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Home Page: http://faunus.thinkaurelius.com
License: Apache License 2.0
Graph Analytics Engine
Home Page: http://faunus.thinkaurelius.com
License: Apache License 2.0
FaunusVertexRelationLoader loader = new FaunusVertexRelationLoader(IDHandler.getKeyID(row.left.duplicate()));
public FaunusVertexRelationLoader(final long id) {
Preconditions.checkArgument(id > 0);
vertex = new FaunusVertex(id);
}
In the JSONRecordReader/Writer use GraphSONFactory for graph Element serialization/deserialization:
If the v.estimate is set to -1 then do a g.V.count() to get the vertex count for a proper split.
...so that data can be loaded with filters.
gremlin.bat is basically identical to other gremlin.bat files save we now have -i parameter.
...then submit all those to global JobConf.
As Map+Reduce?Map* is the general solution for in-memory mapping.
g.V.groupCount(Vertex,"{it.age}")
Make the Writable and in-memory representation more efficient.
titanconfig.setProperty("storage.backend","cassandra"); // todo: astyanax
We might also be able to accomodate that through Rexster.
char direction.
try/catch logger in GraphSONRecordReader.
...also, make use of Titan edge compression techniques.
solve the super node problem for a vertex (can't always assume we can hold in memory)
Still faster than serializing to disk?
As it stands, there are three representations of the same information in memory at every record parse:
If we could go from String -> FaunsVertex, that would be good. However, the best would be InputStreamReader -> FaunusVertex.
...I like Faunus semantics as its a "hop" not a "drop." Think about which model to resolve to.
public void initialize(final InputSplit genericSplit, final TaskAttemptContext context) throws IOException {
final FileSplit split = (FileSplit) genericSplit;
final Configuration job = context.getConfiguration();
//this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength", Integer.MAX_VALUE);
Each Map is parsing some file of data and inserting it into a different machine in Titan.
so hadoop properties percolate?
(author -> paper <- author) -> coauthor
...can lead to multiple coauthor relationships between two people. As such, allow traverse to have a "weight" or "count" argument so that same edges are grouped and property weighted.
traverse(OUT,"author",IN,"author","coauthor",DROP,"weight")
Separate the configuration from the job propagation?
Add configuration options for username/password.
Keep faunus from getting banged up by line wrapping on consoles with lower width. The faunus tag line tends to break him up...maybe move it above or below the art.
v[0] 10
v[1] 1
v[2] 4
....with ability to control the toString().
Would be serial, on the initial load, but once in Hadoop, all operations are parallel.
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