Exploring Pig Data Flow Language for MapReduce Jobs

Pig Data flow language (abstraction for MR jobs) B. Ramamurthy 3/18/2025 1

Abstraction layer for MR Raw MR is difficult to install and program (Do we know about this? Then why did I ask you do this?) There are many models that simplify designing MR applications: MRJob for python developers Elastic Map Reduce (EMR) from amazon aws Pig from Apache via Yahoo Hive from Apache via Facebook And others We will look at Pig in detail. It is a data flow language, so conceptually closer to the way we solve problems 3/18/2025 2

References Pig ebook is available from our library: Alan Gates, Programming Pig, O reilly Media, 2011. ISBN: 9781449317683 3/18/2025 3

Pig Data flow Language Pig data flow language describes a directed acyclic graph (DAG) where edges are data flows and nodes are operations. There are no if statements or for loop in pig, since procedural language and object-oriented languages describe control flow and data flow is a side-effect. Pig focuses on data flow. 3/18/2025 4

What is Pig?: example Pig is a scripting language that helps in designing big data solutions using high level primitives. Pig script can be executed locally; it is typically translated into MR job/task workflow and executed on Hadoop Pig itself is a MR job on Hadoop You can access local file system using pig x local (eg. file:/ ) Other file system accessible are hdfs:// and s3:// from grunt> of non-local pig You can transfer data into local file system from s3: hadoop dfs copyToLocal s3n://cse487/pig1/ps5.pig /home/hadoop/pig1/ps5.pig hadoop dfs copyToLocal s3n://cse487/pig1/data2 /home/hadoop/pig1/data2 Then run ps5.pig in the local mode pig x local run ps5.pig 3/18/2025 5

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Simple pig scripts: wordcount A = load 'data2' as (line); words = foreach A generate flatten(TOKENIZE(line)) as word; grpd = group words by word; count = foreach grpd generate group, COUNT(words); store count into 'pig1out'; 3/18/2025 7

Sample Pig script: simple data analysis 2 -2 3 -4 -7 3 4 4 3 5 5 4 5 4 6 7 6 5 A = LOAD 'data3' AS (x,y,z); B = FILTER A by x> 0; C = GROUP B BY x; D = FOREACH C GENERATE group,COUNT(B); STORE D INTO 'p6out'; 3/18/2025 8

See the pattern? LOAD FILTER GROUP GENERATE (apply some function from piggybank) STORE (DUMP for interactive debugging) 3/18/2025 9

Pig Latin Is the language pig script is written in. Is a parallel data flow language Mathematically pig latin describes a directed acyclic graph (DAG) where edges are data flow and the nodes are operators that process data It is data flow not control flow language: no if statements and for loops! (traditional OO programming describes control flow not data flow.) 3/18/2025 10

Pig and query language How about Pig and SQL? SQL describes what or what is the user s question and it does NOT describes how it is to be solved. SQL is built around answering one question: lots of subqueries and temporary tables resulting in one thing: inverted process remember from our earlier discussions if these temp table are NOT in-memory their random access is expensive Pig describes the data pipeline from first step to final step. HDFS vs RDBMS Tables Pig vs Hive Yahoo vs Facebook 3/18/2025 11

SQL (vs. Pig) CREATE TEMP TABLE t1 AS SELECT customer, sum(purchase) AS total_purchases FROM transactions GROUP BY customer; SELECT customer, total_purchases,zipcode FROM t1, customer_profile WHERE t1.customer = customer_profile.customer; 3/18/2025 12

(SQL vs.) Pig txns = load transactions as (customer, purchase) grouped = group txns customer; total = foreach grouped generate group, SUM(txns.purchase) as tp; profile = load customer_profile as (customer, zipcode); answer = join total by group, profile by customer; dump answer; 3/18/2025 13

Pig and HDFS and MR Pig does not require HDFS. Pig can run on any file system as long as you transfer the data flow and the data appropriately. This is great since you can use not just file:// or hdfs:// but also other systems to be developed in the future. Similarly Pig Latin has several advantages over MR (see chapter 1 Programming Pig book) during the conceptual phase.. For later execution on MR 3/18/2025 14

Uses of Pig Traditional Extract, Transform, Load (ETL) data pipelines Research on raw data Iterative processing Prototyping (debugging) on small data and local system before launching a big data, multi-node MR jobs Good for EDA!! Largest use case: data pipelines: raw data , cleanse, load into data warehouse Ad-hoc queries from data where the scheme is unknown What it is not good for? For workloads that will update a few records, the will look up data in some random order, Pig is not a good choice. In 2009, 50% yahoo! Jobs executed were using Pig. Lets execute some Pig scripts on local installation and then on amazon installation. 3/18/2025 15

Apache Pig Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency. Extensibility. Users can create their own functions to do special-purpose processing. 3/18/2025 16

Running Pig You can execute Pig Latin statements: Using grunt shell or command line $ pig ... - Connecting to ... grunt> A = load 'data'; grunt> B = ... ; In local mode or hadoop mapreduce mode $ pig myscript.pig Command Line - batch, local mode mode $ pig -x local myscript.pig Either interactively or in batch 3/18/2025 17

Program/flow organization A LOAD statement reads data from the file system. A series of "transformation" statements process the data. A STORE statement writes output to the file system; or, a DUMP statement displays output to the screen. 3/18/2025 18

Interpretation In general, Pig processes Pig Latin statements as follows: First, Pig validates the syntax and semantics of all statements. Next, if Pig encounters a DUMP or STORE, Pig will execute the statements. A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, gpa:float); B = FOREACH A GENERATE name; DUMP B; (John) (Mary) (Bill) (Joe) Store operator will store it in a file 3/18/2025 19

Simple Examples A = LOAD 'input' AS (x, y, z); B = FILTER A BY x > 5; DUMP B; C = FOREACH B GENERATE y, z; STORE C INTO 'output'; ----------------------------------------------------------------------------- A = LOAD 'input' AS (x, y, z); B = FILTER A BY x > 5; STORE B INTO 'output1'; C = FOREACH B GENERATE y, z; STORE C INTO 'output2' 3/18/2025 20

Lets run Pig Script on AWS See tutorial at http://aws.amazon.com/articles/2729 This is about parsing web log for frequent external referrers, IPs, frequent terms etc. You place the data and pig script on s3 Then start the pig workflow on aws The output also goes back into the s3 space 3/18/2025 21

Pig Script register file:/home/hadoop/lib/pig/piggybank.jar DEFINE EXTRACT org.apache.pig.piggybank.evaluation.string.EXTRACT(); RAW_LOGS = LOAD '$INPUT' USING TextLoader as (line:chararray); LOGS_BASE = foreach RAW_LOGS generate FLATTEN ( EXTRACT (line, '^(\\S+) (\\S+) (\\S+) \\[([\\w:/]+\\s[+\\-]\\d{4})\\] "(.+?)" (\\S+) (\\S+) "([^"]*)" "([^"]*)"') ) as ( remoteAddr:chararray, remoteLogname:chararray, user:chararray, time:chararray, request:chararray, status:int, bytes_string:chararray, referrer:chararray, browser:chararray ) ; REFERRER_ONLY = FOREACH LOGS_BASE GENERATE referrer; FILTERED = FILTER REFERRER_ONLY BY referrer matches '.*bing.*' OR referrer matches '.*google.*'; SEARCH_TERMS = FOREACH FILTERED GENERATE FLATTEN(EXTRACT(referrer, '.*[&\\?]q=([^&]+).*')) as terms:chararray; SEARCH_TERMS_FILTERED = FILTER SEARCH_TERMS BY NOT $0 IS NULL; SEARCH_TERMS_COUNT = FOREACH (GROUP SEARCH_TERMS_FILTERED BY $0) GENERATE $0, COUNT($1) as num; SEARCH_TERMS_COUNT_SORTED = LIMIT(ORDER SEARCH_TERMS_COUNT BY num DESC) 50; STORE SEARCH_TERMS_COUNT_SORTED into '$OUTPUT'; 3/18/2025 22

More examples from Cloudera http://www.cloudera.com/wp- content/uploads/2010/01/IntroToPig.pdf A very nice presentation from Cloudera Also see Apache s pig page: http://pig.apache.org/docs/r0.9.1/index.html 3/18/2025 23

