diff --git a/README.md b/README.md index 5201fc7a..821d9740 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. +* [cloud_export_to_parquet](cloud_export_to_parquet) - Set up schedule workflow to process exported files on an hourly basis * [custom_converter](custom_converter) - Use a custom payload converter to handle custom types. * [custom_decorator](custom_decorator) - Custom decorator to auto-heartbeat a long-running activity. * [dsl](dsl) - DSL workflow that executes steps defined in a YAML file. @@ -68,7 +69,6 @@ Some examples require extra dependencies. See each sample's directory for specif * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. * [worker_versioning](worker_versioning) - Use the Worker Versioning feature to more easily version your workflows & other code. - ## Test Running the tests requires `poe` to be installed. diff --git a/cloud_export_to_parquet/README.md b/cloud_export_to_parquet/README.md new file mode 100644 index 00000000..873e69b9 --- /dev/null +++ b/cloud_export_to_parquet/README.md @@ -0,0 +1,23 @@ +# Cloud Export to parquet sample + +This is an example workflow to convert exported file from proto to parquet file. The workflow is an hourly schedule. + +Please make sure your python is 3.9 above. For this sample, run: + + poetry install --with cloud_export_to_parquet + +Before you start, please modify workflow input in `create_schedule.py` with your s3 bucket and namespace. Also make sure you've the right AWS permission set up in your environment to allow this workflow read and write to your s3 bucket. + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: + +```bash +poetry run python run_worker.py +``` + +This will start the worker. Then, in another terminal, run the following to execute the schedule: + +```bash +poetry run python create_schedule.py +``` + +The workflow should convert exported file in your input s3 bucket to parquet in your specified location. diff --git a/cloud_export_to_parquet/__init__.py b/cloud_export_to_parquet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cloud_export_to_parquet/create_schedule.py b/cloud_export_to_parquet/create_schedule.py new file mode 100644 index 00000000..f425d4c6 --- /dev/null +++ b/cloud_export_to_parquet/create_schedule.py @@ -0,0 +1,64 @@ +import asyncio +import traceback +from datetime import datetime, timedelta + +from temporalio.client import ( + Client, + Schedule, + ScheduleActionStartWorkflow, + ScheduleIntervalSpec, + ScheduleSpec, + WorkflowFailureError, +) + +from cloud_export_to_parquet.workflows import ( + ProtoToParquet, + ProtoToParquetWorkflowInput, +) + + +async def main() -> None: + """Main function to run temporal workflow.""" + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + # TODO: update s3_bucket and namespace to the actual usecase + wf_input = ProtoToParquetWorkflowInput( + num_delay_hour=2, + export_s3_bucket="test-input-bucket", + namespace="test.namespace", + output_s3_bucket="test-output-bucket", + ) + + # Run the workflow + # try: + # await client.start_workflow( + # ProtoToParquet.run, + # wf_input, + # id = f"proto-to-parquet-{datetime.now()}", + # task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + # ) + # except WorkflowFailureError: + # print("Got exception: ", traceback.format_exc()) + + # Create the schedule + try: + await client.create_schedule( + "hourly-proto-to-parquet-wf-schedule", + Schedule( + action=ScheduleActionStartWorkflow( + ProtoToParquet.run, + wf_input, + id=f"proto-to-parquet-{datetime.now()}", + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + ), + spec=ScheduleSpec( + intervals=[ScheduleIntervalSpec(every=timedelta(hours=1))] + ), + ), + ) + except WorkflowFailureError: + print("Got exception: ", traceback.format_exc()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/data_trans_activities.py b/cloud_export_to_parquet/data_trans_activities.py new file mode 100644 index 00000000..b91f6b1a --- /dev/null +++ b/cloud_export_to_parquet/data_trans_activities.py @@ -0,0 +1,123 @@ +import json +import uuid +from dataclasses import dataclass +from typing import List + +import boto3 +import pandas as pd +import temporalio.api.export.v1 as export +from google.protobuf.json_format import MessageToJson +from temporalio import activity + + +@dataclass +class GetObjectKeysActivityInput: + bucket: str + path: str + + +@dataclass +class DataTransAndLandActivityInput: + export_s3_bucket: str + object_key: str + output_s3_bucket: str + write_path: str + + +@activity.defn +def get_object_keys(activity_input: GetObjectKeysActivityInput) -> List[str]: + """Function that list objects by key.""" + object_keys = [] + s3 = boto3.client("s3") + response = s3.list_objects_v2( + Bucket=activity_input.bucket, Prefix=activity_input.path + ) + for obj in response.get("Contents", []): + object_keys.append(obj["Key"]) + if len(object_keys) == 0: + raise FileNotFoundError( + f"No files found in {activity_input.bucket}/{activity_input.path}" + ) + + return object_keys + + +@activity.defn +def data_trans_and_land(activity_input: DataTransAndLandActivityInput) -> str: + """Function that convert proto to parquet and save to S3.""" + key = activity_input.object_key + data = get_data_from_object_key(activity_input.export_s3_bucket, key) + activity.logger.info("Convert proto to parquet for file: %s", key) + parquet_data = convert_proto_to_parquet_flatten(data) + activity.logger.info("Finish transformation for file: %s", key) + return save_to_sink( + parquet_data, activity_input.output_s3_bucket, activity_input.write_path + ) + + +def get_data_from_object_key( + bucket_name: