gaybro8777 / dwmops

Data Washing Machine implemented on Openshift

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Intro

The dwm package is a standalone set of business logic for maintaining marketing database quality. This repo is an Openshift-based implementation which applies said package to an Eloqua instance.

Architecture

Data flow

alt text

The data flow of this app uses a queue-based processing system (using the package pyqm):

Hourly scripts:

  1. Eloqua_Contacts_GetDWM.py
  • Export the specified contacts from Eloqua via Bulk API (using Python package pyeloqua)
  • Add them to the queue dwmQueue
  1. Eloqua_Contacts_PostDWM.py
  • Pick up records that have finished processing and import back to Eloqua
  • limit 30k to avoid data limits
  • References queue processedQueue
  • Removes from shared list on import

Minutely scripts:

  1. Eloqua_Contacts_RunDWM.py
  • Run the dwmAll function on a set of contacts
  • 600, currently
  • when done, remove from dwmQueue and add to indicatorQueue
  1. Eloqua_Contacts_CleanQueues.py
  • Run a queue cleanup script
  • Timeout records with locks older than 300 seconds
  • Report current queue size and timeout stats to Prometheus for monitoring
  1. Eloqua_Contacts_UpdateContactsIndicators.py
  • Retrieve job from indicatorQueue
  • Update record in Contacts.Indicators (by emailAddress) and set Contacts.Indicators.Data_Status='PROCESS as MOD' via Bulk API
  • remove from indicatorQueue and add to processedQueue

Daily Scripts:

  1. Eloqua_Contacts.Indicators_Refresh.py
  • Retrieve a max of 80k Contacts.Indicators records from Eloqua where Contacts.Indicators.Updated_Timestamp>180 days ago and Contacts.Indicators.Data_Status=='PROCESSED'
  • set Contacts.Indicators.Data_Status='PROCESS as MOD'
  • Import records back to Eloqua via Bulk API

This system provides enough redundancy to allow for troubleshooting of a crashed script. Also helps minimize the impact on Bulk API utilization limits.

Custom functions

Current implementation has two custom functions:

  • CleanZipcodeUS
    • Apply zipcode standardization to contacts where country='US'
    • Takes only first string of numbers before a non-digit character
    • Strips down to first 5 digits
    • Adds leading 0s which may have been stripped by Excel auto-formatting
  • CleanAnnualRevenue
    • Remove any "," or "$" characters
    • Try converting to integer
      • If successful, group into pre-determined annualRevenue bucket

Logging

Eloqua_Contacts_CleanQueues.py

  • Logfile: $OPENSHIFT_LOG_DIR/CLEAN_QUEUE_DWM_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_CleanQueues_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
  • Prometheus metrics (SLI):
    • QueueSize: # of records currently in each queue
    • QueueTimeout: # of records "released" back into queue after timeout
    • last_success_unixtime: Last time of successful run

Eloqua_Contacts_GetDWM.py

  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_DWM_GET_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_GetDWM_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
  • Prometheus metrics (SLI):
    • last_success_unixtime: Last time of successful run
    • total_seconds: # of seconds to complete entire script
    • total_records_total: # of records processed

Eloqua_Contacts_RunDWM.py

  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_DWM_RUN_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_RunDWM_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
    • If argument verbose=True, includes tqdm output 'progress bar', showing # records / second
  • Prometheus metrics (SLI):
    • last_success_unixtime: Last time of successful run
    • total_seconds: # of seconds to complete entire script
    • total_records_total: # of records processed
    • total_seconds_dwm: # of seconds to complete DWM functions (not including queue processing time)

Eloqua_Contacts_PostDWM.py

  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_DWM_POST_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_PostDWM_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
  • Prometheus metrics (SLI):
    • last_success_unixtime: Last time of successful run
    • total_seconds: # of seconds to complete entire script
    • total_records_total: # of records processed
    • total_records_errored: # of records from batches which received an an error on import
    • total_records_warning: # of records from batches which received a warning on import
    • total_records_success: # of records which successfully imported to Eloqua

