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The Snowplow Analytics SDK for Python provides you an API to work with run manifests. Run manifests is simple way to mark chunk (particular run) of enriched data as being processed, by for example Apache Spark data-modeling job.
Run manifests functionality resides in new snowplow_analytics_sdk.run_manifests module.
Main class is RunManifests, that proides access to DynamoDB table via contains and add, as well as create method to initialize table with appropriate settings.
Other commonly-used function is list_runids that is gives S3 client and path to folder such as enriched.archive or shredded.archive from config.yml lists all
folders that match Snowplow run id format (run-YYYY-mm-DD-hh-MM-SS).
Using list_runids and RunManifests you can list job runs and safely process them one by one without risk of reprocessing.
Here's a short usage example:
from boto3 import client
from snowplow_analytics_sdk.run_manifests import *
s3 = client('s3')
dynamodb = client('dynamodb')
dynamodb_run_manifests_table = 'snowplow-run-manifests'
enriched_events_archive = 's3://acme-snowplow-data/storage/enriched-archive/'
run_manifests = RunManifests(dynamodb, dynamodb_run_manifests_table)
run_manifests.create() # This should be called only once
for run_id in list_runids(s3, enriched_events_archive):
if not run_manifests.contains(run_id):
process(run_id)
run_manifests.add(run_id)
else:
passIn above example, we create two AWS service clients for S3 (to list job runs) and for DynamoDB (to access manifests). These cliens are provided via [boto3][boto3] Python AWS SDK and can be initialized with static credentials or with system-provided credentials.
Then we list all run ids in particular S3 path and process (by user-provided process function) only those that were not processed already.
Note that run_id is simple string with S3 key of particular job run.
RunManifests class is a simple API wrapper to DynamoDB, using which you can:
-
createDynamoDB table for manifests, -
addrun to table - check if table
containsrun id