Luigi is Spotify's recently open-sourced Python framework for data flow definition and execution. This presentation introduces the basics of how to use it for data processing and ETL, both locally and on the Hadoop distributed computing platform.

1. Luigi
Batch data processing in
Python
Elias Freider
freider@spotify.com

2. Background
Why did we build Luigi?
We have a lot of data
Billions of log messages (several TBs) every day
Usage and backend stats, debug information
What we want to do
AB-testing
Music recommendations
Monthly/daily/hourly reporting
Business metric dashboards
We experiment a lot – need quick development cycles

3. How to do it?
Hadoop
Data storage
Clean, filter, join and aggregate data
Postgres
Semi-aggregated data for dashboards
Cassandra
Time series data

4. Defining a data flow
A bunch of data processing tasks with inter-dependencies

5. Example: Artist Toplist
Input is a list of Timestamp, Track, Artist tuples
Artist
Streams
Aggregation
Top 10 Database

6. Example: Artist Toplist
Naive (non-luigi) approach

7. Errors will occur
If a failed step leaves behind broken data, we want to clean that up

8. Avoid duplicate work
The second step fails, you fix it, then you want to resume

9. Parametrization
To use data flows as command line tools

10. Repeat!
You want to run the dataflow for a set of similar inputs

11. Introducing Luigi
A Python framework for data flow definition and execution
Main features
Dead-simple dependency definitions (think GNU make)
Emphasis on Hadoop/HDFS integration
Atomic file operations
Powerful task templating using OOP
Data flow visualization
Command line integration

12. Luigi - Aggregate Artists

13. Luigi - Aggregate Artists
Run on the command line:
$ python dataflow.py AggregateArtists
DEBUG: Checking if AggregateArtists() is complete
INFO: Scheduled AggregateArtists()
DEBUG: Checking if Streams() is complete
INFO: Done scheduling tasks
DEBUG: Asking scheduler for work...
DEBUG: Pending tasks: 1
INFO: [pid 74375] Running AggregateArtists()
INFO: [pid 74375] Done AggregateArtists()
DEBUG: Asking scheduler for work...
INFO: Done
INFO: There are no more tasks to run at this time

14. Task templates and targets
Reduces code-repetition by utilizing Luigi’s object oriented data model
Luigi comes with pre-implemented Tasks for...
Running Hadoop MapReduce utilizing Hadoop Streaming or custom jar-files
Running Hive and (soon) Pig queries
Inserting data sets into Postgres
Writing new ones are as easy as defining an interface and
implementing run()

15. Hadoop MapReduce
Built-in Hadoop Streaming Python framework
Features
Very slim interface
Fetches error logs from Hadoop cluster and displays them to the user
Class instance variables can be referenced in MapReduce code, which makes it
easy to supply extra data in dictionaries etc. for map side joins
Easy to send along Python modules that might not be installed on the cluster
Basic support for secondary sort
Runs on CPython so you can use your favorite libs (numpy, pandas etc.)

16. Hadoop MapReduce
Built-in Hadoop Streaming Python framework

17. Attach Python modules
So you can use local modules remotely on the cluster

18. Local state is transferred
Set up local variables for map-side joins etc.

19. Completing the top list
Top 10 artists - Wrapped arbitrary Python code

20. Database support
Basic functionality for exporting to Postgres. Cassandra support is in the works

21. Running it all...
DEBUG: Checking if ArtistToplistToDatabase() is complete
INFO: Scheduled ArtistToplistToDatabase()
DEBUG: Checking if Top10Artists() is complete
INFO: Scheduled Top10Artists()
DEBUG: Checking if AggregateArtists() is complete
INFO: Scheduled AggregateArtists()
DEBUG: Checking if Streams() is complete
INFO: Done scheduling tasks
DEBUG: Asking scheduler for work...
DEBUG: Pending tasks: 3
INFO: [pid 74811] Running AggregateArtists()
INFO: [pid 74811] Done AggregateArtists()
DEBUG: Asking scheduler for work...
DEBUG: Pending tasks: 2
INFO: [pid 74811] Running Top10Artists()
INFO: [pid 74811] Done Top10Artists()
DEBUG: Asking scheduler for work...
DEBUG: Pending tasks: 1
INFO: [pid 74811] Running ArtistToplistToDatabase()
INFO: Done writing, importing at 2013-03-13 15:41:09.407138
INFO: [pid 74811] Done ArtistToplistToDatabase()
DEBUG: Asking scheduler for work...
INFO: Done
INFO: There are no more tasks to run at this time

22. Data flow visualization
Light-weight scheduler daemon with a web interface

23. The results
Imagine how cool this would be with real data...

24. Task Parameters
Class variables with some magic
Tasks have implicit __init__
Generates command line interface with typing and documentation
$ python dataflow.py AggregateArtists --date 2013-03-05

