/custom_dc/custom_ui.md b/custom_dc/custom_ui.md index 94aec3646..3f4bac9f4 100644 --- a/custom_dc/custom_ui.md +++ b/custom_dc/custom_ui.md @@ -2,7 +2,7 @@ layout: default title: Customize the site nav_order: 4 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 1a3eb25d9..f63c68503 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -2,17 +2,17 @@ layout: default title: Test data in Google Cloud nav_order: 6 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} # Test data in Google Cloud +This page shows you how to store your custom data in Google Cloud database and load it into a local instance. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). + * TOC {:toc} -This page shows you how to store your custom data in Google Cloud database and load it into a local instance. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). - ## Overview Once you have tested locally, you need to get your data into Google Cloud so you can test it remotely. You can continue to run the custom Data Commons instance locally, but retrieve data from the Cloud. In this scenario, the system is set up like this: diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 2b7191777..cb0e14a89 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -2,16 +2,18 @@ layout: default title: Deploy a custom instance to Google Cloud nav_order: 7 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} # Deploy a custom instance to Google Cloud +This page shows you how to create an artifact and run it in Google Cloud Run. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow). + * TOC {:toc} -This page shows you how to create an artifact and run it in Google Cloud Run. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow). +## System overview When you are ready to launch your custom Data Commons site, we recommend hosting your site in [Google Cloud Run](https://cloud.google.com/run/), which is a serverless solution that is by far the simplest and least expensive option, providing auto-scaling. This is the production setup: diff --git a/custom_dc/faq.md b/custom_dc/faq.md index b755210f9..b69ca2502 100644 --- a/custom_dc/faq.md +++ b/custom_dc/faq.md @@ -2,7 +2,7 @@ layout: default title: Frequently asked questions nav_order: 9 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} diff --git a/custom_dc/index.md b/custom_dc/index.md index aed706e16..caf176012 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -1,12 +1,12 @@ --- layout: default -title: Build and run your own Data Commons +title: Build your own Data Commons nav_order: 90 has_children: true --- {:.no_toc} -# Build and run your own Data Commons +# Build your own Data Commons * TOC {:toc} @@ -15,7 +15,7 @@ has_children: true A custom instance natively joins your data and the base Data Commons data (from datacommons.org) in a unified fashion. Your users can visualize and analyze the data seamlessly without the need for further data preparation. -You have full control over your own data, computing resources, and access, with the ability to limit access to specific individuals or open it to the general public. +You have full control over your own data and computing resources, with the ability to limit access to specific individuals or open it to the general public. Note that each new Data Commons is deployed using the Google Cloud Platform (GCP). @@ -85,7 +85,7 @@ The cost of running a site on Google Cloud Platform depends on the size of your {: #workflow} ## Recommended workflow -1. Work through the [Quickstart](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. +1. Work through the [Get started](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. 1. Prepare your real-world custom data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Work with custom data](/custom_dc/custom_data.html). If you are just testing custom data to add to the base Data Commons site, you don't need to go any further. 1. If you are launching your own Data Commons site, and want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html). 1. If you are launching your own Data Commons site, upload your data to Google Cloud Platform and continue to use the local instance to test and validate the site. We recommend using Google Cloud Storage to store your data, and Google Cloud SQL to receive SQL queries from the local servers. See [Test data in Google Cloud](/custom_dc/data_cloud.html). diff --git a/custom_dc/launch_cloud.md b/custom_dc/launch_cloud.md index 50e2d364f..26a13befe 100644 --- a/custom_dc/launch_cloud.md +++ b/custom_dc/launch_cloud.md @@ -2,7 +2,7 @@ layout: default title: Launch a custom site nav_order: 8 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} @@ -11,6 +11,8 @@ parent: Build and run your own Data Commons * TOC {:toc} +## Overview + When you are ready to launch your site to external traffic, there are many tasks you will need to perform, including: - Configure your Cloud Service to serve external traffic, over SSL. GCP offers many options for this; see [Mapping custom domains](https://cloud.google.com/run/docs/mapping-custom-domains). diff --git a/custom_dc/manage_repo.md b/custom_dc/manage_repo.md index 859ef63a7..c29a777f1 100644 --- a/custom_dc/manage_repo.md +++ b/custom_dc/manage_repo.md @@ -2,7 +2,7 @@ layout: default title: Build and run a custom image nav_order: 5 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 9c00a47aa..6817b7e04 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -1,20 +1,23 @@ --- layout: default -title: Quickstart +title: Get started nav_order: 2 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} -# Quickstart - -* TOC -{:toc} +# Get started This page shows you how to run a local custom Data Commons instance inside a Docker container and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/). This is step 1 of the [recommended workflow](/custom_dc/index.html#workflow). +* TOC +{:toc} + +## System overview + +The instructions in this page use the following setup: ![setup2](/assets/images/custom_dc/customdc_setup2.png) diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index f96225815..df42f0bc5 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -2,7 +2,7 @@ layout: default title: Troubleshooting nav_order: 8 -parent: Build and run your own Data Commons +parent: Build your own Data Commons --- {:.no_toc} From fb337225aa6484423864c28285642d9f2a1a27e2 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 16 Jul 2024 09:20:38 -0700 Subject: [PATCH 06/96] rename manage_repo file, per Bo --- custom_dc/{manage_repo.md => build_image.md} | 0 custom_dc/custom_ui.md | 2 +- custom_dc/deploy_cloud.md | 2 +- custom_dc/quickstart.md | 2 +- custom_dc/troubleshooting.md | 2 +- 5 files changed, 4 insertions(+), 4 deletions(-) rename custom_dc/{manage_repo.md => build_image.md} (100%) diff --git a/custom_dc/manage_repo.md b/custom_dc/build_image.md similarity index 100% rename from custom_dc/manage_repo.md rename to custom_dc/build_image.md diff --git a/custom_dc/custom_ui.md b/custom_dc/custom_ui.md index 3f4bac9f4..b5ac97859 100644 --- a/custom_dc/custom_ui.md +++ b/custom_dc/custom_ui.md @@ -84,4 +84,4 @@ Alternatively, if you have existing existing CSS and Javascript files, put them See [`server/templates/custom_dc/custom/new.html`](https://github.com/datacommonsorg/website/blob/master/server/templates/custom_dc/custom/new.html) as an example. -Note: Currently, making changes to any of the files in the `static/` directory, even if you're testing locally, requires that you rebuild a local version of the repo to pick up the changes, as described in [Build a local image](/custom_dc/manage_repo.html#build-repo). We plan to fix this in the near future. +Note: Currently, making changes to any of the files in the `static/` directory, even if you're testing locally, requires that you rebuild a local version of the repo to pick up the changes, as described in [Build a local image](/custom_dc/build_image.html#build-repo). We plan to fix this in the near future. diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index cb0e14a89..0a1cc8862 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -35,7 +35,7 @@ You push a locally built Docker image to the [Google Cloud Artifact Registry](ht This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry, and then deploy to Google Cloud Run. -1. Build a local version of the Docker image, following the procedure in [Build a local image](/custom_dc/manage_repo.html#build-repo). +1. Build a local version of the Docker image, following the procedure in [Build a local image](/custom_dc/build_image.html#build-repo). 1. Authenticate to gcloud: ```shell diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 6817b7e04..d6ff9c1b3 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -69,7 +69,7 @@ The instructions in this page use the following setup: Warning: Do not use any quotes (single or double) or spaces when specifying the values. -Note: If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables files containing secrets. Instead, store them locally only. +Note: If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables files containing secrets. Instead, store them locally only. If you are using git/Github, you can add the file to the `.gitignore` file. ## About the downloaded files diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index df42f0bc5..aa8c378bd 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -41,7 +41,7 @@ If you are building a local instance and get this error: Step 7/62 : COPY mixer/go.mod mixer/go.sum ./ COPY failed: file not found in build context or excluded by .dockerignore: stat mixer/go.mod: file does not exist ``` -You need to download/update additional submodules (derived from other repos). See [Build a local image](/custom_dc/manage_repo.html#build-repo). +You need to download/update additional submodules (derived from other repos). See [Build a local image](/custom_dc/build_image.html#build-repo). ## Data loading problems From 0ebdd67fe975da4693e689da69cdfe8186cf29f9 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 17 Jul 2024 11:41:18 -0700 Subject: [PATCH 07/96] Merge. --- data_model.md | 166 +++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 152 insertions(+), 14 deletions(-) diff --git a/data_model.md b/data_model.md index efd30bacb..5d90d64ff 100644 --- a/data_model.md +++ b/data_model.md @@ -1,27 +1,165 @@ --- layout: default -title: Data Models +title: Key concepts nav_order: 10 ---- +---git + +{: .no_toc} +# Key concepts + +Whether you're just exploring the data on [datacommons.org](http://datacommons.org), using the programmatic APIs, or contributing data, it's helpful to have a basic understanding of some of the key concepts in Data Commons. Use the following guidance: +- If you are only using Data Commons interactive tools, Google Sheets, CSV download, or BigQuery, you should at least be familiar with [entities](#entity) and [statistical variables](#statistical-variable). You may wish to just skip directly to those sections. +- If you plan to use the programmatic APIs, contribute data, or run your own Data Commons, you should read this entire page. + +{:toc} +* TOC + +## Schema + +To allow data from hundreds of organizations around the world, in a myriad of models and formats to be interoperable and queryable in a unified way, Data Commons needs to have a common way of understanding and representing this data. To do so, it applies a schema, or vocabulary to all its data, that is largely derived from earlier schemes developed for semantic understanding of web pages – most notably, the data models and schemas of [Schema.org](http://Schema.org) (which were in turn based on earlier schemes such as Microformats and Resource Description Framework (RDF)). + +The Data Commons schema is in fact a superset of [Schema.org schemas](https://schema.org/docs/schemas.html), with a particular emphasis on time series and statistical data. Every data set must have an associated schema, written in [Meta Content Format](https://en.wikipedia.org/wiki/Meta_Content_Framework) (MCF) language, that maps the provider's data to existing concepts in the Data Commons. + +## Knowledge Graph + +Data Commons models the world as a directed labeled graph, consisting of a set of _nodes_ and edges with labels, known as _properties_. This general framework allows Data Commons to represent information about a wide range of domains: from time series about demographics and employment, to hurricanes, to protein structures. + +As a simple example, here are a set of nodes and edges that represent the following statements: + +- California is a state +- Santa Clara county and Berkeley are contained in the state of California +- The latitude of Berkeley, CA is 37.8703 + +![knowledge graph](/assets/images/dc/concept1.png){: width="600"} + +Each node consists of some kind of entity or value, and each edge describes some kind of property. More specifically, each node consists of the following objects: + +- One or more [types](#type): an [entity](#entity), [event](#event), [statistical variable](#statistical-variable), or [statistical observation](#observation) +- A [unique identifier](#unique-identifier-dcid), known as a DCID +- Various [properties](#property) +- A [provenance](#provenance-source-dataset) + +As in other knowledge graphs, each pair of connected nodes is a _triple_ consisting of a subject node, predicate (or "edge") and object node. The Data Commons knowledge graph is made up of billions of triples. The triple is not generally exposed in Data Commons as a concept that you need to know (although it can be queried from some APIs). + +You can get all the information about a node and its edges by looking at the Knowledge Graph browser. If you know the [DCID](#unique-identifier-dcid) for a node, you can access it directly by typing https://datacommons.org/browser/DCID. For example, here is the entry for the `City` node, available at [https://datacommons.org/browser/City](https://datacommons.org/browser/City): + +![KG browser](/assets/images/dc/concept2.png){: width="900"} + +Every node entry shows a list of outgoing edges, or _properties,_ and incoming edges. [Properties](#property) are discussed in more detail below. + +## Type + +Every node has at least one type, where each type may be a sub-class of multiple types. For entities and events, their type is typically another entity. For example, `Berkeley` is a type of `City`. At the root, all types are instances of the Class type. For statistical variables and observations, their type is always `StatisticalVariable` and `StatVarObservation`, respectively. + +## Entity + +An _entity_ represents a persistent, physical thing in the real world. While Data Commons has information about a wide variety of types of entities (cities, states, countries, schools, companies, facilities, etc.), most of the information today is about _places_. Data Commons contains a catalog of about 2.9 million places. In addition to basic metadata like the location, type and containment information, many places also contain information about their shape, area, etc. For a list of available place types, take a look at the [place types page](/place_types.html). + +## Event + +An _event_ is what it sounds like: an occurrence at a specific point in time, such as an extreme weather event, a criminal incident, an election, etc. + +## Statistical variable + +In Data Commons, even statistical measurements and time series data are modeled as nodes. A _statistical variable_ represents any type of metric, statistic, or measurement that can be taken at a place and time, such as a count, an average, a percentage, etc. A statistical variable for a specific place is a _time series_, consisting of a set of observed values over a time period. + +Data Commons comprises hundreds of thousands of statistical variables, which you can view using the [Statistical Variable Explorer](https://datacommons.org/tools/statvar). + +The type of a statistical variable is always the special sub-class [`StatisticalVariable`](https://datacommons.org/browser/StatisticalVariable). For example, the metric [`Median Age of Female Population`](https://datacommons.org/browser/Median_Age_Person_Female) is a node whose type is a statistical variable. + +A statistical variable can be simple, such as [`Total Population`](https://datacommons.org/browser/Count_Person), or more complex, such as [`Hispanic Female Population`](https://datacommons.org/tools/statvar#Count_Household_NoHealthInsurance=&sv=Count_Person_Female_HispanicOrLatino)`. Complex variables may be broken down into constituent parts, or not. + +### Find places available for a statistical variable + +Note that not all statistical variables have observations for all places or other entities. To find out which places have data for a given variable, you can do the following: + +1. Open the [Statistical Variable Explorer](https://datacommons.org/tools/statvar), and search for a variable of interest. +1. Optionally, filter by data source and data set. +1. Scroll to the **Places** section. + +For example, inspecting [Health > Health Insurance (Household) > No Health Insurance > Households Without Health Insurance](https://datacommons.org/tools/statvar#sv=Count_Household_NoHealthInsurance) shows us that the statistical variable `Count_Household_NoHealthInsurance` is available in the United States at state, county, and city levels: + +![Stat Var Explorer](/assets/images/dc/concept4.png){: width="900"} + +On the other hand, the [Average Retail Price of Electricity](https://datacommons.org/tools/statvar#Quarterly_Average_RetailPrice_Electricity=&sv=Quarterly_Average_RetailPrice_Electricity), or `Quarterly_Average_RetailPrice_Electricity`, is only available at the state level states in the US but not at the city or county level. + +![Stat Var Explorer](/assets/images/dc/concept5.png){: width="900"} + +## Unique identifier: DCID + +Every node has a unique identifier, called a Data Commons ID, or DCID. In the [Knowledge Graph browser](https://datacommons.org/browser/), you can view the DCID for any node or edge. For example, the DCID for the city of Berkeley is `geoid/0606000`: + +![KG browser](/assets/images/dc/concept6.png){: width="600"} + +DCIDs are not restricted to entities; statistical variables also have DCIDs. For example, the DCID for the Gini Index of Economic Activity is `GiniIndex_EconomicActivity`: + +![Stat Var Explorer](/assets/images/dc/concept7.png){: width="900"} + +### Find a DCID for an entity or variable + +Many Data Commons tools and APIs require that you provide a DCID as input for a query. There are a few ways to do this. + +To find the DCID for a place: + +1. Go to [https://docs.datacommons.org/place_types.html](https://docs.datacommons.org/place_types.html). +1. Click the link on the place type of interest and click its link to open the Knowledge Graph page for the entity; for example, **City**. +1. Scroll to the **In Arcs** section to look up the places of interest. +1. If necessary, continue to drill down on links until you find the place of interest. + +![KG browser](/assets/images/dc/concept8.png){: width="900"} + +To find the DCID for a statistical variable: + +1. Open the Statistical Variable Explorer. +1. Search for the variable of interest, and optionally filter by data source and dataset. +1. Look under the heading for the DCID. + +![Stat Var Explorer](/assets/images/dc/concept9.png){: width="900"} + +To find a DCID programmatically for both entities and variables, you can use the REST v2 [Resolve API](/api/rest/v2/resolve.html). + +## Property + +Every node also contains properties or characteristics that describe its entity, event, or statistical variable. Each property is actually an edge to another node, with a label. If the object node is a primitive type, such as a string, date, or number, it is a "leaf", or terminal node, which we call an _attribute_. Examples are properties such as `latitude`, `year`, various unique IDs and so on. + +Other properties are links to other entities/events/ etc. In the Knowledge Graph, you can click through links to non-terminal nodes. + +For example, in this node for the city of Addis Ababa, Ethiopia, the `typeOf` and `containedInPlace` edges link to other entities, namely `City` and `Ethiopia`, whereas all the other values are terminal. + +![KG browser](/assets/images/dc/concept10.png){: width="600"} + + +Note that the DCID for a property is the same as its name. + +## Observation + +An _observation_ is a single measured value for a statistical variable, at or during a specified period of time, for a specific entity. + +For example, the value of the statistical variable [`Median Age of Female Population`](https://datacommons.org/browser/Median_Age_Person_Female) for the city of San Antonio, Texas in 2014 could have an observation `Observation_Median_Age_Person_Female_SanAntonio_TX_2014`. The type of an observation is always the special sub-class [`StatVarObservation`](https://datacommons.org/browser/StatVarObservation). + +Time series made up of many observations underlie the data available in the [Timeline Explorer](https://datacommons.org/tools/visualization#visType=timeline) and timeline graphs. For example, here is the [median income in Berkeley, CA over a period of ten years](https://datacommons.org/tools/visualization#visType%3Dtimeline%26place%3DgeoId%2F0606000%26placeType%3DCensusZipCodeTabulationArea%26sv%3D%7B%22dcid%22%3A%22Median_Income_Person%22%7D), according to the US Census Bureau: + +![Timeline Explorer](/assets/images/dc/concept11.png){: width="900"} + -# Data Models +## Provenance, Source, Dataset -The data included in Data Commons, even today, covers a wider range of domains: ranging from time series about demographics and employment, to hurricanes, to election results, and to protein structures. There is an inherent tension between using domain specific data models versus a more expressive, but likely verbose, data model capable of covering the breadth of domains we hope to have in Data Commons. +Every node and triple also have some important properties that indicate the origin of the data. -It is important to have an underlying model capable of expressing the breadth of data we might have about a single entity. For example, consider a place such as [Cook County, IL](https://datacommons.org/place/geoId/17031). There are time series about this place (related to demographics, jobs, etc.), there are specific events (like winter storms), information about historic events, etc. We would like a single uniform schema and query API for a client to access all this data. At the same time, for many applications that access a narrower slice of the data, it would be convenient to use data models that enable more compact encodings and/or more standard query languages such as SQL. To accomplish this, we use an expressive though verbose 'base' representation layer into which everything can be mapped. And on top of this we layer APIs which provide alternate views of the data in more specific data models. +- [`Provenance`](https://datacommons.org/browser/Provenance): All triples have a provenance, typically the URL of the data provider's website; for example, [www.abs.gov.au](https://datacommons.org/browser/dc/base/AustraliaStatistics). In addition, all entity types also have a provenance, defined with a DCID, such as [`AustraliaStatistics`](https://datacommons.org/browser/dc/base/AustraliaStatistics). It also (For many property types, which are defined by the Data Commons schema, their provenance is always [datacommons.org](datacommons.org).) +- [`Source`](https://datacommons.org/browser/Source): This is a property of a provenance, and a dataset, usually the name of an organization that provides the data or the schema. For example, for provenance [www.abs.gov.au](www.abs.gov.au), the source is the [Australian Bureau of Statistics](https://datacommons.org/browser/dc/s/AustralianBureauOfStatistics). +- [`Dataset`](https://datacommons.org/browser/Dataset): This is the name of a specific dataset provided by a provider. Many sources provide multiple datasets. For example, the source Australian Bureau of Statistics provides two datasets, [Australia Statistics](https://datacommons.org/browser/dc/d/AustralianBureauOfStatistics_AustraliaStatistics) (not to be confused with the provenance above), and [Australia Subnational Administrative Boundaries](https://datacommons.org/browser/dc/d/AustralianBureauOfStatistics_AustraliaSubnationalAdministrativeBoundaries). -Data Commons also provides access to the data in the following different views: +![Knowledge graph](/assets/images/dc/concept12.png){: width="600"} -1. The data model for the base layer is the one used by [Schema.org](https://schema.org). This models the world as a set of entities, with attributes and relationships between entities. There is a taxonomy of entities and each entity is an instance of at least one of the types in the taxonomy. The types and relations types are also entities. This kind of structure has its origins in knowledge representation systems such as KRL and Cyc, and has recently found adoption under the name "knowledge graph". The [Node and SPARQL APIs](/api) provide access to this view. The [Data Commons Knowledge Graph](https://datacommons.org/browser) allows one to browse through Data Commons in this raw graph view -1. Time series view provides a set of time series for combinations of entities and variables ([Statistical Variable](https://datacommons.org/browser/StatisticalVariable), in Data Commons parlance). The [DCGET API](/api/sheets/get_variable.html) provides API access to this view of the data and the [Data Commons Timeline Tool](https://datacommons.org/tools/timeline) allows one to browse Data Commons in this view. -1. The relational view provides access to a subset of the Data Commons data as a set of relational tables in Big Query (coming soon). This makes it easier for users to combine their data with data from Data Commons. -## Schemas +Note that a given statistical variable may have multiple provenances, since many data sets define the same variables. You can see the list of all the data sources for a given statistical variable in the Statistical Variable Explorer. For example, the explorer shows multiple sources (Censuses from India, Mexico, Vietnam, OECD, World Bank, etc.) for the variable [Life Expectancy](https://datacommons.org/tools/statvar#LifeExpectancy_Person=&sv=LifeExpectancy_Person): -A single schema (to the extent possible) for all the data is one of Data Common’s main goals. We would like this schema to be 'web friendly' in the sense that it is an extension of some of the most widely used schemas on the web for structured data. To this end, Data Commons is built on top of [Schema.org](https://schema.org). We make heavy use of some of Schema.org's terms (notably [StatisticalPopulation](https://schema.org/StatisticalPopulation) and [Observation](https://schema.org/Observation) and extend Schema.org as required, introducing both general constructs (such as Intervals) and values for common attribute values (e.g., [Ethnicities](https://browser.datacommons.org/browser/race), [EducationalAttainments](https://browser.datacommons.org/browser/educationalAttainment), etc.). +![Stat Var Explorer](/assets/images/dc/concept13.png){: width="900"} -## CrossWalks +You can see a list of all sources and data sets in several places: -A significant part of the work in building Data Commons is in aligning terms used to refer to the same or overlapping concepts across different datasets. Certain kinds of terms have widely shared meaning, e.g., [age](https://browser.datacommons.org/browser/age), [life expectancy](https://browser.datacommons.org/browser/lifeExpectancy). Others, such as [educational attainment](https://browser.datacommons.org/browser/educationalAttainment) are measured differently across different regions. Sometimes, different data sets about the same topic will use slightly different definitions of a term (e.g., [BLS](https://www.bls.gov/bls/employment.htm) vs [Census](https://www.census.gov/topics/employment.html) on the definition of what it means to be employed) and in some cases, the same dataset might even change its definition over time. Even in these cases, for many applications that aim to perform comparisons, it is useful to have mappings or aggregations between these different terminologies. +- The [Data sources](/datasets/) pages in this site. +- The **Data source** and **Dataset** drop-down menus in the Statistical Variable Explorer. -In Data Commons, to the extent possible, we preserve the original encodings. We also introduce new derived attributes/time series that capture mappings. We hope that this will enable useful applications for end users, while preserving the ability for researchers to explore implications of alternate mappings. +![Stat Var Explorer](/assets/images/dc/concept14.png){: width="600"} From 37f3e8760b6d592a77cbfc47681c1ef9bf4657a1 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 22 Jul 2024 17:22:04 -0700 Subject: [PATCH 08/96] formatting edits --- custom_dc/build_image.md | 10 +++++----- custom_dc/custom_data.md | 22 ++++++++++++---------- custom_dc/custom_ui.md | 2 +- custom_dc/data_cloud.md | 27 +++++++++++++++------------ custom_dc/deploy_cloud.md | 4 ++-- custom_dc/launch_cloud.md | 2 +- custom_dc/quickstart.md | 25 +++++++++++-------------- custom_dc/troubleshooting.md | 2 +- 8 files changed, 48 insertions(+), 46 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index a8dfff82f..be81d81f8 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -34,8 +34,8 @@ If you want to pick up the latest prebuilt version, do the following: docker run -it \ -p 8080:8080 \ -e DEBUG=true \ -- -env-file $PWD/custom_dc/sqlite_env.list \ -- v $PWD/custom_dc/sample:/userdata \ + -env-file $PWD/custom_dc/env.list \ + -v $PWD/custom_dc/sample:/$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:latest ``` @@ -141,14 +141,14 @@ To upload and deploy the container to the Cloud, see [Deploy a custom instance t ## Run the container with the local SQLite database -To start the services using the locally built repo. If you have made changes to any of the UI components, be sure to map the `custom` directories to the Docker `workspace` directory. +Start the services using the locally built repo. If you have made changes to any of the UI components (or directories), be sure to map the `custom` directories (or alternative directories) to the Docker `workspace` directory.
  
 docker run -it \
---env-file $PWD/custom_dc/sqlite_env.list \
+--env-file $PWD/custom_dc/env.list \
 -p 8080:8080 \
 -e DEBUG=true \
-[-v $PWD/custom_dc/CUSTOM_DATA_DIRECTORY:/userdata \] 
+-v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 [-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \]
 [-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \]
 datacommons-website-compose:DOCKER_TAG
diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md
index b5f79ed9a..e6103e502 100644
--- a/custom_dc/custom_data.md
+++ b/custom_dc/custom_data.md
@@ -21,7 +21,7 @@ Custom Data Commons provides a simple mechanism to import your own data, but it
 
 - All data must be in CSV format, using the schema described below.
 - You must also provide a JSON configuration file, named `config.json`, to map the CSV contents to the Data Commons schema knowledge graph. The contents of the JSON file are described below.
-- All CSV files and the JSON file must be in the same directory
+- All CSV files and the JSON file _must_ be in the same directory
 
 Examples are provided in [`custom_dc/sample`](https://github.com/datacommonsorg/website/tree/master/custom_dc/sample) and [`custom_dc/examples`](https://github.com/datacommonsorg/website/tree/master/custom_dc/examples) directories.
 
