Re: Spark 1.1.0 and Hive 0.12.0 Compatibility Issue
Hi, My Steps: ### HIVE CREATE TABLE CUSTOMER ( C_CUSTKEYBIGINT, C_NAME VARCHAR(25), C_ADDRESSVARCHAR(40), C_NATIONKEY BIGINT, C_PHONE VARCHAR(15), C_ACCTBALDECIMAL, C_MKTSEGMENT VARCHAR(10), C_COMMENTVARCHAR(117) ) row format serde 'com.bizo.hive.serde.csv.CSVSerde'; LOAD DATA LOCAL INPATH '/usr/local/pdgf/output/CUSTOMER.csv' INTO TABLE CUSTOMER; CREATE TABLE ORDERS ( O_ORDERKEY BIGINT, O_CUSTKEYBIGINT, O_ORDERSTATUSstring, O_TOTALPRICE DECIMAL, O_ORDERDATE STRING, O_ORDERPRIORITY VARCHAR(15), O_CLERK VARCHAR(15), O_SHIPPRIORITY INT, O_COMMENTVARCHAR(79) ) ROW FORMAT serde 'com.bizo.hive.serde.csv.CSVSerde’; LOAD DATA LOCAL INPATH '/usr/local/pdgf/output/ORDERS.csv' INTO TABLE ORDERS; CREATE TABLE LINEITEM ( L_ORDERKEY BIGINT, L_PARTKEYBIGINT, L_SUPPKEYBIGINT, L_LINENUMBER INT, L_QUANTITY DECIMAL, L_EXTENDEDPRICE DECIMAL, L_DISCOUNT DECIMAL, L_TAXDECIMAL, L_SHIPDATE STRING, L_COMMITDATE STRING, L_RECEIPTDATESTRING, L_RETURNFLAG STRING, L_LINESTATUS STRING, L_SHIPINSTRUCT VARCHAR(25), L_SHIPMODE VARCHAR(10), L_COMMENTVARCHAR(44) ) ROW FORMAT serde 'com.bizo.hive.serde.csv.CSVSerde'; LOAD DATA LOCAL INPATH 'vpdgf/output/LINEITEM.csv' INTO TABLE LINEITEM; … (same for other tables) hive add jar /hadoop/hive/csv-serde-1.1.2-0.11.0-all.jar; hive select l_orderkey, sum(l_extendedprice*(1-l_discount)) as revenue, o_orderdate, o_shippriority from customer c join orders o on c.c_mktsegment = 'BUILDING' and c.c_custkey = o.o_custkey join lineitem l on l.l_orderkey = o.o_orderkey where o_orderdate '1995-03-15' and l_shipdate '1995-03-15' group by l_orderkey, o_orderdate, o_shippriority order by revenue desc, o_orderdate limit 10; MapReduce Total cumulative CPU time: 25 seconds 110 msec Ended Job = job_1414101999739_0004 MapReduce Jobs Launched: Job 0: Map: 26 Reduce: 7 Cumulative CPU: 378.14 sec HDFS Read: 6502040850 HDFS Write: 173752818 SUCCESS Job 1: Map: 100 Reduce: 27 Cumulative CPU: 1376.06 sec HDFS Read: 26273646797 HDFS Write: 183687996 SUCCESS Job 2: Map: 3 Reduce: 1 Cumulative CPU: 32.25 sec HDFS Read: 183694290 HDFS Write: 183706480 SUCCESS Job 3: Map: 1 Reduce: 1 Cumulative CPU: 25.11 sec HDFS Read: 183707750 HDFS Write: 349 SUCCESS Total MapReduce CPU Time Spent: 30 minutes 11 seconds 560 msec ### Run the same SQL in Spark scala val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc) scala sqlContext.sql(select l_orderkey, sum(l_extendedprice*(1-l_discount)) as revenue, o_orderdate, o_shippriority from customer c join orders o on c.c_mktsegment = 'BUILDING' and c.c_custkey = o.o_custkey join lineitem l on l.l_orderkey = o.o_orderkey where o_orderdate '1995-03-15' and l_shipdate '1995-03-15' group by l_orderkey, o_orderdate, o_shippriority order by revenue desc, o_orderdate limit 10).collect().foreach(println); java.lang.ClassCastException: java.lang.String cannot be cast to scala.math.BigDecimal scala.math.Numeric$BigDecimalIsFractional$.minus(Numeric.scala:182) org.apache.spark.sql.catalyst.expressions.Subtract$$anonfun$eval$3.apply(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Subtract$$anonfun$eval$3.apply(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:114) org.apache.spark.sql.catalyst.expressions.Subtract.eval(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:108) org.apache.spark.sql.catalyst.expressions.Multiply.eval(arithmetic.scala:70) org.apache.spark.sql.catalyst.expressions.Coalesce.eval(nullFunctions.scala:47) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:108) org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:58) org.apache.spark.sql.catalyst.expressions.MutableLiteral.update(literals.scala:69) org.apache.spark.sql.catalyst.expressions.SumFunction.update(aggregates.scala:433) org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:167) org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:151) org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) org.apache.spark.rdd.RDD.iterator(RDD.scala:229) org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) org.apache.spark.rdd.RDD.iterator(RDD.scala:229) org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
