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

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Presentation on theme: "Putting Lipstick on Apache Pig Big Data Gurus Meetup August 14, 2013."— Presentation transcript:

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 ToolsetRich 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 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) MapReduce Plan (grouping of Physical Operators into map or reduce jobs) Pig Script Unoptimized Logical Plan (~1:1 logical operator / line of Pig) 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 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 My Job has stalled. Solving Problems with Lipstick - Common Problem #1


30 Unoptimized/Optimized Logical Plan Toggle Dangling Operator

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



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

35 Failed Job (light red background) Successful Job (light blue background)

36 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

37 Lipstick on Hive Honey?

38 A closer look…

39 Wrapping up Lipstick is part of Netflix OSS. Clone it on github at Check out the quickstart guide – Started#1-quick-start Started#1-quick-start – Get started playing with Lipstick in under 5 minutes! We happily welcome your feedback and contributions!

40  Jeff Magnusson: | |@jeffmagnusson Thank you! Jobs: Netflix OSS: http://netflix.github.io Tech Blog:

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