Merge pull request #47 from mindsdb/columns_pool

Run as many processes as columns count

type_infer/infer.py

@@ -392,9 +392,10 @@ def infer_types(
         f'from a total population of {population_size}, this is equivalent to {round(sample_size*100/population_size, 1)}% of your data.') # noqa
 
     nr_procs = get_nr_procs(df=sample_df)
-    if data.size > mp_cutoff and nr_procs > 1:
-        log.info(f'Using {nr_procs} processes to deduct types.')
-        pool = mp.Pool(processes=nr_procs)
+    pool_size = min(nr_procs, len(sample_df.columns.values))
+    if data.size > mp_cutoff and pool_size > 1:
+        log.info(f'Using {pool_size} processes to deduct types.')
+        pool = mp.Pool(processes=pool_size)
         # column-wise parallelization  # TODO: evaluate switching to row-wise split instead
         answer_arr = pool.starmap(get_column_data_type, [
             (sample_df[x].dropna(), data[x], x, pct_invalid) for x in sample_df.columns.values
@@ -422,8 +423,8 @@ def infer_types(
             'dtype_dist': data_dtype_dist
         }
 
-    if data.size > mp_cutoff and nr_procs > 1:
-        pool = mp.Pool(processes=nr_procs)
+    if data.size > mp_cutoff and pool_size > 1:
+        pool = mp.Pool(processes=pool_size)
         answer_arr = pool.map(get_identifier_description_mp, [
             (data[x], x, type_information.dtypes[x])
             for x in sample_df.columns