refactor into using thread pool executor

data/data-pipeline/data_pipeline/etl/runner.py

@@ -1,7 +1,5 @@
 import importlib
 import concurrent.futures
-import time
-from functools import wraps
 
 from data_pipeline.etl.score.etl_score import ScoreETL
 from data_pipeline.etl.score.etl_score_geo import GeoScoreETL
@@ -69,24 +67,6 @@ def build_instance_class_from_dataset(dataset: str = None) -> None:
     etl_instance.cleanup()
 
 
-import time
-from functools import wraps
-
-
-def timeit(method):
-    @wraps(method)
-    def wrapper(*args, **kwargs):
-        start_time = time.time()
-        result = method(*args, **kwargs)
-        end_time = time.time()
-        print(f"{method.__name__} => {(end_time - start_time)*1000} ms")
-
-        return result
-
-    return wrapper
-
-
-@timeit
 def etl_runner(dataset_to_run: str = None) -> None:
     """Runs all etl processes or a specific one
 
@@ -97,20 +77,19 @@ def etl_runner(dataset_to_run: str = None) -> None:
         None
     """
     dataset_list = get_datasets_to_run(dataset_to_run)
+    NUMBER_OF_THREADS = 8
+    executor = concurrent.futures.ThreadPoolExecutor(
+        max_workers=NUMBER_OF_THREADS
+    )
+    futures = []
+    for dataset in dataset_list:
+        futures.append(
+            executor.submit(build_instance_class_from_dataset, dataset)
+        )
 
-    # Create a pool of processes. By default, one is created for each CPU in your machine.
-    with concurrent.futures.ProcessPoolExecutor() as executor:
-        # Process the list of ETL datasets, but split the work across the
-        # process pool to use all CPUs
-        for dataset, _ in zip(
-            dataset_list,
-            executor.map(
-                build_instance_class_from_dataset, dataset_list
-            ),
-        ):
-            print(f"Completed ETL for {dataset}")
+    for future in concurrent.futures.as_completed(futures):
+        pass
 
-    # update the front end JSON/CSV of list of data sources
     pass