Pigs data model Scalar types: int, long, float (early versions, recently float has been dropped), double, chararray, bytearray Complex types: Map, Tuple, Bag Map: chararray to any pig element; in fact , this <key> to <value> mapping; map constants [ name # bob , age #55] will create a map with two keys name and age, first value is chararray and the second value is an integer. Tuple: is a fixed length ordered collection of Pig data elements. Equivalent to a row in SQL. Order, can refer to elements by field position. ( bob , 55) is a tuple with two fields. Bag: unordered collection of tuples. Cannot reference tuple by position. Eg. {( bob ,55), ( sally ,52), ( john , 25)} is a bog with 3 tuples; bags may become large and may spill into disk from in-memory Null: unknown, data missing; any data element can be null; (In Java it is Null pointers the meaning is different in Pig) 3/18/2025 24

Pig schema Very relaxed wrt schema. Scheme is defined at the time you load the data Runtime declaration of schemas is really nice. You can operate without meta data. On the other hand, meta data can be stored in a repository Hcatalog and used. For example JSON format etc. Gently typed: between Java and Perl at two extremes 3/18/2025 25

Schema Definition divs = load NYSE_dividends as (exchange:chararray, symbol:chararray, date:chararray, dividend:double); Or if you are lazy divs = load NYSE_dividends as (exchange, symbol, date, dividend); But what if the data input is really complex? Eg. JSON objects? One can keep a scheme in the HCatalog (apache incubation), a meta data repository for facilitating reading/loading input data in other formats. divs = load mydata using HCatLoader(); 3/18/2025 26

Pig Latin Basics: keywords, relation names, field names; Keywords are not case sensitive but relation and fields names are! User defined functions are also case sensitive Comments /* */ or single line comment Each processing step results in data Relation name = data operation Field names start with alphabet 3/18/2025 27

More examples daily = load NYSE_daily ; calcs = foreach daily generate $7/100.0, SUBSTRING($0,0,1), $6-$3); Here is only numeric on Pig) No-schema filter daily = load NYSE_daily ; fltrd = filter daily by $6 > $3; Here > is allowed for numeric, bytearray or chararray.. Pig is going to guess the type! Math (float cast) daily = load NYSE_daily as (exchange, symbol, date, open, high:float,low:float, close, volume:int, adj_close); rough = foreach daily generate volume * close; -- will convert to float No pig-schema Thus the free typing may result in unintended consequences.. Be aware. Pig is sometimes stupid. For a more in-depth view look at also how casts are done in Pig. 3/18/2025 28

Load (input method) Can easily interface to hbase: read from hbase using clause divs = load NYSE_dividends using HBaseStorage(); divs = load NYSE_dividends using PigStorage(); divs = load NYSE_dividends using PigStorage(,); as clause daily = load NYSE_daily as (exchange, symbol, date, open, high,low, close, volume); 3/18/2025 29

Store & dump Default is PigStorage (it writes as tab separated) store processed into /data/example/processed ; For comma separated use: store processed into /data/example/processed using PigStorage(,); Can write into hbase using HBaseStorage(): store processed using into HBaseStorage(); Dump for interactive debugging, and prototyping 3/18/2025 30

Relational operations Allow you to transform by sorting, grouping, joining, projecting and filtering foreach supports as array of expressions: simplest is constants and field references. rough = foreach daily generate volume * close; calcs = foreach daily generate $7/100.0, SUBSTRING($0,0,1), $6-$3); UDF (User Defined Functions) can also be used in expressions Filter operation CMsyms = filter divs by symbol matches CM* ; 3/18/2025 31

Operations (cntd) Group operation collects together records with the same key. grpd = group daily by stock; -- output is <key, bag> counts = foreach grpd generate group, COUNT(daily); Can also group by multiple keys grpd = group daily by (stock, exchange); Group forces the reduce phase of MR Pig offers mechanism for addressing data skew and unbalanced use of reducers (we will not worry about this now) Order by: strict ordering Maps, tuples, bags 3/18/2025 32