Pig, a high level data processing system on Hadoop Is MapReduce not Good Enough? Restricted programming model Only two phases ◼ Job chain for long data flow Too many lines of code even for simple logic How many lines do you have for word count? ◼ Programmers are responsible for this Pig to the Rescue High level dataflow language (Pig Latin) Much simpler than Java ◼ Simplifies the data processing Puts the operations at the apropriate phases ◼ Chains multiple MR jobs How Pig is used in the Industry At Yahoo, 70% MapReduce jobs are written in Pig ◼ Used to Process web logs ◼ Build user behavior models ◼ Process images ◼ Data mining Also used by Twitter, LinkedIn, eBay, AOL, ... Motivation by Example Suppose we have user data in one file, website data in another file. ◼ We need to find the top 5 most visited pages by users aged 18-25 In MapReduce In Pig Latin Pig runs over Hadoop Wait a minute How to map the data to records By default, one line → one record User can customize the loading process How to identify attributes and map them to the schema Delimiter to separate different attributes By default, delimiter is tab. Customizable. MapReduce Vs. Pig cont. Join in MapReduce Various algorithms. None of them are easy to implement in MapReduce Multi-way join is more complicated Hard to integrate into SPJA workflow MapReduce Vs. Pig cont. Join in Pig Various algorithms are already available. Some of them are generic to support multi-way join No need to consider integration into SPJA workflow. Pig does that for you! A = LOAD 'input/join/A'; B = LOAD 'input/join/B'; C = JOIN A BY $0, B BY $1; DUMP C; Pig Latin Data flow language Users specify a sequence of operations to process data More control on the process, compared with declarative language Various data types are supported ◼ Schema is supported ◼ User-defined functions are supported

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