@@ -242,22 +242,24 @@ The `sources` section is optional. It encodes the sources and provenances associ
 
 To load custom data uploaded to Google Cloud, see instead [Pointing the local Data Commons site to the Cloud data](/custom_dc/data_cloud.html) for procedures.
 
+### Configure custom directories
+
+If you are using a directory other than `custom_dc/sample` to store your CSV files, edit the `env.list` file as follows:
+- Set the `OUTPUT_DIR` variable to the directory where your input files are stored. The load step will create a `datacommons` subdirectory under this directory.
+
 ### Start the Docker container with local custom data {#docker-data}
 
-Once you have your CSV files and config.json set up, use the following command to restart the Docker container, mapping your custom data directory to the Docker userdata directory.
+Once you have configured everything, use the following command to restart the Docker container, mapping your output directory to the same path in Docker:
 
 
 docker run -it \
 -p 8080:8080 \
 -e DEBUG=true \
---env-file $PWD/custom_dc/sqlite_env.list \
--v $PWD/custom_dc/CUSTOM_DATA_DIRECTORY:/userdata \
-[-v $PWD/custom_dc/CUSTOM_DATA_DIRECTORY/datacommons:/sqlite] \
+--env-file $PWD/custom_dc/env.list \ \
+-v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 gcr.io/datcom-ci/datacommons-website-compose:stable
 
-The optional `-v` flag preserves the SQLite data so it loads automatically when you restart the Docker container. - Every time you make changes to the CSV or JSON files, you should reload the data, as described below. ## Load custom data in SQLite @@ -266,9 +268,9 @@ As you are iterating on changes to the source CSV and JSON files, you will need You can load the new/updated data from SQLite using the `/admin` page on the site: -1. Optionally, in the `sqlite_env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data. +1. Optionally, in the `env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data. 1. Start the Docker container as usual, being sure to map the path to the directory containing the custom data (see command above). -1. With the services running, navigate to the `/admin page`. If a secret is required, enter it in the text field, and click **Load**. This runs a script inside the Docker container, that converts the CSV data into SQL tables, and generates embeddings in the container as well. The database is created as custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/datacommons.db and embeddings are generated in custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/nl/. +1. With the services running, navigate to the `/admin` page. If a secret is required, enter it in the text field, and click **Load**. This runs a script inside the Docker container, that converts the CSV data into SQL tables, and generates embeddings in the container as well. The database is created as OUTPUT_DIRECTORY/datacommons/datacommons.db and embeddings are generated in OUTPUT_DIRECTORY/datacommons/nl/. ## Inspect the SQLite database @@ -277,7 +279,7 @@ If you need to troubleshoot custom data, it is helpful to inspect the contents o To do so, from a terminal window, open the database:
  
-sqlite3 website/custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/datacommons.db
+sqlite3 WEBSITE_ROOT/OUTPUT_DIRECTORY/datacommons/datacommons.db
 
This starts the interactive SQLite shell. To view a list of tables, at the prompt type `.tables`. The relevant table is `observations`. diff --git a/custom_dc/custom_ui.md b/custom_dc/custom_ui.md index b5ac97859..7beb2b5f7 100644 --- a/custom_dc/custom_ui.md +++ b/custom_dc/custom_ui.md @@ -53,7 +53,7 @@ HTML and CSS customization files are provided as samples to get you started. The -Note that the `custom` parent directory is customizable as the `FLASK_ENV` environment variable. You can rename the directory as desired and update the environment variable. +Note that the `custom` parent directory is customizable as the `FLASK_ENV` environment variable. You can rename the directory as desired and update the environment variable in `custom_dc/env.list`. To enable the changes to be picked up by the Docker image, and allow you to refresh the browser for further changes, restart the Docker image with this additional flag to map the directories to the Docker workspace: diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 7d4668111..7f5c54fd0 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -21,6 +21,7 @@ Once you have tested locally, you need to get your data into Google Cloud so you You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and the custom Data Commons importer will transform, store, and query the data in a [Google Cloud SQL](https://cloud.google.com/sql) database. + ## Prerequisites - A [GCP](https://console.cloud.google.com/welcome) billing account and project. @@ -57,22 +58,22 @@ While you are testing, you can start with a single Google Cloud region; to be cl 1. For the **Location type**, choose the same regional options as for Cloud SQL above. 1. When you have finished setting all the configuration options, click **Create**. 1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data. -1. Name the folder as desired. Record the folder path as gs://BUCKET_NAME/FOLDER_PATH for setting environment variables below. You can start with the sample data provided under `custom_dc/sample` and update to your own data later. +1. Name the folder as desired. Record the folder path as gs://BUCKET_NAME/FOLDER_PATH for setting the `OUTPUT_DIR` environment variable below. -### Set up environment variables +### Set environment variables -1. Using your favorite editor, open `custom_dc/cloudsql_env.list`. -1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. +1. Using your favorite editor, open `custom_dc/env.list`. +1. Set `USE_SQLITE=false` and `USE_CLOUDSQL=true` 1. Set values for all of the following: - - `GCS_DATA_PATH` - `CLOUDSQL_INSTANCE` - `GOOGLE_CLOUD_PROJECT` - `DB_NAME` - `DB_USER` - `DB_PASS` + - `OUTPUT_DIR` - See comments in the [`cloudsql_env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/cloudsql_env.list) file for the correct format for each option. + See comments in the [`env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/env.list) file for the correct format for each option. 1. Optionally, set an `ADMIN_SECRET` to use when loading the data through the `/admin` page later. @@ -117,23 +118,25 @@ If you are prompted to install the Cloud Resource Manager API, press `y` to acce If you have not made changes that require a local build, and just want to run the pre-downloaded image, from your repository root, run: -```shell +
 docker run -it \
---env-file $PWD/custom_dc/cloudsql_env.list \
+--env-file $PWD/custom_dc/env.list \
+-v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 -p 8080:8080 \
 -e DEBUG=true \
 -e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \
 -v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro \
 gcr.io/datcom-ci/datacommons-website-compose:stable
-```
+
#### Run with a locally built repo -If you have made local changes and have a [locally built repo](/custom_dc/manage_repo.html#build-repo), from the root of the repository, run the following: +If you have made local changes and have a [locally built repo](/custom_dc/build_image.html#build-repo), from the root of the repository, run the following:
 docker run -it \  
---env-file $PWD/custom_dc/cloudsql_env.list \  
+--env-file $PWD/custom_dc/env.list \  
+-v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 -p 8080:8080 \  
 -e DEBUG=true \  
 -e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \  
@@ -149,7 +152,7 @@ Each time you upload new versions of the source CSV and JSON files, you need to
 
 You can load the new/updated data from Cloud Storage using the `/admin` page on the site:
 
-1. Optionally, in the `cloudsql_env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data.
+1. Optionally, in the `env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data.
 1. Start the Docker container as described above.
 1. With the services running, navigate to the `/admin` page. If a secret is required, enter it in the text field, and click **Load**.
    This runs a script inside the Docker container, that converts the CSV data in Cloud Storage into SQL tables, and stores them in the Cloud SQL database you created earlier. It also generates embeddings in the Google Cloud Storage folder into which you uploaded the CSV/JSON files, in a `datacommons/nl/` subfolder.
diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md
index 0a1cc8862..bfe3bcb2b 100644
--- a/custom_dc/deploy_cloud.md
+++ b/custom_dc/deploy_cloud.md
@@ -76,10 +76,10 @@ When it completes, verify that the container has been uploaded in the Cloud Cons
 
 To deploy the image in Google Cloud Run, you need to create a Run service. Because we need to set the environment variables for the running image as options in the service, it is actually more convenient to create the service and deploy the image at the same time using `gcloud` rather than the Cloud Console. Once the service is created in this way, you can edit it and redeploy using the Console.
 
-1. Copy the settings from the cloudsql_env.list file to a local environment variable:
+1. Copy the settings from the `env.list` file to a local environment variable:
 
    ```shell
-   env_vars=$(awk -F '=' 'NF==2 {print $1"="$2}' custom_dc/cloudsql_env.list | tr '\n' ',' | sed 's/,$//')
+   env_vars=$(awk -F '=' 'NF==2 {print $1"="$2}' custom_dc/env.list | tr '\n' ',' | sed 's/,$//')
    ```
 
 1. Create a new Cloud Run service and deploy the image in the Artifact Registry. `
diff --git a/custom_dc/launch_cloud.md b/custom_dc/launch_cloud.md
index 26a13befe..80e7101c1 100644
--- a/custom_dc/launch_cloud.md
+++ b/custom_dc/launch_cloud.md
@@ -43,7 +43,7 @@ The following is a sample configuration that you can tune as needed. For additio
 **Step 2: Set the environment variable**
 
 1. When the Redis instance is created above, go to the **Instances > Redis** tab, look up your instance and note the **Primary Endpoint** IP address.
-1. In `custom_dc/cloudsql_env.list`, set the value of the `REDIS_HOST` option to the IP address. 
+1. In `custom_dc/env.list`, set the value of the `REDIS_HOST` option to the IP address. 
 
 **Step 3: Create the VPC connector**
 
diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index bf2ff6e5e..aef93e3c4 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -63,15 +63,16 @@ When the downloads are complete, navigate to the root directory of the repo (e.g
 cd website | DIRECTORY
 
-### Set API keys as environment variables +### Set environment variables -1. Using your favorite editor, open `custom_dc/sqlite_env.list`. +1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. +1. For the `OUTPUT_DIR`, set it to `$PWD/custom_dc/sample/`. 1. Leave `ADMIN_SECRET` blank for now. Warning: Do not use any quotes (single or double) or spaces when specifying the values. -Note: If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables files containing secrets. Instead, store them locally only. If you are using git/Github, you can add the file to the `.gitignore` file. +Note: If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables file containing secrets. Instead, store it locally only. If you are using git/Github, you can add the file name to the `.gitignore` file. ## About the downloaded files @@ -85,7 +86,7 @@ Note: If you are storing your source code in a public/open-source version contro custom_dc/sample/ - Sample supplemental data that is added to the base data in Data Commons. This page shows you how to easily load and view this data. The data is in CSV format and mapped to Data Commons entity definitions using the config.json file. + Sample supplemental data that is added to the base data in Data Commons. This page shows you how to easily load and view this data. The data is in CSV format and mapped to Data Commons entity definitions using the `config.json` file. custom_dc/examples/ @@ -100,12 +101,8 @@ Note: If you are storing your source code in a public/open-source version contro Contains customizable CSS file and default logo. To modify the styles or replace the logo, see Customize Javascript and styles. - custom_dc/sqlite_env.list - Contains environment variables for a development environment using SQLite as the database. For details of the variables, see the comments in the file. - - - custom_dc/cloudsql_env.list - Contains environment variables for a development or production environment using Cloud SQL as the database. For details of the variables, see the comments in the file. + custom_dc/env.list + Contains environment variables for the Data Commons services. For details of the variables, see the comments in the file. @@ -119,19 +116,19 @@ Note: If you are storing your source code in a public/open-source version contro docker run -it \ -p 8080:8080 \ -e DEBUG=true \ ---env-file $PWD/custom_dc/sqlite_env.list \ --v $PWD/custom_dc/sample:/userdata \ +--env-file $PWD/custom_dc/env.list \ +-v $PWD/custom_dc/sample:/$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:stable ``` -Note: If you are running on Linux, depending on whether you have created a ["sudoless" Docker group](https://docs.docker.com/engine/install/linux-postinstall/), you will need to preface every `docker` invocation with `sudo`. +Note: If you are running on Linux, depending on whether you have created a ["sudoless" Docker group](https://docs.docker.com/engine/install/linux-postinstall/), you may need to preface every `docker` invocation with `sudo`. This command does the following: - The first time you run it, downloads the latest stable Data Commons image, `gcr.io/datcom-ci/datacommons-website-compose:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. - Starts a Docker container in interactive mode. - Starts development/debug versions of the Web Server, NL Server, and Mixer, as well as the Nginx proxy, inside the container -- Maps the sample data to the Docker path `/userdata`, so the servers do not need to be restarted when you load the sample data +- Maps the sample data to a Docker path, so the servers do not need to be restarted when you load the sample data ### Stop and restart the services diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index 9acd4dbb7..4e6b1a910 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -65,7 +65,7 @@ There is a problem with how you have set up your CSV files and/or config.json fi If the load page does not show any errors but data still does not load, try checking the following: -1. In the `_env.list` file you are using, check that you are not using single or double quotes around any of the values. +1. In the `env.list` file, check that you are not using single or double quotes around any of the values. 1. Check your Docker command line for invalid arguments. Often Docker won't give any error messages but failures will show up at runtime. ## Website display problems From d83db88a8dc0ca374ba9b08fde4c9cd11105c389 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 24 Jul 2024 09:51:35 -0700 Subject: [PATCH 09/96] updates per Keyur's feedback --- custom_dc/build_image.md | 4 ++-- custom_dc/custom_data.md | 6 +++--- custom_dc/data_cloud.md | 2 -- custom_dc/deploy_cloud.md | 2 +- custom_dc/quickstart.md | 2 +- 5 files changed, 7 insertions(+), 9 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index be81d81f8..f71044dd9 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -35,7 +35,7 @@ If you want to pick up the latest prebuilt version, do the following: -p 8080:8080 \ -e DEBUG=true \ -env-file $PWD/custom_dc/env.list \ - -v $PWD/custom_dc/sample:/$PWD/custom_dc/sample \ + -v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:latest ``` @@ -148,7 +148,7 @@ docker run -it \ --env-file $PWD/custom_dc/env.list \ -p 8080:8080 \ -e DEBUG=true \ --v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \ +-v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \ [-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \] [-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \] datacommons-website-compose:DOCKER_TAG diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index e6103e502..5060e725d 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -244,7 +244,7 @@ To load custom data uploaded to Google Cloud, see instead [Pointing the local Da ### Configure custom directories -If you are using a directory other than `custom_dc/sample` to store your CSV files, edit the `env.list` file as follows: +Edit the `env.list` file as follows: - Set the `OUTPUT_DIR` variable to the directory where your input files are stored. The load step will create a `datacommons` subdirectory under this directory. ### Start the Docker container with local custom data {#docker-data} @@ -256,7 +256,7 @@ docker run -it \ -p 8080:8080 \ -e DEBUG=true \ --env-file $PWD/custom_dc/env.list \ \ --v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \ +-v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \ gcr.io/datcom-ci/datacommons-website-compose:stable
@@ -279,7 +279,7 @@ If you need to troubleshoot custom data, it is helpful to inspect the contents o To do so, from a terminal window, open the database:
  
-sqlite3 WEBSITE_ROOT/OUTPUT_DIRECTORY/datacommons/datacommons.db
+sqlite3 OUTPUT_DIRECTORY/datacommons/datacommons.db
 
This starts the interactive SQLite shell. To view a list of tables, at the prompt type `.tables`. The relevant table is `observations`. diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 7f5c54fd0..5664aaf7b 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -121,7 +121,6 @@ If you have not made changes that require a local build, and just want to run th
 docker run -it \
 --env-file $PWD/custom_dc/env.list \
--v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 -p 8080:8080 \
 -e DEBUG=true \
 -e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \
@@ -136,7 +135,6 @@ If you have made local changes and have a [locally built repo](/custom_dc/build_
 
 docker run -it \  
 --env-file $PWD/custom_dc/env.list \  
--v OUTPUT_DIRECTORY:/OUTPUT_DIRECTORY \
 -p 8080:8080 \  
 -e DEBUG=true \  
 -e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \  
diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md
index bfe3bcb2b..4d6f36fcf 100644
--- a/custom_dc/deploy_cloud.md
+++ b/custom_dc/deploy_cloud.md
@@ -76,7 +76,7 @@ When it completes, verify that the container has been uploaded in the Cloud Cons
 
 To deploy the image in Google Cloud Run, you need to create a Run service. Because we need to set the environment variables for the running image as options in the service, it is actually more convenient to create the service and deploy the image at the same time using `gcloud` rather than the Cloud Console. Once the service is created in this way, you can edit it and redeploy using the Console.
 
-1. Copy the settings from the `env.list` file to a local environment variable:
+1. Copy the settings from the `env.list`g file to a local environment variable:
 
    ```shell
    env_vars=$(awk -F '=' 'NF==2 {print $1"="$2}' custom_dc/env.list | tr '\n' ',' | sed 's/,$//')
diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index aef93e3c4..f12e257ff 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -117,7 +117,7 @@ docker run -it \
 -p 8080:8080 \
 -e DEBUG=true \
 --env-file $PWD/custom_dc/env.list \
--v $PWD/custom_dc/sample:/$PWD/custom_dc/sample  \
+-v $PWD/custom_dc/sample:$PWD/custom_dc/sample  \
 gcr.io/datcom-ci/datacommons-website-compose:stable
 ```
 

From 03b906fde50ff94c7c30a86088d0a81ed409b905 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 24 Jul 2024 11:06:34 -0700
Subject: [PATCH 10/96] Fix typos

---
 custom_dc/deploy_cloud.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md
index 4d6f36fcf..6a3bf3fba 100644
--- a/custom_dc/deploy_cloud.md
+++ b/custom_dc/deploy_cloud.md
@@ -76,13 +76,13 @@ When it completes, verify that the container has been uploaded in the Cloud Cons
 
 To deploy the image in Google Cloud Run, you need to create a Run service. Because we need to set the environment variables for the running image as options in the service, it is actually more convenient to create the service and deploy the image at the same time using `gcloud` rather than the Cloud Console. Once the service is created in this way, you can edit it and redeploy using the Console.
 
-1. Copy the settings from the `env.list`g file to a local environment variable:
+1. Copy the settings from the `env.list` file to a local environment variable:
 
    ```shell
    env_vars=$(awk -F '=' 'NF==2 {print $1"="$2}' custom_dc/env.list | tr '\n' ',' | sed 's/,$//')
    ```
 
-1. Create a new Cloud Run service and deploy the image in the Artifact Registry. `
+1. Create a new Cloud Run service and deploy the image in the Artifact Registry.
 
    
    gcloud run deploy SERVICE_NAME \  

From 03538afa0bdc7d9bdff20c6ba1dd9e7adf8ec6b6 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 24 Jul 2024 11:37:13 -0700
Subject: [PATCH 11/96] fix nav order

---
 custom_dc/troubleshooting.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md
index 4e6b1a910..f4394341d 100644
--- a/custom_dc/troubleshooting.md
+++ b/custom_dc/troubleshooting.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Troubleshooting
-nav_order: 8
+nav_order: 9
 parent: Build your own Data Commons
 ---
 

From d7fb58fa87d628d0d9e5ca0093fe84171774ff21 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 24 Jul 2024 13:38:17 -0700
Subject: [PATCH 12/96] fix link to API key request form

---
 custom_dc/quickstart.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index f12e257ff..5b855c91e 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -27,7 +27,7 @@ The instructions in this page use the following setup:
 - If you are developing on Windows, install [WSL 2](https://learn.microsoft.com/en-us/windows/wsl/install) (any distribution will do, but we recommend the default, Ubuntu), and enable [WSL 2 integration with Docker](https://docs.docker.com/desktop/wsl/). 
 - Install [Docker Desktop/Engine](https://docs.docker.com/engine/install/).
 - Install [Git](https://git-scm.com/).
-- Get an API key for Data Commons by submitting the [Data Commons API key request form](https://docs.google.com/forms/d/e/1FAIpQLSePrkVfss9lUIHFClQsVPwPcAVWvX7WaZZyZjJWS99wRQNW4Q/viewform?resourcekey=0-euQU6Kly7YIWVRNS2p4zjw). The key is needed to authorize requests from your site to the base Data Commons site. Typical turnaround times are 24-48 hours.
+- Get an API key to authorize requests from your site to the base Data Commons, by [filling out this form](https://docs.google.com/forms/d/17XRFdbXHomz5cwAwSAg1jUj3r3TxGT_HABgXhSM1204). Typical turnaround times are 24-48 hours.
 - Optional: Get a [Github](http://github.com) account, if you would like to browse the Data Commons source repos using your browser.
 
 ## One-time setup steps {#setup}

From c746a7e258c83b68303a3025f0b78be2c1a78582 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 24 Jul 2024 13:40:09 -0700
Subject: [PATCH 13/96] update form link

---
 custom_dc/quickstart.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index 5b855c91e..6af965657 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -27,7 +27,7 @@ The instructions in this page use the following setup:
 - If you are developing on Windows, install [WSL 2](https://learn.microsoft.com/en-us/windows/wsl/install) (any distribution will do, but we recommend the default, Ubuntu), and enable [WSL 2 integration with Docker](https://docs.docker.com/desktop/wsl/). 
 - Install [Docker Desktop/Engine](https://docs.docker.com/engine/install/).
 - Install [Git](https://git-scm.com/).
-- Get an API key to authorize requests from your site to the base Data Commons, by [filling out this form](https://docs.google.com/forms/d/17XRFdbXHomz5cwAwSAg1jUj3r3TxGT_HABgXhSM1204). Typical turnaround times are 24-48 hours.
+- Get an API key to authorize requests from your site to the base Data Commons, by [filling out this form](https://docs.google.com/forms/d/e/1FAIpQLSeVCR95YOZ56ABsPwdH1tPAjjIeVDtisLF-8oDYlOxYmNZ7LQ/viewform?usp=dialog). Typical turnaround times are 24-48 hours.
 - Optional: Get a [Github](http://github.com) account, if you would like to browse the Data Commons source repos using your browser.
 
 ## One-time setup steps {#setup}

From d5e04af04d1caffe2a82fa460176c3c88567a552 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 24 Jul 2024 13:54:49 -0700
Subject: [PATCH 14/96] update key request form and output dir env var

---
 custom_dc/build_image.md | 16 ++++++++--------
 custom_dc/quickstart.md  |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md
index f71044dd9..1a0130b7b 100644
--- a/custom_dc/build_image.md
+++ b/custom_dc/build_image.md
@@ -30,14 +30,14 @@ If you want to pick up the latest prebuilt version, do the following:
    ```
 1. Rerun the container, specifying that repo as the argument to the `docker run` command:
 
