Merge branch 'main' into migration-guides

Author: matea16

pages/_meta.ts

@@ -13,8 +13,8 @@ export default {
   "database-management": "Database management",
   "deployment": "Deployment",
   "clustering": "Clustering",
+  "memgraph-in-production": "Memgraph in production",
   "data-streams": "Data streams",
   "help-center": "Help center",
   "release-notes": "Release notes",
-  "memgraph-in-production": "Memgraph in production"
 }

pages/getting-started/install-memgraph.mdx

@@ -109,13 +109,15 @@ Before running Memgraph, please check the [system configuration guidelines](/dat
 
 Below are minimum and recommended system requirements for installing Memgraph.
 
-|         | Minimum  | Recommended                     |
-| ------- | -------- | ------------------------------- |
-| CPU     | Server or desktop processor: <br/> Intel Xeon <br/> AMD Opteron/Epyc <br/> ARM machines or Apple M1 <br/> Amazon Graviton | Server processor: <br/> Intel Xeon <br/> AMD Opteron/Epyc <br/> ARM machines or Apple M1 <br/> Amazon Graviton |
-| RAM     | 1 GB     | ≥ 16 GB ECC                     |
-| Disk    | 1 GB     | at least 3x the amount of RAM (NVMe SSD) |
-| Cores   | 1 vCPU   | ≥ 8 vCPUs (≥ 4 physical cores)  |
-| Network | 100 Mbps | ≥ 1 Gbps                        |
+|                    | Minimum                                                                                                                   | Recommended                                                                                                    |
+| ------------------ | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
+| CPU                | Server or desktop processor: <br/> Intel Xeon <br/> AMD Opteron/Epyc <br/> ARM machines or Apple M1 <br/> Amazon Graviton | Server processor: <br/> Intel Xeon <br/> AMD Opteron/Epyc <br/> ARM machines or Apple M1 <br/> Amazon Graviton |
+| RAM                | 1 GB                                                                                                                      | ≥ 16 GB ECC                                                                                                    |
+| Disk               | 1 GB                                                                                                                      | at least 3x the amount of RAM (NVMe SSD, mirroring RAID 1)                                                     |
+| Cores              | 1 vCPU                                                                                                                    | ≥ 8 vCPUs (≥ 4 physical cores)                                                                                 |
+| Network Throughput | 100 Mbps                                                                                                                  | ≥ 1 Gbps                                                                                                       |
+| Network Latency    | < 10ms                                                                                                                    | < 1ms                                                                                                          |
+
 
 The disk is used for storing database [durability files](/configuration/data-durability-and-backup), including snapshots and write-ahead
 logs. By default, Memgraph stores the **three most recent snapshots** in the database  (this can be configured using the `--storage-snapshot-retention-count` flag).

pages/getting-started/install-memgraph/kubernetes.mdx

@@ -509,8 +509,10 @@ stays always the same:
 
