Pig, Making Hadoop Easy Alan F. Gates Yahoo!

Who Am I? Pig committer Hadoop PMC Member An architect in Yahoo! grid team Or, as one coworker put it, “the lipstick on the Pig”

Who are you? How many have used Pig? How many have looked at it and have a basic understanding of it?

Motivation By Example Suppose you have user data in one file, website data in another, and you need to find the top 5 most visited pages by users aged 18 - 25. Load Users Load Pages Filter by age Join on name Group on url Count clicks Order by clicks Take top 5

In Map Reduce

In Pig Latin Users = load ‘users’ as (name, age); Fltrd = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url); Jnd = join Fltrd by name, Pages by user; Grpd = group Jnd by url; Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks; Srtd = order Smmd by clicks desc; Top5 = limit Srtd 5; store Top5 into ‘top5sites’;

Performance 0.1 0.2 0.3 0.4, 0.5 0.6, 0.7

Why not SQL? Data Collection Data Factory Pig Pipelines Iterative Processing Research Data Warehouse Hive BI Tools Analysis

Pig Highlights User defined functions (UDFs) can be written for column transformation (TOUPPER), or aggregation (SUM) UDFs can be written to take advantage of the combiner Four join implementations built in: hash, fragment-replicate, merge, skewed Multi-query: Pig will combine certain types of operations together in a single pipeline to reduce the number of times data is scanned Order by provides total ordering across reducers in a balanced way Writing load and store functions is easy once an InputFormat and OutputFormat exist Piggybank, a collection of user contributed UDFs

Who uses Pig for What? 70% of production jobs at Yahoo (10ks per day) Also used by Twitter, LinkedIn, Ebay, AOL, … Used to Process web logs Build user behavior models Process images Build maps of the web Do research on raw data sets

Submit a script directly Grunt, the pig shell Accessing Pig Submit a script directly Grunt, the pig shell PigServer Java class, a JDBC like interface

Components Job executes on cluster Pig resides on user machine Hadoop Cluster Pig resides on user machine User machine No need to install anything extra on your Hadoop cluster.

How It Works Pig Latin pig.jar: parses Execution Plan checks Map: A = LOAD ‘myfile’ AS (x, y, z); B = FILTER A by x > 0; C = GROUP B BY x; D = FOREACH A GENERATE x, COUNT(B); STORE D INTO ‘output’; pig.jar: parses checks optimizes plans execution submits jar to Hadoop monitors job progress Execution Plan Map: Filter Count Combine/Reduce: Sum

Demo s3://hadoopday/pig_tutorial Demo script: Show group query first, talk about: load and schema (none, declared, from data) data types data sources need not be from HDFS or even from files parallel clause, how parallelism is determined on maps how grouping works in Pig Latin So far what I’ve shown you is a simple join/group query. Now let’s look at something less straight forward in SQL Often people want to group data a number of different ways. Look at multiquery script: Note how there’s a branch in the logic now Often want to operate on the result of each record in a previous statement. Look at top5 query Note nested foreach allows you to operate on each record coming out of group by Since result of group by is a bag in each record, can apply operators to that bag Currently support order, distinct, filter, limit Use of flatten at the end Use of positional parameters There will always be logic you need to write that you can’t get from Pig Latin. This is where rich support of UDFs come in. Look at session query Note registering UDF UDF now called like any other Pig builtin function (in fact Pig builtins implemented as UDFs) Look at SessionAnalysis.java Class name is UDF name Input to UDF is always a Tuple, avoids need to declare expected input, means UDF has to check what it gets Talk about how projection of bags works Talk about how EvalFunc is templatized on return type Also easy to write load and store functions to fit your data needs

Upcoming Features In 0.8 (plan to branch end of August, release this fall): Runtime statistics collection UDFs in scripting languages (e.g. python) Ability to specify a custom partitioner Adding many string and math functions as Pig supported UDFs Post 0.8 Adding branches, loops, functions, and modules Usability Better error messages Fix ILLUSTRATE Improved integration with workflow systems

Learn More Read the online documentation: http://hadoop.apache.org/pig/ On line tutorials From Yahoo, http://developer.yahoo.com/hadoop/tutorial/ From Cloudera, http://www.cloudera.com/hadoop-training Using Pig on EC2: http://developer.amazonwebservices.com/connect/entry.jspa?externalID=2728 A couple of Hadoop books available that include chapters on Pig, search at your favorite bookstore Join the mailing lists: pig-user@hadoop.apache.org for user questions pig-dev@hadoop.apache.com for developer issues howldev@yahoogroups.com for Howl