Eloqua_Contacts_UpdateContactsIndicators.py

  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_DWM_INDICATORS_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts_UpdateContactsIndicators_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
  • Prometheus metrics (SLI):
    • last_success_unixtime: Last time of successful run
    • total_seconds: # of seconds to complete entire script
    • total_records_total: # of records processed
    • total_records_errored: # of records from batches which received an an error on import
    • total_records_warning: # of records from batches which received a warning on import
    • total_records_success: # of records which successfully imported to Eloqua

Eloqua_Contacts.Indicators_Refresh.py

  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts.Indicators_Refresh_YYYY_MM_DD.log
    • Explicitly logged runtime info
    • Accounted for exceptions
  • Logfile: $OPENSHIFT_LOG_DIR/Eloqua_Contacts.Indicators_Refresh_Console_YYYY_MM_DD.log
    • Runtime console output (including uncaught exceptions)
  • Prometheus metrics (SLI):
    • last_success_unixtime: Last time of successful run
    • total_seconds: # of seconds to complete entire script
    • total_records_total: # of records processed
    • total_records_errored: # of records from batches which received an an error on import
    • total_records_warning: # of records from batches which received a warning on import
    • total_records_success: # of records which successfully imported to Eloqua

Setup

Gears

This implementation use ITOS (Red Hat IT-Hosted Openshift v2; comparable to Openshift Enterprise). Using the Openshift PaaS is a good rapid deployment solution in that it's fast and consistent in setup.

Python

  • Internal-hosted medium gear
  • Python 3.3
  • Scalable; set to 1
  • 1GB storage

Environment Variables

  • Eloqua variables (service account)
    • ELOQUA_COMPANY
    • ELOQUA_USERNAME
    • ELOQUA_PASSWORD
  • Monitoring
    • PUSHGATEWAY (for Prometheus monitoring of batch jobs)

MongoDB

Cron

Testing and Deployment

Best practices for testing in a Openshift DEV environment, then promoting to and Openshift PROD environment.

Testing in DEV

  • Test under database load by replicating prod DB
    • New features interacting with MongoDB may require additional indexing; testing with a full data replication has a greater chance of catching these issues
    1. Establish port forward to PROD:
    rhc port-forward dwmops -n PRODUCTION_NAMESPACE
    
    1. In a separate terminal:
    mongodump --port 12345
    
    1. Kill original port forward
    2. Establish port forward to DEV:
    rhc port-forward dwmops -n DEV_NAMESPACE
    
    1. In a separate terminal:
    mongorestore
    
  • Ensure runscripts are uncommented for non-PROD environments
    • In DEV, runscripts for non-PROD should normally be commented out to avoid extra load on Eloqua's Bulk API
  • Wait up to 2 hours for next load from Eloqua
  • Monitor queues via Prometheus to ensure proper flow
  • If testing features expecting a different return result, check sample data from the final queue and the dwmdev.contactHistory collection to ensure proper application of business rules

Deployment to PROD

  • SSH into the gear and manually comment out python command in runscripts/halfhour_getdwm.sh
  • Wait until all queues have been emptied
  • Manually backup MongoDB
  • Create a new release using git flow release start vX.Y.Z
  • Populate any relevant release notes in the CHANGELOG.md
  • Update the version in setup.py
  • Finish release using git flow release finish vX.Y.Z
  • Verify that PROD app deployment-branch==master
  • Push to PROD
  • Monitor queues regularly for next 8 hours, then next 2 mornings, to ensure proper data flow

Rollback procedure

  • Set deployment branch to previous stable release rhc app-configure dwmops -n PRODUCTION_NAMESPACE --deployment-branch vA.B.C
  • git push

TODO

  • Build an API operating off the same MongoDB

About

Data Washing Machine implemented on Openshift

License:GNU General Public License v3.0


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