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Hadoop library for large-scale data processing
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README.md

DataFu Build Status

DataFu is a collection of user-defined functions for working with large-scale data in Hadoop and Pig. This library was born out of the need for a stable, well-tested library of UDFs for data mining and statistics. It is used at LinkedIn in many of our off-line workflows for data derived products like "People You May Know" and "Skills & Endorsements". It contains functions for:

  • PageRank
  • Statistics (e.g. quantiles, median, variance, etc.)
  • Sampling (e.g. weighted, reservoir, etc.)
  • Sessionization
  • Convenience bag functions (e.g. enumerating items)
  • Convenience utility functions (e.g. assertions, easier writing of EvalFuncs)
  • Set operations (intersect, union)
  • and more...

Each function is unit tested and code coverage is being tracked for the entire library.

We have also contributed a framework called Hourglass for incrementally processing data in Hadoop.

Pig Compatibility

The current version of DataFu has been tested against Pig 0.11.1 and 0.12.0. DataFu should be compatible with some older versions of Pig, however we do not do any sort of testing with prior versions of Pig and do not guarantee compatibility.
Our policy is to test against the most recent version of Pig whenever we release and make sure DataFu works with that version.

Blog Posts

Presentations

Papers

What can you do with it?

Here's a taste of what you can do in Pig.

Statistics

Compute the median with the Median UDF:

define Median datafu.pig.stats.StreamingMedian();

-- input: 3,5,4,1,2
input = LOAD 'input' AS (val:int);

grouped = GROUP input ALL;
-- produces median of 3
medians = FOREACH grouped GENERATE Median(sorted.val);

Similarly, compute any arbitrary quantiles with StreamingQuantile:

define Quantile datafu.pig.stats.StreamingQuantile('0.0','0.5','1.0');

-- input: 9,10,2,3,5,8,1,4,6,7
input = LOAD 'input' AS (val:int);

grouped = GROUP input ALL;
-- produces: (1,5.5,10)
quantiles = FOREACH grouped GENERATE Quantile(sorted.val);

Or how about the variance using VAR:

define VAR datafu.pig.stats.VAR();

-- input: 1,2,3,4,5,6,7,8,9
input = LOAD 'input' AS (val:int);

grouped = GROUP input ALL;
-- produces variance of 6.666666666666668
variance = FOREACH grouped GENERATE VAR(input.val);

Set Operations

Treat sorted bags as sets and compute their intersection with SetIntersect:

define SetIntersect datafu.pig.sets.SetIntersect();

-- ({(3),(4),(1),(2),(7),(5),(6)},{(0),(5),(10),(1),(4)})
input = LOAD 'input' AS (B1:bag{T:tuple(val:int)},B2:bag{T:tuple(val:int)});

-- ({(1),(4),(5)})
intersected = FOREACH input {
  sorted_b1 = ORDER B1 by val;
  sorted_b2 = ORDER B2 by val;
  GENERATE SetIntersect(sorted_b1,sorted_b2);
}

Compute the set union with SetUnion:

define SetUnion datafu.pig.sets.SetUnion();

-- ({(3),(4),(1),(2),(7),(5),(6)},{(0),(5),(10),(1),(4)})
input = LOAD 'input' AS (B1:bag{T:tuple(val:int)},B2:bag{T:tuple(val:int)});

-- ({(3),(4),(1),(2),(7),(5),(6),(0),(10)})
unioned = FOREACH input GENERATE SetUnion(B1,B2);

Operate on several bags even:

intersected = FOREACH input GENERATE SetUnion(B1,B2,B3);

Bag operations

Concatenate two or more bags with BagConcat:

define BagConcat datafu.pig.bags.BagConcat();

-- ({(1),(2),(3)},{(4),(5)},{(6),(7)})
input = LOAD 'input' AS (B1: bag{T: tuple(v:INT)}, B2: bag{T: tuple(v:INT)}, B3: bag{T: tuple(v:INT)});

-- ({(1),(2),(3),(4),(5),(6),(7)})
output = FOREACH input GENERATE BagConcat(B1,B2,B3);

Append a tuple to a bag with AppendToBag:

define AppendToBag datafu.pig.bags.AppendToBag();

-- ({(1),(2),(3)},(4))
input = LOAD 'input' AS (B: bag{T: tuple(v:INT)}, T: tuple(v:INT));

-- ({(1),(2),(3),(4)})
output = FOREACH input GENERATE AppendToBag(B,T);

PageRank

Run PageRank on a large number of independent graphs through the PageRank UDF:

define PageRank datafu.pig.linkanalysis.PageRank('dangling_nodes','true');

topic_edges = LOAD 'input_edges' as (topic:INT,source:INT,dest:INT,weight:DOUBLE);

topic_edges_grouped = GROUP topic_edges by (topic, source) ;
topic_edges_grouped = FOREACH topic_edges_grouped GENERATE
  group.topic as topic,
  group.source as source,
  topic_edges.(dest,weight) as edges;

topic_edges_grouped_by_topic = GROUP topic_edges_grouped BY topic; 

topic_ranks = FOREACH topic_edges_grouped_by_topic GENERATE
  group as topic,
  FLATTEN(PageRank(topic_edges_grouped.(source,edges))) as (source,rank);

skill_ranks = FOREACH skill_ranks GENERATE
  topic, source, rank;

This implementation stores the nodes and edges (mostly) in memory. It is therefore best suited when one needs to compute PageRank on many reasonably sized graphs in parallel.

Start Using It

The JAR can be found here in the Maven central repository. The GroupId and ArtifactId are com.linkedin.datafu and datafu, respectively.

If you are using Ivy:

<dependency org="com.linkedin.datafu" name="datafu" rev="1.0.0"/>

If you are using Maven:

<dependency>
  <groupId>com.linkedin.datafu</groupId>
  <artifactId>datafu</artifactId>
  <version>1.0.0</version>
</dependency>

Or download the code.

Working with the source code

Here are some common tasks when working with the source code.

Eclipse

To generate eclipse files:

ant eclipse

Build the JAR

ant jar

Run all tests

ant test

Run specific tests

Override testclasses.pattern, which defaults to **/*.class. For example, to run all tests defined in QuantileTests:

ant test -Dtestclasses.pattern=**/QuantileTests.class

Compute code coverage

ant coverage

Notes on eclipse

Adjusting heap size for TestNG plugin

You may run out of heap when executing tests in Eclipse. To fix this adjust your heap settings for the TestNG plugin. Go to Eclipse->Preferences. Select TestNG->Run/Debug. Add "-Xmx1G" to the JVM args.

Contribute

The source code is available under the Apache 2.0 license.

For help please see the discussion group. Bugs and feature requests can be filed here.

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