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From "Pablo Alcaraz (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-20859) SQL Loader does not recognize multidimensional columns in postgresql (like integer[]][])
Date Tue, 23 May 2017 22:40:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-20859?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16022011#comment-16022011
] 

Pablo Alcaraz commented on SPARK-20859:
---------------------------------------

The patch on SPARK-14536 only works for unidimensional arrays.

> SQL Loader does not recognize multidimensional columns in postgresql (like integer[]][])
> ----------------------------------------------------------------------------------------
>
>                 Key: SPARK-20859
>                 URL: https://issues.apache.org/jira/browse/SPARK-20859
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.1
>            Reporter: Pablo Alcaraz
>            Priority: Critical
>
> The fix in SPARK-14536 is not accepting columns like integer[][]  (multidimensional arrays)
> To reproduce this error:
> 1) Create a SQL table in postgresql
> {code:sql}
> CREATE TABLE arrays_test
> (
>   eid integer NOT NULL,
>   simple integer[],
>   multi integer[][]
> );
> {code}
> 2) Insert a row like this one:
> {code:xml}
> insert into arrays_test (eid, simple, multi)
> values
> (1, '{1, 1}', NULL);
> {code}
> 3) Execute a SPQL query like this one and observe how it works:
> {code:python}
> from pyspark import SparkConf
> from pyspark import SparkContext
> from pyspark.sql import SQLContext
> master = "spark://spark211:7077"  # local is OK too
> conf = (
>     SparkConf()
>         .setMaster(master)
>         .setAppName("Connection Test 5")
>         .set("spark.jars.packages", "org.postgresql:postgresql:9.4.1212")   ## This one
works ok
>         .set("spark.driver.memory", "2G")
>         .set("spark.executor.memory", "2G")
>         .set("spark.driver.cores", "10")
> )
> sc = SparkContext(conf=conf)
> # sc.setLogLevel("ALL")
> print "====>", 1
> print(sc)
> sqlContext = SQLContext(sc)
> print "====>", 2
> print sqlContext
> url = "postgresql://localhost:5432/test"   # change properly
> url = 'jdbc:'+url
> properties = {'user': 'user', 'password': 'password'}   # change user and password if
needed
> df = sqlContext.read.format("jdbc"). \
>     option("url", url). \
>     option("driver", "org.postgresql.Driver"). \
>     option("useUnicode", "true"). \
>     option("continueBatchOnError","true"). \
>     option("useSSL", "false"). \
>     option("user", "user"). \
>     option("password", "password"). \
>     option("dbtable", "arrays_test"). \
>     option("partitionColumn", "eid"). \
>     option("lowerBound", "1000015"). \
>     option("upperBound", "6026289"). \
>     option("numPartitions", "100"). \
>     load()
> print "====>", 3
> df.registerTempTable("arrays_test")
> df = sqlContext.sql("SELECT * FROM arrays_test limit 5")
> print "====>", 4
> print df.collect()
> {code}
> 4) Observe how it works.
> 5) Now, to reproduce the error, insert a multi dimensional array into the SQL table:
> {code:sql}
> insert into arrays_test (eid, simple, multi)
> values
> (2, '{1, 1}', '{{1, 1},{2, 2}}');
> {code}
> 6) Execute step 3) again.
> 7) Observe the exception
> {code}
> 17/05/23 15:23:38 ERROR TaskSetManager: Task 0 in stage 0.0 failed 4 times; aborting
job
> Traceback (most recent call last):
>   File "/home/pablo/develop/physiosigns/livebetter/modelling2/modelling2/scripts/runSparkTest2.py",
line 65, in <module>
>     print df.collect()
>   File "/home/pablo/myProgs/virt-pablo/local/lib/python2.7/site-packages/pyspark/sql/dataframe.py",
line 391, in collect
>     port = self._jdf.collectToPython()
>   File "/home/pablo/myProgs/virt-pablo/local/lib/python2.7/site-packages/py4j/java_gateway.py",
line 1133, in __call__
>     answer, self.gateway_client, self.target_id, self.name)
>   File "/home/pablo/myProgs/virt-pablo/local/lib/python2.7/site-packages/pyspark/sql/utils.py",
line 63, in deco
>     return f(*a, **kw)
>   File "/home/pablo/myProgs/virt-pablo/local/lib/python2.7/site-packages/py4j/protocol.py",
line 319, in get_return_value
>     format(target_id, ".", name), value)
> py4j.protocol.Py4JJavaError: An error occurred while calling o49.collectToPython.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, 172.17.0.58, executor
0): java.lang.ClassCastException: [Ljava.lang.Integer; cannot be cast to java.lang.Integer
> 	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:101)
> 	at org.apache.spark.sql.catalyst.util.GenericArrayData.getInt(GenericArrayData.scala:62)
> 	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
> 	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> 	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> 	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> 	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:99)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> 	at java.lang.Thread.run(Thread.java:748)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> 	at scala.Option.foreach(Option.scala:257)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> 	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1925)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1938)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1951)
> 	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:333)
> 	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
> 	at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2768)
> 	at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2765)
> 	at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2765)
> 	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> 	at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2788)
> 	at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2765)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> 	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> 	at java.lang.reflect.Method.invoke(Method.java:498)
> 	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> 	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> 	at py4j.Gateway.invoke(Gateway.java:280)
> 	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> 	at py4j.commands.CallCommand.execute(CallCommand.java:79)
> 	at py4j.GatewayConnection.run(GatewayConnection.java:214)
> 	at java.lang.Thread.run(Thread.java:748)
> Caused by: java.lang.ClassCastException: [Ljava.lang.Integer; cannot be cast to java.lang.Integer
> 	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:101)
> 	at org.apache.spark.sql.catalyst.util.GenericArrayData.getInt(GenericArrayData.scala:62)
> 	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
> 	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> 	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> 	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> 	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:99)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> 	... 1 more
> {code}



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