-   ```shell
-   docker run -it \
-   -p 8080:8080 \
-   -e DEBUG=true \
-   -env-file $PWD/custom_dc/env.list \
-   -v $PWD/custom_dc/sample:$PWD/custom_dc/sample \
-   gcr.io/datcom-ci/datacommons-website-compose:latest
-   ```
+```shell
+docker run -it \
+-p 8080:8080 \
+-e DEBUG=true \
+-env-file $PWD/custom_dc/env.list \
+-v $PWD/custom_dc/sample:$PWD/custom_dc/sample \
+gcr.io/datcom-ci/datacommons-website-compose:latest
+```
 
 ## Build a local image {#build-repo}
 
diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index 6af965657..5027271d7 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -67,7 +67,7 @@ cd website | DIRECTORY
 
 1. Using your favorite editor, open `custom_dc/env.list`.
 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`.
-1. For the `OUTPUT_DIR`, set it to `$PWD/custom_dc/sample/`.
+1. For the `OUTPUT_DIR`, set it to the full path to the `sample` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample.
 1. Leave `ADMIN_SECRET` blank for now.
 
 Warning: Do not use any quotes (single or double) or spaces when specifying the values.

From b6f53cf097c7cd0012b91eaf502c8125a824a397 Mon Sep 17 00:00:00 2001
From: kmoscoe <165203920+kmoscoe@users.noreply.github.com>
Date: Thu, 25 Jul 2024 18:03:16 -0700
Subject: [PATCH 15/96] Revert to gerund

Though the style guide says to just use imperatives, "get started" just sounds weird. Also this is more consistent with "troubleshooting"
---
 custom_dc/quickstart.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index 5027271d7..ac05585b2 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -1,12 +1,12 @@
 ---
 layout: default
-title: Get started
+title: Getting started
 nav_order: 2
 parent: Build your own Data Commons
 ---
 
 {:.no_toc}
-# Get started
+# Getting started
 
 This page shows you how to run a local custom Data Commons instance inside a Docker container and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/).
 

From d368ba49ce7336bb4105c3835c150d642485ebfd Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 31 Jul 2024 09:47:01 -0700
Subject: [PATCH 16/96] new troubleshooting entry

---
 custom_dc/quickstart.md      | 4 ++--
 custom_dc/troubleshooting.md | 9 +++++++++
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md
index 5027271d7..ac05585b2 100644
--- a/custom_dc/quickstart.md
+++ b/custom_dc/quickstart.md
@@ -1,12 +1,12 @@
 ---
 layout: default
-title: Get started
+title: Getting started
 nav_order: 2
 parent: Build your own Data Commons
 ---
 
 {:.no_toc}
-# Get started
+# Getting started
 
 This page shows you how to run a local custom Data Commons instance inside a Docker container and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/).
 
diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md
index f4394341d..0a18d5c92 100644
--- a/custom_dc/troubleshooting.md
+++ b/custom_dc/troubleshooting.md
@@ -84,6 +84,15 @@ This is because you are missing a valid API key or the necessary APIs are not en
 
 In general, whenever you encounter problems with any Google Cloud Run service, check the **Logs** page for your Cloud Run service, to get detailed output from the services.
 
+### "403 Forbidden: Your client does not have permission to get URL / from this server"
+
+This error indicats that your application requires authenticated requests but you have not provided an authentication token. If your site is intended to be public, first check to see that the Cloud Run service is not set up to require authentication:
+1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run) page for your project.
+1. From the list of services, select the relevant service and select the **Security** tab.
+1. Ensure that you have enabled **Allow unauthenticated invocations** and restart the Cloud Run service.
+
+If you are unable to select this option, this indicates that there is an IAM permissions setup issue with your project or account. See the [Cloud Run Troubleshooting](https://cloud.google.com/run/docs/troubleshooting#unauthorized-client) for details on how to fix this.
+
 ### "502 Bad Gateway"
 
 If you see no errors in the logs, except `connect() failed (111: Connection refused) while connecting to upstream`, try the following:

From 771ccca8431e442d4a264ea86814bfa2bbab810f Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Wed, 31 Jul 2024 10:25:17 -0700
Subject: [PATCH 17/96] fix typo

---
 custom_dc/troubleshooting.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md
index 0a18d5c92..a56838d94 100644
--- a/custom_dc/troubleshooting.md
+++ b/custom_dc/troubleshooting.md
@@ -86,7 +86,7 @@ In general, whenever you encounter problems with any Google Cloud Run service, c
 
 ### "403 Forbidden: Your client does not have permission to get URL / from this server"
 
-This error indicats that your application requires authenticated requests but you have not provided an authentication token. If your site is intended to be public, first check to see that the Cloud Run service is not set up to require authentication:
+This error indicates that your application requires authenticated requests but you have not provided an authentication token. If your site is intended to be public, first check to see that the Cloud Run service is not set up to require authentication:
 1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run) page for your project.
 1. From the list of services, select the relevant service and select the **Security** tab.
 1. Ensure that you have enabled **Allow unauthenticated invocations** and restart the Cloud Run service.

From 6e61dac04d9f88f40156334067caa8b180bcc26a Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Mon, 5 Aug 2024 15:22:01 -0700
Subject: [PATCH 18/96] new data container procedures

---
 custom_dc/build_image.md  |  2 +-
 custom_dc/custom_data.md  | 30 ++++++++++---
 custom_dc/data_cloud.md   | 76 +++++++++------------------------
 custom_dc/deploy_cloud.md | 12 +++++-
 custom_dc/index.md        | 24 ++++-------
 custom_dc/quickstart.md   | 89 +++++++++++++++++++++------------------
 6 files changed, 111 insertions(+), 122 deletions(-)

diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md
index 1a0130b7b..e28018b6d 100644
--- a/custom_dc/build_image.md
+++ b/custom_dc/build_image.md
@@ -139,7 +139,7 @@ To run the container with a remote Cloud SQL database, see [Start the Docker con
 
 To upload and deploy the container to the Cloud, see [Deploy a custom instance to Google Cloud](/custom_dc/deploy_cloud.html) for procedures.
 
-## Run the container with the local SQLite database
+## Run the custom services container locally
 
 Start the services using the locally built repo. If you have made changes to any of the UI components (or directories), be sure to map the `custom` directories (or alternative directories) to the Docker `workspace` directory.
 
diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md
index 5060e725d..56f7fb0e5 100644
--- a/custom_dc/custom_data.md
+++ b/custom_dc/custom_data.md
@@ -240,16 +240,32 @@ The `sources` section is optional. It encodes the sources and provenances associ
 
 ## Load local custom data
 
-To load custom data uploaded to Google Cloud, see instead [Pointing the local Data Commons site to the Cloud data](/custom_dc/data_cloud.html) for procedures.
+To load custom data uploaded to Google Cloud, see instead [ ](/custom_dc/deploy_cloud.html) for procedures.
 
-### Configure custom directories
+### Configure environment variables
 
 Edit the `env.list` file as follows:
-- Set the `OUTPUT_DIR` variable to the directory where your input files are stored. The load step will create a `datacommons` subdirectory under this directory.
+- Set the `INPUT_DIR` variable to the directory where your input files are stored. 
+- Set the `OUTPUT_DIR` variable to the directory where you would like the output files to be store. This can be the same or different from the input directory. When you rerun the Docker data management container, it will create a `datacommons` subdirectory under this directory.
 
-### Start the Docker container with local custom data {#docker-data}
+**Notes:**
+- NL support increases the startup time of your server and consumes more resources. If you don't want NL functionality, you can disable it by updating the `ENABLE_MODEL` flag from `true` to `false`.
+- If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables file containing secrets. Instead, store it locally only. If you are using git/Github, you can add the file name to the `.gitignore` file.
 
-Once you have configured everything, use the following command to restart the Docker container, mapping your output directory to the same path in Docker:
+### Start the Docker containers with local custom data {#docker-data}
+
+Once you have configured everything, use the following commands to restart the Docker containers, mapping your input and output directories to the same path in Docker.
+
+In one terminal window, from the root directory, run:
+
+
+docker run \
+--env-file $PWD/custom_dc/env.list \ \
+-v INPUT_DIRECTORY:INPUT_DIRECTORY \
+gcr.io/datcom-ci/datacommons-data:stable
+
+ +In another terminal window, from the root directory, run:
 docker run -it \
@@ -260,7 +276,7 @@ docker run -it \
 gcr.io/datcom-ci/datacommons-website-compose:stable
 
-Every time you make changes to the CSV or JSON files, you should reload the data, as described below. +Every time you make changes to the CSV or JSON files, you will need to rerun both of these commands. ## Load custom data in SQLite @@ -268,7 +284,7 @@ As you are iterating on changes to the source CSV and JSON files, you will need You can load the new/updated data from SQLite using the `/admin` page on the site: -1. Optionally, in the `env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data. + 1. Start the Docker container as usual, being sure to map the path to the directory containing the custom data (see command above). 1. With the services running, navigate to the `/admin` page. If a secret is required, enter it in the text field, and click **Load**. This runs a script inside the Docker container, that converts the CSV data into SQL tables, and generates embeddings in the container as well. The database is created as OUTPUT_DIRECTORY/datacommons/datacommons.db and embeddings are generated in OUTPUT_DIRECTORY/datacommons/nl/. diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 5664aaf7b..64cfe98a0 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -8,18 +8,14 @@ parent: Build your own Data Commons {:.no_toc} # Test data in Google Cloud -This page shows you how to store your custom data in Google Cloud database and load it into a local instance. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). +This page shows you how to store your custom data in Google Cloud Storage load it into Google Cloud SQL. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). * TOC {:toc} ## Overview -Once you have tested locally, you need to get your data into Google Cloud so you can test it remotely. You can continue to run the custom Data Commons instance locally, but retrieve data from the Cloud. In this scenario, the system is set up like this: - -![setup3](/assets/images/custom_dc/customdc_setup3.png) - -You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and the custom Data Commons importer will transform, store, and query the data in a [Google Cloud SQL](https://cloud.google.com/sql) database. +Once you have tested locally, you need to get your data into Google Cloud so you can test it remotely. You pload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. ## Prerequisites @@ -33,8 +29,22 @@ You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud. While you are testing, you can start with a single Google Cloud region; to be close to the base Data Commons data, you can use `us-central1`. However, once you launch, you may want to host your data and application closer to where your users will be. In any case, you should use the _same region_ for your Google Cloud SQL instance, the Google Cloud Storage buckets, and the [Google Cloud Run service](deploy_cloud.md) where you will host the site. For a list of supported regions, see Cloud SQL [Manage instance locations](https://cloud.google.com/sql/docs/mysql/locations). +### Create a Google Cloud Storage bucket + +This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated embeddings files in the + +1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project. +1. Next to **Buckets**, click **Create**. +1. Enter a name for this bucket. +1. For the **Location type**, choose the same regional options as for Cloud SQL above. +1. When you have finished setting all the configuration options, click **Create**. +1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data. +1. Name the folder as desired. Record the folder path as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. + ### Create a Google Cloud SQL instance +This stores the data that will be served at run time. The Data Commons data management + 1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances) for your project. 1. Next to **Instances**, click **Create Instance**. 1. Click **Choose MySQL.** @@ -50,15 +60,7 @@ While you are testing, you can start with a single Google Cloud region; to be cl 1. Choose a name for the database or use the default, `datacommons`. 1. Click **Create**. -### Create a Google Cloud Storage bucket - -1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project. -1. Next to **Buckets**, click **Create**. -1. Enter a name for this bucket. -1. For the **Location type**, choose the same regional options as for Cloud SQL above. -1. When you have finished setting all the configuration options, click **Create**. -1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data. -1. Name the folder as desired. Record the folder path as gs://BUCKET_NAME/FOLDER_PATH for setting the `OUTPUT_DIR` environment variable below. +### ### Set environment variables @@ -71,12 +73,11 @@ While you are testing, you can start with a single Google Cloud region; to be cl - `DB_NAME` - `DB_USER` - `DB_PASS` + - `INPUT_DIR` - `OUTPUT_DIR` See comments in the [`env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/env.list) file for the correct format for each option. -1. Optionally, set an `ADMIN_SECRET` to use when loading the data through the `/admin` page later. - Warning: Do not use any quotes (single or double) or spaces when specifying the values. ## Upload data files to Google Cloud Storage @@ -89,11 +90,11 @@ Note: Do not upload the local `datacommons` subdirectory or its files. As you are iterating on changes to the source CSV and JSON files, you can re-upload them at any time, either overwriting existing files or creating new folders. To load them into Google SQL, follow the procedures below. -## Point the local Data Commons site to the Cloud data +## Load the data into Cloud SQL ### Generate credentials for Google Cloud default application -Before you can connect to the Cloud SQL instance, you need to generate credentials that can be used in the local Docker container. You should refresh the credentials every time you restart the Docker container. +Before you can connect to the Cloud SQL instance, you need to generate credentials that can be used in the data management job. You should refresh the credentials every time you rerun the Cloud Run job. Open a terminal window and run the following command: @@ -114,45 +115,8 @@ If you are prompted to install the Cloud Resource Manager API, press `y` to acce ### Start the Docker container with Cloud data {#docker-data} -#### Run with a prebuilt image - -If you have not made changes that require a local build, and just want to run the pre-downloaded image, from your repository root, run: - -
-docker run -it \
---env-file $PWD/custom_dc/env.list \
--p 8080:8080 \
--e DEBUG=true \
--e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \
--v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro \
-gcr.io/datcom-ci/datacommons-website-compose:stable
-
- -#### Run with a locally built repo - -If you have made local changes and have a [locally built repo](/custom_dc/build_image.html#build-repo), from the root of the repository, run the following: - -
-docker run -it \  
---env-file $PWD/custom_dc/env.list \  
--p 8080:8080 \  
--e DEBUG=true \  
--e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \  
--v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro \  
-datacommons-website-compose:DOCKER_TAG  
-
- -_`DOCKER_TAG`_ is the tag you specified when you built the repo. - -### Load custom data in Cloud SQL {#load-data-cloudsql} - -Each time you upload new versions of the source CSV and JSON files, you need to load the new/updated data into Google Cloud SQL. Custom Data Commons allows you to reload data on the fly, while the website is running, so even multiple users can reload data. -You can load the new/updated data from Cloud Storage using the `/admin` page on the site: -1. Optionally, in the `env.list` file, set the `ADMIN_SECRET` environment variable to a string that authorizes users to load data. -1. Start the Docker container as described above. -1. With the services running, navigate to the `/admin` page. If a secret is required, enter it in the text field, and click **Load**. This runs a script inside the Docker container, that converts the CSV data in Cloud Storage into SQL tables, and stores them in the Cloud SQL database you created earlier. It also generates embeddings in the Google Cloud Storage folder into which you uploaded the CSV/JSON files, in a `datacommons/nl/` subfolder. ## Inspect the Cloud SQL database diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 6a3bf3fba..811c31864 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -8,19 +8,27 @@ parent: Build your own Data Commons {:.no_toc} # Deploy a custom instance to Google Cloud -This page shows you how to create an artifact and run it in Google Cloud Run. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow). +This page shows you how to store your custom data in Google Cloud database and create the data management and services containers as Google Cloud Platform artifacts and run them Google Cloud Run. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). * TOC {:toc} ## System overview -When you are ready to launch your custom Data Commons site, we recommend hosting your site in [Google Cloud Run](https://cloud.google.com/run/), which is a serverless solution that is by far the simplest and least expensive option, providing auto-scaling. This is the production setup: +Once you have tested locally, you need to get your data and code into Google Cloud so you can test it there. In this scenario, the system is set up like this:When you are ready to launch your custom Data Commons site, we recommend hosting it [Google Cloud Run](https://cloud.google.com/run/), which is a serverless solution that is by far the simplest and least expensive option, providing auto-scaling. This is the production setup: ![setup4](/assets/images/custom_dc/customdc_setup4.png) You push a locally built Docker image to the [Google Cloud Artifact Registry](https://cloud.google.com/artifact-registry), and then deploy the image in Cloud Run. +## Overview + +![setup3](/assets/images/custom_dc/customdc_setup3.png) + +You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and the custom Data Commons importer will transform, store, and query the data in a [Google Cloud SQL](https://cloud.google.com/sql) database. + + + ## One-time setup: Create a Google Artifact Registry repository 1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project. diff --git a/custom_dc/index.md b/custom_dc/index.md index bdbe6e548..4f7454fec 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -6,10 +6,8 @@ has_children: true --- {:.no_toc} -# Build your own Data Commons -* TOC -{:toc} +# Build your own Data Commons ## Overview @@ -55,18 +53,15 @@ For the following use cases, a custom Data Commons instance is not necessary: ## System overview -Essentially, a custom Data Commons instance is a mirror of the public Data Commons, that runs in a [Docker](http://docker.com) container hosted in the cloud. In the browsing tools, the custom data appears alongside the base data in the list of variables. When a query is sent to the custom website, the Mixer server fetches both the custom and base data to provide multiple visualizations. At a high level, here is a conceptual view of a custom Data Commons instance: +Essentially, a custom Data Commons instance is a mirror of the public Data Commons, that runs in [Docker](http://docker.com) containers hosted in the cloud. In the browsing tools, the custom data appears alongside the base data in the list of variables. When a query is sent to the custom website, a Data Commons server fetches both the custom and base data to provide multiple visualizations. At a high level, here is a conceptual view of a custom Data Commons instance: ![setup1](/assets/images/custom_dc/customdc_setup1.png){: height="450" } -The custom Data Commons Docker container consists of the following components: +A custom Data Commons instance uses custom data that you provide as raw CSV files. An importer script converts the CSV data into the Data Commons format and stores this in a SQL database. For local development, we provide a lightweight, open-source [SQLite](http://sqlite.org) database; for production, we recommend that you use [Google Cloud SQL](https://cloud.google.com/sql/). -- A [Nginx reverse proxy server](https://www.nginx.com/resources/glossary/reverse-proxy-server/), which routes incoming requests to the web or API server -- A Python-Flask web server, which handles interactive requests from users -- An Python-Flask NL server, for generating embeddings and serving natural language queries -- A Go Mixer, also known as the API server, which serves programmatic requests using Data Commons APIs. The SQL query engine is built into the Mixer, which sends queries to both the local and remote data stores to find the right data. If the Mixer determines that it cannot fully resolve a user query from the custom data, it will make an REST API call, as an anonymous "user" to the base Data Commons Mixer and data. +In addition to the data, a custom Data Commons instance consists of two Docker containers: one with the core services that serve the data and website; and one with additional utilities for managing and loading custom data and embeddings used for natural-language processing. -In addition to the Docker container, a custom Data Commons instance uses custom data that you provide as raw CSV files. The web server calls an importer script that converts the CSV data into the Data Commons format and stores this in a SQL database. For local development, we provide a lightweight, open-source [SQLite](http://sqlite.org) database; for production, we recommend that you use [Google Cloud SQL](https://cloud.google.com/sql/). +Details about the components that make up the containers are provided in the [Getting started](quickstart.md) guide. ## Requirements and cost @@ -88,9 +83,8 @@ The cost of running a site on Google Cloud Platform depends on the size of your ## Recommended workflow 1. Work through the [Get started](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. -1. Prepare your real-world custom data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Work with custom data](/custom_dc/custom_data.html). If you are just testing custom data to add to the base Data Commons site, you don't need to go any further. -1. If you are launching your own Data Commons site, and want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html). -1. If you are launching your own Data Commons site, upload your data to Google Cloud Platform and continue to use the local instance to test and validate the site. We recommend using Google Cloud Storage to store your data, and Google Cloud SQL to receive SQL queries from the local servers. See [Test data in Google Cloud](/custom_dc/data_cloud.html). -1. When you are satisfied that everything is working correctly, and are getting closer to launch, upload your custom site to Google Cloud Run and continue to test in the Cloud. See [Deploy the custom instance to Google Cloud](/custom_dc/deploy_cloud.html) +1. Prepare your real-world custom data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Work with custom data](/custom_dc/custom_data.html). +1. If you want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html). +1. When you have finished testing locally, host your data and code in Google Cloud Platform: upload your data to store your data in Google Cloud Storage, use Google Cloud SQL to receive SQL queries, and upload the Docker containers as artifacts to Google Cloud Run. See [Run in Google Cloud](/custom_dc/data_cloud.html). 1. Launch and productionize your site for external traffic. See [Launch a custom site](/custom_dc/launch_cloud.html). -1. For future updates and launches, continue to make UI and data changes locally and upload the data to Cloud Storage, before deploying the changes to Cloud Run. +1. For future updates and launches, continue to make UI and data changes locally, before deploying the changes to GCP. diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index ac05585b2..cce3c21f4 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -8,7 +8,7 @@ parent: Build your own Data Commons {:.no_toc} # Getting started -This page shows you how to run a local custom Data Commons instance inside a Docker container and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/). +This page shows you how to run a local custom Data Commons instance inside Docker containers and load sample custom data from a local SQLite database. A custom Data Commons instance uses code from the public open-source repo, available at [https://github.com/datacommonsorg/](https://github.com/datacommonsorg/). This is step 1 of the [recommended workflow](/custom_dc/index.html#workflow). @@ -21,6 +21,14 @@ The instructions in this page use the following setup: ![setup2](/assets/images/custom_dc/customdc_setup2.png) +The "data management" Docker container consists of scripts that convert custom CSV file data into SQL tables in a local SQLite database, and generate embeddings for custom data. + +The "services" Docker container consists of the following Data Commons components: +- A [Nginx reverse proxy server](https://www.nginx.com/resources/glossary/reverse-proxy-server/), which routes incoming requests to the web or API server +- A Python-Flask web server, which handles interactive requests from users +- An Python-Flask NL server, for generating embeddings and serving natural language queries +- A Go Mixer, also known as the API server, which serves programmatic requests using Data Commons APIs. The SQL query engine is built into the Mixer, which sends queries to both the local and remote data stores to find the right data. If the Mixer determines that it cannot fully resolve a user query from the custom data, it will make an REST API call, as an anonymous "user" to the base Data Commons Mixer and data. + ## Prerequisites - Obtain a [GCP](https://console.cloud.google.com/welcome) billing account and project. @@ -67,12 +75,10 @@ cd website | DIRECTORY 1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. -1. For the `OUTPUT_DIR`, set it to the full path to the `sample` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample. -1. Leave `ADMIN_SECRET` blank for now. - -Warning: Do not use any quotes (single or double) or spaces when specifying the values. +1. Set the `INPUT_DIR`, set it to the full path to the `sample` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample. +1. For the `OUTPUT_DIR`, set it to the same path as the `INPUT_DIR`. -Note: If you are storing your source code in a public/open-source version control system, we recommend that you do not store the environment variables file containing secrets. Instead, store it locally only. If you are using git/Github, you can add the file name to the `.gitignore` file. +**Warning:** Do not use any quotes (single or double) or spaces when specifying the values. ## About the downloaded files @@ -107,17 +113,43 @@ Note: If you are storing your source code in a public/open-source version contro -## Start the services {#start-services} +## Load data + +In this step, we will add sample data that we have included as part of the download for you to load it into your custom instance. This data is from the Organisation for Economic Co-operation and Development (OECD): "per country data for annual average wages" and "gender wage gaps". + +To load the sample data: 1. If you are running on Windows or Mac, start Docker Desktop and ensure that the Docker Engine is running. -1. Open a terminal window, and from the root directory, run the following command: +1. Open a terminal window, and from the root directory, run the following command to start the data management Docker container: + +```shell +docker run -it \ +--env-file $PWD/custom_dc/env.list \ +-v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ +gcr.io/datcom-ci/datacommons-data:stable +``` + +This does the following: + +- The first time you run it, downloads the latest stable Data Commons data image, `gcr.io/datcom-ci/datacommons-data:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. +- Maps the input sample data to a Docker path. +- Starts a Docker container. +- Imports the data from the CSV files, resolves entities, and writes the data to a SQLite database