 **5.** Connect instances to form the cluster
 
-The last step, connecting instances, needs to be done manually to provide information to instances about their external addresses
-on which they are available. Otherwise, client-side routing wouldn't work. To connect instances, we use the following queries:
+The last step, connecting instances, needs to be done manually to provide
+information to instances about their external addresses on which they are
+available. Otherwise, client-side routing wouldn't work. To connect instances,
+we use the following queries and execute them against any coordinator:
 
 ```cypher
 ADD COORDINATOR 1 WITH CONFIG {"bolt_server": "<bolt-server-coord1>", "management_server":  "memgraph-coordinator-1.default.svc.cluster.local:10000", "coordinator_server":  "memgraph-coordinator-1.default.svc.cluster.local:12000"};
@@ -523,7 +525,11 @@ SET INSTANCE instance_1 TO MAIN;
 
 Note that the only part which you need to change from the above template is the
 Bolt server address. The value of the Bolt server address depends on the type of
-the external network you are using for accessing instances from outside. 
+the external network you are using for accessing instances from outside.
+
+For more details, see our documentation on the Memgraph HA 
+[User API](/clustering/high-availability#user-api), including how to add 
+coordinators and register data instances.
 
 #### Using Memgraph HA chart with `IngressNginx` resource
 
@@ -626,6 +632,8 @@ The following table lists the configurable parameters of the Memgraph HA chart a
 | `secrets.name`                                           | The name of the Kubernetes secret containing Memgraph credentials                                            | `memgraph-secrets`         |
 | `secrets.userKey`                                        | The key in the Kubernetes secret for the Memgraph user, the value is passed to the `MEMGRAPH_USER` env.      | `USER`                     |
 | `secrets.passwordKey`                                    | The key in the Kubernetes secret for the Memgraph password, the value is passed to the `MEMGRAPH_PASSWORD`.  | `PASSWORD`                 |
+| `resources.coordinators`                                 | CPU/Memory resource requests/limits. Left empty by default.                                                  | `{}`                        |
+| `resources.data`                                         | CPU/Memory resource requests/limits. Left empty by default.                                                  | `{}`                        |
 | `prometheus.enabled`                                     | Enable the use of `prometheus-community` chart                                                               | `false`                    |
 | `prometheus.server.service.type`                         | The type of service through which `prometheus-server` will be exposed                                        | `LoadBalancer`             |
 

pages/help-center/faq.mdx

@@ -125,11 +125,21 @@ least 16 GB of ECC RAM, the same amount of disk storage and at least 8 vCPUs or
 
 We recommend twice as many GB of RAM as the data size.  If you have 8 GB of
 data, we recommend having at least 16 GB of RAM. Of course, the actual memory
-needs depend on the complexity of executed queries. The more graph objects query
-needs to return as a result, the more RAM will be required. To calculate the
-Memgraph RAM instance requirements based on your data, check out [how Memgraph
-uses memory](/fundamentals/storage-memory-usage) or use the [Storage memory
-calculator](https://memgraph.com/storage-calculator). 
+needs depend on the complexity of executed queries and the number of concurrent
+users/requests. The more graph objects query needs to return as a result, the
+more RAM will be required.
+
+The recommendation complicates under environments such as k8s because there is
+an additional overhead to run the engine. In addition, each cloud provider has
+a different configuration so that also has to be take into account. E.g. [here
+is the link on how k8s is using resources under
+Azure](https://learn.microsoft.com/en-us/azure/aks/node-resource-reservations).
+Please make sure you understand the underlying infrastructure and reserve
+enough resources for Memgraph to operate.
+
+To calculate the Memgraph RAM instance requirements based on your data, check
+out [how Memgraph uses memory](/fundamentals/storage-memory-usage) or use the
+[Storage memory calculator](https://memgraph.com/storage-calculator).
 
 ### Are there any graph size limits?
 
@@ -491,4 +501,4 @@ Yes, Memgraph cloud is running at AWS.
 ### Is GCP available?
 No, at the moment, Memgraph cloud is not available on the Google Cloud Platform.
 
-<CommunityLinks/>
+<CommunityLinks/>

pages/memgraph-in-production.mdx

@@ -44,14 +44,16 @@ Here are the currently available guides to help you deploy Memgraph effectively:
 ### [General suggestions](/memgraph-in-production/general-suggestions)
 A foundational guide covering universal best practices for any production deployment - recommended reading before anything else.
 
+### [Memgraph in high-throughput workloads](/memgraph-in-production/memgraph-in-high-throughput-workloads)
+Scale your write throughput while keeping up with fast-changing, high-velocity graph data.
+
 ### [Memgraph in GraphRAG use cases](/memgraph-in-production/memgraph-in-graphrag)
 Learn how to optimize Memgraph for Retrieval-Augmented Generation (RAG) systems using graph data.
 
 ## 🚧 Guides in construction
 - Memgraph in transactional workloads
 - Memgraph in analytical workloads
 - Memgraph in mission critical workloads
-- Memgraph in high throughput workloads
 - Memgraph in supply chain use cases
 - Memgraph in cyber security use cases
 - Memgraph in fraud detection use cases
@@ -70,7 +72,7 @@ smooth transition to production.
 These guides focus on areas like performance benchmarking, testing, and operational readiness—offering additional tools 
 and frameworks that can help you get the most out of your Memgraph deployment.
 
-### [📊 Evaluating Memgraph](/memgraph-in-production/evaluating-memgraph)
+### [📊 Benchmarking Memgraph](/memgraph-in-production/benchmarking-memgraph)
   Learn how to properly **test Memgraph for performance and scalability**. 
   This guide walks you through performance and stress testing scenarios, benchmarking with real-world data, 
   and identifying key metrics that can help validate Memgraph’s fit for your application needs.

pages/memgraph-in-production/_meta.ts

@@ -1,5 +1,6 @@
 export default {
   "general-suggestions": "General suggestions",
   "memgraph-in-graphrag": "Memgraph in GraphRAG use cases",
+  "memgraph-in-high-throughput-workloads": "Memgraph in high-throughput workloads",
   "benchmarking-memgraph": "Benchmarking Memgraph",
 }

pages/memgraph-in-production/general-suggestions.mdx

@@ -44,7 +44,7 @@ based on your specific use case.
 8. [Backup considerations](#backup-considerations) <br />
 Learn about how to preserve your data in Memgraph to prevent any data loss.
 
-9. [Importing mechanisms](#importing-mechanisms) <br /> 
+9. [Importing mechanisms](#importing-mechanisms) <br />
 Discover the best methods for importing your dataset into Memgraph, including Cypher queries, bulk loading, and integrations with other data sources.
 
 10. [Enterprise features you might require](#enterprise-features-you-might-require)  <br />