Lipstick on Pig

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Netflix engineer Jeff Magnusson talks about Pig Lipstick at Big Data Gurus meetup (

Transcript of Lipstick on Pig

  • 1. Putting Lipstick on Apache Pig Big Data Gurus Meetup August 14, 2013

2. Data should be accessible, easy to discover, and easy to process for everyone. Motivation 3. Big Data Users at Netflix Analysts Engineers Desires Self Service Easy Rich Toolset Rich APIs A Single Platform / Data Architecture that Serves Both Groups 4. Netflix Data Warehouse - Storage S3 is the source of truth Decouples storage from processing. Persistent data; multiple/ transient Hadoop clusters Data sources Event data from cloud services via Ursula/Honu Dimension data from Cassandra via Aegisthus ~100 billion events processed / day Petabytes of data persisted and available to queries on S3. 5. Netflix Data Platform - Processing Long running clusters sla and ad-hoc Supplemental nightly bonus clusters For high priority ETL jobs 2,000+ instances in aggregate across the clusters 6. Netflix Hadoop Platform as a Service S3 7. Netflix Data Platform Primitive Service Layer Primitive, decoupled services Building blocks for more complicated tools/services/apps Serves 1000s of MapReduce Jobs / day 100+ jobs concurrently 8. Netflix Data Platform Tools Sting (Adhoc Visualization) Looper (Backloading) Forklift (Data Movement) Ignite (A/B Test Analytics) Lipstick (Workflow Visualization) Spock (Data Auditing) Heavily utilize services in the primitive layer. Follow the same design philosophy as primitive apps: RESTful API Decoupled javascript interfaces 9. Pig and Hive at Netflix Hive AdHoc queries Lightweight aggregation Pig Complex Dataflows / ETL Data movement glue between complex operations 10. What is Pig? A data flow language Simple to learn Very few reserved words Comparable to a SQL logical query plan Easy to extend and optimize Extendable via UDFs written in multiple languages Java, Python, Ruby, Groovy, Javascript 11. Sample Pig Script* (Word Count) input_lines = LOAD '/tmp/my-copy-of-all-pages-on-internet' AS (line:chararray); -- Extract words from each line and put them into a pig bag -- datatype, then flatten the bag to get one word on each row words = FOREACH input_lines GENERATE FLATTEN(TOKENIZE(line)) AS word; -- filter out any words that are just white spaces filtered_words = FILTER words BY word MATCHES 'w+'; -- create a group for each word word_groups = GROUP filtered_words BY word; -- count the entries in each group word_count = FOREACH word_groups GENERATE COUNT(filtered_words) AS count, group AS word; -- order the records by count ordered_word_count = ORDER word_count BY count DESC; STORE ordered_word_count INTO '/tmp/number-of-words-on-internet'; * 12. A Typical Pig Script 13. Pig Data flows are easy & flexible to express in text Facilitates code reuse via UDFs and macros Allows logical grouping of operations vs grouping by order of execution. But errors are easy to make and overlook. Scripts can quickly get complicated Visualization quickly draws attention to: Common errors Execution order / logical flow Optimization opportunities 14. Lipstick Generates graphical representations of Pig data flows. Compatible with Apache Pig v11+ Has been used to monitor more than 25,000 Pig jobs at Netflix 15. Lipstick 16. Overall Job Progress 17. Logical Plan Overall Job Progress 18. Logical Operator (reduce side) Logical Operator (map side) Map/Reduce Job Intermediate Row Count Records Loaded 19. Hadoop Counters 20. Lipstick for Fast Development During development: Keep track of data flow Spot common errors Omitted (hanging) operators Data type issues Easily estimate and optimize complexity Number of MR jobs generated Map only vs full Map/Reduce jobs Opportunities to rejigger logic to: Combine multiple jobs into a single job Manipulate execution order to achieve better parallelism (e.g. less blocking) 21. Lipstick for Job Monitoring During execution: Graphically monitor execution status from a single console Spot optimization opportunities Map vs reduce side joins Data skew Better parallelism settings 22. Lipstick for Support Empowers users to support themselves Better operational visibility What is my script currently doing? Why is my script slow? Examine intermediate output of jobs All execution information in one place Facilitates communication between infrastructure / support teams and end users Lipstick link contains all information needed to provide support. 23. Lipstick Architecture Pig v11+ lipstick-console.jar Lipstick Server (RESTful Grails app) Javascript Client (Frontend GUI) RDS Persistence 24. Lipstick Architecture - Console Implements PigProgressNotificationListener interface Listens for: 1. New statements to be registered (unoptimized plan) 2. Script launched event (optimized, physical, M/R plan) 3. MR Job completion/failure event 4. Heartbeat progress (during execution) Pig Plans and Progress Lipstick objects Communicates with Lipstick Server 25. Pig Compilation Plans Optimized Logical Plan Physical Plan MapReduce Plan (grouping of Physical Operators into map or reduce jobs) Pig Script Unoptimized Logical Plan (~1:1 logical operator / line of Pig) Lipstick associates Logical Operators with MapReduce jobs by inferring relationships between Logical and Physical Operations. 26. Lipstick Architecture - Server Simple REST interface Its a Grails app! Pig client posts plans and puts progress Javascript client gets plans and progress Searches jobs by job name and user name 27. Lipstick Architecture JS Client Displays and annotates graphs with status / progress Completely decoupled from Server Event based design Periodically polls Server for job progress Usability is a key focus 28. My Job has stalled. Solving Problems with Lipstick - Common Problem #1 29. Unoptimized/Optimized Logical Plan Toggle Dangling Operator 30. I didnt get the data I was expecting Common Problem #2 31. I dont understand why my job failed. Common Problem #3 32. Failed Job (light red background) Successful Job (light blue background) 33. Future of Lipstick Annotate common errors and inefficiencies on the graph Skew / map side join opportunities / scalar issues E.g. Warnings / error dashboard Provide better details of runtime performance Timings annotated on graph Min / median / max mapper and reducer times Map / reduce completion over time Search through execution history Examine trends in runtime and data volumes History of failure / success Search jobs for commonalities Common datasets loaded / saved Better grasp data lineage Common uses of UDFs and macros 34. Lipstick on Hive Honey? 35. A closer look 36. Wrapping up Lipstick is part of Netflix OSS. Clone it on github at Check out the quickstart guide Started#1-quick-start Get started playing with Lipstick in under 5 minutes! We happily welcome your feedback and contributions! 37. Jeff Magnusson: [email protected] | |@jeffmagnusson Thank you! Jobs: Netflix OSS: Tech Blog: