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 <http://imgur.com/a/cUC3d> ). 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 AWS EMR cluster, with 3 m3.2xlarge nodes in cluster (master + 2 slaves). Here is result of explain: http://pastebin.com/ZRUdUuYT -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-SQL-poor-join-performance-tp27235.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org