Apache pig presentation_siddharth_mathur
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Apache pig presentation_siddharth_mathur



Overview of MapReduce and Details of Apache Pig features, special commands and the Debugger

Overview of MapReduce and Details of Apache Pig features, special commands and the Debugger



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Apache pig presentation_siddharth_mathur Apache pig presentation_siddharth_mathur Presentation Transcript

  • CSC 5800: Pig Latin: A Not-So-Foreign Language for Data Intelligent Systems: Processing Algorithms and Tools By Siddharth Mathur 1
  • What we will be covering  Introduction  MapReduce Overview  Pig Overview  Pig Features  Pig Latin  Pig Debugger  Demo 2
  • Introduction  Enormous data  Innovation critically depends upon analyzing terabytes of data collected everyday  SQL can resolve the structure data problems  Parallel Database processing – Data is enormous can‟t be analyzed serially. – Has to be analyzed in parallel. – Shared nothing clusters are the way to go. 3
  • Parallel DB Products  Teradata, Oracle RAC, Netezza  Expensive at web scale  Programmers have to write complex SQL queries because of this declarative programming is not preferred 4
  • Procedural programming  Map-Reduce programming model  It can easily perform a group by aggregation in parallel over a cluster of machines  The programmer provides map functions which is used as a filter or transforming method  The reduce function performs the aggregation  Appealing to the programmer because there are only 2 high level declarative functions to enable parallel processing 5
  • MapReduce Overview  Programming Model – To cater large data analytics – Works over Hadoop – Java based – Splits data into independent chunks and process them in-parallel  Program structure – Mapper – Reducer – Driver Program 6
  • MapReduce Driver Program  Works as „Main‟ function for MR job  Takes care of – Number of arguments – Input Data Location – Input Data Types – Output Data Location – Output Data types – Number of Mappers – Number of Reducers 7
  • Mapper and Reducer Class  Mapper Class – Main task is to perform any function logic – Computes tasks like: • Filtering • Splitting • Tokenizing • Transforming  Reducer Class – Works as an aggregator – Aggregates the intermediate results gathered from Mapper 8
  • Word Count Execution Input the quick brown fox Map Shuffle & Sort Reduce the, 1 brown, 1 fox, 1 Output Reduce Map brown, 2 fox, 2 how, 1 now, 1 the, 3 Reduce ate, 1 cow, 1 mouse, 1 quick, 1 the, 1 fox, 1 the, 1 the fox ate the mouse Map quick, 1 how, 1 now, 1 brown, 1 how now brown cow Map ate, 1 mouse, 1 cow, 1 9
  • MapReduce Word Count Program public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } 10
  • Map Reduce Limitations  1 input – 2 stage data flow is extremely rigid. – To perform a task like join or sum iteration task, workaround has to be devised. – Custom code for common task like filtering or transforming or projection – The code is difficult to reuse and maintain  Moreover, because of its own data types, workflow and the fact that people have to learn java, makes it‟s a tough choice to take. 11
  • Pig  An Apache open source project.  Provides an engine for executing data flows in parallel on Hadoop.  Includes a language called „Pig Latin‟ for expressing these data flows.  High level declarative data workflow language.  It has best of both worlds: – High Level declarative querying like SQL – Low Level procedural like Map Reduce 12
  • Hadoop Stack Hive … HBase Data Processing Layer Pig Hadoop MR Hadoop Yarn Resource Management Layer HDFS Storage Layer 13
  • Why Choose Pig  Written like SQL, compiled into MapReduce  Fully nested data model  Extensive support for UDFs  Can answer multiple questions in one single workflow. A = load './input.txt'; B = foreach A generate flatten(TOKENIZE((chararray)$0)) as word; C = group B by word; D = foreach C generate COUNT(B), group; store D into './output'; 14
  • Features and Motivation  Design goal of pig is to provide programmers with appealing experience for performing ad-hoc analysis of extremely large data sets. – DataFlow Language – QuickStart and Interoperability – Nested Data Model – UDF‟s – Debugging Environment 15