file, `custom_dc/sample/datacommons/datacommons.db`. +- Generates embeddings in `custom_dc/sample/datacommons/nl` and loads them. (To learn more about embeddings generation, see the [FAQ](faq.md#natural-language-processing). + +Once the container has executed all the functions in the scripts, it shuts down. + +## Start the services {#start-services} + +1. Open a new terminal window. +1. From the root directory, run the following command to start the services Docker container: ```shell docker run -it \ -p 8080:8080 \ -e DEBUG=true \ --env-file $PWD/custom_dc/env.list \ --v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ +-v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:stable ``` @@ -126,18 +158,18 @@ Note: If you are running on Linux, depending on whether you have created a ["sud This command does the following: - The first time you run it, downloads the latest stable Data Commons image, `gcr.io/datcom-ci/datacommons-website-compose:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. -- Starts a Docker container in interactive mode. -- Starts development/debug versions of the Web Server, NL Server, and Mixer, as well as the Nginx proxy, inside the container -- Maps the sample data to a Docker path, so the servers do not need to be restarted when you load the sample data +- Starts a long-running Docker container. +- Starts development/debug versions of the Web Server, NL Server, and Mixer, as well as the Nginx proxy, inside the container. +- Maps the output sample data to a Docker path. ### Stop and restart the services If you need to restart the services for any reason, do the following: -1. In the terminal window where the services are running, press Ctrl-c to kill the Docker container. -1. Rerun the `docker run` command as usual. +1. In the terminal window where the container is running, press Ctrl-c to kill the Docker container. +1. Rerun the `docker run` command as described in [Start the services](#start-services). -Tip: If you close the terminal window in which you started the Docker container, you can kill it as follows: +Tip: If you close the terminal window in which you started the Docker services container, you can kill it as follows: 1. Open another terminal window, and from the root directory, get the Docker container ID. @@ -155,33 +187,10 @@ Tip: If you close the terminal window in which you started the Docker container, ## View the local website -Once Docker is up and running, visit your local instance by pointing your browser to [http://localhost:8080](http://localhost:8080). You should see something like this: +Once the services are up and running, visit your local instance by pointing your browser to [http://localhost:8080](http://localhost:8080). You should see something like this: ![screenshot_homepage](/assets/images/custom_dc/customdc_screenshot1.png){: width="900"} -You can browse the various Data Commons tools (Variables, Map, Timelines, etc.) and work with the entire base dataset. - -## Load sample data - -In this step, we will add sample data that we have included as part of the download for you to load it into your custom instance. This data is from the Organisation for Economic Co-operation and Development (OECD): "per country data for annual average wages" and "gender wage gaps". - -To load and view the sample data: - -1. Point your browser to the admin page at [http://localhost:8080/admin](http://localhost:8080/admin). -1. Since you have not yet specified an `ADMIN_SECRET`, leave it blank. -1. Click **Load Data**. It may take a few seconds to load. - -This does the following: - -- Imports the data from the CSV files, resolves entities, and writes the data to a SQLite database file, `custom_dc/sample/datacommons/datacommons.db`. -- Generates embeddings in the Docker image and loads them. (To learn more about embeddings generation, see the [FAQ](faq.md#natural-language-processing). - -Tip: When you restart the Docker instance, all data in the SQLite database is lost. If you want to preserve the sample data and have it automatically always load after restarting your Docker instance, without having to run the load data function each time, include this additional flag in your Docker run command: - -```shell --v $PWD/custom_dc/sample/datacommons:/sqlite -``` - Now click the **Timeline** link to visit the Timeline explorer. Try entering a country and click **Continue**. Now, in the **Select variables** tools, you'll see the new variables: ![screenshot_timeline](/assets/images/custom_dc/customdc_screenshot2.png){: width="900"} @@ -194,8 +203,6 @@ To issue natural language queries, click the **Search** link. Try NL queries aga ![screenshot_search](/assets/images/custom_dc/customdc_screenshot3a.png){: width="900"} -Note that NL support increases the startup time of your server and consumes more resources. If you don't want NL functionality, you can disable it by updating the `ENABLE_MODEL` flag in `sqlite_env.list` from `true` to `false`. - ## Send an API request A custom instance can accept REST API requests at the endpoint `/core/api/v2/`. To try it out, here's an example request that returns the same data as in the interactive queries above, using the `observation` API. You can enter this query in your browser to get nice output: From 24ea3c4dbfab5e73e76045a94f14fddc1d0a873f Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 5 Aug 2024 16:29:31 -0700 Subject: [PATCH 19/96] more work --- custom_dc/build_image.md | 2 -- custom_dc/data_cloud.md | 23 ++++++----------------- custom_dc/deploy_cloud.md | 18 ++++++++++++++++++ custom_dc/troubleshooting.md | 18 ++++++++++++++++++ 4 files changed, 42 insertions(+), 19 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index e28018b6d..7503e0d08 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -135,8 +135,6 @@ It will take several minutes to build. To run the container with the local SQLite database, start the Docker container as described below. -To run the container with a remote Cloud SQL database, see [Start the Docker container with Cloud data](/custom_dc/data_cloud.html#docker-data) for procedures. - To upload and deploy the container to the Cloud, see [Deploy a custom instance to Google Cloud](/custom_dc/deploy_cloud.html) for procedures. ## Run the custom services container locally diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 64cfe98a0..bba22fbc7 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -43,7 +43,7 @@ This stores the CSV and JSON files that you will upload whenever your data chang ### Create a Google Cloud SQL instance -This stores the data that will be served at run time. The Data Commons data management +This stores the data that will be served at run time. The Data Commons data management job will create this data when it is started. 1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances) for your project. 1. Next to **Instances**, click **Create Instance**. @@ -54,31 +54,20 @@ This stores the data that will be served at run time. The Data Commons data mana 1. For the **Location type**, choose the relevant regional option. 1. When you have finished setting all the configuration options, click **Create Instance**. It may take several minutes for the instance to be created. 1. When the instance is created and the left navigation bar appears, select **Users**. -1. Add at least one user and password. +1. Add at least one user and password. Be sure to at least assign a password to the "root" user. 1. Select **Databases**. 1. Click **Create Database**. 1. Choose a name for the database or use the default, `datacommons`. 1. Click **Create**. -### +### Create a Google Cloud Run -### Set environment variables +Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. -1. Using your favorite editor, open `custom_dc/env.list`. -1. Set `USE_SQLITE=false` and `USE_CLOUDSQL=true` -1. Set values for all of the following: +1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/run) for your project. +1. Click **Create job**. - - `CLOUDSQL_INSTANCE` - - `GOOGLE_CLOUD_PROJECT` - - `DB_NAME` - - `DB_USER` - - `DB_PASS` - - `INPUT_DIR` - - `OUTPUT_DIR` - See comments in the [`env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/env.list) file for the correct format for each option. - -Warning: Do not use any quotes (single or double) or spaces when specifying the values. ## Upload data files to Google Cloud Storage diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 811c31864..ffaabb4b1 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -39,6 +39,24 @@ You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud. 1. Enable or disable **Immutable image tags** according to the workflow you prefer; that is, if you want to be able to reuse the same Docker tag for new images, keep this option disabled. 1. Click **Create**. +### Set environment variables + +1. Using your favorite editor, open `custom_dc/env.list`. +1. Set `USE_SQLITE=false` and `USE_CLOUDSQL=true` +1. Set values for all of the following: + + - `CLOUDSQL_INSTANCE` + - `GOOGLE_CLOUD_PROJECT` + - `DB_NAME` + - `DB_USER` + - `DB_PASS` + - `INPUT_DIR` + - `OUTPUT_DIR` + + See comments in the [`env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/env.list) file for the correct format for each option. + +Warning: Do not use any quotes (single or double) or spaces when specifying the values. + ## Upload the Docker container to Google Cloud This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry, and then deploy to Google Cloud Run. diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index a56838d94..1166332aa 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -68,6 +68,12 @@ If the load page does not show any errors but data still does not load, try chec 1. In the `env.list` file, check that you are not using single or double quotes around any of the values. 1. Check your Docker command line for invalid arguments. Often Docker won't give any error messages but failures will show up at runtime. +## NL queries not returning custom data + +If you have previously been able to get custom data in your natural-language query results, but this has suddenly stopped working, this is due to embeddings incompatibility issues between releases. To fix this, do the following: +1. Delete the `datacommons` subdirectory from your output directory. +1. Restart the services, and reload the data from the /admin page. + ## Website display problems If styles aren't rendering properly because CSS, logo files or JS files are not loading, check your Docker command line for invalid arguments. Often Docker won't give any error messages but failures will show up at runtime. @@ -93,8 +99,20 @@ This error indicates that your application requires authenticated requests but y If you are unable to select this option, this indicates that there is an IAM permissions setup issue with your project or account. See the [Cloud Run Troubleshooting](https://cloud.google.com/run/docs/troubleshooting#unauthorized-client) for details on how to fix this. +### "403: Not authorized to access resources" + ### "502 Bad Gateway" +This is a general indication that the Data Commons servers are not running. Check the **Logs ** page for the Cloud Run service in the Google Cloud Console. Here are common errors: + +`403 Forbidden: Not authorized to access resources` + +This may be . To fix this: +1. In the Cloud Run service page in the Cloud Console, select the **Revisions** tab, and scroll to view the **Environment variables**. +1. Ensure that the `DB_USER` and `DB_PASS` variables are set to the values you set when creating the [SQL database](/custom_dc/data_cloud.html#create-sql) + + + If you see no errors in the logs, except `connect() failed (111: Connection refused) while connecting to upstream`, try the following: 1. Wait a few minutes and try to access the app again. Sometimes it appears to be deployed, but some of the services haven't yet started up. From d4d81c8031320912fc023625f901cc010cf7ea90 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 6 Aug 2024 13:41:41 -0700 Subject: [PATCH 20/96] more work --- custom_dc/data_cloud.md | 44 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 40 insertions(+), 4 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index bba22fbc7..a100334c4 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -31,15 +31,16 @@ While you are testing, you can start with a single Google Cloud region; to be cl ### Create a Google Cloud Storage bucket -This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated embeddings files in the +This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated embeddings files in a `datacommons/nl` subdirectory. 1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project. 1. Next to **Buckets**, click **Create**. 1. Enter a name for this bucket. 1. For the **Location type**, choose the same regional options as for Cloud SQL above. 1. When you have finished setting all the configuration options, click **Create**. -1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data. -1. Name the folder as desired. Record the folder path as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. +1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your input CSV and JSON files and name it as desired. +1. Optionally, create a separate folder to hold the output embeddings files, or just use the same one as for the input. The Docker data management container, when you run it, will create subdirectory in the output directory called `datacommons`. +1. Record the folder path(s) as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. ### Create a Google Cloud SQL instance @@ -60,12 +61,47 @@ This stores the data that will be served at run time. The Data Commons data mana 1. Choose a name for the database or use the default, `datacommons`. 1. Click **Create**. -### Create a Google Cloud Run +### Optional: Edit environment variables file + +If for some reason you would like to access the Cloud data from a locally running custom instance, or you would like to maintain your settings in a local file for reference purposes, edit the `custom_dc/env.list` file and set the following variables. (Otherwise, you can simply provide them using the Cloud Console, and skip this step.) + +- `INPUT_DIR`: Set to the + +- + + + + + + + + +### Create a Google Cloud Run job Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. 1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/run) for your project. 1. Click **Create job**. +1. In the **Container image URL** field, click **Select** to open the **Select container image** window. +1. Click the **Container Registry** tab. +1. Next to the **Project**, click **Change**, search for **datcom-ci** and select it. +1. In the list of images that appears, navigate to and expand **gcr.io/datcom-ci/datacommons-data**, highlight the image you want, **stable** or **latest**, and click **Select**. +1. Optionally, in the **Job name** field, enter an alternative name as desired. +1. In the **Region** field, select the region you chose as your location. +1. Leave the default **Number of tasks** as 1. +1. Expand **Container, Volumes, Connections, Security** and expand **Settings**, and set the following options: + - **Resources** > **Memory**: **8 GiB** + - **Resources** > **CPU**: **2** + - **Task timeout** > **Number of retries per failed task**: **3** +1. Click **Create** to save the job (but not run it yet). + +#### Set environment variables + +1. Click the **Variables and Secrets** tab. +1. Click **Add variable**. + + + From 166e7e1655be87bf3e404e4c0a0d6917813480b2 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 6 Aug 2024 14:55:22 -0700 Subject: [PATCH 21/96] complete data draft --- custom_dc/data_cloud.md | 86 +++++++++++++++-------------------------- 1 file changed, 31 insertions(+), 55 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index a100334c4..500f63fa3 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -25,13 +25,13 @@ Once you have tested locally, you need to get your data into Google Cloud so you ## One-time setup steps -### Choose a location +### Step 1: Choose a location While you are testing, you can start with a single Google Cloud region; to be close to the base Data Commons data, you can use `us-central1`. However, once you launch, you may want to host your data and application closer to where your users will be. In any case, you should use the _same region_ for your Google Cloud SQL instance, the Google Cloud Storage buckets, and the [Google Cloud Run service](deploy_cloud.md) where you will host the site. For a list of supported regions, see Cloud SQL [Manage instance locations](https://cloud.google.com/sql/docs/mysql/locations). -### Create a Google Cloud Storage bucket +### Step 2: Create a Google Cloud Storage bucket -This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated embeddings files in a `datacommons/nl` subdirectory. +This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated files files in a `datacommons` subdirectory when you run the data management job. 1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project. 1. Next to **Buckets**, click **Create**. @@ -39,14 +39,14 @@ This stores the CSV and JSON files that you will upload whenever your data chang 1. For the **Location type**, choose the same regional options as for Cloud SQL above. 1. When you have finished setting all the configuration options, click **Create**. 1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your input CSV and JSON files and name it as desired. -1. Optionally, create a separate folder to hold the output embeddings files, or just use the same one as for the input. The Docker data management container, when you run it, will create subdirectory in the output directory called `datacommons`. +1. Optionally, create a separate folder to hold the output embeddings files, or just use the same one as for the input. 1. Record the folder path(s) as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. -### Create a Google Cloud SQL instance +### Step 3: Create a Google Cloud SQL instance This stores the data that will be served at run time. The Data Commons data management job will create this data when it is started. -1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances) for your project. +1. Go to https://console.cloud.google.com/sql/instances for your project. 1. Next to **Instances**, click **Create Instance**. 1. Click **Choose MySQL.** 1. If necessary, enable APIs as directed. @@ -59,28 +59,14 @@ This stores the data that will be served at run time. The Data Commons data mana 1. Select **Databases**. 1. Click **Create Database**. 1. Choose a name for the database or use the default, `datacommons`. -1. Click **Create**. +1. Click **Create**. +1. In the **Overview** page for the new instance, record the **Connection name** to set in environment variables in the next step. -### Optional: Edit environment variables file - -If for some reason you would like to access the Cloud data from a locally running custom instance, or you would like to maintain your settings in a local file for reference purposes, edit the `custom_dc/env.list` file and set the following variables. (Otherwise, you can simply provide them using the Cloud Console, and skip this step.) - -- `INPUT_DIR`: Set to the - -- - - - - - - - - -### Create a Google Cloud Run job +### Step 4: Create a Google Cloud Run job Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. -1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/run) for your project. +1. Go to https://console.cloud.google.com/run/ for your project. 1. Click **Create job**. 1. In the **Container image URL** field, click **Select** to open the **Select container image** window. 1. Click the **Container Registry** tab. @@ -97,17 +83,24 @@ Since you won't need to customize the data management container, you can simply #### Set environment variables +This procedure shows you how to set the environment variables using the Cloud Console. Alternatively, you could set them in the `custom_dc/env.list` file, pipe the file contents to a system variable, and use the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/run/jobs?hl=en) to update the job. + 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. - - - - - +1. Add names and values for the following environment variables: +- `USE_CLOUDSQL`: set to `true` +- `DC_API_KEY`: set to the API key for your instance +- `INPUT_DIR`: set to the Cloud Storage bucket and input folder that you created in step 2 above. +- `OUTPUT_DIR`: set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. +- `CLOUDSQL_INSTANCE`: set to the full connection name of the instance you created in step 3 above. +- `DB_USER`: set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. +- `DB_PASS`: set to the user's or root password you configured when you created the instance in step 3. +- `DB_NAME`: if you set it to a name other than `datacommons` +1. Click **Done** and **Update** to save. ## Upload data files to Google Cloud Storage -1. Go to [https://console.cloud.google.com/storage/browse](https://console.cloud.google.com/storage/browse) and select your custom Data Commons bucket. +1. Go to https://console.cloud.google.com/storage/browse and select your custom Data Commons bucket. 1. Navigate to the folder you created in the earlier step. 1. Click **Upload Files**, and select all your CSV files and `config.json`. @@ -115,36 +108,19 @@ Note: Do not upload the local `datacommons` subdirectory or its files. As you are iterating on changes to the source CSV and JSON files, you can re-upload them at any time, either overwriting existing files or creating new folders. To load them into Google SQL, follow the procedures below. -## Load the data into Cloud SQL - -### Generate credentials for Google Cloud default application - -Before you can connect to the Cloud SQL instance, you need to generate credentials that can be used in the data management job. You should refresh the credentials every time you rerun the Cloud Run job. - -Open a terminal window and run the following command: - -```shell -gcloud auth application-default login -``` - -This opens a browser window that prompts you to enter credentials, sign in to Google Auth Library and allow Google Auth Library to access your account. Accept the prompts. When it has completed, a credential JSON file is created in -`$HOME/.config/gcloud/application_default_credentials.json`. Use this in the command below to authenticate from the docker container. - -If you are prompted to specify a quota project for billing that will be used in the credentials file, run this command: - -
  
-gcloud auth application-default set-quota-project PROJECT_ID  
-
- -If you are prompted to install the Cloud Resource Manager API, press `y` to accept. +## Start the data management Cloud Run job -### Start the Docker container with Cloud data {#docker-data} +Now that everything is configured, and you have uploaded your data in Google Cloud Storage, you simply have to start the Cloud Run data management job to convert the CSV data into tables in the Cloud SQL database and generate the embeddings (in a `datacommons/nl` subfolder). +Every time you upload new data files or changes to the JSON file to Google Cloud Storage, you will need to rerun the job. +To run the job: - This runs a script inside the Docker container, that converts the CSV data in Cloud Storage into SQL tables, and stores them in the Cloud SQL database you created earlier. It also generates embeddings in the Google Cloud Storage folder into which you uploaded the CSV/JSON files, in a `datacommons/nl/` subfolder. +1. Go to https://console.cloud.google.com/run/jobs for your project. +1. From the list of jobs, click the link of the "datacommons-data" job you created above. +1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. To verify that the data has been loaded correctly, see the next step. -## Inspect the Cloud SQL database +### Inspect the Cloud SQL database To view information about the created tables: From 079fe62e942a060e482d52129f94bef5237bf8a2 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 7 Aug 2024 11:34:48 -0700 Subject: [PATCH 22/96] more changes --- custom_dc/data_cloud.md | 8 ++++---- custom_dc/deploy_cloud.md | 4 ++-- custom_dc/index.md | 5 +++-- custom_dc/launch_cloud.md | 4 ++-- 4 files changed, 11 insertions(+), 10 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 500f63fa3..4daff9212 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -1,21 +1,21 @@ --- layout: default -title: Test data in Google Cloud +title: Load data in Google Cloud nav_order: 6 parent: Build your own Data Commons --- {:.no_toc} -# Test data in Google Cloud +# Load data in Google Cloud -This page shows you how to store your custom data in Google Cloud Storage load it into Google Cloud SQL. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). +This page shows you how to store your custom data in Google Cloud, and create the data management container as a Google Cloud Run job. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). * TOC {:toc} ## Overview -Once you have tested locally, you need to get your data into Google Cloud so you can test it remotely. You pload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. +Once you have tested locally, the next step is to get your data into the Google Cloud Platform. You upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. ## Prerequisites diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index ffaabb4b1..fd6125880 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -1,12 +1,12 @@ --- layout: default -title: Deploy a custom instance to Google Cloud +title: Deploy services to Google Cloud nav_order: 7 parent: Build your own Data Commons --- {:.no_toc} -# Deploy a custom instance to Google Cloud +# Deploy services to Google Cloud This page shows you how to store your custom data in Google Cloud database and create the data management and services containers as Google Cloud Platform artifacts and run them Google Cloud Run. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). diff --git a/custom_dc/index.md b/custom_dc/index.md index 4f7454fec..ba65d3e2b 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -85,6 +85,7 @@ The cost of running a site on Google Cloud Platform depends on the size of your 1. Work through the [Get started](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. 1. Prepare your real-world custom data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Work with custom data](/custom_dc/custom_data.html). 1. If you want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html). -1. When you have finished testing locally, host your data and code in Google Cloud Platform: upload your data to store your data in Google Cloud Storage, use Google Cloud SQL to receive SQL queries, and upload the Docker containers as artifacts to Google Cloud Run. See [Run in Google Cloud](/custom_dc/data_cloud.html). -1. Launch and productionize your site for external traffic. See [Launch a custom site](/custom_dc/launch_cloud.html). +1. When you have finished testing locally, host your data and code in Google Cloud Platform: first, upload your data to Google Cloud Storage and create a Cloud Run job to load the data into Google Cloud SQL. See [Load data in Google Cloud](/custom_dc/data_cloud.html). +1. Build a custom image, upload it to the Google Cloud Artifact Registry and create a Cloud Run service to run the site. See [Deploy services to Google Cloud](deploy_cloud.md) +1. Launch and productionize your site for external traffic. See [Launch your Data Commons](/custom_dc/launch_cloud.html). 