HDFS Handling

The Hadoop Distributed File System (HDFS) is a filesystem that is supported by the nodes in the Hadoop compute cluster. HDFS has numerous similarities to typical filesystems such as those of the *nix variety. The hadoop CLI utility provides a collection of commands that can be run from the local command line.

Gremlin provides methods to easily interact with both the local file system and HDFS from within the REPL. These methods are provided in the table below. Note that the global variables local and hdfs reference the local and HDFS file systems, respectively. Finally, pattern refers to a regular expression pattern and path refers to an explicit path to a file or directory.

Method	Description
ls()	list all the contents in the home directory
ls(pattern)	list all the contents that match the pattern
result(path)	display the directory of the final result of a Faunus job
exists(path)	return true or false on whether path exists
rm(pattern)	remove all paths that match the pattern
rmr(pattern)	recursively remove all paths that match the pattern
copyToLocal(from,to)	copy the HDFS path to the local filesystem
copyFromLocal(from,to)	copy the local path to HDFS
mergeToLocal(from,to)	merge the files at HDFS path to a single file on the local filesystem
head(pattern,lines?)	look at the top ?lines of the files that match pattern
unzip(from,to,delete)	BZ2 unzip the path to another path

Useful Tricks

• Use Gremlin/HDFS like *nix/file system.

gremlin> hdfs.ls('dbpedia')._().count()
==>228
gremlin> hdfs.head('output')._().sort{-(it.split()[1] as Integer)}
==>wikiPageWikiLink	158373970
==>sameAs	18707022
==>subject	15184863
==>wikiPageInterLanguageLink	13184401
==>wasDerivedFrom	11547302
…

• Use Gremlin on a side-effect to configure another job.

gremlin> g.E.label.groupCount()
gremlin> x = hdfs.head('output')._().filter{(it.split()[1] as Integer) > 1000}.transform{it.split()[0]}.toList().asArray()
gremlin> g.E.has('label',x).keep()

1. Generate an edge label distribution and store the side-effect in output.
2. Get the side-effect and set x to only those edge labels that have more than 1000 counts.
3. Trim all edges from the graph that don’t have a label in x.