Python in an Evolving Enterprise System (PyData SV 2013)
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Python in an Evolving Enterprise System (PyData SV 2013)
- 2. PYTHON IN AN EVOLVING ENTERPRISE SYSTEM EVALUATING INTEGRATION SOLUTIONS WITH HADOOP DAVE HIMROD STEVE KANNAN ANGELICA PANDO
- 3. Building today’s most powerful, open, and customizable advertising technology platform.
- 4. Ad is served in <100 milliseconds WINNING AUCTION BID REQUEST 300x250 AD ADVERTISER 1 ADVERTISER 2 ADVERTISER 3 RESPONSE BID: $2.50 BID: $3.25 BID: $4.10 APPNEXUS OPTIMIZATION
- 5. Evolution of AppNexus 20 350 430 PEOPLE FROM 100M 39B 45B AD REQUESTS 5000+ MYSQL, HADOOP/HBASE, AEROSPIKE, SERVERS NETEZZA, VERTICA 38+ TB OF DATA EVERY DAY 99.99% UPTIME
- 6. Evolution of AppNexus ENG OFFICES ENGINEERING IN PORTLAND HQ IN NYC & SF
- 7. Data-Driven Decisioning (D3) Bidder Bidder Bidder BIDDERS DATA D3 PIPELINE PROCESSING
- 8. Python at AppNexus Python enables us to scale our team and rapidly iterate and prototype technologies.
- 9. Hadoop at AppNexus Hadoop enables us to 1PB CLUSTER do aggregations for reporting and other 862 NODES ACROSS SEVERAL CLUSTERS data pipeline jobs 40B BILLION LOG RECORDS DAILY BILLION 5.6B LOG RECORDS/HOUR AT PEAK
- 10. Data modeling today BIG DATA: TBS/HOUR MEDIUM DATA: GBS/HOUR Task Task Task Task logs logs CACHE logs VERTICA logs HADOOP Σ DATA DATA DRIVEN SERVICES DECISIONING
- 11. To enable the next generation of data modeling, we need to leverage our Hadoop cluster
- 12. What are we trying to do Access the data on Hadoop Continue to use Python to model à No consensus on the best solution So we conducted our own research to evaluate integration options
- 13. The budget problem We have thousands of bidders buying billions of ads per hour in real-time auctions. We need to create a model that can manipulate how our bidders spend their budgets and purchase ads.
- 14. Data modeling today BIG DATA: TBS/HOUR MEDIUM DATA: GBS/HOUR Task Task Task Task logs logs CACHE logs DATA DRIVEN VERTICA logs DECISIONING HADOOP Σ DATA DATA DRIVEN SERVICES DECISIONING
- 15. Test problem: Budget aggregation SCENARIO: Each auction creates a row in a log. timestamp, auction_id, object_type, object_id, method, value We need to aggregate and model to update bidders.
- 16. Method: Budget aggregation STEP 1: De-duplicate records where KEY: object_type, object_id, method, auction_id STEP 2: Aggregate value where KEY: object_type, object_id, method
- 17. HARDWARE • 300 GB of log data • 5 nodes running Scientific Linux 6.3 (Carbon) • Intel Xeon CPU @ 2.13 GHz, 4 cores • 2 TB Disk • CDH4 • 45 map, 35 reduce tasks at a time
- 18. Research: Potential solutions 1. Native Java 2. Streaming ‒ no framework 3. mrjob 4. Happy / Jython / PyCascading 5. Pig + Jython UDF 6. Pydoop prohibitive installation 7. Disco evaluating Hadoop 8. Hadoopy / dumbo similar to mrjob 9. Hipy Effectively ORM for Hive
- 19. Research: Criteria 1. Usability 2. Performance 3. Versatility / Flexibility
- 20. Research: Native Java Benchmark for comparison, using new Hadoop Java API BudgetAgg.java Mapper class BudgetAgg.java Reducer class
- 21. Research: Native Java USABILITY: › Not straightforward for analysts to implement, launch, or tweak PERFORMANCE: › Fastest implementation. › Can further enhance by overriding comparators for grouping and sorting
- 22. Research: Native Java VERSATILITY / FLEXIBILITY: › Abilityto customize pretty much everything › CustomPartitioner, Comparator, Grouping Comparator in our implementation › Canuse complex objects as keys or values
- 23. Research: Streaming Supplies an executable to Hadoop that reads from stdin and writes to stdout mapper.py reducer.py
