[
https://issues.apache.org/jira/browse/SPARK-11868?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15557944#comment-15557944
]
Hyukjin Kwon commented on SPARK-11868:
--------------------------------------
FYI, it now prints differently:
{code}
>>> dicts = [{'1':1,'2':2,'3':3}]*10+[{'1':1,'3':3}]
>>> rows = [pyspark.sql.Row(**r) for r in dicts]
>>> rows_rdd = sc.parallelize(rows)
>>> dicts_rdd = sc.parallelize(dicts)
>>> rows_df = sqlContext.createDataFrame(rows_rdd)
>>> dicts_df = sqlContext.createDataFrame(dicts_rdd)
/Users/hyukjinkwon/Desktop/workspace/local/forked/spark/python/pyspark/sql/session.py:336:
UserWarning: Using RDD of dict to inferSchema is deprecated. Use
pyspark.sql.Row instead
warnings.warn("Using RDD of dict to inferSchema is deprecated. "
>>>
>>> print(rows_df.select(['2']).collect()[10])
16/10/08 22:10:03 ERROR Executor: Exception in task 7.0 in stage 2.0 (TID 9)
java.lang.IllegalStateException: Input row doesn't have expected number of
values required by the schema. 3 fields are required while 2 values are
provided.
at
org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:136)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:370)
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$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
16/10/08 22:10:03 WARN TaskSetManager: Lost task 7.0 in stage 2.0 (TID 9,
localhost): java.lang.IllegalStateException: Input row doesn't have expected
number of values required by the schema. 3 fields are required while 2 values
are provided.
at
org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:136)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:370)
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$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
16/10/08 22:10:03 ERROR TaskSetManager: Task 7 in stage 2.0 failed 1 times;
aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"/Users/hyukjinkwon/Desktop/workspace/local/forked/spark/python/pyspark/sql/dataframe.py",
line 322, in collect
port = self._jdf.collectToPython()
File
"/Users/hyukjinkwon/Desktop/workspace/local/forked/spark/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py",
line 1133, in __call__
File
"/Users/hyukjinkwon/Desktop/workspace/local/forked/spark/python/pyspark/sql/utils.py",
line 63, in deco
return f(*a, **kw)
File
"/Users/hyukjinkwon/Desktop/workspace/local/forked/spark/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py",
line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling
o75.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in
stage 2.0 failed 1 times, most recent failure: Lost task 7.0 in stage 2.0 (TID
9, localhost): java.lang.IllegalStateException: Input row doesn't have expected
number of values required by the schema. 3 fields are required while 2 values
are provided.
at
org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:136)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:370)
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$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$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:1901)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1914)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1927)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1941)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:912)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.collect(RDD.scala:911)
at
org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:275)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2574)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2571)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2571)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2594)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2571)
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:497)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
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:745)
Caused by: java.lang.IllegalStateException: Input row doesn't have expected
number of values required by the schema. 3 fields are required while 2 values
are provided.
at
org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:136)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at
org.apache.spark.sql.SparkSession$$anonfun$5.apply(SparkSession.scala:656)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:370)
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$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
>>> print(dicts_df.select(['2']).collect()[10])
Row(2=None)
{code}
> wrong results returned from dataframe create from Rows without consistent
> schma on pyspark
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-11868
> URL: https://issues.apache.org/jira/browse/SPARK-11868
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.5.2
> Environment: pyspark
> Reporter: Yuval Tanny
>
> When schema is inconsistent (but is the sames for the 10 first rows), it's
> possible to create a dataframe form dictionaries and if a key is missing, its
> value is None. But when trying to create dataframe from corresponding rows,
> we get inconsistent behavior (wrong values for keys) without exception. See
> example below.
> The problems seems to be:
> 1. Not verifying all rows in schema.
> 2. In pyspark.sql.types._create_converter, None is being set when converting
> dictionary and field is not exist:
> {code}
> return tuple([conv(d.get(name)) for name, conv in zip(names, converters)])
> {code}
> But for Rows, it is just assumed that the number of fields in tuple is equal
> the number of in the inferred schema, and we place wrong values for wrong
> keys otherwise:
> {code}
> return tuple(conv(v) for v, conv in zip(obj, converters))
> {code}
> Thanks.
> example:
> {code}
> dicts = [{'1':1,'2':2,'3':3}]*10+[{'1':1,'3':3}]
> rows = [pyspark.sql.Row(**r) for r in dicts]
> rows_rdd = sc.parallelize(rows)
> dicts_rdd = sc.parallelize(dicts)
> rows_df = sqlContext.createDataFrame(rows_rdd)
> dicts_df = sqlContext.createDataFrame(dicts_rdd)
> print(rows_df.select(['2']).collect()[10])
> print(dicts_df.select(['2']).collect()[10])
> {code}
> output:
> {code}
> Row(2=3)
> Row(2=None)
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