Re: Spark 1.1.0 and Hive 0.12.0 Compatibility Issue
Can you show the DDL for the table? It looks like the SerDe might be saying it will produce a decimal type but is actually producing a string. On Thu, Oct 23, 2014 at 3:17 PM, arthur.hk.c...@gmail.com arthur.hk.c...@gmail.com wrote: Hi My Spark is 1.1.0 and Hive is 0.12, I tried to run the same query in both Hive-0.12.0 then Spark-1.1.0, HiveQL works while SparkSQL failed. hive select l_orderkey, sum(l_extendedprice*(1-l_discount)) as revenue, o_orderdate, o_shippriority from customer c join orders o on c.c_mktsegment = 'BUILDING' and c.c_custkey = o.o_custkey join lineitem l on l.l_orderkey = o.o_orderkey where o_orderdate '1995-03-15' and l_shipdate '1995-03-15' group by l_orderkey, o_orderdate, o_shippriority order by revenue desc, o_orderdate limit 10; Ended Job = job_1414067367860_0011 MapReduce Jobs Launched: Job 0: Map: 1 Reduce: 1 Cumulative CPU: 2.0 sec HDFS Read: 261 HDFS Write: 96 SUCCESS Job 1: Map: 1 Reduce: 1 Cumulative CPU: 0.88 sec HDFS Read: 458 HDFS Write: 0 SUCCESS Total MapReduce CPU Time Spent: 2 seconds 880 msec OK Time taken: 38.771 seconds scala sqlContext.sql(select l_orderkey, sum(l_extendedprice*(1-l_discount)) as revenue, o_orderdate, o_shippriority from customer c join orders o on c.c_mktsegment = 'BUILDING' and c.c_custkey = o.o_custkey join lineitem l on l.l_orderkey = o.o_orderkey where o_orderdate '1995-03-15' and l_shipdate '1995-03-15' group by l_orderkey, o_orderdate, o_shippriority order by revenue desc, o_orderdate limit 10).collect().foreach(println); org.apache.spark.SparkException: Job aborted due to stage failure: Task 14 in stage 5.0 failed 4 times, most recent failure: Lost task 14.3 in stage 5.0 (TID 568, m34): java.lang.ClassCastException: java.lang.String cannot be cast to scala.math.BigDecimal scala.math.Numeric$BigDecimalIsFractional$.minus(Numeric.scala:182) org.apache.spark.sql.catalyst.expressions.Subtract$$anonfun$eval$3.apply(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Subtract$$anonfun$eval$3.apply(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:114) org.apache.spark.sql.catalyst.expressions.Subtract.eval(arithmetic.scala:64) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:108) org.apache.spark.sql.catalyst.expressions.Multiply.eval(arithmetic.scala:70) org.apache.spark.sql.catalyst.expressions.Coalesce.eval(nullFunctions.scala:47) org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:108) org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:58) org.apache.spark.sql.catalyst.expressions.MutableLiteral.update(literals.scala:69) org.apache.spark.sql.catalyst.expressions.SumFunction.update(aggregates.scala:433) org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:167) org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:151) org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) org.apache.spark.rdd.RDD.iterator(RDD.scala:229) org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) org.apache.spark.rdd.RDD.iterator(RDD.scala:229) org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) org.apache.spark.scheduler.Task.run(Task.scala:54) org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177) java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