str, object_key: str +) -> export.WorkflowExecutions: + """Function that get object by key.""" + v = export.WorkflowExecutions() + + s3 = boto3.client("s3") + try: + data = s3.get_object(Bucket=bucket_name, Key=object_key)["Body"].read() + except Exception as e: + activity.logger.error(f"Error reading object: {e}") + raise e + v.ParseFromString(data) + return v + + +def convert_proto_to_parquet_flatten(wfs: export.WorkflowExecutions) -> pd.DataFrame: + """Function that convert flatten proto data to parquet.""" + dfs = [] + for wf in wfs.items: + start_attributes = wf.history.events[ + 0 + ].workflow_execution_started_event_attributes + histories = wf.history + json_str = MessageToJson(histories) + row = { + "WorkflowID": start_attributes.workflow_id, + "RunID": start_attributes.original_execution_run_id, + "Histories": json.loads(json_str), + } + dfs.append(pd.DataFrame([row])) + df = pd.concat(dfs, ignore_index=True) + rows_flatten = [] + for _, row in df.iterrows(): + wf_histories_raw = row["Histories"]["events"] + worfkow_id = row["WorkflowID"] + run_id = row["RunID"] + for history_event in wf_histories_raw: + row_flatten = pd.json_normalize(history_event, sep="_") + skip_name = ["payloads", "."] + columns_to_drop = [ + col for col in row_flatten.columns for skip in skip_name if skip in col + ] + row_flatten.drop(columns_to_drop, axis=1, inplace=True) + row_flatten.insert(0, "WorkflowId", worfkow_id) + row_flatten.insert(1, "RunId", run_id) + rows_flatten.append(row_flatten) + df_flatten = pd.concat(rows_flatten, ignore_index=True) + return df_flatten + + +def save_to_sink(data: pd.DataFrame, s3_bucket: str, write_path: str) -> str: + """Function that save object to s3 bucket.""" + write_bytes = data.to_parquet(None, compression="snappy", index=False) + uuid_name = uuid.uuid1() + file_name = f"{uuid_name}.parquet" + activity.logger.info("Writing to S3 bucket: %s", file_name) + + s3 = boto3.client("s3") + try: + key = f"{write_path}/{file_name}" + s3.put_object(Bucket=s3_bucket, Key=key, Body=write_bytes) + return key + except Exception as e: + activity.logger.error(f"Error saving to sink: {e}") + raise e diff --git a/cloud_export_to_parquet/run_worker.py b/cloud_export_to_parquet/run_worker.py new file mode 100644 index 00000000..df02de11 --- /dev/null +++ b/cloud_export_to_parquet/run_worker.py @@ -0,0 +1,38 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio.client import Client +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +from cloud_export_to_parquet.data_trans_activities import ( + data_trans_and_land, + get_object_keys, +) +from cloud_export_to_parquet.workflows import ProtoToParquet + + +async def main() -> None: + """Main worker function.""" + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + # Run the worker + worker: Worker = Worker( + client, + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + workflows=[ProtoToParquet], + activities=[get_object_keys, data_trans_and_land], + workflow_runner=SandboxedWorkflowRunner( + restrictions=SandboxRestrictions.default.with_passthrough_modules("boto3") + ), + activity_executor=ThreadPoolExecutor(100), + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/workflows.py b/cloud_export_to_parquet/workflows.py new file mode 100644 index 00000000..2d3edd88 --- /dev/null +++ b/cloud_export_to_parquet/workflows.py @@ -0,0 +1,74 @@ +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.exceptions import ActivityError + +with workflow.unsafe.imports_passed_through(): + from cloud_export_to_parquet.data_trans_activities import ( + DataTransAndLandActivityInput, + data_trans_and_land, + get_object_keys, + GetObjectKeysActivityInput, + ) +from dataclasses import dataclass + + +@dataclass +class ProtoToParquetWorkflowInput: + num_delay_hour: int + export_s3_bucket: str + namespace: str + output_s3_bucket: str + + +@workflow.defn +class ProtoToParquet: + """Proto to parquet workflow.""" + + @workflow.run + async def run(self, workflow_input: ProtoToParquetWorkflowInput) -> str: + """Run proto to parquet workflow.""" + retry_policy = RetryPolicy( + maximum_attempts=10, maximum_interval=timedelta(seconds=5) + ) + + # Read from export S3 bucket and given at least 2 hour delay to ensure the file has been uploaded + read_time = workflow.now() - timedelta(hours=workflow_input.num_delay_hour) + common_path = f"{workflow_input.namespace}/{read_time.year}/{read_time.month:02}/{read_time.day:02}/{read_time.hour:02}/00" + path = f"temporal-workflow-history/export/{common_path}" + get_object_keys_input = GetObjectKeysActivityInput( + workflow_input.export_s3_bucket, path + ) + + # Read Input File + object_keys_output = await workflow.execute_activity( + get_object_keys, + get_object_keys_input, + start_to_close_timeout=timedelta(minutes=5), + retry_policy=retry_policy, + ) + + write_path = f"temporal-workflow-history/parquet/{common_path}" + + try: + # Could create a list of corountine objects to process files in parallel + for key in object_keys_output: + data_trans_and_land_input = DataTransAndLandActivityInput( + workflow_input.export_s3_bucket, + key, + workflow_input.output_s3_bucket, + write_path, + ) + # Convert proto to parquet and save to S3 + await workflow.execute_activity( + data_trans_and_land, + data_trans_and_land_input, + start_to_close_timeout=timedelta(minutes=15), + retry_policy=retry_policy, + ) + except ActivityError as output_err: + workflow.logger.error(f"Data transformation failed: {output_err}") + raise output_err + + return write_path diff --git a/poetry.lock b/poetry.lock index 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