Follow the steps to install GoFFish

1. Install Hadoop and Hama using this link.

2. Build

   git clone https://github.com/dream-lab/goffish_v3
   cd goffish_v3/goffish-api
   mvn clean install						# Build api project
   cd ../sample
   mvn clean install						# Build sample project
   cd ../hama/v3.1/
   mvn clean install assembly:single		# Build goffish-hama
   

3. Graphs

   3.1 Generate random graph using hama-examples:

   $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar gen fastgen -v 100 -e 10 -o randomgraph -t 2
   

   3.2 Supported graph formats

   3.2.1 Adjacency List (LongTextAdjacencyListReader.java)

   ​ srcId sinkVertexId sinkVertexId

   job.setInputFormat(NonSplitTextInputFormat.class);
   job.setInputReaderClass(LongTextAdjacencyListReader.class);
   

   If no reader is specified in the job, this is used as default reader.

   3.2.2 Partitioned Adjacency List ( PartitionsLongTextAdjacencyReader.java)

   ​ srcId partitionID sinkVertexId sinkVertexId

   job.setInputFormat(TextInputFormat.class);
   job.setInputReaderClass(PartitionsLongTextAdjacencyReader.class);
   

   3.2.3 JSON Reader (LongTextJSONReader.java)

   ​ [srcid,partitionid,srcvalue,[[sinkid1,edgeid1,edgevalue1],[sinkid2,edgeid2,edgevalue2]... ]]

   job.setInputFormat(TextInputFormat.class);
   job.setInputReaderClass(LongTextJSONReader.class);
   

   Note: Partition ID starts from 0. And if partitionId is specified, the number of input files to the job should be atleast as many as there are partitions.

4. Running sample

   Place the jars generated in step 2 in Hama classpath:

   cd goffish_v3/
   cp -t $HAMA_HOME/lib/ goffish-api/target/goffish-api-3.1.jar sample/target/goffish-sample-3.1.jar hama/v3.1/target/goffish-hama-3.1-jar-with-dependencies.jar
   

   General format of running goffish-hama job:

   hama JobClass properties-file input-path output-path 
   

   where input-path and output-path is the path of the graph in HDFS and path of the output in HDFS, respectively and properties-file is local file used for loading properties of job; e.g:

   hama in.dream_lab.goffish.job.DefaultJob ConnectedComponents.properties facebook_graph fbout
   

   Output and logs can be found at $HAMA_HOME/logs/tasklogs/job_id/

5. Writing custom application

   Your application has to extend AbstractSubgraphComputation.java and implement the compute function. The job configuration (equivalent of driver class in MapReduce) can be written in the form of properties file or as a Java class. See DefaultJob.java and ConnectedComponents.properties for more details.

   Add the following dependencies to your project in pom.xml:

   <dependency>
   	<groupId>in.dream_lab.goffish</groupId>
   	<artifactId>goffish-api</artifactId>
   	<version>3.1</version>
   </dependency>
   <dependency>
   	<groupId>in.dream_lab.goffish</groupId>
   	<artifactId>goffish-hama</artifactId>
   	<version>3.1</version>
   </dependency>
   

   Your application can be run in the similar way as described above for the sample job, you just have to put the jar file in Hama classpath:

   export HAMA_CLASSPATH=/home/user/my_application.jar
   

   ​