  • Data Flow Language  Each step specifies a single high level data transformation  Different from SQL where all these results are a single output.  The system has given opportunity to provide optimization function. – Example: A= Load „input.txt‟; B= Filter A by UDF (Column1); C= Filter B by Column1 > 0.8; 16
  • Quick start and Interoperability  Data Load – Capability of Ad-Hoc analysis – Can run queries directly on Data from dump of search engines – Just have to provide a function that tells Pig how to parse the content of file into tuple. – Similarly for output • Any output format. • These function can be reused. • Used for visualization or dumped to excel directly 17
  • Pig as part of workflow  Pig easily becomes a part of workflow eco-system – Can take most of the input types – Can output in many of the forms – Doesn‟t take over the data, i.e., it does not lock the data that is being processed. – Read only data analysis 18
  • Optional data schemas  Schema can be provided by the user : – In the beginning – On the fly – Example: • A= LOAD „input.txt‟ as (Column1;Column2); • B= Filter A by Column1>5;  If the schema is not provided then the columns can be referred by „$0‟, „$1‟, „$2‟…. for the 1st, 2nd, 3rd column etc.  Example:  A= LOAD „input.txt‟;  B= Filter A by $0>5; 19
  • Nested Data Model  Suppose, for a document, we want to extract the term and its position.  Format of output : Map<document Set<position>>  SQL data model: Term Document ID Position Hi 1 2 Hi 1 5  Or keep in normalized form, i.e., – term_info(termid, String) – position_info(termid, position, document) 20
  • Problem resolved using Pig  In pig we have complex data types like map, tuple or bag to occur as a field of a table itself.  Example: Term Document ID Position Hi 1 (2,5,8..)  This approach is good because its more closer to what a programmer thinks.  Data is stored on disk in a nested fashion only  It gives user an ease in writing UDFs. 21
  • UDFs  Significant part of data analysis is custom processing  For example, user might want to process natural language stemming  Or checking if the page is spam or not, or many other tasks  To work on this, Pig Latin has extensive support for UDFs, most of the tasks can be resolved using the UDFs  It can take non-atomic input and can provide a nonatomic output also  Currently the UDFs can be written in java or python 22
  • Debugging Environment  In any language, getting a data processing program work correctly usually takes many iterations  First few iterations mostly produce errors  With a large scale data this would result in serious time and resource wastage  Debuggers can help  Pig has a novel debugging environment  Generates concise examples from input data  Data samples are carefully chosen to resemble real data as far as possible  Sample data is carved specially 23
  • Pig Latin  Language in which data workflow statements are written  It runs on the shell called „Grunt‟  It has a shared repository name Piggybank  We can create our custom UDFs and add them to Piggybank 24
  • Data Model  Rich, yet simple data models  Atoms – Simple atomic values like string or number  Tuple – A collection of fields each of which can be of any data type – Analogous to rows in SQL  Bag – Collection of tuples or both tuples and atoms – Can also be heterogeneous 25
  • Data Model (cont.)  Example of a relation Atom Tuple Bag T= „alice‟, (labours,1), {(„ipod‟, 2),„james‟}  Tuple is represented with round braces  Bag is represented with curly braces 26
  • Specifying Input Data : LOAD  Its the first step in Pig Latin program  Specifying what the input files are  How are its contents to be deserialized, i.e., converted to pig data model.  LOAD command – Example queries= LOAD „query_log.csv‟ USING PigStorage(„,‟) AS (userId,queryString,timestamp); 27
  • LOAD (cont.)  Both the „USING‟ clause and the „AS‟ clause are optional  We can work without them as shown earlier ($0 for first field)  Pig Storage is a pre-defined function  Can use custom function instead of Pig Storage 28