- 24. Research: Streaming USABILITY: › Key/value detection has to be done by the user › Still, straightforward for relatively simple jobs hadoop jar /usr/lib/hadoop-0.23.0-mr1-cdh4b1/contrib/streaming/hadoop-*streaming*.jar -D stream.num.map.output.key.fields=4 -D num.key.fields.for.partition=3 -D mapred.reduce.tasks=35 -file mapper.py -mapper mapper.py -file reducer.py -reducer reducer_nongroup.py -partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner -input /logs/log_budget/v002/2013/03/06/19/ -output bidder_logs/streaming_output
- 25. Research: Streaming PERFORMANCE: › ~50% slower than Java VERSATILITY / FLEXIBILITY: › Inputs in reducer are iterated line-by-line › Straightforward to get de-duplication and agg to work in a single step
- 26. Research: mrjob Open-source Python framework that wraps Hadoop Streaming USABILITY: › “Simplified Java” › Great docs, actively developed python budget_agg.py -r hadoop --hadoop-bin /usr/bin/hadoop --jobconf stream.num.map.output.key.fields=4 --jobconf num.key.fields.for.partition=3 --jobconf mapred.reduce.tasks=35 --partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner -o hdfs:///user/apando/budget_logs/mrjob_output hdfs:///logs/log_budget/v002/2013/03/06/19/
- 27. Research: mrjob PERFORMANCE: › Not much slower than Streaming if only using RawValueProtocol
- 28. Research: mrjob PERFORMANCE: › Involvingobjects or multiple steps slow it down a lot VERSATILITY / FLEXIBILITY: › Candefine Input / Internal / Output protocols
- 29. Research: Happy / Jython HAPPY: › Full access to Java MapReduce API › Happy project is deprecated › Depends on Hadoop 0.17 JYTHON: › Doesn’t work easily out of the box › Relies on deprecated Jython compiler in Jython 2.2 › Limited to Jython implementation of Python › Numpy/SciPy and Pandas unavailable
- 30. Research: PyCascading Python wrapper around Cascading framework for data processing workflow. Uses Jython as high level language for defining workflows.
- 31. Research: PyCascading USABILITY: › Relatively new project › Cascading API is simple and intuitive › Job Planner abstracts details of MapReduce PERFORMANCE: › Abstraction makes performance tuning challenging › Does not support Combiner operation › Dev time was fast, runtime was slow
- 32. Research: PyCascading VERSATILITY / FLEXIBILITY: › Allows Jython UDFs › Rich set of built-in functions: GroupBy, Join, Merge
- 33. Research: Pig Provides a high-level language for data analysis which is compiled into a sequence of MapReduce operations. USABILITY:
- 34. Research: Pig USABILITY: › Powerful debugging and optimization tools (e.g. explain, illustrate) › Automatically optimizes MapReduce operations: › Applies Combiner operations where applicable › Reorders and conflates data flow for efficiency
- 35. Research: Pig PERFORMANCE: › Pig compiler produces performant code › Complex operations might require manual optimization › Budget Aggregation require the implementation of a User Defined Function in Jython to eliminate unnecessary MapReduce step
- 36. Research: Pig VERSATILITY / FLEXIBILITY: USING PIG + JYTHON UDF › PigLatin is expressive and can capture most use cases › Define custom data operations in Jython called UDFs › UDFs can implement custom loaders, partitioners, and other advanced features
- 37. Research: Summary Running Time / Lines of Code for Implementations Pig PyCascading MRJob Lines of Code Running Time Streaming Java 0 50 100 150 200 250 300 Running Time (minutes), Lines of Code
- 38. Research: Recommendations • Pig and PyCascading enable complex pipelines to be expressed simply • Pig is more mature and the most viable option for ad-hoc analysis
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