1. The Idea of Pig Or Pig Concepts
B. Ramamurthy
1/2/2019

2. References http://pig.apache.org/
1/2/2019

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
1/2/2019

4. 1/2/2019

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';
1/2/2019

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';
1/2/2019

7. See the pattern? LOAD FILTER GROUP
GENERATE (apply some function from piggybank)
STORE (DUMP for interactive debugging)
1/2/2019

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.)
1/2/2019

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
1/2/2019

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;
1/2/2019

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;
1/2/2019

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)
1/2/2019

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
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 amazon installation.
1/2/2019