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Posted to issues@spark.apache.org by "Pablo Alcaraz (JIRA)" <ji...@apache.org> on 2017/05/23 22:39:04 UTC
[jira] [Created] (SPARK-20859) SQL Loader does not recognize
multidimensional columns in postgresql (like integer[]][])
Pablo Alcaraz created SPARK-20859:
-------------------------------------
Summary: 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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