Luigi-Warehouse

A boilerplate implementation of Luigi at Groupon

• Luigi is a Python package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more

• Luigi-Warehouse adds

• example workflows (i.e. replicating postgresql tables to redshift)

• more data sources

• variable data sources that do not rely on default luigi behavior/configs (i.e. VariableS3Client)

Install / Setup

• Install python3 - This repo has been tested against python 3.4+

Simple

python setup.py install

Developers - if you're wanting to modify/use the workflows with your custom logic

• Clone this repo
• pip3 install -r requirements.txt if you want full functionality of all data sources

Post-Install

• mkdir your-path-to/data
• Put your credentials and settings in luigi.cfg. luigi.cfg-example shows some possible options. You can also $ export LUIGI_CONFIG_PATH=/path/to/your/luigi.cfg && python...
• You're ready to replicate or move data around...

Getting Started

• Some example workflows are included. Assumptions, Args & Comments are in the File

• Example to start the luigi scheduler daemon

$ ./start_luigi_server.bash

• Example to run a workflow with multiple workers in parallel

$ LUIGI_CONFIG_PATH=/path/to/your/luigi.cfg && python3 luigi_warehouse/postgres_to_redshift.py Run --params here --workers 50

Data Sources

Dependent python packages required & API reference

Luigi - Spotify/Luigi

Postgres / Redshift - psycopg2

MySQL - pymysql

Adwords - googleads : API Reference

Googlesheets - gspread : API Reference

Slack - slackclient : API Reference

Five9 - suds : API Reference

Twilio - twilio : API Reference

Livechat - API Reference

Zendesk - zdesk : API Reference

Shiftplanning - API Reference

Kochava - API Reference

Teradata - teradata

• requires some configuring to install. We typically have to do

$ mv ~/.odbc.ini ~/.odbc.ini.orig 
$ cp /opt/teradata/client/15.10/odbc_64/odbcinst.ini ~/.odbcinst.ini 
$ cp /opt/teradata/client/15.10/odbc_64/odbc.ini ~/.odbc.ini

OnboardIQ - API Reference

AppBoy - API Reference

Salesforce - simple-salesforce : API Reference

• Props to cghall for the capability to query salesforce reports directly using the analytics API

• Also available are SalesforceBulk and SalesforceBulkJob classes which use the Salesforce bulk API

Braintree - braintree : API Reference

Typeform - API Reference

Checkr - API Reference

AWS - boto : boto3

Notifications

• We currently use slack or email for job status notifications which can easily be added

• luigi-slack

from luigi_slack import SlackBot, notify
slack_channel = 'luigi-status-messages'
...
...
...

if __name__ == '__main__':
  slack_channel = 'luigi-status-messages'
  slacker = SlackBot(token=luigi.configuration.get_config().get('slackbots', 'BOWSER_SLACK_API_KEY'),
                   channels=[slack_channel])
  with notify(slacker):
    luigi.run() 

• AWS SES

import boto3

class Email:
  def __init__(self, region, aws_access_key_id, aws_secret_access_key):
    self.client = boto3.client('ses',region_name=region,aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key)

  def send(self, from_, to_list, subject, body):
    return self.client.send_email(Source=from_,
                                  Destination={'ToAddresses': to_list},
                                  Message={'Subject':
                                                     {'Data': subject},
                                           'Body':
                                                     {'Text':
                                                             {'Data': body},
                                                      'Html':
                                                              {'Data':' '}
                                                      }
                                            }
                                   )

Data Validation

• Targeted towards ensuring successful replication of data to Redshift (see modules/validation.py)

Structure

• if the same number of columns in the csv are in the target table
• if the columns have the same datatypes in the same order (VARCHAR is acceptable for any python datatype)
  • uses python_redshift_dtypes to convert

LoadError

• Checks for load errors for the target:schema:table provided since the load_start provided timestamp

RunAnywayTarget

• Use the wrapper class RunAnywayTarget if you want to make it easier as we make each validation scheme better

• pass in the taskobj with the following attributes

  • type = ['LoadError', 'Structure']
  • target = Redshift
  • table =
  • schema =
  • local_file = local csv file path
  • load_start = when you started to copy the records from S3

• doing RunAnywayTarget(self).done() will not do validation

• doing RunAnywayTarget(self).validation() will do the validation and if successful also say we're done the task

OrderedDF

• Takes the following args

1. target_cols : a list of columns ordered for how you want your dataframe to be structured
2. df : your dataframe you want restructured

• example: I my dataframe to have columns in this order ['one','two','three','four','five','six']

>>> from validation import OrderedDF
>>> import pandas as pd
>>> test = [[None,'',1,7,8],[None,'',2,5,6]]
>>> test = pd.DataFrame(test,columns=['one','two','four','five','three'])
>>> test
    one two  four  five  three
0  None         1     7      8
1  None         2     5      6
>>> result = OrderedDF(['one','two','three','four','five','six'],t)
>>> result.df
    one two  three  four  five   six
0  None          8     1     7  None
1  None          6     2     5  None

StructureDynamic

• This class will fix tables for you

1. Check for copy errors
2. Handle the copy errors

• Add column(s) if needed
• Change dtype(s) if needed

3. Get orig table's schema
4. Craft new table's schema with changes from errors
5. Make the change and retry the copy and remove duplicate * records
6. While there are copy errors

• handle the errors

• attempt to fix

• retry copy

• remove duplicate * records

• To run use

StructureDynamic(target_schema=  ,# redshift schema your table is in
                 target_table=    # your table
                 )
                 .run(
                      add_cols=  ,# True or False for if you want columns added in attempting to fix
                      change_dtypes=  ,# True or False if you want column data types changed in attempting to fix
                      copy=           ,# copy command you attempted
                      load_start=      # when you started the copy command, '%Y-%m-%d %H:%M:$S
                      )

• Example usage:
  • sql prep: create the table

CREATE TABLE public.test(id INT, col VARCHAR);
INSERT INTO test VALUES (1,'2');
INSERT INTO test VALUES (2, 'two');

• test.csv: create the csv you want to attempt to copy

1,2
two,2
3,4
5,6
ab,test

• we attempt to copy normally but we get load errors because one of the columns isn't right

COPY public.test FROM 's3://luigi-godata/test.csv' 
CREDENTIALS 'aws_access_key_id=XXXX;aws_secret_access_key=XXXX'
CSV DELIMITER ',' COMPUPDATE ON MAXERROR 0;

• we run ValidationDynamic

from validation import StructureDynamic
copy = '''COPY public.test FROM 's3://luigi-godata/test.csv' 
          CREDENTIALS 'aws_access_key_id=XXXX;aws_secret_access_key=XXXX'
          CSV DELIMITER ',' COMPUPDATE ON MAXERROR 0;'''
StructureDynamic(target_schema='public',target_table='test').run(add_cols=True,change_dtypes=True,copy=copy,load_start='2016-10-6 10:15:00')

• our table is fixed and called public.test
• our original table is kept as public.test_orig_backup
• stdout lists the stl_load_errors
• the changes made to the table's ddl is printed to stdout