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Registered: ‎06-27-2016

spark sql poor join performance

I'm using SparkSQL to make fact table out of 5 dimensions. I'm facing performance issue (job is taking several hours to complete), and even after exhaustive googleing I see no solution. These are settings I have tried turing, but no sucess.

sqlContext.sql("set spark.sql.shuffle.partitions=10"); // varied between 10 and 5000
sqlContext.sql("set spark.sql.autoBroadcastJoinThreshold=500000000"); // 500 MB, tried 1 GB

Most of RDDs are nicely parittions (500 partitions each), however largest dimension is not partitioned at all (images). Maybe this can lead to solution ? Below is code I have used for making fact table.

resultDmn1.registerTempTable("Dmn1");
    resultDmn2.registerTempTable("Dmn2");
    resultDmn3.registerTempTable("Dmn3");
    resultDmn4.registerTempTable("Dmn4");
    resultDmn5.registerTempTable("Dmn5");

    DataFrame resultFact = sqlContext.sql("SELECT DISTINCT\n" +
            "    0 AS FactId,\n" +
            "    rs.c28 AS c28,\n" +
            "    dop.DmnId AS dmn_id_dim4,\n" +
            "    dh.DmnId AS dmn_id_dim5,\n" +
            "    op.DmnId AS dmn_id_dim3,\n" +
            "    du.DmnId AS dmn_id_dim2,\n" +
            "    dc.DmnId AS dmn_id_dim1\n" +
            "FROM\n" +
            "    t10 rs\n" +
            "        JOIN\n" +
            "    t11 r ON rs.c29 = r.id\n" +
            "        JOIN\n" +
            "    Dmn4 dop ON dop.c26 = r.c25\n" +
            "        JOIN\n" +
            "    Dmn5 dh ON dh.Date = r.c27\n" +
            "        JOIN\n" +
            "    Dmn3 du ON du.c9 = r.c16\n" +
            "        JOIN\n" +
            "    t1 d ON r.c5 = d.id\n" +
            "        JOIN\n" +
            "    t2 di ON d.id = di.c5\n" +
            "        JOIN\n" +
            "    t3 s ON d.c6 = s.id\n" +
            "        JOIN\n" +
            "    t4 p ON s.c7 = p.id\n" +
            "        JOIN\n" +
            "    t5 o ON p.c8 = o.id\n" +
            "        JOIN\n" +
            "    Dmn1 op ON op.c1 = di.c1\n" +
            "        JOIN\n" +
            "    t9 ci ON ci.id = r.c24\n" +
            "        JOIN\n" +
            "    Dmn3 dc ON dc.c18 = ci.c23\n" +
            "WHERE\n" +
            "    op.c2 = di.c2\n" +
            "        AND o.name = op.c30\n" +
            "        AND di.c3 = op.c3\n" +
            "        AND di.c4 = op.c4").toSchemaRDD();

     resultFact.count();
     resultFact.cache();

Dmn1 has 56 rows, dmn2 11, dmn3 10, dmn4 12, and dmn5 1275533 rows prior this join. Everything is running on one master + 2 slaves.

Here is result of explain: http://pastebin.com/ZRUdUuYT