1. Pig Data flow language (abstraction for MR jobs)
B. Ramamurthy
11/11/2018

2. 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:
MR made easy from Google App Engine
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 data flow language, so conceptually closer to the way we solve problems…
11/11/2018

3. 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
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4. 11/11/2018

5. Simple pig scripts: wordcount
A = load 'data2' as (line); words = foreach A generate flatten(TOKENIZE(line)) as word; grpd = group words by word; cntd = foreach grpd generate group, COUNT(words); store cntd into 'pig1out';
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6. Sample Pig script: simple data analysis
2 4 5
-2 3 4
3 5 6
-4 5 7
-7 4 6
4 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';
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7. See the pattern? LOAD FILTER GROUP
GENERATE (apply some function from piggybank)
STORE (DUMP for interactive debugging)
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8. 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.)
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9. 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
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10. 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;
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11. (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;
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12. 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
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13. 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.
11/11/2018

14. 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.
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15. 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
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16. 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.
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17. 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
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18. 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'; STORE B INTO 'output1'; STORE C INTO 'output2'
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19. 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';
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20. More examples from Cloudera
A very nice presentation from Cloudera…
Also see Apache’s pig page:
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21. Pig’s 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)
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22. 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
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23. 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();
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24. 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
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25. More examples No pig-schema No-schema filter
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
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
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.
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26. 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);
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27. 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
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28. 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*’;
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29. 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
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30. Pig from Alan Gates’ book (In preparation for exam2)

31. Introduction
Download pig from pig.apache.org (into timberlake or your local computer/laptop)
Unzip and untar it. You are set to go.
You can execute in local mode for learning purposes. Later on you can test it on your hadoop installation.
Navigate to the director where pig is installed.
./bin/pig –x local
Will put you in grunt mode or local mode

32. Data and pig Script
Create a data (called data) directory in the directory where bin is located.
Download from github all the data files related to pig book and store in the data directory
NYSE_divdidends
NYSE_daily
Etc.
Now go thru’ the examples in chapters 1-4, either by typing them in line by line or by creating script files.
Mystockanalysis.pig can be executed by
./bin/pig –x local Mystockanalysis.pig
or line by line on grunt

33. Chapter 1 Hello world of pig. Mary had little lamb example.
Go through the example in page.3
Create “mary” file in your data directory
Type in the commands line by line as in p.3
Now create a ch1.pig file out of the commands
Run the script file using the pig command
Try some other commands not listed there.
Understand the examples discussed in p.5,6

34. Chapter 2 Discusses installing and running pig
Go through the example in p.14.
That’s all.

35. Chapter 3
Discuss the grunt shell that is the prompt for the local mode
pig –x local
Results in grunt
grunt>
See the example in page 20

36. Chapter 4 Pig data model Scalars like: int, long, float, double, etc.
Complex types: Map, chararray to element mapping, sort of like key, value pair
Tuple ordered collection of Pig elements (‘bob, 55)
Bag is an unordered collection of tuples
Nulls
Schemas: Pig has lax attitude towards schemas
Explicit:
dividends = load ‘NYSE_dividends’ as (exchange:chararray, symbol:chararray, date: chararray, dividend:float);
Or you could say
divs = load ‘NYSE_dividends’ as (exchange, symbol, date, dividend);
See the table on page 28
See the example p.28,29,30.

37. Chapter 5 Pig Latin Look at the examples p.33-50
Commands discussed are:
Load, store, dump
Relational operations: foreach, filter, group, order ..by, distinct, join
Data operation: limit, sample, parallel.