Hive Vs Pig
Apache Pig and Hive are two projects that layer on top of Hadoop, and provide a higher-level language for using Hadoop's MapReduce library. Apache Pig provides a scripting language for describing operations like reading, filtering, transforming, joining, and writing data -- exactly the operations that MapReduce was originally designed for. Rather than expressing these operations in thousands of lines of Java code that uses MapReduce directly, Pig lets users express them in a language not unlike a bash or perl script. Pig is excellent for prototyping and rapidly developing MapReduce-based jobs, as opposed to coding MapReduce jobs in Java itself.
If Pig is "Scripting for Hadoop", then Hive is "SQL queries for Hadoop". Apache Hive offers an even more specific and higher-level language, for querying data by running Hadoop jobs, rather than directly scripting step-by-step the operation of several MapReduce jobs on Hadoop. The language is, by design, extremely SQL-like. Hive is still intended as a tool for long-running batch-oriented queries over massive data; it's not "real-time" in any sense. Hive is an excellent tool for analysts and business development types who are accustomed to SQL-like queries and Business Intelligence systems; it will let them easily leverage your shiny new Hadoop cluster to perform ad-hoc queries or generate report data across data stored in storage systems mentioned above.
WORD COUNT EXAMPLE - PIG SCRIPT
Q) How to find the number of occurrences of the words in a file using the pig script?
You can find the famous word count example written in map reduce programs in apache website. Here we will write a simple pig script for the word count problem.
The following pig script finds the number of times a word repeated in a file:
Word Count Example Using Pig Script
The above pig script, first splits each line into words using the TOKENIZE operator. The tokenize function creates a bag of words. Using the FLATTEN function, the bag is converted into a tuple. In the third statement, the words are grouped together so that the count can be computed which is done in fourth statement.
You can see just with 5 lines of pig program, we have solved the word count problem very easily.
|
Feature
|
Hive
|
Pig
|
|
Language
|
SQL-like
|
PigLatin
|
|
Schemas/Types
|
Yes (explicit)
|
Yes (implicit)
|
|
Partitions
|
Yes
|
No
|
|
Server
|
Optional (Thrift)
|
No
|
|
User Defined Functions (UDF)
|
Yes (Java)
|
Yes (Java)
|
|
Custom Serializer/Deserializer
|
Yes
|
Yes
|
|
DFS Direct Access
|
Yes (implicit)
|
Yes (explicit)
|
|
Join/Order/Sort
|
Yes
|
Yes
|
|
Shell
|
Yes
|
Yes
|
|
Streaming
|
Yes
|
Yes
|
|
Web Interface
|
Yes
|
No
|
|
JDBC/ODBC
|
Yes (limited)
|
No
|
Apache Pig and Hive are two projects that layer on top of Hadoop, and provide a higher-level language for using Hadoop's MapReduce library. Apache Pig provides a scripting language for describing operations like reading, filtering, transforming, joining, and writing data -- exactly the operations that MapReduce was originally designed for. Rather than expressing these operations in thousands of lines of Java code that uses MapReduce directly, Pig lets users express them in a language not unlike a bash or perl script. Pig is excellent for prototyping and rapidly developing MapReduce-based jobs, as opposed to coding MapReduce jobs in Java itself.
If Pig is "Scripting for Hadoop", then Hive is "SQL queries for Hadoop". Apache Hive offers an even more specific and higher-level language, for querying data by running Hadoop jobs, rather than directly scripting step-by-step the operation of several MapReduce jobs on Hadoop. The language is, by design, extremely SQL-like. Hive is still intended as a tool for long-running batch-oriented queries over massive data; it's not "real-time" in any sense. Hive is an excellent tool for analysts and business development types who are accustomed to SQL-like queries and Business Intelligence systems; it will let them easily leverage your shiny new Hadoop cluster to perform ad-hoc queries or generate report data across data stored in storage systems mentioned above.
WORD COUNT EXAMPLE - PIG SCRIPT
Q) How to find the number of occurrences of the words in a file using the pig script?
You can find the famous word count example written in map reduce programs in apache website. Here we will write a simple pig script for the word count problem.
The following pig script finds the number of times a word repeated in a file:
Word Count Example Using Pig Script
lines = LOAD
'/user/hadoop/HDFS_File.txt' AS (line:chararray);
words = FOREACH lines GENERATE
FLATTEN(TOKENIZE(line)) as word;
grouped = GROUP words BY word;
wordcount = FOREACH grouped GENERATE
group, COUNT(words);
DUMP wordcount;
The above pig script, first splits each line into words using the TOKENIZE operator. The tokenize function creates a bag of words. Using the FLATTEN function, the bag is converted into a tuple. In the third statement, the words are grouped together so that the count can be computed which is done in fourth statement.
You can see just with 5 lines of pig program, we have solved the word count problem very easily.