1. For future updates and launches, continue to make UI and data changes locally, before deploying the changes to GCP. diff --git a/custom_dc/launch_cloud.md b/custom_dc/launch_cloud.md index 80e7101c1..e50e12b4b 100644 --- a/custom_dc/launch_cloud.md +++ b/custom_dc/launch_cloud.md @@ -1,12 +1,12 @@ --- layout: default -title: Launch a custom site +title: Launch your Data Commons nav_order: 8 parent: Build your own Data Commons --- {:.no_toc} -# Launch a custom site +# Launch your Data Commons * TOC {:toc} From 5ccaba5a6054aa588bcc91a2e9fb5ad0023e3121 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Thu, 8 Aug 2024 13:10:11 -0700 Subject: [PATCH 23/96] more changes --- custom_dc/build_image.md | 2 +- custom_dc/custom_data.md | 11 +--- custom_dc/data_cloud.md | 113 +++++++++++++++++++++++++++++++++---- custom_dc/deploy_cloud.md | 116 ++++++++++++++------------------------ custom_dc/index.md | 2 +- custom_dc/quickstart.md | 5 +- 6 files changed, 150 insertions(+), 99 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index 7503e0d08..f639c72be 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -135,7 +135,7 @@ It will take several minutes to build. To run the container with the local SQLite database, start the Docker container as described below. -To upload and deploy the container to the Cloud, see [Deploy a custom instance to Google Cloud](/custom_dc/deploy_cloud.html) for procedures. +To upload and deploy the container to the Cloud, see [Deploy services to Google Cloud](/custom_dc/deploy_cloud.html) for procedures. ## Run the custom services container locally diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index 56f7fb0e5..56e3e72cf 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -278,16 +278,6 @@ gcr.io/datcom-ci/datacommons-website-compose:stable Every time you make changes to the CSV or JSON files, you will need to rerun both of these commands. -## Load custom data in SQLite - -As you are iterating on changes to the source CSV and JSON files, you will need to reload the data. Custom Data Commons allows you to reload data on the fly, while the website is running, so even multiple users can reload data with a shared Docker instance. - -You can load the new/updated data from SQLite using the `/admin` page on the site: - - -1. Start the Docker container as usual, being sure to map the path to the directory containing the custom data (see command above). -1. With the services running, navigate to the `/admin` page. If a secret is required, enter it in the text field, and click **Load**. This runs a script inside the Docker container, that converts the CSV data into SQL tables, and generates embeddings in the container as well. The database is created as OUTPUT_DIRECTORY/datacommons/datacommons.db and embeddings are generated in OUTPUT_DIRECTORY/datacommons/nl/. - ## Inspect the SQLite database If you need to troubleshoot custom data, it is helpful to inspect the contents of the generated SQLite database. @@ -317,3 +307,4 @@ country/BEL|average_annual_wage|2004|56195.68432|c/p/1 country/BEL|average_annual_wage|2005|55662.21541|c/p/1 ... ``` + diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 4daff9212..cb046bb2c 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -15,17 +15,20 @@ This page shows you how to store your custom data in Google Cloud, and create th ## Overview -Once you have tested locally, the next step is to get your data into the Google Cloud Platform. You upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. +Once you have tested locally, the next step is to get your data into the Google Cloud Platform. You upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data management Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. +TODO: add image + +Alternatively, if you have a very large data set, you may find it faster to store your input files and run the data management container locally, and output the data to Google Cloud Storage. If you would like to use this approach, see xxx ## Prerequisites - A [GCP](https://console.cloud.google.com/welcome) billing account and project. -- Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install-sdk). +- Optional: Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install-sdk). ## One-time setup steps -### Step 1: Choose a location +### Step 1: Choose a location {#location} While you are testing, you can start with a single Google Cloud region; to be close to the base Data Commons data, you can use `us-central1`. However, once you launch, you may want to host your data and application closer to where your users will be. In any case, you should use the _same region_ for your Google Cloud SQL instance, the Google Cloud Storage buckets, and the [Google Cloud Run service](deploy_cloud.md) where you will host the site. For a list of supported regions, see Cloud SQL [Manage instance locations](https://cloud.google.com/sql/docs/mysql/locations). @@ -38,13 +41,15 @@ This stores the CSV and JSON files that you will upload whenever your data chang 1. Enter a name for this bucket. 1. For the **Location type**, choose the same regional options as for Cloud SQL above. 1. When you have finished setting all the configuration options, click **Create**. -1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your input CSV and JSON files and name it as desired. -1. Optionally, create a separate folder to hold the output embeddings files, or just use the same one as for the input. +1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data and name it as desired. +1. Optionally, create separate folders to hold input and output files, or just use the same one as for the input. + + Note: If you plan to run the data management container locally, you only need an output folder to hold the embeddings files. 1. Record the folder path(s) as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. ### Step 3: Create a Google Cloud SQL instance -This stores the data that will be served at run time. The Data Commons data management job will create this data when it is started. +This stores the data that will be served at run time. The Data Commons data management job will create the SQL tables and populate them when you start the job. 1. Go to https://console.cloud.google.com/sql/instances for your project. 1. Next to **Instances**, click **Create Instance**. @@ -66,6 +71,10 @@ This stores the data that will be served at run time. The Data Commons data mana Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. +See also https://cloud.google.com/run/docs/create-jobs for links to more information on all the options you may set on your jobs. + +**Note:** If you plan to the always run the data management container locally, skip this step entirely and see instead xxx + 1. Go to https://console.cloud.google.com/run/ for your project. 1. Click **Create job**. 1. In the **Container image URL** field, click **Select** to open the **Select container image** window. @@ -81,16 +90,15 @@ Since you won't need to customize the data management container, you can simply - **Task timeout** > **Number of retries per failed task**: **3** 1. Click **Create** to save the job (but not run it yet). -#### Set environment variables - -This procedure shows you how to set the environment variables using the Cloud Console. Alternatively, you could set them in the `custom_dc/env.list` file, pipe the file contents to a system variable, and use the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/run/jobs?hl=en) to update the job. +{#env-vars} +Now set environment variables: 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. 1. Add names and values for the following environment variables: - `USE_CLOUDSQL`: set to `true` - `DC_API_KEY`: set to the API key for your instance -- `INPUT_DIR`: set to the Cloud Storage bucket and input folder that you created in step 2 above. +- `INPUT_DIR`: set to the Cloud Storage bucket and input folder that you created in step 2 above. - `OUTPUT_DIR`: set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. - `CLOUDSQL_INSTANCE`: set to the full connection name of the instance you created in step 3 above. - `DB_USER`: set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. @@ -100,6 +108,8 @@ This procedure shows you how to set the environment variables using the Cloud Co ## Upload data files to Google Cloud Storage +Note: If you plan to run the data management container locally, skip this step and see instead .... + 1. Go to https://console.cloud.google.com/storage/browse and select your custom Data Commons bucket. 1. Navigate to the folder you created in the earlier step. 1. Click **Upload Files**, and select all your CSV files and `config.json`. @@ -114,13 +124,15 @@ Now that everything is configured, and you have uploaded your data in Google Clo Every time you upload new data files or changes to the JSON file to Google Cloud Storage, you will need to rerun the job. +Note: If you plan to run the data management container locally, skip this step and see instead .... + To run the job: 1. Go to https://console.cloud.google.com/run/jobs for your project. 1. From the list of jobs, click the link of the "datacommons-data" job you created above. 1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. To verify that the data has been loaded correctly, see the next step. -### Inspect the Cloud SQL database +## Inspect the Cloud SQL database To view information about the created tables: @@ -133,3 +145,82 @@ To view information about the created tables: 1. Enter a query and click **Run**. For example, for the sample OECD data, if you do `select * from observations limit 10;`, you should see output like this: ![screenshot_sqlite](/assets/images/custom_dc/customdc_screenshot6.png){: height="400"} + +## Advanced setup (optional): Run the data management container locally + +This process is similar to running both data management and services containers locally, with a few exceptions: +- Your input directory will be the local file system, while the output directory will be a Google Cloud Storage bucket and folder. +- You must start the job with credentials to be passed to Google Cloud, to access the Cloud SQL instance. + +### Set environment variables + +To run a local instance of the services container, you need to set all the environment variables in the `custom_dc/env.list` file. See [above](#set-vars) for the details, with the following differences: +- For the `INPUT_DIR`, specify the full local path where your CSV and JSON files are stored, as described in the [Getting started](/custom_dc/). +- Set `GOOGLE_CLOUD_PROJECT` to your GCP project name. + +### Generate credentials for Google Cloud default application {#gen-creds} + +For the services to connect to the Cloud SQL instance, you need to generate credentials that can be used in the local Docker container for authentication. You should refresh the credentials every time you restart the Docker container. + +Open a terminal window and run the following command: + +```shell +gcloud auth application-default login +``` + +This opens a browser window that prompts you to enter credentials, sign in to Google Auth Library and allow Google Auth Library to access your account. Accept the prompts. When it has completed, a credential JSON file is created in +`$HOME/.config/gcloud/application_default_credentials.json`. Use this in the command below to authenticate from the docker container. + +The first time you run it, may be prompted to specify a quota project for billing that will be used in the credentials file. If so, run this command: + +
  
+gcloud auth application-default set-quota-project PROJECT_ID  
+
+ +If you are prompted to install the Cloud Resource Manager API, press `y` to accept. + +### Run the Docker container + +From your project root directory, run: + +
+docker run \
+--env-file $PWD/custom_dc/env.list \
+-v INPUT_DIRECTORY:INPUT_DIRECTORY \
+-e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \
+-v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro \
+gcr.io/datcom-ci/datacommons-data:VERSION
+
+ +The version is `latest` or `stable`. + +## Advanced setup (optional): Access Cloud data from a local services container + +For testing purposes, if you wish to run the services Docker container locally but access the data in Google Cloud, use the following procedures. + +### Set environment variables + +To run a local instance of the services container, you will need to set all the environment variables, as described [above](#env-vars) in the `custom_dc/env.list`. You must also set the `MAPS_API_KEY` to your Maps API key. + +### Generate credentials for Google Cloud default application + +See the section [above](#gen-creds) for procedures. + +### Run the Docker container + +From the root directory of your repo, run the following command, assuming you are using a locally built image: + +
  
+docker run -it \
+--env-file $PWD/custom_dc/env.list \
+-p 8080:8080 \
+-e DEBUG=true \
+-e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json \
+-v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro \
+-v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \
+[-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \]
+[-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \]
+datacommons-website-compose:DOCKER_TAG
+
+ + diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index fd6125880..4cdeb83e0 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -8,26 +8,18 @@ parent: Build your own Data Commons {:.no_toc} # Deploy services to Google Cloud -This page shows you how to store your custom data in Google Cloud database and create the data management and services containers as Google Cloud Platform artifacts and run them Google Cloud Run. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow). +This page shows you how to build a custom services Docker container as a GCP artifact, upload it to the Artifact Registry, and create a Google Cloud Run service. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow). * TOC {:toc} ## System overview -Once you have tested locally, you need to get your data and code into Google Cloud so you can test it there. In this scenario, the system is set up like this:When you are ready to launch your custom Data Commons site, we recommend hosting it [Google Cloud Run](https://cloud.google.com/run/), which is a serverless solution that is by far the simplest and least expensive option, providing auto-scaling. This is the production setup: +When you are ready to host your custom Data Commons site in production, you create a [Google Cloud Run](https://cloud.google.com/run/) service for the site. This is the production setup: ![setup4](/assets/images/custom_dc/customdc_setup4.png) -You push a locally built Docker image to the [Google Cloud Artifact Registry](https://cloud.google.com/artifact-registry), and then deploy the image in Cloud Run. - -## Overview - -![setup3](/assets/images/custom_dc/customdc_setup3.png) - -You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and the custom Data Commons importer will transform, store, and query the data in a [Google Cloud SQL](https://cloud.google.com/sql) database. - - +You push a locally built Docker image to the [Google Cloud Artifact Registry](https://cloud.google.com/artifact-registry), and then deploy the image as a Cloud Run service. ## One-time setup: Create a Google Artifact Registry repository @@ -35,31 +27,13 @@ You will upload your CSV and JSON files to [Google Cloud Storage](https://cloud. 1. Click **Create Repository**. 1. Specify a name for the repository. 1. In the **Format** option, select **Docker**. -1. In **Location type**, select **Region**, and specify a region. +1. In **Location type**, select **Region**, and specify the region you chose for your [Google Cloud SQL instance](/custom_dc/data_cloud.html#location) . 1. Enable or disable **Immutable image tags** according to the workflow you prefer; that is, if you want to be able to reuse the same Docker tag for new images, keep this option disabled. 1. Click **Create**. -### Set environment variables - -1. Using your favorite editor, open `custom_dc/env.list`. -1. Set `USE_SQLITE=false` and `USE_CLOUDSQL=true` -1. Set values for all of the following: +## Upload the Docker container to the Artifact Registry - - `CLOUDSQL_INSTANCE` - - `GOOGLE_CLOUD_PROJECT` - - `DB_NAME` - - `DB_USER` - - `DB_PASS` - - `INPUT_DIR` - - `OUTPUT_DIR` - - See comments in the [`env.list`](https://github.com/datacommonsorg/website/blob/master/custom_dc/env.list) file for the correct format for each option. - -Warning: Do not use any quotes (single or double) or spaces when specifying the values. - -## Upload the Docker container to Google Cloud - -This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry, and then deploy to Google Cloud Run. +This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry. Every time you rebuild the image, you need ro rerun this procedure. 1. Build a local version of the Docker image, following the procedure in [Build a local image](/custom_dc/build_image.html#build-repo). 1. Authenticate to gcloud: @@ -78,13 +52,17 @@ This procedure creates a "dev" Docker package that you upload to the Google Clou When prompted to confirm creating the credentials file, click `Y` to accept. -1. Build a target package from the source image in the previous step. The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. The `_IMAGE_NAME`_ may be the same as the source (`datacommons-website-compose`) or any other string. The _`TARGET_IMAGE_TAG`_ can be the same as the source or any other string. +1. Create a package from the source image created in step 1:
 
 docker tag datacommons-website-compose:DOCKER_TAG \  
 LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
 
+ - The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. + - The `_IMAGE_NAME`_ may be the same as the source (`datacommons-website-compose`) or any other string. + - The _`TARGET_IMAGE_TAG`_ can be the same as the source, or any other string. + 1. Push the image to the registry:
@@ -96,50 +74,42 @@ This will take several minutes to upload.
 When it completes, verify that the container has been uploaded in the Cloud Console:
 
 1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project.
-1. In the list of repositories, click on the one you created earlier. Under **Repository Details**, you should see the Docker image listed.
-
-## Deploy the image
+1. In the list of repositories, click on the one you created above. Under **Repository Details**, you should see the Docker image listed.
 
-To deploy the image in Google Cloud Run, you need to create a Run service. Because we need to set the environment variables for the running image as options in the service, it is actually more convenient to create the service and deploy the image at the same time using `gcloud` rather than the Cloud Console. Once the service is created in this way, you can edit it and redeploy using the Console.
+## One-time setup: Create a Google Run service
 
-1. Copy the settings from the `env.list` file to a local environment variable:
-
-   ```shell
-   env_vars=$(awk -F '=' 'NF==2 {print $1"="$2}' custom_dc/env.list | tr '\n' ',' | sed 's/,$//')
-   ```
-
-1. Create a new Cloud Run service and deploy the image in the Artifact Registry.
-
-   
-   gcloud run deploy SERVICE_NAME \  
-   --allow-unauthenticated \  
-   --memory 8Gi \  
-   --cpu 2 \  
-   --region LOCATION \  
-   --image LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  \
-   --add-cloudsql-instances=PROJECT_ID:LOCATION:INSTANCE_ID \  
-   --set-env-vars="$env_vars" \  
-   --port 8080  
-   --no-cpu-throttling
-   
- - The _SERVICE_NAME_ can be any string of your choice. - -1. Follow the screen output to see the status details of the operation. Once it completes, a link to the deployed image is output. Visit the link in your browser to see the running instance. - -To inspect the service in the Cloud Console: - -1. Go to [https://console.cloud.google.com/run](https://console.cloud.google.com/run) for your project. -1. Select the service you just created to show the **Service Details** page. -1. Select **Revisions**. Scroll down and you can see the environment variables and other settings. - -For future deployments, select **Edit & Deploy Revision** from this page to select a new repository\ container and locate the link to the running instance. +This procedure shows you how to create a service and set environment variables using the Cloud Console. Alternatively, you could set them in the `custom_dc/env.list` file, pipe the file contents to a system variable, and use the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/run/jobs?hl=en) to update the job. See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) for more information on all the options you may set on your service. -## Load custom data to a deployed instance +1. Go to https://console.cloud.google.com/run/ for your project. +1. Click **Create service**. +1. Keep the default **Deploy one revision from an existing container image** enabled. +1. In the **Container image URL** field, click **Select** to open the **Select container image** window. +1. In the list of images that appears, navigate to container image you pushed in the previous step, highlight it and click **Select**. +1. In the **Service name** field, enter a name for your service. +1. In the **Region** field, select your [location](/custom_dc/data_cloud.html#location). +1. Under **Authentication**, select the relevant option depending on whether your site will be public or not. If it is public, enable **Allow unauthenticated invocations**. +1. Set the following options: + - **CPU allocation and pricing**: **CPU is always allocated** + - **Service autoscaling** > **Minimum number of instances**: **1** +1. Expand **Container, Volumes, Connections, Security** and expand **Settings**, and set the following options: + - **Resources** > **Memory**: **8 GiB** + - **Resources** > **CPU**: **2** + - **Task timeout** > **Number of retries per failed task**: **3** + - **Revision autoscaling** > **Minimum number of instances**: **1** + - **Revision autoscaling** > **Maximum number of instances**: **1** +1. Disable **Startup CPU boost**. +1. Click the **Variables and Secrets** tab. +1. Click **Add variable**. +1. Add the same environment variables, with the same names and values as you did when you created the [data management run job](#/custom_dc/data_cloud.html#env-vars) You can omit the `INPUT_DIR` variable). + Tip: You can copy from the job by going to +1. Add a variable for the `MAPS_API_KEY` and set it to your Maps API key. +1. Click **Done**. +1. Click **Deploy**. +1. Follow the screen output to see the status details of the operation. Once it completes, a link to the deployed image is listed at the top of the page. Click on the link to see the running instance. + +For future deployments, select **Edit & Deploy Revision** from this page to select a new repository / container and locate the link to the running instance. + -Once you have deployed a custom instance to Google Cloud, you can continue to update your custom data in two ways: -- Load the data from a local running instance, as described in [Load custom data in Cloud SQL](/custom_dc/data_cloud.html#load-data-cloudsql) -- Use the `/admin` page from the running Cloud app. diff --git a/custom_dc/index.md b/custom_dc/index.md index ba65d3e2b..275a8d1d7 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -65,7 +65,7 @@ Details about the components that make up the containers are provided in the [Ge ## Requirements and cost -A custom Data Commons site runs in a Docker container on Google Cloud Platform (GCP). You will need the following: +A custom Data Commons site runs in a Docker container on Google Cloud Platform (GCP), using Google Cloud Run a serverless solution that is by far the simplest and least expensive option, providing auto-scaling and other benefits. You will need the following: - A [GCP](http://console.cloud.google.com) billing account and project - A [Docker](http://docker.com) account diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 1e3e8b750..79975ae6d 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -71,7 +71,7 @@ When the downloads are complete, navigate to the root directory of the repo (e.g cd website | DIRECTORY
-### Set environment variables +### Set environment variables {#env-vars} 1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. @@ -126,9 +126,8 @@ To load the sample data: docker run -it \ --env-file $PWD/custom_dc/env.list \ -v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ -gcr.io/datcom-ci/datacommons-data:stable +gcr.io/datcom-ci/datacommons-data:stable | latest ``` - This does the following: - The first time you run it, downloads the latest stable Data Commons data image, `gcr.io/datcom-ci/datacommons-data:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. From 6d80bcc4fad41935a85fb8b6bd4f77c1db241735 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Thu, 8 Aug 2024 14:54:41 -0700 Subject: [PATCH 24/96] more revisions --- custom_dc/custom_data.md | 15 +++++---- custom_dc/data_cloud.md | 70 +++++++++++++++++++++------------------ custom_dc/deploy_cloud.md | 36 +++++++++++--------- custom_dc/index.md | 8 +++-- custom_dc/quickstart.md | 25 +++++++------- 5 files changed, 84 insertions(+), 70 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index 56e3e72cf..fa2927f7f 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -54,7 +54,8 @@ San Francisco,2023,300,300,200,50 San Jose,2023,400,400,300,0 ``` -The _ENTITY_ is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see [Place types](/place_types.html) for a full list. It may also be any of the special DCID prefixes listed in [Special place names](#special-names). +The _ENTITY_ is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see [Place types](/place_types.html) for a full list. It may also be any of the special DCID prefixes listed in [Special place names](#special-names). Avoid combining multiple entity types in the same CSV file; the type of the entities in a single file should be the same. + The _DATE_ is the date of the observation and should be in the format _YYYY_, _YYYY_-_MM_, or _YYYY_-_MM_-_DD_. The heading can be anything, although as a best practice, we recommend using a corresponding identifier, such as `year`, `month` or `date`. The _VARIABLE_ should contain a metric [observation](/glossary.html#observation) at a particular time. We recommend that you try to reuse existing statistical variables where feasible; use the base Data Commons [Statistical Variable Explorer](https://datacommons.org/tools/statvar) to find them. If there is no existing statistical variable you can use, name the heading with an illustrative name and the importer will create a new variable for you. @@ -240,13 +241,15 @@ The `sources` section is optional. It encodes the sources and provenances associ ## Load local custom data -To load custom data uploaded to Google Cloud, see instead [ ](/custom_dc/deploy_cloud.html) for procedures. +The following procedures show you how to load and serve your custom data locally. + +To load data in Google Cloud, see instead [Load data in Google Cloud](/custom_dc/deploy_cloud.html) for procedures. ### Configure environment variables Edit the `env.list` file as follows: - Set the `INPUT_DIR` variable to the directory where your input files are stored. -- Set the `OUTPUT_DIR` variable to the directory where you would like the output files to be store. This can be the same or different from the input directory. When you rerun the Docker data management container, it will create a `datacommons` subdirectory under this directory. +- Set the `OUTPUT_DIR` variable to the directory where you would like the output files to be stored. This can be the same or different from the input directory. When you rerun the Docker data management container, it will create a `datacommons` subdirectory under this directory. **Notes:** - NL support increases the startup time of your server and consumes more resources. If you don't want NL functionality, you can disable it by updating the `ENABLE_MODEL` flag from `true` to `false`. @@ -254,7 +257,7 @@ Edit the `env.list` file as follows: ### Start the Docker containers with local custom data {#docker-data} -Once you have configured everything, use the following commands to restart the Docker containers, mapping your input and output directories to the same path in Docker. +Once you have configured everything, use the following commands to restart the Docker containers, mapping your input and output directories to the same paths in Docker. In one terminal window, from the root directory, run: @@ -276,9 +279,9 @@ docker run -it \ gcr.io/datcom-ci/datacommons-website-compose:stable
