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Putting Lipstick on Apache Pig

Lipstick is a tool that generates graphical representations of Pig data flows, allowing for easy monitoring, debugging, and optimization of Pig jobs.

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Putting Lipstick on Apache Pig

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

  2. Motivation Data should be accessible, easy to discover, and easy to process for everyone.

  3. Big Data Users at Netflix Analysts Engineers • Desires Rich Toolset Self Service Easy 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 https://github.com/Netflix/genie 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 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 Sting (Adhoc Visualization) Looper (Backloading) Forklift (Data Movement)

  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 = FOREACHinput_linesGENERATEFLATTEN(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 = GROUPfiltered_wordsBY word; -- count the entries in each group word_count = FOREACHword_groupsGENERATE COUNT(filtered_words) AScount, group AS word; -- order the records by count ordered_word_count = ORDERword_countBY count DESC; STOREordered_word_countINTO '/tmp/number-of-words-on-internet'; * http://en.wikipedia.org/wiki/Pig_(programming_tool)#Example

  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. Overall Job Progress Logical Plan

  18. Records Loaded Logical Operator (map side) Map/Reduce Job Logical Operator (reduce side) Intermediate Row Count

  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 Server (RESTful Grails app) Javascript Client (Frontend GUI) lipstick-console.jar RDS Persistence

  24. Lipstick Architecture - Console • Implements PigProgressNotificationListener interface • Listens for: • New statements to be registered (unoptimized plan) • Script launched event (optimized, physical, M/R plan) • MR Job completion/failure event • Heartbeat progress (during execution) • Pig Plans and Progress  Lipstick objects • Communicates with Lipstick Server

  25. Pig Compilation Plans 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. Optimized Logical Plan Physical Plan MapReduce Plan (grouping of Physical Operators into map or reduce jobs)

  26. Lipstick Architecture - Server • Simple REST interface • It’s 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. Solving Problems with Lipstick - Common Problem #1 My Job has stalled.

  29. Unoptimized/Optimized Logical Plan Toggle Dangling Operator

  30. Common Problem #2 I didn’t get the data I was expecting

  31. Common Problem #3 I don’t understand why my job failed.

  32. SuccessfulJob (light blue background) Failed Job (light red 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. Honey? Lipstick on Hive

  35. A closer look…

  36. Wrapping up • Lipstick is part of Netflix OSS. • Clone it on githubat http://github.com/Netflix/Lipstick • Check out the quickstart guide • https://github.com/Netflix/Lipstick/wiki/Getting-Started#1-quick-start • Get started playing with Lipstick in under 5 minutes! • We happily welcome your feedback and contributions!

  37. Thank you! • Jeff Magnusson: jmagnusson@netflix.com| http://www.linkedin.com/in/jmagnuss |@jeffmagnusson Jobs: http://jobs.netflix.com Netflix OSS: http://netflix.github.io Tech Blog: http://techblog.netflix.com/

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