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Pig, Making Hadoop Easy



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Pig, Making Hadoop Easy

  1. 1. Alan F. Gates<br />Yahoo!<br />Pig, Making Hadoop Easy<br />
  2. 2. Who Am I?<br />Pig committer<br />Hadoop PMC Member<br />An architect in Yahoo!grid team<br />Or, as one coworker put it, “the lipstick on the Pig”<br />
  3. 3. Who are you?<br />
  4. 4. Motivation By Example<br /> 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.<br />Load Users<br />Load Pages<br />Filter by age<br />Join on name<br />Group on url<br />Count clicks<br />Order by clicks<br />Take top 5<br />
  5. 5. In Map Reduce<br />
  6. 6. In Pig Latin<br />Users = load‘users’as (name, age);Fltrd = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url);Jnd = joinFltrdby name, Pages by user;Grpd = groupJndbyurl;Smmd = foreachGrpdgenerate group,COUNT(Jnd) as clicks;Srtd = orderSmmdby clicks desc;Top5 = limitSrtd 5;store Top5 into‘top5sites’;<br />
  7. 7. Performance<br />0.1<br />0.4,<br />0.5<br />0.2<br />0.3<br />0.6, <br />0.7<br />
  8. 8. Why not SQL?<br />Data Factory<br />Pig<br />Pipelines<br />Iterative Processing<br />Research<br />Data Warehouse<br />Hive<br />BI Tools<br />Analysis<br />Data Collection<br />
  9. 9. Pig Highlights<br />User defined functions (UDFs) can be written for column transformation (TOUPPER), or aggregation (SUM)<br />UDFs can be written to take advantage of the combiner<br />Four join implementations built in: hash, fragment-replicate, merge, skewed<br />Multi-query: Pig will combine certain types of operations together in a single pipeline to reduce the number of times data is scanned<br />Order by provides total ordering across reducers in a balanced way<br />Writing load and store functions is easy once an InputFormat and OutputFormat exist<br />Piggybank, a collection of user contributed UDFs<br />
  10. 10. Who uses Pig for What?<br />70% of production jobs at Yahoo (10ks per day)<br />Also used by Twitter, LinkedIn, Ebay, AOL, …<br />Used to<br />Process web logs<br />Build user behavior models<br />Process images<br />Build maps of the web<br />Do research on raw data sets<br />
  11. 11. Accessing Pig<br />Submit a script directly<br />Grunt, the pig shell<br />PigServer Java class, a JDBC like interface<br />
  12. 12. Components<br />Job executes on cluster<br />Hadoop Cluster<br />Pig resides on user machine<br />User machine<br />No need to install anything extra on your Hadoop cluster.<br />
  13. 13. How It Works<br />Pig Latin<br />A = LOAD ‘myfile’<br /> AS (x, y, z);<br />B = FILTER A by x > 0; <br />C = GROUP B BY x;<br />D = FOREACH A GENERATE<br />x, COUNT(B);<br />STORE D INTO ‘output’;<br />pig.jar:<br /><ul><li>parses
  14. 14. checks
  15. 15. optimizes
  16. 16. plans execution
  17. 17. submits jar to Hadoop
  18. 18. monitors job progress</li></ul>Execution Plan<br />Map:<br />Filter<br /> Count<br />Combine/Reduce:<br />Sum<br />
  19. 19. Demo<br />s3://hadoopday/pig_tutorial<br />
  20. 20. Upcoming Features<br />In 0.8 (plan to branch end of August, release this fall):<br />Runtime statistics collection<br />UDFs in scripting languages (e.g. python)<br />Ability to specify a custom partitioner<br />Adding many string and math functions as Pig supported UDFs<br />Post 0.8<br />Adding branches, loops, functions, and modules<br />Usability<br />Better error messages<br />Fix ILLUSTRATE<br />Improved integration with workflow systems<br />
  21. 21. Learn More<br />Read the online documentation:<br />On line tutorials<br />From Yahoo,<br />From Cloudera,<br />Using Pig on EC2:<br />A couple of Hadoop books available that include chapters on Pig, search at your favorite bookstore<br />Join the mailing lists:<br /> for user questions<br /> for developer issues<br /> for Howl<br />

Editor's Notes

  • How many have used Pig? How many have looked at it and have a basic understanding of it?
  • 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 LatinSo far what I’ve shown you is a simple join/group query. Now let’s look at something less straight forward in SQLOften people want to group data a number of different ways. Look at multiquery script: Note how there’s a branch in the logic nowOften 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 parametersThere 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 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 typeAlso easy to write load and store functions to fit your data needs
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