[
https://issues.apache.org/jira/browse/SPARK-20527?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-20527.
----------------------------------
Resolution: Duplicate
I tried to reproduces this but I could not reproduce as reported in the current
master as below:
{code}
from datetime import datetime
from pyspark.sql import Row
from datetime import datetime
spark.sql('CREATE DATABASE test_trash')
rows = [
dict(number=1, letters='real1', some_date=datetime(2017,12,1,3,15)),
dict(number=2, letters='real2', some_date=datetime(2017,12,2,3,15)),
dict(number=3, letters='real3', some_date=datetime(2017,12,3,3,15)),
]
rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
df = spark.createDataFrame(rows_rdd)
df.write.mode(saveMode='overwrite').saveAsTable('test_trash.thingy')
{code}
{code}
##############################
# this works as I would expect
schema = spark.sql('SELECT number, some_date FROM test_trash.thingy').schema
rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
df = spark.createDataFrame(rows_rdd, schema)
assert df.count() == 1 # force evaluation so that the schema is evaluated
print "Made it here! "
{code}
produces the output as below:
{code}
AttributeError: 'str' object has no attribute 'tzinfo'
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at
org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
...
{code}
{code}
########################################################################
# this fails and the only difference is in the order of the query fields
schema = spark.sql('SELECT some_date, number FROM test_trash.thingy').schema
rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
df = spark.createDataFrame(rows_rdd, schema)
try:
df.count() # force evaluation so that the schema is evaluated
except:
print "Oh no! Why?!"
raise
else:
print "Everything worked just fine."
{code}
produces the output below:
{code}
AttributeError: 'int' object has no attribute 'tzinfo'
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at
org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
...
{code}
It looks the problem is, PySPark sorts the fields in {{Row}} as below:
{code}
>>> Row(b=2, a=2)
Row(a=2, b=2)
{code}
and it seems the schema ended up with mismatching. For this issue, I think it
duplicates SPARK-12467 in this case.
I am resolving this as a duplicate of this. Please reopen this if I
misunderstood.
> Schema issues when fields are queries in different order
> --------------------------------------------------------
>
> Key: SPARK-20527
> URL: https://issues.apache.org/jira/browse/SPARK-20527
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: John Berryman
>
> In the code below I
> * create a dataframe
> * shove it into hive
> * use spark.sql to query it with different field orderings, and retrieve the
> schema
> * try to to apply the same data to the schema
> Observation: the order of the fields in the spark.sql query matters, in one
> order the schema is successfully applied, in the other order we get an error
> Expectation: since the ``rows`` all have named field, I would suspect that
> the ordering should not matter and the error appears to be a bug
> Here is the code to trigger this behavior:
> {code}
> #######
> # setup
> from datetime import datetime
> spark.sql('CREATE DATABASE test_trash')
> rows = [
> dict(number=1, letters='real1', some_date=datetime(2017,12,1,3,15)),
> dict(number=2, letters='real2', some_date=datetime(2017,12,2,3,15)),
> dict(number=3, letters='real3', some_date=datetime(2017,12,3,3,15)),
> ]
> rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
> df = spark.createDataFrame(rows_rdd)
> df.write.mode(saveMode='overwrite').saveAsTable('test_trash.thingy')
> ##############################
> # this works as I would expect
> schema = spark.sql('SELECT number, some_date FROM test_trash.thingy').schema
> rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
> df = spark.createDataFrame(rows_rdd, schema)
> assert df.count() == 1 # force evaluation so that the schema is evaluated
> print "Made it here! "
> ########################################################################
> # this fails and the only difference is in the order of the query fields
> schema = spark.sql('SELECT some_date, number FROM test_trash.thingy').schema
> rows_rdd = spark.sparkContext.parallelize(rows).map(lambda r: Row(**r))
> df = spark.createDataFrame(rows_rdd, schema)
> try:
> df.count() # force evaluation so that the schema is evaluated
> except:
> print "Oh no! Why?!"
> raise
> else:
> print "Everything worked just fine."
> {code}
> results in
> {code}
> Made it here!
> Oh no! Why?!
> Py4JJavaErrorTraceback (most recent call last)
> <ipython-input-90-353e9a10b2b7> in <module>()
> 35 df = spark.createDataFrame(rows_rdd, schema)
> 36 try:
> ---> 37 df.count() # force evaluation so that the schema is evaluated
> 38 except:
> 39 print "Oh no! Why?!"
> /usr/local/spark/python/pyspark/sql/dataframe.py in count(self)
> 378 2
> 379 """
> --> 380 return int(self._jdf.count())
> 381
> 382 @ignore_unicode_prefix
> /usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in
> __call__(self, *args)
> 1131 answer = self.gateway_client.send_command(command)
> 1132 return_value = get_return_value(
> -> 1133 answer, self.gateway_client, self.target_id, self.name)
> 1134
> 1135 for temp_arg in temp_args:
> /usr/local/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
> 61 def deco(*a, **kw):
> 62 try:
> ---> 63 return f(*a, **kw)
> 64 except py4j.protocol.Py4JJavaError as e:
> 65 s = e.java_exception.toString()
> /usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in
> get_return_value(answer, gateway_client, target_id, name)
> 317 raise Py4JJavaError(
> 318 "An error occurred while calling {0}{1}{2}.\n".
> --> 319 format(target_id, ".", name), value)
> 320 else:
> 321 raise Py4JError(
> Py4JJavaError: An error occurred while calling o2469.count.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 127.0 failed 1 times, most recent failure: Lost task 0.0 in stage
> 127.0 (TID 100, localhost, executor driver):
> org.apache.spark.api.python.PythonException: Traceback (most recent call
> last):
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 174,
> in main
> process()
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 169,
> in process
> serializer.dump_stream(func(split_index, iterator), outfile)
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/serializers.py", line
> 268, in dump_stream
> vs = list(itertools.islice(iterator, batch))
> File "/usr/local/spark/python/pyspark/sql/types.py", line 576, in toInternal
> return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
> File "/usr/local/spark/python/pyspark/sql/types.py", line 576, in <genexpr>
> return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line
> 436, in toInternal
> return self.dataType.toInternal(obj)
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line
> 190, in toInternal
> seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo
> AttributeError: 'int' object has no attribute 'tzinfo'
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$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:1918)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:275)
> at
> org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2405)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2404)
> at org.apache.spark.sql.Dataset.withCallback(Dataset.scala:2778)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2404)
> at sun.reflect.GeneratedMethodAccessor40.invoke(Unknown Source)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:497)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.spark.api.python.PythonException: Traceback (most
> recent call last):
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 174,
> in main
> process()
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 169,
> in process
> serializer.dump_stream(func(split_index, iterator), outfile)
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/serializers.py", line
> 268, in dump_stream
> vs = list(itertools.islice(iterator, batch))
> File "/usr/local/spark/python/pyspark/sql/types.py", line 576, in toInternal
> return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
> File "/usr/local/spark/python/pyspark/sql/types.py", line 576, in <genexpr>
> return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line
> 436, in toInternal
> return self.dataType.toInternal(obj)
> File "/usr/local/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line
> 190, in toInternal
> seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo
> AttributeError: 'int' object has no attribute 'tzinfo'
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$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
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