25. Task Parameters
Combined usage example

26. Task Parameters
Makes it really easy to create aggregates over time

27. Multiple workers
Basic multi-processing
$ python dataflow.py --workers 3 AggregateArtists --date_interval 2013-W11

28. Large data flows
(Screenshot from web interface)
EndSongCleaned EndSongCleaned
(date=2013-03-16) (date=2013-03-15)
AggregateTracks AggregateTracks UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay UserLocationDay
(test=False, MasterMetadata (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False,
date=2013-03-16, (date=2013-03-15) date=2013-03-15, date=2013-03-16, date=2013-03-14, date=2013-03-13, date=2013-03-08, date=2013-03-12, date=2013-03-07, date=2013-03-11, date=2013-03-10, date=2013-03-09, date=2013-03-15, date=2013-03-06,
test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False)
JoinArtistGids JoinArtistGids UserLocationPeriod UserLocationPeriod
(test=False, (test=False, (test=False, (test=False,
date=2013-03-16, date=2013-03-15, period=10, period=10,
nshards=12, nshards=12, date=2013-03-17, date=2013-03-16,
test_users=False) test_users=False) test_users=False) test_users=False)
AggregateUserMatrices AggregateUserMatrices AggregateUserMatrices AggregateUserMatrices AggregateUserMatrices AggregateUserMatrices
AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists AggregateByArtists
(test=False, (test=False, (test=False, (test=False, (test=False, (test=False,
(test=False, (test=False, ArtistFollows (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False, (test=False,
date=2013-03-16, date=2013-03-14, date=2013-03-13, date=2013-03-15, date=2013-03-12, date=2013-03-11,
date=2013-03-16, date=2013-03-13, (date=2013-03-14) date=2013-03-14, date=2013-03-09, date=2013-03-08, date=2013-03-15, date=2013-03-12, date=2013-03-11, date=2013-03-07, date=2013-03-10, date=2013-03-06,
index_version=1362433077, index_version=1362433077, index_version=1362433077, index_version=1362433077, index_version=1362433077, index_version=1362433077,
test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False)
test_users=False) test_users=False) test_users=False) test_users=False) test_users=False) test_users=False)
AccumulateUserMatrices AccumulateUserMatrices AccumulateByArtists AccumulateByArtists
(test=False, (test=False, (test=False, (test=False,
JoinArtistGids JoinArtistGids JoinArtistGids JoinArtistGids
date=2013-03-16, date=2013-03-15, date=2013-03-16, PlaylistVectors date=2013-03-15,
(test=False, (test=False, (test=False, (test=False,
index_version=1362433077, index_version=1362433077, test_users=False, (test=False, RelatedArtistsTC test_users=False,
date=2013-03-14, date=2013-03-13, date=2013-03-12, date=2013-03-11,
test_users=False, test_users=False, max_days=90, date=2013-03-14, () max_days=90,
nshards=12, nshards=12, nshards=12, nshards=12,
decay_factor=0.99, decay_factor=0.99, decay_factor=0.99, index_version=1362433077) decay_factor=0.99,
test_users=False) test_users=False) test_users=False) test_users=False)
days=5, days=5, days=10, days=10,
build_from_scratch=True) build_from_scratch=True) build_from_scratch=True) build_from_scratch=True)
UserRecs UserRecs
(test=False, (test=False,
date=2013-03-17, date=2013-03-16,
rec_days=5, rec_days=5,
IngestUserRecs
exp_days=10, exp_days=10,
(date=2013-03-15,
test_users=False, test_users=False,
test_users=False,
force_updates=False, force_updates=False,
ttl=604800)
build_from_scratch=True, build_from_scratch=True,
index_path=/spotify/discover/index, index_path=/spotify/discover/index,
index_version=None, index_version=None,
FOLLOWS_SCORE=5.0) FOLLOWS_SCORE=5.0)
IngestUserRecs
(date=2013-03-16,
test_users=False,
ttl=604800)
IngestUserRecs
(date=2013-03-17,
test_users=False,
ttl=604800)

29. Really large...
Visualization needs some work

30. Error notifications
Great for automated execution

31. Process Synchronization
Prevents two identical tasks from running simultaneously
luigid
Simple task synchronization
Data flow 1 Data flow 2
Common dependency
Task

32. What we use Luigi for
Not just another Hadoop streaming framework!
Hadoop Streaming
Java Hadoop MapReduce
Hive
Pig
Local (non-hadoop) data processing
Import/Export data to/from Postgres
Insert data into Cassandra
scp/rsync/ftp data files and reports
Dump and load databases
Others using it with Scala MapReduce and MRJob as well

33. Luigi is open source
http://github.com/spotify/luigi
Originated at Spotify
Based on many years of experience with data processing
Recent contributions by Foursquare and Bitly
Open source since September 2012

34. Thank you!
For more information feel free to reach out at
http://github.com/spotify/luigi
Oh, and we’re hiring – http://spotify.com/jobs
Elias Freider
freider@spotify.com