-Every time you make changes to the CSV or JSON files, you will need to rerun both of these commands. +Every time you make changes to the CSV or JSON files, you will need to restart both containers. -## Inspect the SQLite database +### Inspect the SQLite database If you need to troubleshoot custom data, it is helpful to inspect the contents of the generated SQLite database. diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index cb046bb2c..a7fc1c24e 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -15,18 +15,18 @@ This page shows you how to store your custom data in Google Cloud, and create th ## Overview -Once you have tested locally, the next step is to get your data into the Google Cloud Platform. You upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data management Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. +Once you have tested locally, the next step is to get your data into the Google Cloud Platform. You upload your CSV and JSON files to [Google Cloud Storage](https://cloud.google.com/storage), and run the Data Commons data management Docker container as a Cloud Run job. The job will transform and store the data in a [Google Cloud SQL](https://cloud.google.com/sql) database, and generate NL embeddings stored in Cloud Storage. -TODO: add image +![data management setup](/assets/images/custom_dc/customdc_setup3.png) -Alternatively, if you have a very large data set, you may find it faster to store your input files and run the data management container locally, and output the data to Google Cloud Storage. If you would like to use this approach, see xxx +Alternatively, if you have a very large data set, you may find it faster to store your input files and run the data management container locally, and output the data to Google Cloud Storage. If you would like to use this approach, follow steps 1 to 3 of the one-time setup steps below and then skip to [Run the data management container locally](#run-local). ## Prerequisites - A [GCP](https://console.cloud.google.com/welcome) billing account and project. - Optional: Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install-sdk). -## One-time setup steps +## One-time setup steps {#setup} ### Step 1: Choose a location {#location} @@ -44,7 +44,7 @@ This stores the CSV and JSON files that you will upload whenever your data chang 1. In the **Bucket Details** page, click **Create Folder** to create a new folder to hold your data and name it as desired. 1. Optionally, create separate folders to hold input and output files, or just use the same one as for the input. - Note: If you plan to run the data management container locally, you only need an output folder to hold the embeddings files. + Note: If you plan to run the data management container locally, you only need to create a single folder to hold the output files. 1. Record the folder path(s) as gs://BUCKET_NAME/FOLDER_PATH for setting the `INPUT_DIR` and `OUTPUT_DIR` environment variables below. ### Step 3: Create a Google Cloud SQL instance @@ -71,9 +71,7 @@ This stores the data that will be served at run time. The Data Commons data mana Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. -See also https://cloud.google.com/run/docs/create-jobs for links to more information on all the options you may set on your jobs. - -**Note:** If you plan to the always run the data management container locally, skip this step entirely and see instead xxx +See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) documentation for links to more information on all the options you may set on your jobs. 1. Go to https://console.cloud.google.com/run/ for your project. 1. Click **Create job**. @@ -90,25 +88,25 @@ See also https://cloud.google.com/run/docs/create-jobs for links to more informa - **Task timeout** > **Number of retries per failed task**: **3** 1. Click **Create** to save the job (but not run it yet). -{#env-vars} +{:.#env-vars} Now set environment variables: 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. 1. Add names and values for the following environment variables: -- `USE_CLOUDSQL`: set to `true` -- `DC_API_KEY`: set to the API key for your instance -- `INPUT_DIR`: set to the Cloud Storage bucket and input folder that you created in step 2 above. -- `OUTPUT_DIR`: set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. -- `CLOUDSQL_INSTANCE`: set to the full connection name of the instance you created in step 3 above. -- `DB_USER`: set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. -- `DB_PASS`: set to the user's or root password you configured when you created the instance in step 3. -- `DB_NAME`: if you set it to a name other than `datacommons` +- `USE_CLOUDSQL`: Set to `true`. +- `DC_API_KEY`: Set to your API key. +- `INPUT_DIR`: Set to the Cloud Storage bucket and input folder that you created in step 2 above. +- `OUTPUT_DIR`: Set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. +- `CLOUDSQL_INSTANCE`: Set to the full connection name of the instance you created in step 3 above. +- `DB_USER`: Set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. +- `DB_PASS`: Set to the user's or root password you configured when you created the instance in step 3. +- `DB_NAME`: Only set this if you configured the database name to something other than `datacommons`. 1. Click **Done** and **Update** to save. -## Upload data files to Google Cloud Storage +## Manage your data -Note: If you plan to run the data management container locally, skip this step and see instead .... +### Step 1: Upload data files to Google Cloud Storage 1. Go to https://console.cloud.google.com/storage/browse and select your custom Data Commons bucket. 1. Navigate to the folder you created in the earlier step. @@ -116,23 +114,23 @@ Note: If you plan to run the data management container locally, skip this step a Note: Do not upload the local `datacommons` subdirectory or its files. -As you are iterating on changes to the source CSV and JSON files, you can re-upload them at any time, either overwriting existing files or creating new folders. To load them into Google SQL, follow the procedures below. +As you are iterating on changes to the source CSV and JSON files, you can re-upload them at any time, either overwriting existing files or creating new folders. To load them into Cloud SQL, you run the Cloud Run job you created above. -## Start the data management Cloud Run job +### Step 2: Start the data management Cloud Run job {#run-job} Now that everything is configured, and you have uploaded your data in Google Cloud Storage, you simply have to start the Cloud Run data management job to convert the CSV data into tables in the Cloud SQL database and generate the embeddings (in a `datacommons/nl` subfolder). -Every time you upload new data files or changes to the JSON file to Google Cloud Storage, you will need to rerun the job. - -Note: If you plan to run the data management container locally, skip this step and see instead .... +Every time you upload new input CSV or JSON files to Google Cloud Storage, you will need to restart the job. To run the job: 1. Go to https://console.cloud.google.com/run/jobs for your project. 1. From the list of jobs, click the link of the "datacommons-data" job you created above. -1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. To verify that the data has been loaded correctly, see the next step. +1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. -## Inspect the Cloud SQL database +To verify that the data has been loaded correctly, see the next step. + +### Inspect the Cloud SQL database {#inspect-sql} To view information about the created tables: @@ -146,19 +144,21 @@ To view information about the created tables: ![screenshot_sqlite](/assets/images/custom_dc/customdc_screenshot6.png){: height="400"} -## Advanced setup (optional): Run the data management container locally +## Advanced setup (optional): Run the data management container locally {#run-local} This process is similar to running both data management and services containers locally, with a few exceptions: - Your input directory will be the local file system, while the output directory will be a Google Cloud Storage bucket and folder. - You must start the job with credentials to be passed to Google Cloud, to access the Cloud SQL instance. -### Set environment variables +Before you proceed, ensure you have completed steps 1 to 3 of the [One-time setup steps](#setup) above. + +### Step 1: Set environment variables To run a local instance of the services container, you need to set all the environment variables in the `custom_dc/env.list` file. See [above](#set-vars) for the details, with the following differences: - For the `INPUT_DIR`, specify the full local path where your CSV and JSON files are stored, as described in the [Getting started](/custom_dc/). - Set `GOOGLE_CLOUD_PROJECT` to your GCP project name. -### Generate credentials for Google Cloud default application {#gen-creds} +### Step 2: Generate credentials for Google Cloud authentication {#gen-creds} For the services to connect to the Cloud SQL instance, you need to generate credentials that can be used in the local Docker container for authentication. You should refresh the credentials every time you restart the Docker container. @@ -179,7 +179,7 @@ gcloud auth application-default set-quota-project PROJECT_ID If you are prompted to install the Cloud Resource Manager API, press `y` to accept. -### Run the Docker container +### Step 3: Run the Docker container From your project root directory, run: @@ -194,19 +194,21 @@ gcr.io/datcom-ci/datacommons-data:VERSION The version is `latest` or `stable`. +To verify that the data is correctly created in your Cloud SQL database, use the procedure in [Inspect the Cloud SQL database](#inspect-sql) above. + ## Advanced setup (optional): Access Cloud data from a local services container For testing purposes, if you wish to run the services Docker container locally but access the data in Google Cloud, use the following procedures. -### Set environment variables +### Step 1: Set environment variables To run a local instance of the services container, you will need to set all the environment variables, as described [above](#env-vars) in the `custom_dc/env.list`. You must also set the `MAPS_API_KEY` to your Maps API key. -### Generate credentials for Google Cloud default application +### Step 2: Generate credentials for Google Cloud default application See the section [above](#gen-creds) for procedures. -### Run the Docker container +### Step 3: Run the Docker container From the root directory of your repo, run the following command, assuming you are using a locally built image: @@ -224,3 +226,5 @@ datacommons-website-compose:DOCKER_TAG
+ + diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 4cdeb83e0..d4c461995 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -17,7 +17,7 @@ This page shows you how to build a custom services Docker container as a GCP art When you are ready to host your custom Data Commons site in production, you create a [Google Cloud Run](https://cloud.google.com/run/) service for the site. This is the production setup: -![setup4](/assets/images/custom_dc/customdc_setup4.png) +![services setup](/assets/images/custom_dc/customdc_setup4.png) You push a locally built Docker image to the [Google Cloud Artifact Registry](https://cloud.google.com/artifact-registry), and then deploy the image as a Cloud Run service. @@ -54,20 +54,20 @@ This procedure creates a "dev" Docker package that you upload to the Google Clou 1. Create a package from the source image created in step 1: -
 
-docker tag datacommons-website-compose:DOCKER_TAG \  
-LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
-
+
 
+   docker tag datacommons-website-compose:DOCKER_TAG \  
+   LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
+   
- - The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. - - The `_IMAGE_NAME`_ may be the same as the source (`datacommons-website-compose`) or any other string. - - The _`TARGET_IMAGE_TAG`_ can be the same as the source, or any other string. + - The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. + - The `_IMAGE_NAME`_ may be the same as the source (`datacommons-website-compose`) or any other string. + - The _`TARGET_IMAGE_TAG`_ can be the same as the source, or any other string. 1. Push the image to the registry: -
-docker push LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
-
+
+   docker push LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
+   
This will take several minutes to upload. @@ -78,7 +78,7 @@ When it completes, verify that the container has been uploaded in the Cloud Cons ## One-time setup: Create a Google Run service -This procedure shows you how to create a service and set environment variables using the Cloud Console. Alternatively, you could set them in the `custom_dc/env.list` file, pipe the file contents to a system variable, and use the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/run/jobs?hl=en) to update the job. +This procedure shows you how to create a service and set environment variables using the Cloud Console. See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) for more information on all the options you may set on your service. @@ -102,14 +102,18 @@ See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) f 1. Disable **Startup CPU boost**. 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. -1. Add the same environment variables, with the same names and values as you did when you created the [data management run job](#/custom_dc/data_cloud.html#env-vars) You can omit the `INPUT_DIR` variable). - Tip: You can copy from the job by going to +1. Add the same environment variables, with the same names and values as you did when you created the [data management run job](/custom_dc/data_cloud.html#env-vars) You can omit the `INPUT_DIR` variable. 1. Add a variable for the `MAPS_API_KEY` and set it to your Maps API key. 1. Click **Done**. 1. Click **Deploy**. -1. Follow the screen output to see the status details of the operation. Once it completes, a link to the deployed image is listed at the top of the page. Click on the link to see the running instance. -For future deployments, select **Edit & Deploy Revision** from this page to select a new repository / container and locate the link to the running instance. +Follow the screen output to see the status details of the operation. Once it completes, a link to the deployed image is listed at the top of the page. Click on the link to see the running instance. + +## Manage the service + +Every time you make changes to the code and release a new Docker artifact, or restart the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: + +1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. diff --git a/custom_dc/index.md b/custom_dc/index.md index 275a8d1d7..c8faafc69 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -6,9 +6,11 @@ has_children: true --- {:.no_toc} - # Build your own Data Commons +* TOC +{:toc} + ## Overview A custom instance natively joins your data and the base Data Commons data (from datacommons.org) in a unified fashion. Your users can visualize and analyze the data seamlessly without the need for further data preparation. @@ -43,7 +45,7 @@ For the following use cases, a custom Data Commons instance is not necessary: | Python and Pandas API wappers | yes | yes, but requires additional setup2 | | Bigquery interface | yes | no | Google Spreadsheets | yes | yes, but requires additional setup2 | -| Site ccess controls | yes, using any supported Cloud Run mechanisms3 | n/a | +| Site access controls | yes, using any supported Cloud Run mechanisms3 | n/a | | Fine-grained data access controls4 | no | n/a | 1. Open-source Python ML library, Sentence Transformers model, from [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). @@ -82,7 +84,7 @@ The cost of running a site on Google Cloud Platform depends on the size of your {: #workflow} ## Recommended workflow -1. Work through the [Get started](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. +1. Work through the [Getting started](/custom_dc/quickstart.html) page to learn how to run a local Data Commons instance and load some sample custom data. 1. Prepare your real-world custom data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Work with custom data](/custom_dc/custom_data.html). 1. If you want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html). 1. When you have finished testing locally, host your data and code in Google Cloud Platform: first, upload your data to Google Cloud Storage and create a Cloud Run job to load the data into Google Cloud SQL. See [Load data in Google Cloud](/custom_dc/data_cloud.html). diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 79975ae6d..df83948f8 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -19,9 +19,11 @@ This is step 1 of the [recommended workflow](/custom_dc/index.html#workflow). The instructions in this page use the following setup: -![setup2](/assets/images/custom_dc/customdc_setup2.png) +![local setup](/assets/images/custom_dc/customdc_setup2.png) -The "data management" Docker container consists of scripts that convert custom CSV file data into SQL tables in a local SQLite database, and generate embeddings for custom data. +The "data management" Docker container consists of scripts that do the following: +- Convert custom CSV file data into SQL tables and store them in a data store -- for now, in a local SQLite database +- Generate ML embeddings for custom data and store them -- for now, in the local file system The "services" Docker container consists of the following Data Commons components: - A [Nginx reverse proxy server](https://www.nginx.com/resources/glossary/reverse-proxy-server/), which routes incoming requests to the web or API server @@ -68,14 +70,14 @@ The "services" Docker container consists of the following Data Commons component When the downloads are complete, navigate to the root directory of the repo (e.g. `website`). References to various files and commands in these procedures are relative to this root.
-cd website | DIRECTORY
+cd website
 
### Set environment variables {#env-vars} 1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. -1. Set the `INPUT_DIR`, set it to the full path to the `sample` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample. +1. Set the `INPUT_DIR` to the full path to the `sample/` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample/. 1. For the `OUTPUT_DIR`, set it to the same path as the `INPUT_DIR`. **Warning:** Do not use any quotes (single or double) or spaces when specifying the values. @@ -108,7 +110,7 @@ cd website | DIRECTORY custom_dc/env.list - Contains environment variables for the Data Commons services. For details of the variables, see the comments in the file. + Contains environment variables for locally run Data Commons data management and services containers. For details of the variables, see the comments in the file. @@ -123,10 +125,10 @@ To load the sample data: 1. Open a terminal window, and from the root directory, run the following command to start the data management Docker container: ```shell -docker run -it \ +docker run \ --env-file $PWD/custom_dc/env.list \ -v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ -gcr.io/datcom-ci/datacommons-data:stable | latest +gcr.io/datcom-ci/datacommons-data:stable ``` This does the following: @@ -172,11 +174,10 @@ Tip: If you close the terminal window in which you started the Docker services c 1. Open another terminal window, and from the root directory, get the Docker container ID. - ```shell - docker ps - ``` - - The `CONTAINER ID` is the first column in the output. + ```shell + docker ps + ``` + The `CONTAINER ID` is the first column in the output. 1. Run: From 21917fa053939bae0a53fa0474b8b2c9e5afeb19 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Fri, 9 Aug 2024 12:50:13 -0700 Subject: [PATCH 25/96] update troubleshooting doc etc. --- custom_dc/custom_data.md | 4 +++- custom_dc/deploy_cloud.md | 5 +---- custom_dc/troubleshooting.md | 12 +++++------- 3 files changed, 9 insertions(+), 12 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index fa2927f7f..5017551ee 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -54,7 +54,9 @@ San Francisco,2023,300,300,200,50 San Jose,2023,400,400,300,0 ``` -The _ENTITY_ is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see [Place types](/place_types.html) for a full list. It may also be any of the special DCID prefixes listed in [Special place names](#special-names). Avoid combining multiple entity types in the same CSV file; the type of the entities in a single file should be the same. +The _ENTITY_ is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see [Place types](/place_types.html) for a full list. It may also be any of the special DCID prefixes listed in [Special place names](#special-names). + +**Note:** Do not mix multiple entity types in the same CSV file; the type of the entities in a single file should be the same. The _DATE_ is the date of the observation and should be in the format _YYYY_, _YYYY_-_MM_, or _YYYY_-_MM_-_DD_. The heading can be anything, although as a best practice, we recommend using a corresponding identifier, such as `year`, `month` or `date`. diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index d4c461995..ff28e55b5 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -113,7 +113,4 @@ Follow the screen output to see the status details of the operation. Once it com Every time you make changes to the code and release a new Docker artifact, or restart the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: -1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. - - - +1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. \ No newline at end of file diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index 1166332aa..6c9a74ede 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -57,7 +57,7 @@ You need to download/update additional submodules (derived from other repos). Se ## Data loading problems -If you try to load data using the `/admin page`, and see the following errors: +TODO: write this for new Docker container `Error running import` or `invalid input` @@ -71,8 +71,8 @@ If the load page does not show any errors but data still does not load, try chec ## NL queries not returning custom data If you have previously been able to get custom data in your natural-language query results, but this has suddenly stopped working, this is due to embeddings incompatibility issues between releases. To fix this, do the following: -1. Delete the `datacommons` subdirectory from your output directory. -1. Restart the services, and reload the data from the /admin page. +1. Delete the `datacommons` subdirectory from your output directory, either locally or in your Google Cloud Storage bucket. +1. Restart the data management container, as described in . ## Website display problems @@ -107,11 +107,9 @@ This is a general indication that the Data Commons servers are not running. Chec `403 Forbidden: Not authorized to access resources` -This may be . To fix this: +This may be due to multiple reasons. First try the following: 1. In the Cloud Run service page in the Cloud Console, select the **Revisions** tab, and scroll to view the **Environment variables**. -1. Ensure that the `DB_USER` and `DB_PASS` variables are set to the values you set when creating the [SQL database](/custom_dc/data_cloud.html#create-sql) - - +1. Ensure that the `DB_USER` and `DB_PASS` variables are set to the values you set when creating the [SQL database](/custom_dc/data_cloud.html#create-sql). 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c207f6b709dcbb749215a4dc90852e302b05de83 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 12 Aug 2024 11:15:54 -0700 Subject: [PATCH 27/96] remove data loading problems troubleshooting entry; can't reproduce --- custom_dc/custom_data.md | 4 ++-- custom_dc/troubleshooting.md | 13 ------------- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index 5017551ee..138d93599 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -1,12 +1,12 @@ --- layout: default -title: Work with custom data +title: Load custom data nav_order: 3 parent: Build your own Data Commons --- {:.no_toc} -# Work with custom data +# Load custom data This page shows you how to format and load your own custom data into your local instance. This is step 2 of the [recommended workflow](/custom_dc/index.html#workflow). diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index 6c9a74ede..f1e30b9c3 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -55,19 +55,6 @@ COPY failed: file not found in build context or excluded by .dockerignore: stat ``` You need to download/update additional submodules (derived from other repos). See [Build a local image](/custom_dc/build_image.html#build-repo). -## Data loading problems - -TODO: write this for new Docker container - -`Error running import` or `invalid input` - -There is a problem with how you have set up your CSV files and/or config.json file. Check that your CSV files conform to the structure described in [Prepare the CSV files](/custom_dc/custom_data.html#prepare-csv). - -If the load page does not show any errors but data still does not load, try checking the following: - -1. In the `env.list` file, check that you are not using single or double quotes around any of the values. -1. Check your Docker command line for invalid arguments. Often Docker won't give any error messages but failures will show up at runtime. - ## NL queries not returning custom data If you have previously been able to get custom data in your natural-language query results, but this has suddenly stopped working, this is due to embeddings incompatibility issues between releases. To fix this, do the following: From 6fa91d3bf6b80abedd9121567ec944a4f88615ed Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 12 Aug 2024 11:19:39 -0700 Subject: [PATCH 28/96] revert title change --- custom_dc/custom_data.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index 138d93599..9943d837d 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -1,12 +1,12 @@ --- layout: default -title: Load custom data +title: Work with custom data nav_order: 3 parent: Build your own Data Commons --- {:.no_toc} -# Load custom data +# Work with custom data This page shows you how to format and load your own custom data into your local instance. This is step 2 of the [recommended workflow](/custom_dc/index.html#workflow). @@ -261,7 +261,7 @@ Edit the `env.list` file as follows: Once you have configured everything, use the following commands to restart the Docker containers, mapping your input and output directories to the same paths in Docker. -In one terminal window, from the root directory, run: +In one terminal window, from the root directory, run the following command to start the data management container:

 docker run \
@@ -270,7 +270,7 @@ docker run \
 gcr.io/datcom-ci/datacommons-data:stable
 
-In another terminal window, from the root directory, run: +In another terminal window, from the root directory, run the following command to start the services container:
 docker run -it \

From 9cde771f685ccf50611ef636cb55f8ea26111db3 Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Mon, 12 Aug 2024 12:07:13 -0700
Subject: [PATCH 29/96] add example for not mixing entity types

---
 custom_dc/custom_data.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md
index 9943d837d..0ca5f9699 100644
--- a/custom_dc/custom_data.md
+++ b/custom_dc/custom_data.md
@@ -56,7 +56,7 @@ San Jose,2023,400,400,300,0
 
 The _ENTITY_ is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see [Place types](/place_types.html) for a full list. It may also be any of the special DCID prefixes listed in [Special place names](#special-names). 
 
-**Note:** Do not mix multiple entity types in the same CSV file; the type of the entities in a single file should be the same.
+**Note:** The type of the entities in a single file should be unique; do not mix multiple entity types in the same CSV file. For example, if you have observations for cities and counties, put all the city data in one CSV file and all the county data in another one.
 
 The _DATE_ is the date of the observation and should be in the format _YYYY_, _YYYY_-_MM_, or _YYYY_-_MM_-_DD_. The heading can be anything, although as a best practice, we recommend using a corresponding identifier, such as `year`, `month` or `date`.
 

From 83bbaf26ac940bac1a37cc9b8900fea4f957ce0c Mon Sep 17 00:00:00 2001
From: Kara Moscoe 
Date: Mon, 12 Aug 2024 13:40:20 -0700
Subject: [PATCH 30/96] changes from Keyur

---
 custom_dc/build_image.md     | 2 +-
 custom_dc/custom_data.md     | 4 ++--
 custom_dc/deploy_cloud.md    | 2 +-
 custom_dc/quickstart.md      | 4 ++--
 custom_dc/troubleshooting.md | 2 --
 5 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md
index f639c72be..640263b3e 100644
--- a/custom_dc/build_image.md
+++ b/custom_dc/build_image.md
@@ -137,7 +137,7 @@ To run the container with the local SQLite database, start the Docker container
 
 To upload and deploy the container to the Cloud, see [Deploy services to Google Cloud](/custom_dc/deploy_cloud.html) for procedures.
 
-## Run the custom services container locally
+## Run the services container locally
 
 Start the services using the locally built repo. If you have made changes to any of the UI components (or directories), be sure to map the `custom` directories (or alternative directories) to the Docker `workspace` directory.
 
diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md
index 0ca5f9699..d062bbdef 100644
--- a/custom_dc/custom_data.md
+++ b/custom_dc/custom_data.md
@@ -259,7 +259,7 @@ Edit the `env.list` file as follows:
 
 ### Start the Docker containers with local custom data {#docker-data}
 
-Once you have configured everything, use the following commands to restart the Docker containers, mapping your input and output directories to the same paths in Docker.
+Once you have configured everything, use the following commands to run the data management container and restart the services container, mapping your input and output directories to the same paths in Docker.
 
 In one terminal window, from the root directory, run the following command to start the data management container:
 
@@ -281,7 +281,7 @@ docker run -it \
 gcr.io/datcom-ci/datacommons-website-compose:stable
 