  • Per Tuple Processing : FOREACH  Similar to FOR statements  Its used for applying special processing to each tuple of the dataset  Example – Expanded_query = FOREACH queries GENERATE UserId, Expand(queryString), timeStamp;  Its not a FILTERING command  „Expand‟ can take atomic input and can generate a bag of outputs 29
  • Per Tuple Processing : FOREACH(cont.)  The semantics of FOREACH is such that there is no dependency between different tuples of input, therefore permitting efficient parallel implementation 30
  • Discarding Unwanted Data : FILTER  Used as a where clause  Can provide anything in the expression – Query = FILTER queries By user_id neq „bot‟;  We can provide a UDF also, like – Query = FILTER queries by Isbot(user_id); 31
  • COGROUP  Similar to Join  Groups bags of different inputs together  Ease of use for UDF‟s – Grouped_data = COGROUP results by querystring, revenue by querystring; 32
  • JOIN  Not all users want to use COGROUP  Simple equi-join is all that is required – Example Join_result = JOIN results by querystring, revenue by querystring;  Other types of join are also supported: – Left outer – Right outer – Full outer 33
  • Other Commands  Relational Operators – UNION – CROSS – ORDER – DISTINCT – LIMIT  Eval Functions – Concat – Count – Diff 34
  • PARALLEL clause  It is used to increase the parallelization of the job  We can specify the number of reduce tasks of the MR jobs created by Pig  It only effects the reduce task  No control over map  The system also can figure out number of reducers  Mostly one reduce task is required 35
  • PARALLEL clause (cont.)  Can be applied to only those commands which come under reduce phase – COGROUP – CROSS – DISTINCT – GROUP – JOINS – ORDER A = LOAD „ File1‟; B = LOAD „ File2‟; C = CROSS A, B PARALLEL 10; 36
  • Split Clause  We can split the input record into many by providing condition A = LOAD „data‟ AS (F1:int, F2:int, F3:int) (1,2;3) (2,3;7) SPLIT A INTO B IF F1>7, C IF F2==5; B (1,2,3) C (2,5,7) (2,5,7)  Any expression can be written  UDFs can be used  It is not partitioning 37
  • Output  There are two ways to display – STORE • If you want to store the output in any location STORE output_1 INTO „hadoopuser/output‟ – DUMP • Basically used to display the result in the GRUNT shell itself • Dumping doesn‟t store the output anywhere DUMP query_result; 38
  • Building a Logical Plan  Pig interpreter first parses all the commands which the client issues  Verifies that the input files, bags or columns referred by the command are valid  Builds a logical plan for every bag the user defines  No processing is carried out  Processing triggers where a user invokes STORE/DUMP command  Called as a Lazy execution approach  Helps in FILTER reordering 39
  • Debugging Environment  This is used to avoid running the complete code on the entire dataset  User can create a sample data  Difficult to tailor these datasets and end up in self cooked data  Pig Pen is Pig‟s debugging environment  Creates side dataset automatically, called as sandbox dataset  Pig Pen has its own user interface 40
  • Pig Pen  Outputs can be easily analyzed  Errors can be rectified earlier 41
  • Future Work  User Interface – Drag-Drop style would help – Logical plan diagram create made easy  UDF support for other languages  Unified Environment – Currently, lacks in control structures like loops – Has to embedded for all iterative tasks 42
  • Summary  Not So Foreign Language  Aims a sweet spot between SQL and MapReduce  Reusable and easy to use  Novel Debugging Environment: Pig Pen  Pig has an active and growing user base in Yahoo!  Pigs – Eats anything – Live anywhere – Are domestic 43
  • 44
  • Based on “Pig Latin: A Not-So-Foreign Language for Data Processing” SIGMOD‟08,June 9–12, 2008, Vancouver,BC,Canada Christopher Olston Yahoo! Research Benjamin Reed Yahoo! Research Utkarsh Srivastava Yahoo! Research Ravi Kumar Yahoo! Research Andrew Tomkins Yahoo! Research 45
  • References  http://infolab.stanford.edu/~usriv/papers/pig-latin.pdf  http://pig.apache.org/docs/r0.7.0/piglatin_ref2.html  Book: Programming pig  http://www.brentozar.com/archive/2011/11/good-pig/  http://hortonworks.com/hadoop/pig/ 46