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
- Quantiles (median), variance, etc.
- Sessionization
- Variance
- Convenience bag functions (e.g., set operations, enumerating bags, etc)
- Convenience utility functions (e.g., assertions, easier writing of EvalFuncs)
- and more...
Each function is unit tested and code coverage is being tracked for the entire library. It has been tested against Pig 0.11.
Here's a taste of what you can do in Pig.
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 7.5
variance = FOREACH grouped GENERATE VAR(input.val);
Treat sorted bags as sets and compute their intersection with SetIntersect:
define SetIntersect datafu.pig.bags.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.bags.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);
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);
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.
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="0.0.6"/>
If you are using Maven:
<dependency>
<groupId>com.linkedin.datafu</groupId>
<artifactId>datafu</artifactId>
<version>0.0.6</version>
</dependency>
Or download the code.
Here are some common tasks when working with the source code.
ant jar
ant test
Override testclasses.pattern, which defaults to **/*.class. For example, to run all tests defined in QuantileTests:
ant test -Dtestclasses.pattern=**/QuantileTests.class
ant coverage
We use Sonatype to release artifacts. Information on how this is set up can be found here. Most of this has already been set up with the build.xml file. You will however need a Sonatype account and must create a Maven ~/.m2/settings.xml with your account information, as described here.
Create the settings.xml from our template:
mkdir ~/.m2
cp settings.xml.template ~/.m2/settings.xml
Then edit ~/.m2/settings.xml and add your user name and password.
We use gpg to sign the artifacts, so you'll need gpg set up as well. Information on generating PGP signatures with gpg can be found here. Make sure you follow the section Deleting a Sub Key.
First run the tests to make sure all is well:
ant test
If it succeeds, build artifacts and upload to sonatype:
ant deploy
Login to Sonatype. In Staging Repositories you should see a repository for the articacts just uploaded. Select the repository and click Close.
Now that the repository is closed you can download the artifacts and do some manual testing if you would like before doing a final release. If you find a problem you can find drop the release by selecting the repository and clicking Drop. Else to release select the repository and click Release.
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.