Run a for loop concurrently and not sequentially in pyspark

Question

Below there is a for loop execution I am running on a Databricks cluster:

datalake_spark_dataframe_downsampled = pd.DataFrame( 
                           {'IMEI' : ['001', '001', '001', '001', '001', '002', '002'],
                            'OuterSensorConnected':[0, 0, 0, 1, 0, 0, 0], 
                            'OuterHumidity':[31.784826, 32.784826, 33.784826, 43.784826, 23.784826, 54.784826, 31.784826],
                            'EnergyConsumption': [70, 70, 70, 70, 70, 70, 70],
                            'DaysDeploymentDate': [0, 0, 1, 1, 1, 1, 1],
                            'label': [0, 0, 1, 1, 0, 0, ]}
                           )
datalake_spark_dataframe_downsampled = spark.createDataFrame(datalake_spark_dataframe_downsampled )

# printSchema of the datalake_spark_dataframe_downsampled (spark df):

"root
 |-- IMEI: string (nullable = true)
 |-- OuterSensorConnected: integer (nullable = false)
 |-- OuterHumidity: float (nullable = true)
 |-- EnergyConsumption: float (nullable = true)
 |-- DaysDeploymentDate: integer (nullable = true)
 |-- label: integer (nullable = false)"

device_ids=datalake_spark_dataframe_downsampled.select(sql_function.collect_set('IMEI').alias('unique_IMEIS')).collect()[0]['unique_IMEIS']

print(device_ids) #["001", "002", ..."030"] 30 ids

for i in device_ids:

  #filtered_dataset=datalake_spark_dataframe_downsampled.where(datalake_spark_dataframe_downsampled.IMEI.isin([i])) 
  #The above operation is executed inside the function training_models_operation_testing()

  try:
      training_models_operation_testing(i, datalake_spark_dataframe_downsampled, drop_columns_not_used_in_training, training_split_ratio_value, testing_split_ratio_value, mlflow_folder, cross_validation_rounds_value, features_column_name, optimization_metric_value, pretrained_models_T_minus_one, folder_name_T_minus_one, timestamp_snap, instrumentation_key_value, canditate_asset_ids, executor, device_ids)

  except Exception as e:
      custom_logging_function("ERROR", instrumentation_key_value, "ERROR EXCEPTION: {0}".format(e))

For the sake of the problem I have attached a sample data to have a general idea of how my data is..And imagine that many more rows and IDs exist. I have just created a few only for demonstration

As you can see this is a simple function call inside a for loop in a Databricks cluster running with pyspark.

Briefly, I first create a list of the unique ids (IMEI column) existing in my dataset. This is equal to 30. Thus, I am running 30 iterations with the for loop. In each iteration I am executing the following steps:

• Filter the rows of the datalake_spark_dataframe_downsampled (spark df) matching to each of the 30 asset ids. So let's say for example that out of the 40,000 rows of the initial df only 140 correspond to the first device id.
• Based on those 140 rows (filtered_dataset) the function does preprocessing , train-test-split and trains two Spark ML algorithms only for the rows of the filtered dataset.

The code snippet attached is working successfully. Although the for loop is executed sequentially, one iteration at a time. The function is called for the first id and only after completes it goes to the next id. However, what I want is to transform the above for loop in a way that the 30 iterations will run concurrently in pyspark and NOT one-by-one . How could I achieve this in pyspark?

I am open to discussion and ideas testing, because I understand that what I am asking may not be so simple to be executed in a Spark environment.

My current output in logging (this is something I print the way below)

Iteration 1
Starting execution...
- Executing the function for id 001
Finished execution...

Iteration 2
Starting execution...
- Executing the function for id 002
Finished execution...

My desired output in logging (this is something I print the way below)

Starting execution...
- Executing the function for id 001
- Executing the function for id 002
- Executing the function for id 003
- Executing the function for id 004

. . . .
- Executing the function for id 030
Finished execution...

All at the same time (concurrently) once

[Update] Based on the answer on the comments (threading module):

Answer 1

"for loop" is linear execution/ Sequential execution and can be considered as single threaded execution.

If you want to run your code concurrently, you need to create multiple threads/processes to execute your code.

Below is the example to achieve multi threading. I didn't test the code, but should work:)

#importing threading library

import threading

# Creating a list of threads
thread_list = []

#looping all objects, creating a thread for each element in the loop, and append them to thread_list
for items in device_ids:
    thread = threading.Thread(target=training_models_operation_testing,args=(items, datalake_spark_dataframe_downsampled, drop_columns_not_used_in_training,
                                                   training_split_ratio_value, testing_split_ratio_value, mlflow_folder,
                                                   cross_validation_rounds_value, features_column_name,
                                                   optimization_metric_value, pretrained_models_T_minus_one,
                                                   folder_name_T_minus_one, timestamp_snap, instrumentation_key_value,
                                                   canditate_asset_ids, executor, device_ids,))
    thread_list.append(thread)

#Start multi threaded exucution
for thread in thread_list:
    thread.start()

#Wait for all threads to finish
for thread in thread_list:
    thread.join()

print("Finished executing all threads")