-Every time you make changes to the CSV or JSON files, you will need to restart both containers. +Any time you make changes to the CSV or JSON files and want to reload the data, you will need to rerun the data management container, and then restart the services container. ### Inspect the SQLite database diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index ff28e55b5..cefb53ade 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -33,7 +33,7 @@ You push a locally built Docker image to the [Google Cloud Artifact Registry](ht ## Upload the Docker container to the Artifact Registry -This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry. Every time you rebuild the image, you need ro rerun this procedure. +This procedure creates a "dev" Docker package that you upload to the Google Cloud Artifact Registry. Any time you rebuild the image and want to deploy it to the cloud, you need to rerun this procedure. 1. Build a local version of the Docker image, following the procedure in [Build a local image](/custom_dc/build_image.html#build-repo). 1. Authenticate to gcloud: diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index df83948f8..2e85a7fce 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -136,7 +136,7 @@ This does the following: - Maps the input sample data to a Docker path. - Starts a Docker container. - Imports the data from the CSV files, resolves entities, and writes the data to a SQLite database file, `custom_dc/sample/datacommons/datacommons.db`. -- Generates embeddings in `custom_dc/sample/datacommons/nl` and loads them. (To learn more about embeddings generation, see the [FAQ](faq.md#natural-language-processing). +- Generates embeddings in `custom_dc/sample/datacommons/nl`. (To learn more about embeddings generation, see the [FAQ](faq.md#natural-language-processing). Once the container has executed all the functions in the scripts, it shuts down. @@ -159,7 +159,7 @@ Note: If you are running on Linux, depending on whether you have created a ["sud This command does the following: - The first time you run it, downloads the latest stable Data Commons image, `gcr.io/datcom-ci/datacommons-website-compose:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. -- Starts a long-running Docker container. +- Starts a services Docker container. - Starts development/debug versions of the Web Server, NL Server, and Mixer, as well as the Nginx proxy, inside the container. - Maps the output sample data to a Docker path. diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index f1e30b9c3..a0bf61020 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -86,8 +86,6 @@ This error indicates that your application requires authenticated requests but y If you are unable to select this option, this indicates that there is an IAM permissions setup issue with your project or account. See the [Cloud Run Troubleshooting](https://cloud.google.com/run/docs/troubleshooting#unauthorized-client) for details on how to fix this. -### "403: Not authorized to access resources" - ### "502 Bad Gateway" This is a general indication that the Data Commons servers are not running. Check the **Logs ** page for the Cloud Run service in the Google Cloud Console. Here are common errors: From 86800bafabcf92e1628d4af91fdcb0bcd8da0073 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 09:28:04 -0700 Subject: [PATCH 31/96] add screenshots for GCP, and related changes --- assets/images/custom_dc/gcp_screenshot1.png | Bin 0 -> 85580 bytes assets/images/custom_dc/gcp_screenshot2.png | Bin 0 -> 181290 bytes assets/images/custom_dc/gcp_screenshot3.png | Bin 0 -> 144104 bytes assets/images/custom_dc/gcp_screenshot4.png | Bin 0 -> 79505 bytes custom_dc/custom_data.md | 3 ++- custom_dc/data_cloud.md | 26 +++++++++++++++----- custom_dc/deploy_cloud.md | 23 +++++++++++------ custom_dc/index.md | 4 +-- custom_dc/quickstart.md | 6 ++--- custom_dc/troubleshooting.md | 2 +- 10 files changed, 44 insertions(+), 20 deletions(-) create mode 100644 assets/images/custom_dc/gcp_screenshot1.png create mode 100644 assets/images/custom_dc/gcp_screenshot2.png create mode 100644 assets/images/custom_dc/gcp_screenshot3.png create mode 100644 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eaYx}yYsB<{M^#?joT0nm-#K+%wcNAjfB!F6hTzHo literal 0 HcmV?d00001 diff --git a/custom_dc/custom_data.md b/custom_dc/custom_data.md index d062bbdef..e74871f27 100644 --- a/custom_dc/custom_data.md +++ b/custom_dc/custom_data.md @@ -300,7 +300,6 @@ At the prompt, enter SQL queries. For example, for the sample OECD data, this qu ```shell sqlite> select * from observations limit 10; ``` - returns output like this: ```shell @@ -313,3 +312,5 @@ country/BEL|average_annual_wage|2005|55662.21541|c/p/1 ... ``` +To exit the sqlite shell, press Ctrl-D. + diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index a7fc1c24e..e805ea6e8 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -79,14 +79,17 @@ See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) document 1. Click the **Container Registry** tab. 1. Next to the **Project**, click **Change**, search for **datcom-ci** and select it. 1. In the list of images that appears, navigate to and expand **gcr.io/datcom-ci/datacommons-data**, highlight the image you want, **stable** or **latest**, and click **Select**. + + ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot1.png) + 1. Optionally, in the **Job name** field, enter an alternative name as desired. 1. In the **Region** field, select the region you chose as your location. 1. Leave the default **Number of tasks** as 1. 1. Expand **Container, Volumes, Connections, Security** and expand **Settings**, and set the following options: - **Resources** > **Memory**: **8 GiB** - **Resources** > **CPU**: **2** - - **Task timeout** > **Number of retries per failed task**: **3** -1. Click **Create** to save the job (but not run it yet). + + ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot2.png) {:.#env-vars} Now set environment variables: @@ -102,7 +105,18 @@ Now set environment variables: - `DB_USER`: Set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. - `DB_PASS`: Set to the user's or root password you configured when you created the instance in step 3. - `DB_NAME`: Only set this if you configured the database name to something other than `datacommons`. -1. Click **Done** and **Update** to save. +1. Click **Done**. + + ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot3.png) + +Finally, set the task options under **Task capacity**: + - **Task timeout** > **10** + - **Time unit** > **minute** + - **Number of retries per failed task**: **3** + + ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot4.png) + +1. Click **Create** (but don't run it immediately). ## Manage your data @@ -120,7 +134,7 @@ As you are iterating on changes to the source CSV and JSON files, you can re-upl Now that everything is configured, and you have uploaded your data in Google Cloud Storage, you simply have to start the Cloud Run data management job to convert the CSV data into tables in the Cloud SQL database and generate the embeddings (in a `datacommons/nl` subfolder). -Every time you upload new input CSV or JSON files to Google Cloud Storage, you will need to restart the job. +Every time you upload new input CSV or JSON files to Google Cloud Storage, you will need to rerun the job. To run the job: @@ -128,7 +142,7 @@ To run the job: 1. From the list of jobs, click the link of the "datacommons-data" job you created above. 1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. -To verify that the data has been loaded correctly, see the next step. +When it completes, to verify that the data has been loaded correctly, see the next step. ### Inspect the Cloud SQL database {#inspect-sql} @@ -160,7 +174,7 @@ To run a local instance of the services container, you need to set all the envir ### Step 2: Generate credentials for Google Cloud authentication {#gen-creds} -For the services to connect to the Cloud SQL instance, you need to generate credentials that can be used in the local Docker container for authentication. You should refresh the credentials every time you restart the Docker container. +For the services to connect to the Cloud SQL instance, you need to generate credentials that can be used in the local Docker container for authentication. You should refresh the credentials every time you rerun the Docker container. Open a terminal window and run the following command: diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index cefb53ade..dbc1a5fc6 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -93,24 +93,33 @@ See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) f 1. Set the following options: - **CPU allocation and pricing**: **CPU is always allocated** - **Service autoscaling** > **Minimum number of instances**: **1** -1. Expand **Container, Volumes, Connections, Security** and expand **Settings**, and set the following options: + +[add image here] +1. Expand **Container, Volumes, Connections, Security** > **Container** > **Settings**, and set the following options: - **Resources** > **Memory**: **8 GiB** - **Resources** > **CPU**: **2** - - **Task timeout** > **Number of retries per failed task**: **3** - - **Revision autoscaling** > **Minimum number of instances**: **1** - - **Revision autoscaling** > **Maximum number of instances**: **1** -1. Disable **Startup CPU boost**. +1. Expand the **Variables and secrets** tab. 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. 1. Add the same environment variables, with the same names and values as you did when you created the [data management run job](/custom_dc/data_cloud.html#env-vars) You can omit the `INPUT_DIR` variable. 1. Add a variable for the `MAPS_API_KEY` and set it to your Maps API key. 1. Click **Done**. + +[add image here] +1. Under **Execution environment** > **Autoscaling**, set the following options: + - **Minimum number of instances**: **1** + - **Maximum number of instances**: **1** +1. Disable **Startup CPU boost**. +1. + +[add image here] + 1. Click **Deploy**. -Follow the screen output to see the status details of the operation. Once it completes, a link to the deployed image is listed at the top of the page. Click on the link to see the running instance. +Click the **Logs** tab to see the status details of the operation. Once it completes, a link to the deployed image URL is listed at the top of the page. Click on the link to see the running instance. ## Manage the service -Every time you make changes to the code and release a new Docker artifact, or restart the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: +Every time you make changes to the code and release a new Docker artifact, or rerun the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: 1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. \ No newline at end of file diff --git a/custom_dc/index.md b/custom_dc/index.md index c8faafc69..b56af8f0a 100644 --- a/custom_dc/index.md +++ b/custom_dc/index.md @@ -61,13 +61,13 @@ Essentially, a custom Data Commons instance is a mirror of the public Data Commo A custom Data Commons instance uses custom data that you provide as raw CSV files. An importer script converts the CSV data into the Data Commons format and stores this in a SQL database. For local development, we provide a lightweight, open-source [SQLite](http://sqlite.org) database; for production, we recommend that you use [Google Cloud SQL](https://cloud.google.com/sql/). -In addition to the data, a custom Data Commons instance consists of two Docker containers: one with the core services that serve the data and website; and one with additional utilities for managing and loading custom data and embeddings used for natural-language processing. +In addition to the data, a custom Data Commons instance consists of two Docker containers: one with the core services that serve the data and website; and one with utilities for managing and loading custom data and embeddings used for natural-language processing. Details about the components that make up the containers are provided in the [Getting started](quickstart.md) guide. ## Requirements and cost -A custom Data Commons site runs in a Docker container on Google Cloud Platform (GCP), using Google Cloud Run a serverless solution that is by far the simplest and least expensive option, providing auto-scaling and other benefits. You will need the following: +A custom Data Commons site runs in a Docker container on Google Cloud Platform (GCP), using Google Cloud Run, a serverless solution that provides auto-scaling and other benefits. You will need the following: - A [GCP](http://console.cloud.google.com) billing account and project - A [Docker](http://docker.com) account diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 2e85a7fce..8bf44e5f4 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -23,12 +23,12 @@ The instructions in this page use the following setup: The "data management" Docker container consists of scripts that do the following: - Convert custom CSV file data into SQL tables and store them in a data store -- for now, in a local SQLite database -- Generate ML embeddings for custom data and store them -- for now, in the local file system +- Generate NL embeddings for custom data and store them -- for now, in the local file system The "services" Docker container consists of the following Data Commons components: - A [Nginx reverse proxy server](https://www.nginx.com/resources/glossary/reverse-proxy-server/), which routes incoming requests to the web or API server - A Python-Flask web server, which handles interactive requests from users -- An Python-Flask NL server, for generating embeddings and serving natural language queries +- An Python-Flask NL server, for serving natural language queries - A Go Mixer, also known as the API server, which serves programmatic requests using Data Commons APIs. The SQL query engine is built into the Mixer, which sends queries to both the local and remote data stores to find the right data. If the Mixer determines that it cannot fully resolve a user query from the custom data, it will make an REST API call, as an anonymous "user" to the base Data Commons Mixer and data. ## Prerequisites @@ -122,7 +122,7 @@ In this step, we will add sample data that we have included as part of the downl To load the sample data: 1. If you are running on Windows or Mac, start Docker Desktop and ensure that the Docker Engine is running. -1. Open a terminal window, and from the root directory, run the following command to start the data management Docker container: +1. Open a terminal window, and from the root directory, run the following command to run the data management Docker container: ```shell docker run \ diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index a0bf61020..d9067b33a 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -59,7 +59,7 @@ You need to download/update additional submodules (derived from other repos). Se If you have previously been able to get custom data in your natural-language query results, but this has suddenly stopped working, this is due to embeddings incompatibility issues between releases. To fix this, do the following: 1. Delete the `datacommons` subdirectory from your output directory, either locally or in your Google Cloud Storage bucket. -1. Restart the data management container, as described in . +1. Rerun the data management container, as described in [Load data in Google Cloud](data_cloud.md), and restart the services container. ## Website display problems From 1a235593a4b703df2a7125cde28e0b1a1c491f2d Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 09:44:41 -0700 Subject: [PATCH 32/96] fixed one image --- assets/images/custom_dc/gcp_screenshot4.png | Bin 79505 -> 78776 bytes custom_dc/data_cloud.md | 20 ++++++++++---------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/assets/images/custom_dc/gcp_screenshot4.png b/assets/images/custom_dc/gcp_screenshot4.png index e7eea479ae255924aeb351c2f8473231c54d5662..c2e9a958c8e65818bb547437eb95f231ab4a0c74 100644 GIT binary patch literal 78776 zcmeFZhhNYA|29l^A)-Or18q&(8rnnB9-`8qw3lR6655jn8bYPCCqhemX-iw$OQpJw zk8z&oecku{2V9Tq=kfUZIzQ|6e!s@^c^t>{c)tDARpoXOA0*zeVZ$y3c^Qoj8#aBw z|Bh@Y!Yh_{TQ1`NHacj?No`20Jv6#u1N{aCnUk8X8^3q&(9sJJ|hsrCIuYcem{*ra)Td;9rS#)^4R`HY@2+W()U6`zX})8ptP&cn=n`)YVbW~P;; 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eaYx}yYsB<{M^#?joT0nm-#K+%wcNAjfB!F6hTzHo diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index e805ea6e8..622d0c8fd 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -97,15 +97,15 @@ Now set environment variables: 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. 1. Add names and values for the following environment variables: -- `USE_CLOUDSQL`: Set to `true`. -- `DC_API_KEY`: Set to your API key. -- `INPUT_DIR`: Set to the Cloud Storage bucket and input folder that you created in step 2 above. -- `OUTPUT_DIR`: Set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. -- `CLOUDSQL_INSTANCE`: Set to the full connection name of the instance you created in step 3 above. -- `DB_USER`: Set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. -- `DB_PASS`: Set to the user's or root password you configured when you created the instance in step 3. -- `DB_NAME`: Only set this if you configured the database name to something other than `datacommons`. -1. Click **Done**. + - `USE_CLOUDSQL`: Set to `true`. + - `DC_API_KEY`: Set to your API key. + - `INPUT_DIR`: Set to the Cloud Storage bucket and input folder that you created in step 2 above. + - `OUTPUT_DIR`: Set to the Cloud Storage bucket (and, optionally, output folder) that you created in step 2 above. If you didn't create a separate folder for output, specify the same folder as the `INPUT_DIR`. + - `CLOUDSQL_INSTANCE`: Set to the full connection name of the instance you created in step 3 above. + - `DB_USER`: Set to a user you configured when you created the instance in step 3, or to `root` if you didn't create a new user. + - `DB_PASS`: Set to the user's or root password you configured when you created the instance in step 3. + - `DB_NAME`: Only set this if you configured the database name to something other than `datacommons`. +1. When you finished, click **Done**. ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot3.png) @@ -116,7 +116,7 @@ Finally, set the task options under **Task capacity**: ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot4.png) -1. 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- **CPU allocation and pricing**: **CPU is always allocated** - **Service autoscaling** > **Minimum number of instances**: **1** -[add image here] + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot5.png) + 1. Expand **Container, Volumes, Connections, Security** > **Container** > **Settings**, and set the following options: - **Resources** > **Memory**: **8 GiB** - **Resources** > **CPU**: **2** + + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot6.png) + 1. Expand the **Variables and secrets** tab. 1. Click the **Variables and Secrets** tab. 1. Click **Add variable**. 1. Add the same environment variables, with the same names and values as you did when you created the [data management run job](/custom_dc/data_cloud.html#env-vars) You can omit the `INPUT_DIR` variable. 1. Add a variable for the `MAPS_API_KEY` and set it to your Maps API key. -1. Click **Done**. +1. When you are finished, click **Done**. + + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot7.png) -[add image here] 1. Under **Execution environment** > **Autoscaling**, set the following options: - **Minimum number of instances**: **1** - **Maximum number of instances**: **1** 1. Disable **Startup CPU boost**. -1. - -[add image here] +1. Under **Cloud SQL connections** click **Add connection** and select your Cloud SQL instance from the menu. -1. Click **Deploy**. + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot8.png) -Click the **Logs** tab to see the status details of the operation. Once it completes, a link to the deployed image URL is listed at the top of the page. Click on the link to see the running instance. +Click **Create** to kick off the deployment. Click the **Logs** tab to see the status details of the operation. Once it completes, a link to the deployed image URL is listed at the top of the page. Click on the link to see the running instance. ## Manage the service From a68f8b428c0b4c5fd7650a93e80352232bd11877 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 10:31:29 -0700 Subject: [PATCH 34/96] resize images --- custom_dc/data_cloud.md | 12 ++++++------ custom_dc/deploy_cloud.md | 11 ++++++----- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 622d0c8fd..c7c5b16d3 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -80,7 +80,7 @@ See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) document 1. Next to the **Project**, click **Change**, search for **datcom-ci** and select it. 1. In the list of images that appears, navigate to and expand **gcr.io/datcom-ci/datacommons-data**, highlight the image you want, **stable** or **latest**, and click **Select**. - ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot1.png) + ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot1.png){: width="450" } 1. Optionally, in the **Job name** field, enter an alternative name as desired. 1. In the **Region** field, select the region you chose as your location. @@ -89,7 +89,7 @@ See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) document - **Resources** > **Memory**: **8 GiB** - **Resources** > **CPU**: **2** - ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot2.png) + ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot2.png){: width="450" } {:.#env-vars} Now set environment variables: @@ -107,14 +107,14 @@ Now set environment variables: - `DB_NAME`: Only set this if you configured the database name to something other than `datacommons`. 1. When you finished, click **Done**. - ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot3.png) + ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot3.png){: width="450" } Finally, set the task options under **Task capacity**: - - **Task timeout** > **10** - - **Time unit** > **minute** + - **Task timeout**: **10** + - **Time unit**: **minute** - **Number of retries per failed task**: **3** - ![Cloud Run Job](/assets/images/custom_dc/gcp_screenshot4.png) + ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot4.png){: width="450" } Click **Create** (but don't run it immediately). diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 3c755a801..ae01780fa 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -94,13 +94,13 @@ See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) f - **CPU allocation and pricing**: **CPU is always allocated** - **Service autoscaling** > **Minimum number of instances**: **1** - ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot5.png) + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot5.png){: width="450"} 1. Expand **Container, Volumes, Connections, Security** > **Container** > **Settings**, and set the following options: - **Resources** > **Memory**: **8 GiB** - **Resources** > **CPU**: **2** - ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot6.png) + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot6.png){: width="450"} 1. Expand the **Variables and secrets** tab. 1. Click the **Variables and Secrets** tab. @@ -109,7 +109,7 @@ See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) f 1. Add a variable for the `MAPS_API_KEY` and set it to your Maps API key. 1. When you are finished, click **Done**. - ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot7.png) + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot7.png){: width="450"} 1. Under **Execution environment** > **Autoscaling**, set the following options: - **Minimum number of instances**: **1** @@ -117,7 +117,7 @@ See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) f 1. Disable **Startup CPU boost**. 1. Under **Cloud SQL connections** click **Add connection** and select your Cloud SQL instance from the menu. - ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot8.png) + ![Cloud Run service](/assets/images/custom_dc/gcp_screenshot8.png){: width="450"} Click **Create** to kick off the deployment. Click the **Logs** tab to see the status details of the operation. Once it completes, a link to the deployed image URL is listed at the top of the page. Click on the link to see the running instance. @@ -125,4 +125,5 @@ Click **Create** to kick off the deployment. Click the **Logs** tab to see the Every time you make changes to the code and release a new Docker artifact, or rerun the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: -1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. \ No newline at end of file +1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. +1. 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Q*We!&MGb}gW9R(;2UO*eD*ylh diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index c7c5b16d3..3ae17f1fd 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -109,14 +109,8 @@ Now set environment variables: ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot3.png){: width="450" } -Finally, set the task options under **Task capacity**: - - **Task timeout**: **10** - - **Time unit**: **minute** - - **Number of retries per failed task**: **3** - - ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot4.png){: width="450" } - -Click **Create** (but don't run it immediately). +1. If you have a large amount of data, adjust the **Task capacity** > **Task timeout** option accordingly. See [Set task timeout (jobs)](https://cloud.google.com/run/docs/configuring/task-timeout) for more details. +1. Click **Create** (but don't run it immediately). ## Manage your data diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 8bf44e5f4..3d2a518e0 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -127,7 +127,7 @@ To load the sample data: ```shell docker run \ --env-file $PWD/custom_dc/env.list \ --v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ +-v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-data:stable ``` This does the following: @@ -150,7 +150,7 @@ docker run -it \ -p 8080:8080 \ -e DEBUG=true \ --env-file $PWD/custom_dc/env.list \ --v $PWD/custom_dc/sample/:$PWD/custom_dc/sample \ +-v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:stable ``` From c6060378caf2c60e58875d7eb600b4f61f357cf5 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 12:22:48 -0700 Subject: [PATCH 36/96] fix a tiny error --- custom_dc/data_cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 3ae17f1fd..73960d5ee 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -60,7 +60,7 @@ This stores the data that will be served at run time. The Data Commons data mana 1. For the **Location type**, choose the relevant regional option. 1. When you have finished setting all the configuration options, click **Create Instance**. It may take several minutes for the instance to be created. 1. When the instance is created and the left navigation bar appears, select **Users**. -1. Add at least one user and password. Be sure to at least assign a password to the "root" user. +1. Add at least one user and password. 1. Select **Databases**. 1. Click **Create Database**. 1. Choose a name for the database or use the default, `datacommons`. From 9bb03557cb8a687e39b0d6ef9edbff2201a7618e Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 12:33:20 -0700 Subject: [PATCH 37/96] delete unused images --- assets/images/custom_dc/home-heart-food.png | Bin 425457 -> 0 bytes assets/images/custom_dc/iitm.png | Bin 385127 -> 0 bytes assets/images/custom_dc/install_step_1.png | Bin 65280 -> 0 bytes assets/images/custom_dc/install_step_2.png | Bin 102756 -> 0 bytes assets/images/custom_dc/install_step_3.png | Bin 34326 -> 0 bytes assets/images/custom_dc/install_step_4.png | Bin 90223 -> 0 bytes 6 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 assets/images/custom_dc/home-heart-food.png delete mode 100644 assets/images/custom_dc/iitm.png delete mode 100644 assets/images/custom_dc/install_step_1.png delete mode 100644 assets/images/custom_dc/install_step_2.png delete mode 100644 assets/images/custom_dc/install_step_3.png delete mode 100644 assets/images/custom_dc/install_step_4.png diff --git 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zO47_G5wlnfe765i7BA};qB69rO?Q`uF$I54Q&5DhC`AdDj{bPZg-bNJmAyfNx;MYG z*OAcBzMJ&xYY6o`v$WSCuoq$*k@b}3OHZK}@lv#sXwr2Hx|5e!s!cQhi7Ym~ z;u{lVlxpbu5VU5L9a#1m+7Y&eV`#eV%*pX&ZS`uc26ob=NX#4YSP99RNz;3D`UOCC@~Go+hOzw^B3{=CuU$mIuYqFO zy?&U#OvuyNwnE*|+Z$U;U)anGxCQ&2G=E2cIWle0hciF@vUw?}7L>w@R1z^zcZ{M`?{o zDPcz9-%)eMdl&sqTUsmH!1vnRpsZ&e1Abg}4860&4G^}wu}ogbiMsRaf8Y)uAM@;2 z?c)rHdXsZD%o#Bo8e0uJmGY{D!G8k(6RZ@fWjW0DqnEEN9mGu+O}j0BNXaxXGj)l5 zdXB-!Y^;T?ZI`my*_YiizOsi!B93aQ7oCP`miqI`iq`)2kE&V*4W?klP<9uIIAN1I zTvxb?klSj|0-r?0H^J#K5f1VTOvZJ8OX^XqXyh;cEIo48`;3LmFOiLp!`alx%N}j` zZ+5S!{D92MROj4wxvr@wM^RG|Md^vXnVOz%RM-RYC+t2G?XSCsKPh=7%EY`d1qej`mrF3($+ zF-t9kW<~n|WDYz7(CkMWQLapR2?}jH{_2j6jz?4@=qIaP%j8dsu5whF{^VmH^dcS) zH+uS700&qe+md_;0#p86X$HFlf)4u zE9>7W>!{d!uCCk;whJ>j05cQT$PO-xGD;sit4}{ zxX3U8^XFDX?GFzJ+5#|9){W^Z3o_(%=r5YzppNNQ02R5mC^sx zdwmYq?#V-)Eb94+zWsy(KeCdF5|v^`f&UF)a{qS# From edcb5bce5156b8d7565e5724b4e3c91933d68fa3 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 13 Aug 2024 15:07:50 -0700 Subject: [PATCH 38/96] fix missing dash --- custom_dc/build_image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index 640263b3e..1ad43fde6 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -34,7 +34,7 @@ If you want to pick up the latest prebuilt version, do the following: docker run -it \ -p 8080:8080 \ -e DEBUG=true \ --env-file $PWD/custom_dc/env.list \ +--env-file $PWD/custom_dc/env.list \ -v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-website-compose:latest ``` From ae46ea2122d596e504d5dcfcdd673bd79be09222 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 10:06:24 -0700 Subject: [PATCH 39/96] update build file --- custom_dc/build_image.md | 9 +++++---- custom_dc/deploy_cloud.md | 2 +- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index 98f507433..b0dc21871 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -124,12 +124,13 @@ If you want to create a new source directory and start from scratch, clone the r Run the following command to build the repo:

      -docker build --tag datacommons-services:DOCKER_TAG \
      --f build/web_compose/Dockerfile \
      +docker build --tag IMAGE_NAME:DOCKER_TAG \
      +-f build/cdc_services/Dockerfile \
       -t services .
       
      -The _DOCKER_TAG_ is a meaningful description of the version you are building. +- The _IMAGE_NAME_ is a meaningful name for the build, such as `datacommons-services`. +- The _DOCKER_TAG_ is a meaningful description of the version you are building, such as `latest`. It will take several minutes to build. @@ -149,5 +150,5 @@ docker run -it \ -v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \ [-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \] [-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \] -datacommons-services:DOCKER_TAG +IMAGE_NAME:DOCKER_TAG

