Hi Shivani, The issue is that by the time the Hash Join is executed, the MutableHashTable cannot allocate enough memory segments. That means that your other operators are occupying them. It is fine that this also occurs on Travis because the workers there have limited memory as well.
Till suggested to change the memory fraction through the ExuectionEnvironment. Can you try that? Cheers, Max On Mon, Jul 20, 2015 at 2:23 PM, Shivani Ghatge <shgha...@gmail.com> wrote: > Hello Maximilian, > > Thanks for the suggestion. I will use it to check the program. But when I > am creating a PR for the same implementation with a Test, I am getting the > same error even on Travis build. So for that what would be the solution? > > Here is my PR https://github.com/apache/flink/pull/923 > And here is the Travis build status > https://travis-ci.org/apache/flink/builds/71695078 > > Also on the IDE it is working fine in Collection execution mode. > > Thanks and Regards, > Shivani > > On Mon, Jul 20, 2015 at 2:14 PM, Maximilian Michels <m...@apache.org> > wrote: > >> Hi Shivani, >> >> Flink doesn't have enough memory to perform a hash join. You need to >> provide Flink with more memory. You can either increase the >> "taskmanager.heap.mb" config variable or set "taskmanager.memory.fraction" >> to some value greater than 0.7 and smaller then 1.0. The first config >> variable allocates more overall memory for Flink; the latter changes the >> ratio between Flink managed memory (e.g. for hash join) and user memory >> (for you functions and Gelly's code). >> >> If you run this inside an IDE, the memory is configured automatically and >> you don't have control over that at the moment. You could, however, start a >> local cluster (./bin/start-local) after you adjusted your flink-conf.yaml >> and run your programs against that configured cluster. You can do that >> either through your IDE using a RemoteEnvironment or by submitting the >> packaged JAR to the local cluster using the command-line tool (./bin/flink). >> >> Hope that helps. >> >> Cheers, >> Max >> >> On Mon, Jul 20, 2015 at 2:04 PM, Shivani Ghatge <shgha...@gmail.com> >> wrote: >> >>> Hello, >>> I am working on a problem which implements Adamic Adar Algorithm using >>> Gelly. >>> I am running into this exception for all the Joins (including the one >>> that are part of the reduceOnNeighbors function) >>> >>> Too few memory segments provided. Hash Join needs at least 33 memory >>> segments. >>> >>> >>> The problem persists even when I comment out some of the joins. >>> >>> Even after using edg = edg.join(graph.getEdges(), >>> JoinOperatorBase.JoinHint.BROADCAST_HASH_SECOND).where(0,1).equalTo(0,1).with(new >>> JoinEdge()); >>> >>> as suggested by @AndraLungu the problem persists. >>> >>> The code is >>> >>> >>> DataSet<Tuple2<Long, Long>> degrees = graph.getDegrees(); >>> >>> //get neighbors of each vertex in the HashSet for it's value >>> computedNeighbors = graph.reduceOnNeighbors(new >>> GatherNeighbors(), EdgeDirection.ALL); >>> >>> //get vertices with updated values for the final Graph which >>> will be used to get Adamic Edges >>> Vertices = computedNeighbors.join(degrees, >>> JoinOperatorBase.JoinHint.BROADCAST_HASH_FIRST).where(0).equalTo(0).with(new >>> JoinNeighborDegrees()); >>> >>> Graph<Long, Tuple3<Double, HashSet<Long>, List<Tuple3<Long, >>> Long, Double>>>, Double> updatedGraph = >>> Graph.fromDataSet(Vertices, edges, env); >>> >>> //configure Vertex Centric Iteration >>> VertexCentricConfiguration parameters = new >>> VertexCentricConfiguration(); >>> >>> parameters.setName("Find Adamic Adar Edge Weights"); >>> >>> parameters.setDirection(EdgeDirection.ALL); >>> >>> //run Vertex Centric Iteration to get the Adamic Adar Edges into >>> the vertex Value >>> updatedGraph = updatedGraph.runVertexCentricIteration(new >>> GetAdamicAdarEdges<Long>(), new NeighborsMessenger<Long>(), 1, parameters); >>> >>> //Extract Vertices of the updated graph >>> DataSet<Vertex<Long, Tuple3<Double, HashSet<Long>, >>> List<Tuple3<Long, Long, Double>>>>> vertices = updatedGraph.getVertices(); >>> >>> //Extract the list of Edges from the vertex values >>> DataSet<Tuple3<Long, Long, Double>> edg = vertices.flatMap(new >>> GetAdamicList()); >>> >>> //Partial weights for the edges are added >>> edg = edg.groupBy(0,1).reduce(new AdamGroup()); >>> >>> //Graph is updated with the Adamic Adar Edges >>> edg = edg.join(graph.getEdges(), >>> JoinOperatorBase.JoinHint.BROADCAST_HASH_SECOND).where(0,1).equalTo(0,1).with(new >>> JoinEdge()); >>> >>> Any idea how I could tackle this Exception? >>> >> >> >