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diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 2a6083162..883c147d3 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -60,7 +60,7 @@ This procedure creates a "dev" Docker package that you upload to the Google Clou - The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. - - The `_IMAGE_NAME`_ may be the same as the source (`datacommons-services`) or any other string. + - The `_IMAGE_NAME`_ may be the same as the source (e.g. `datacommons-services`) or any other string. - The _`TARGET_IMAGE_TAG`_ can be the same as the source, or any other string. 1. Push the image to the registry: From 4ce0fb9de27f14a1d1ef28092f110974620215c5 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 10:36:27 -0700 Subject: [PATCH 40/96] adjust build command --- _includes/find_dcid.html | 7 +++++++ custom_dc/build_image.md | 3 +-- 2 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 _includes/find_dcid.html diff --git a/_includes/find_dcid.html b/_includes/find_dcid.html new file mode 100644 index 000000000..ae78237e7 --- /dev/null +++ b/_includes/find_dcid.html @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index b0dc21871..9863ccc70 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -125,8 +125,7 @@ Run the following command to build the repo:
 docker build --tag IMAGE_NAME:DOCKER_TAG \
--f build/cdc_services/Dockerfile \
--t services .
+-f build/cdc_services/Dockerfile .
 
- The _IMAGE_NAME_ is a meaningful name for the build, such as `datacommons-services`. From 68a3f518c071fc6de6bf2e6a6bd70e8fe5cfbccb Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 10:37:40 -0700 Subject: [PATCH 41/96] Revert "adjust build command" This reverts commit 4ce0fb9de27f14a1d1ef28092f110974620215c5. --- _includes/find_dcid.html | 7 ------- custom_dc/build_image.md | 3 ++- 2 files changed, 2 insertions(+), 8 deletions(-) delete mode 100644 _includes/find_dcid.html diff --git a/_includes/find_dcid.html b/_includes/find_dcid.html deleted file mode 100644 index ae78237e7..000000000 --- a/_includes/find_dcid.html +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - \ No newline at end of file diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index 9863ccc70..b0dc21871 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -125,7 +125,8 @@ Run the following command to build the repo:
 docker build --tag IMAGE_NAME:DOCKER_TAG \
--f build/cdc_services/Dockerfile .
+-f build/cdc_services/Dockerfile \
+-t services .
 
- The _IMAGE_NAME_ is a meaningful name for the build, such as `datacommons-services`. From d8f1ab721248148208df70b1c3eae0a7a802fd09 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 10:46:06 -0700 Subject: [PATCH 42/96] update docker file --- custom_dc/build_image.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index b0dc21871..9863ccc70 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -125,8 +125,7 @@ Run the following command to build the repo:
 docker build --tag IMAGE_NAME:DOCKER_TAG \
--f build/cdc_services/Dockerfile \
--t services .
+-f build/cdc_services/Dockerfile .
 
- The _IMAGE_NAME_ is a meaningful name for the build, such as `datacommons-services`. From d67d2808e20cba60d80327a89b6b0b3045375e17 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 11:29:01 -0700 Subject: [PATCH 43/96] more fixes --- custom_dc/build_image.md | 18 +++++++++--------- custom_dc/deploy_cloud.md | 15 +++++++-------- custom_dc/quickstart.md | 12 ++++++------ 3 files changed, 22 insertions(+), 23 deletions(-) diff --git a/custom_dc/build_image.md b/custom_dc/build_image.md index 9863ccc70..f8aac9345 100644 --- a/custom_dc/build_image.md +++ b/custom_dc/build_image.md @@ -37,7 +37,7 @@ If you want to pick up the latest prebuilt version, do the following: --env-file $PWD/custom_dc/env.list \ -v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ gcr.io/datcom-ci/datacommons-services:latest -``` + ``` ## Build a local image {#build-repo} @@ -61,9 +61,9 @@ In Github, use the following procedure. 1. If you want to reuse the root directory you previously created and cloned, skip to step 3. If you want to create a new source directory and start from scratch, clone the repo up to the stable release tag: -
-      git clone https://github.com/datacommonsorg/website --branch customdc_stable --single-branch  [DIRECTORY]
-      
+
+   git clone https://github.com/datacommonsorg/website --branch customdc_stable --single-branch  [DIRECTORY]
+   
1. Change to the root directory:
@@ -88,7 +88,7 @@ If you want to create a new source directory and start from scratch, clone the r
    ...
    ```
 
-   Verify that the last commit in the output matches that listed in [https://github.com/datacommonsorg/website/releases/tag/customdc_stable](https://github.com/datacommonsorg/website/releases/tag/customdc_stable).
+   Verify that the last commit in the output matches that listed in https://github.com/datacommonsorg/website/releases/tag/customdc_stable.
 
 1. Press `q` to exit the output log.
 
@@ -124,12 +124,12 @@ If you want to create a new source directory and start from scratch, clone the r
 Run the following command to build the repo:
 
 
-docker build --tag IMAGE_NAME:DOCKER_TAG \
+docker build --tag IMAGE_NAME:IMAGE_TAG \
 -f build/cdc_services/Dockerfile .
 
-- The _IMAGE_NAME_ is a meaningful name for the build, such as `datacommons-services`. -- The _DOCKER_TAG_ is a meaningful description of the version you are building, such as `latest`. +- The image name is a meaningful name, such as `datacommons-services`. +- The image tag is a meaningful description of the version you are building, such as `latest`. It will take several minutes to build. @@ -149,5 +149,5 @@ docker run -it \ -v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \ [-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \] [-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \] -IMAGE_NAME:DOCKER_TAG +IMAGE_NAME:IMAGE_TAG
diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 883c147d3..655e56db3 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -47,7 +47,7 @@ This procedure creates a "dev" Docker package that you upload to the Google Clou 1. Generate credentials for the Docker package you will build in the next step. Docker package names must be in the format LOCATION-docker-pkg.dev, where the _LOCATION_ is the region you have selected in the repository creation step previously; for example, `us-central1`.
  
-    gcloud auth configure-docker LOCATION-docker.pkg.dev  
+    gcloud auth configure-docker LOCATION-docker.pkg.dev
    
When prompted to confirm creating the credentials file, click `Y` to accept. @@ -55,18 +55,17 @@ This procedure creates a "dev" Docker package that you upload to the Google Clou 1. Create a package from the source image created in step 1:
 
-   docker tag datacommons-services:DOCKER_TAG \  
-   LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
+   docker tag SOURCE_IMAGE_NAME:SOURCE_IMAGE_TAG \  
+   LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/TARGET_IMAGE_NAME:TARGET_IMAGE_TAG  
    
- - The `_ARTIFACT_REPO`_ must be an Artifact Registry repository you have created previously. - - The `_IMAGE_NAME`_ may be the same as the source (e.g. `datacommons-services`) or any other string. - - The _`TARGET_IMAGE_TAG`_ can be the same as the source, or any other string. - + - The artifact repo must be an Artifact Registry repository you have created previously. + - The target image name and tag can be the same as the source or different. + 1. Push the image to the registry:
-   docker push LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/IMAGE_NAME:TARGET_IMAGE_TAG  
+   docker push LOCATION-docker.pkg.dev/PROJECT_ID/ARTIFACT_REPO/TARGET_IMAGE_NAME:TARGET_IMAGE_TAG  
    
This will take several minutes to upload. diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index aaf68666d..e267c1262 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -124,12 +124,12 @@ To load the sample data: 1. If you are running on Windows or Mac, start Docker Desktop and ensure that the Docker Engine is running. 1. Open a terminal window, and from the root directory, run the following command to run the data management Docker container: -```shell -docker run \ ---env-file $PWD/custom_dc/env.list \ --v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ -gcr.io/datcom-ci/datacommons-data:stable -``` + ```shell + docker run \ + --env-file $PWD/custom_dc/env.list \ + -v $PWD/custom_dc/sample:$PWD/custom_dc/sample \ + gcr.io/datcom-ci/datacommons-data:stable + ``` This does the following: - The first time you run it, downloads the latest stable Data Commons data image, `gcr.io/datcom-ci/datacommons-data:stable`, from the Google Cloud Artifact Registry, which may take a few minutes. Subsequent runs use the locally stored image. From 15a0a18a5aeff318fd10e3cc43b0dc856ff6895a Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 14 Aug 2024 11:31:26 -0700 Subject: [PATCH 44/96] one last fix --- custom_dc/data_cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index f437b6df0..c5c7476af 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -230,7 +230,7 @@ docker run -it \ -v OUTPUT_DIRECTORY:OUTPUT_DIRECTORY \ [-v $PWD/server/templates/custom_dc/custom:/workspace/server/templates/custom_dc/custom \] [-v $PWD/static/custom_dc/custom:/workspace/static/custom_dc/custom \] -datacommons-services:DOCKER_TAG +IMAGE_NAME:IMAGE_TAG From 437948cf47c40659878a89d98f4b404545718e92 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Thu, 15 Aug 2024 10:21:14 -0700 Subject: [PATCH 45/96] make links to Cloud Console open in a new page --- custom_dc/data_cloud.md | 16 ++++++++-------- custom_dc/deploy_cloud.md | 8 ++++---- custom_dc/troubleshooting.md | 2 +- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index c5c7476af..14419eb14 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -23,7 +23,7 @@ Alternatively, if you have a very large data set, you may find it faster to stor ## Prerequisites -- A [GCP](https://console.cloud.google.com/welcome) billing account and project. +- A [GCP](https://console.cloud.google.com/welcome){: target="_blank"} billing account and project. - Optional: Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install-sdk). ## One-time setup steps {#setup} @@ -36,7 +36,7 @@ While you are testing, you can start with a single Google Cloud region; to be cl This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated files files in a `datacommons` subdirectory when you run the data management job. -1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project. +1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser){: target="_blank"} for your project. 1. Next to **Buckets**, click **Create**. 1. Enter a name for this bucket. 1. For the **Location type**, choose the same regional options as for Cloud SQL above. @@ -51,7 +51,7 @@ This stores the CSV and JSON files that you will upload whenever your data chang This stores the data that will be served at run time. The Data Commons data management job will create the SQL tables and populate them when you start the job. -1. Go to https://console.cloud.google.com/sql/instances for your project. +1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances){: target="_blank"} for your project. 1. Next to **Instances**, click **Create Instance**. 1. Click **Choose MySQL.** 1. If necessary, enable APIs as directed. @@ -69,11 +69,11 @@ This stores the data that will be served at run time. The Data Commons data mana ### Step 4: Create a Google Cloud Run job -Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data. +Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at [https://console.cloud.google.com/gcr/images/datcom-ci/global/datacommons-data](https://console.cloud.google.com/gcr/images/datcom-ci/global/datacommons-data){: target="_blank"}. See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) documentation for links to more information on all the options you may set on your jobs. -1. Go to https://console.cloud.google.com/run/ for your project. +1. Go to [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} for your project. 1. Click **Create job**. 1. In the **Container image URL** field, click **Select** to open the **Select container image** window. 1. Click the **Container Registry** tab. @@ -116,7 +116,7 @@ Now set environment variables: ### Step 1: Upload data files to Google Cloud Storage -1. Go to https://console.cloud.google.com/storage/browse and select your custom Data Commons bucket. +1. Go to [https://console.cloud.google.com/storage/browse](https://console.cloud.google.com/storage/browse){: target="_blank"} and select your custom Data Commons bucket. 1. Navigate to the folder you created in the earlier step. 1. Click **Upload Files**, and select all your CSV files and `config.json`. @@ -132,7 +132,7 @@ Every time you upload new input CSV or JSON files to Google Cloud Storage, you w To run the job: -1. Go to https://console.cloud.google.com/run/jobs for your project. +1. Go to [https://console.cloud.google.com/run/jobs](https://console.cloud.google.com/run/jobs){: target="_blank"} for your project. 1. From the list of jobs, click the link of the "datacommons-data" job you created above. 1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. @@ -142,7 +142,7 @@ When it completes, to verify that the data has been loaded correctly, see the ne To view information about the created tables: -1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances) for your project and select the instance you created earlier. +1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances){: target="_blank"} for your project and select the instance you created earlier. 1. In the left panel, select **Cloud SQL Studio**. 1. In the **Sign in to SQL Studio** page, from the Database field, select the database you created earlier, e.g. `datacommons`. 1. Enter the user name and password and click **Authenticate**. diff --git a/custom_dc/deploy_cloud.md b/custom_dc/deploy_cloud.md index 655e56db3..ec6e4e9b8 100644 --- a/custom_dc/deploy_cloud.md +++ b/custom_dc/deploy_cloud.md @@ -23,7 +23,7 @@ You push a locally built Docker image to the [Google Cloud Artifact Registry](ht ## One-time setup: Create a Google Artifact Registry repository -1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project. +1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts){: target="_blank"} for your project. 1. Click **Create Repository**. 1. Specify a name for the repository. 1. In the **Format** option, select **Docker**. @@ -72,7 +72,7 @@ This will take several minutes to upload. When it completes, verify that the container has been uploaded in the Cloud Console: -1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project. +1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts){: target="_blank"} for your project. 1. In the list of repositories, click on the one you created above. Under **Repository Details**, you should see the Docker image listed. ## One-time setup: Create a Google Run service @@ -81,7 +81,7 @@ This procedure shows you how to create a service and set environment variables u See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) for more information on all the options you may set on your service. -1. Go to https://console.cloud.google.com/run/ for your project. +1. Go to [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} for your project. 1. Click **Create service**. 1. Keep the default **Deploy one revision from an existing container image** enabled. 1. In the **Container image URL** field, click **Select** to open the **Select container image** window. @@ -124,5 +124,5 @@ Click **Create** to kick off the deployment. Click the **Logs** tab to see the Every time you make changes to the code and release a new Docker artifact, or rerun the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so: -1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. +1. Go to the [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} page, click on the service you created above, and click **Edit & Deploy Revision**. 1. Select a new container and click **Deploy**. \ No newline at end of file diff --git a/custom_dc/troubleshooting.md b/custom_dc/troubleshooting.md index fa1aef85b..eddae0515 100644 --- a/custom_dc/troubleshooting.md +++ b/custom_dc/troubleshooting.md @@ -80,7 +80,7 @@ In general, whenever you encounter problems with any Google Cloud Run service, c ### "403 Forbidden: Your client does not have permission to get URL / from this server" This error indicates that your application requires authenticated requests but you have not provided an authentication token. If your site is intended to be public, first check to see that the Cloud Run service is not set up to require authentication: -1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run) page for your project. +1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run){: target="_blank"} page for your project. 1. From the list of services, select the relevant service and select the **Security** tab. 1. Ensure that you have enabled **Allow unauthenticated invocations** and restart the Cloud Run service. From 1b3fcdfbf043d8c089e0f2b8af15d3b59172a7f3 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Fri, 16 Aug 2024 10:28:54 -0700 Subject: [PATCH 46/96] fixes to quickstart suggested by Prem --- custom_dc/quickstart.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index e267c1262..d1bacaa4c 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -33,7 +33,7 @@ The "services" Docker container consists of the following Data Commons component ## Prerequisites -- Obtain a [GCP](https://console.cloud.google.com/welcome) billing account and project. +- Obtain a [GCP](https://cloud.google.com/docs/get-started) billing account and project. - If you are developing on Windows, install [WSL 2](https://learn.microsoft.com/en-us/windows/wsl/install) (any distribution will do, but we recommend the default, Ubuntu), and enable [WSL 2 integration with Docker](https://docs.docker.com/desktop/wsl/). - Install [Docker Desktop/Engine](https://docs.docker.com/engine/install/). - Install [Git](https://git-scm.com/). @@ -44,15 +44,15 @@ The "services" Docker container consists of the following Data Commons component ### Enable Google Cloud APIs and get a Maps API key {#maps-key} -1. Go to [https://console.cloud.google.com/apis/dashboard](https://console.cloud.google.com/apis/dashboard) for your project. +1. Go to [https://console.cloud.google.com/apis/dashboard](https://console.cloud.google.com/apis/dashboard){: target="_blank"} for your project. 1. Click **Enable APIs & Services**. 1. Under **Maps**, enable **Places API** and **Maps Javascript API**. -1. Go to [https://console.cloud.google.com/google/maps-apis/credentials](https://console.cloud.google.com/google/maps-apis/credentials) for your project. +1. Go to [https://console.cloud.google.com/google/maps-apis/credentials](https://console.cloud.google.com/google/maps-apis/credentials){: target="_blank"} for your project. 1. Click **Create Credentials** > **API Key**. 1. Record the key and click **Close**. 1. Click on the newly created key to open the **Edit API Key** window. -1. Under API restrictions, select Restrict key. -1. From the drop-down menu, enable Places API and Maps Javascript API. (Optionally enable other APIs for which you want to use this key.) +1. Under **API restrictions**, select **Restrict key**. +1. From the drop-down menu, enable **Places API** and **Maps Javascript API**. (Optionally enable other APIs for which you want to use this key.) 1. Click **OK** and **Save**. ### Clone the Data Commons repository @@ -77,7 +77,7 @@ cd website 1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. -1. Set the `INPUT_DIR` to the full path to the `sample/` directory. For example if you have cloned the repo directly to your home directory, this would be /home/USERNAME/website/custom_dc/sample/. +1. Set the `INPUT_DIR` to the full path to the `website/custom_dc/sample/` directory. For example if you have cloned the repo directly to your home directory, this might be /home/USERNAME/website/custom_dc/sample/. (If you're not sure, type `pwd` to get the working directory.) 1. For the `OUTPUT_DIR`, set it to the same path as the `INPUT_DIR`. **Warning:** Do not use any quotes (single or double) or spaces when specifying the values. From 4677644c2abe9a9c995305c799581cd3a00d5421 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Fri, 16 Aug 2024 10:33:32 -0700 Subject: [PATCH 47/96] one more change --- custom_dc/quickstart.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index d1bacaa4c..ac09ea43c 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -191,7 +191,7 @@ Once the services are up and running, visit your local instance by pointing your ![screenshot_homepage](/assets/images/custom_dc/customdc_screenshot1.png){: width="900"} -Now click the **Timeline** link to visit the Timeline explorer. Try entering a country and click **Continue**. Now, in the **Select variables** tools, you'll see the new variables: +Now click the **Timeline** link to visit the Timeline explorer. Click **Start**, enter a country and click **Continue**. Now, in the **Select variables** tools, you'll see the new variables: ![screenshot_timeline](/assets/images/custom_dc/customdc_screenshot2.png){: width="900"} From cba3fe97609b95082b4282b2fb00b704b82d5123 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Wed, 21 Aug 2024 11:38:03 -0700 Subject: [PATCH 48/96] change from Keyur --- custom_dc/quickstart.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index ac09ea43c..b0232b9f2 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -77,7 +77,7 @@ cd website 1. Using your favorite editor, open `custom_dc/env.list`. 1. Enter the relevant values for `DC_API_KEY` and `MAPS_API_KEY`. -1. Set the `INPUT_DIR` to the full path to the `website/custom_dc/sample/` directory. For example if you have cloned the repo directly to your home directory, this might be /home/USERNAME/website/custom_dc/sample/. (If you're not sure, type `pwd` to get the working directory.) +1. Set the `INPUT_DIR` to the full path to the `website/custom_dc/sample/` directory. For example if you have cloned the repo directly to your home directory, this might be /home/USERNAME/website/custom_dc/sample/. (If you're not sure, in your terminal, type `pwd` to get the working directory.) 1. For the `OUTPUT_DIR`, set it to the same path as the `INPUT_DIR`. **Warning:** Do not use any quotes (single or double) or spaces when specifying the values. From 41aacce96a58c6a5d85e9118b45dc0fd4df65857 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Mon, 26 Aug 2024 11:45:20 -0700 Subject: [PATCH 49/96] revise procedure --- custom_dc/data_cloud.md | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/custom_dc/data_cloud.md b/custom_dc/data_cloud.md index 29ce4e224..6404c5c98 100644 --- a/custom_dc/data_cloud.md +++ b/custom_dc/data_cloud.md @@ -75,13 +75,7 @@ See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs){: target 1. Go to [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} for your project. 1. Click **Create job**. -1. In the **Container image URL** field, click **Select** to open the **Select container image** window. -1. Click the **Container Registry** tab. -1. Next to the **Project**, click **Change**, search for **datcom-ci** and select it. -1. In the list of images that appears, navigate to and expand **gcr.io/datcom-ci/datacommons-data**, highlight the image you want, **stable** or **latest**, and click **Select**. - - ![Cloud Run job](/assets/images/custom_dc/gcp_screenshot1.png){: width="450" } - +1. In the **Container image URL** field, enter `gcr.io/datcom-ci/datacommons-data:stable`. 1. Optionally, in the **Job name** field, enter an alternative name as desired. 1. In the **Region** field, select the region you chose as your location. 1. Leave the default **Number of tasks** as 1. From e204fbda9b06541f7576a7c06c8ae6a7214ee5f1 Mon Sep 17 00:00:00 2001 From: Kara Moscoe Date: Tue, 27 Aug 2024 09:32:45 -0700 Subject: [PATCH 50/96] merge --- api/web_components/index.md | 9 +++++++++ bigquery/index.md | 8 +++----- custom_dc/index.md | 1 - data_model.md | 2 +- how_to_use.md | 2 -- index.md | 4 +--- 6 files changed, 14 insertions(+), 12 deletions(-) diff --git a/api/web_components/index.md b/api/web_components/index.md index 47df117fb..bc1840bde 100644 --- a/api/web_components/index.md +++ b/api/web_components/index.md @@ -54,6 +54,7 @@ For example, to embed a line chart: See a live version of this example you can play around with in [Playground](https://lit.dev/playground/#gist=c0c88276739f4f6061807cc943937a14){: target="_blank"} +[Playground](https://lit.dev/playground/#gist=c0c88276739f4f6061807cc943937a14){: target="_blank"} ([source](/assets/examples/web-components/line-chart.html)). ## Components @@ -70,8 +71,10 @@ See a live version of this example you can play around with in ## Code Playground Examples +- [Static page with all chart types playground](https://lit.dev/playground/#gist=822ce6018bb41113c866d703760c1def){: target="_blank"} - [Static page with all chart types playground](https://lit.dev/playground/#gist=822ce6018bb41113c866d703760c1def){: target="_blank"} ([source](/assets/examples/web-components/all-charts.html)) +- [Dynamically updating charts playground](https://lit.dev/playground/#gist=9e3ac88e162248f849dd276ff5895ad0){: target="_blank"} - [Dynamically updating charts playground](https://lit.dev/playground/#gist=9e3ac88e162248f849dd276ff5895ad0){: target="_blank"} ([source](/assets/examples/web-components/dynamic-map.html)) @@ -84,10 +87,13 @@ places. Variables and places are identified by To look up a DCID for an entity or variable, see the different methods described in [this page](/data_model.html#find-dcid). +To find places available for a statistical variable, see [this page](/data_mode.html#find-places). + ## Styling Custom styles are supported through [CSS shadow parts](https://developer.mozilla.org/en-US/docs/Web/CSS/::part){: target="_blank"}. +[CSS shadow parts](https://developer.mozilla.org/en-US/docs/Web/CSS/::part){: target="_blank"}.
@@ -159,23 +157,32 @@ Now that everything is configured, and you have uploaded your data in Google Clo Every time you upload new input CSV or JSON files to Google Cloud Storage, you will need to rerun the job. -To run the job using the Cloud Console: - -1. Go to [https://console.cloud.google.com/run/jobs](https://console.cloud.google.com/run/jobs){: target="_blank"} for your project. -1. From the list of jobs, click the link of the "datacommons-data" job you created above. -1. Optionally, if you have received a `SQL check failed` error when previously trying to start the container, and would like to minimize startup time, click **Execute with overrides** and click **Add variable** to set a new variable with name `DATA_RUN_MODE` and value `schemaupdate`. -1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. +
+ + +
-To run the job using the command line: +
+
    +
  1. Go to https://console.cloud.google.com/run/jobs for your project.
  2. +
  3. From the list of jobs, click the link of the "datacommons-data" job you created above.
  4. +
  5. Optionally, if you have received a SQL check failed error when previously trying to start the container, and would like to minimize startup time, click Execute with overrides and click Add variable to set a new variable with name DATA_RUN_MODE and value schemaupdate.
  6. +
  7. Click Execute. It will take several minutes for the job to run. You can click the Logs tab to view the progress.
  8. +
+
-1. From any local directory, run the following command: -
-   gcloud run jobs execute JOB_NAME
-   
-1. To view the progress of the job, run the following command: -
-   gcloud beta run jobs logs tail JOB_NAME
-   
+
+
    +
  1. From any local directory, run the following command: +
    gcloud run jobs execute JOB_NAME
  2. +
  3. To view the progress of the job, run the following command: +
    gcloud beta run jobs logs tail JOB_NAME
    +
  4. +
+
+ When it completes, to verify that the data has been loaded correctly, see [Inspect the Cloud SQL database](#inspect-sql). diff --git a/custom_dc/quickstart.md b/custom_dc/quickstart.md index 3c7cdc7e8..85f438374 100644 --- a/custom_dc/quickstart.md +++ b/custom_dc/quickstart.md @@ -65,15 +65,13 @@ An API key is required to authorize requests from your site to the base Data Com 1. Open a terminal window, and go to a directory to which you would like to download the Data Commons repository. 1. Clone the website Data Commons repository: -
-   git clone https://github.com/datacommonsorg/website.git [DIRECTORY]
-  
- If you don't specify a directory name, this creates a local `website` subdirectory. If you specify a directory name, all files are created under that directory, without a `website` subdirectory. +
git clone https://github.com/datacommonsorg/website.git [DIRECTORY]
+ If you don't specify a directory name, this creates a local `website` subdirectory. If you specify a directory name, all files are created under that directory, without a `website` subdirectory. When the downloads are complete, navigate to the root directory of the repo (e.g. `website`). References to various files and commands in these procedures are relative to this root.
-cd website
+cd website | DIRECTORY
 
### Set environment variables {#env-vars}