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aws / sagemaker-spark

A Spark library for Amazon SageMaker.
https://aws.github.io/sagemaker-spark/
Apache License 2.0
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Problem with writing RecordIO format, section 'The Amazon Record format' in README.md file probably needs a change - an old import from mllib (should be ml) #131

Open borysrybak opened 3 years ago

borysrybak commented 3 years ago

System Information

Describe the problem

I was trying to write the dataframe object into s3 by following the formatting instructions:

(...) Writing a DataFrame using the "sagemaker" format serializes a column named "label", expected to contain Doubles, and a column named "features", expected to contain a Sparse or Dense org.apache.mllib.linalg.Vector. If the features column contains a SparseVector, SageMaker Spark sparsely-encodes the Vector into the Amazon Record. If the features column contains a DenseVector, SageMaker Spark densely-encodes the Vector into the Amazon Record. (...)

and by executing exact the code as follows:

myDataFrame.write
    .format("sagemaker")
    .option("labelColumnName", "myLabelColumn")
    .option("featuresColumnName", "myFeaturesColumn")
    .save("s3://my-s3-bucket/my-s3-prefix")

In my case, I did prepare the dataset with the schema that was described in the section I mentioned before, so my data types were like:

but whenever I was executing the code:

df.write
    .format("sagemaker")
    .option("labelColumnName", "myLabels")
    .option("featuresColumnName", "myFeatures")
    .save("s3://...")

everytime I was receiving such errors:

scala.MatchError: (11,[0,3,7,10],[1.0,1.0,1.0,1.0]) (of class org.apache.spark.mllib.linalg.SparseVector)

I double-checked on the format of my SparseVector and I was sure that the Vector type that was prepared was explicitly defined and it was a SparseVector from mllib.linalg package

After a few tests and experiments, it turned out that the correct dtype of the feature shouldn't be the SparseVector from mllib.linalg, but from ml.linalg :)

this is the correct/working schema I used and it abled me to write the dataframe into s3 in RecordIO format: 'features': pyspark.ml.linalg.VectorUDT, 'label': pyspark.sql.types.DoubleType,

mwe:

from pyspark.mllib.linalg import VectorUDT, SparseVector

df = spark.createDataFrame([
    (1.0, SparseVector(4, {1: 1.0, 3: 5.5})),
    (0.0, SparseVector(4, {1: 1.0, 2: 5.5}))
], ['labels', 'features'])

df.show():

+------+-------------------+
|labels|           features|
+------+-------------------+
|   1.0|(4,[1,3],[1.0,5.5])|
|   0.0|(2,[1,2],[1.0,5.5])|
+------+-------------------+

{s.name: type(s.dataType) for s in df.schema}:

{'features': pyspark.mllib.linalg.VectorUDT,
 'labels': pyspark.sql.types.DoubleType}

and after executing:

df.write.format(
    "sagemaker"
).option(
    "labelColumnName", "labels"
).option(
    "featuresColumnName", "features"
).save(
    my_path
)

log:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-61-58bc3a4fb49d> in <module>()
      6     "featuresColumnName", "features"
      7 ).save(
----> 8     my_path
      9 )

/usr/lib/spark/python/pyspark/sql/readwriter.py in save(self, path, format, mode, partitionBy, **options)
    736             self._jwrite.save()
    737         else:
--> 738             self._jwrite.save(path)
    739 
    740     @since(1.4)

/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/usr/lib/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/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o8427.save.
: org.apache.spark.SparkException: Job aborted.
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:156)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:83)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:84)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:165)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:74)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 63 in stage 309.0 failed 4 times, most recent failure: Lost task 63.3 in stage 309.0 (TID 15086, ip-10-128-40-43.eu-west-1.compute.internal, executor 19): org.apache.spark.SparkException: Task failed while writing rows.
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: scala.MatchError: (2,[1,2],[1.0,5.5]) (of class org.apache.spark.mllib.linalg.SparseVector)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.ProtobufConverter$.rowToProtobuf(ProtobufConverter.scala:69)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:84)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:72)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:245)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:242)
    at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:248)
    ... 10 more

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
    ... 32 more
Caused by: org.apache.spark.SparkException: Task failed while writing rows.
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more
Caused by: scala.MatchError: (2,[1,2],[1.0,5.5]) (of class org.apache.spark.mllib.linalg.SparseVector)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.ProtobufConverter$.rowToProtobuf(ProtobufConverter.scala:69)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:84)
    at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:72)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:245)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:242)
    at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:248)
    ... 10 more

and once created with ml.linalg:

from pyspark.ml.linalg import VectorUDT, SparseVector

df = spark.createDataFrame([
    (1.0, SparseVector(4,[1,3],[1.0,5.5])),
    (0.0, SparseVector(2,[1,2],[1.0,5.5]))
], ['labels', 'features'])
df.show()

+------+-------------------+
|labels|           features|
+------+-------------------+
|   1.0|(4,[1,3],[1.0,5.5])|
|   0.0|(4,[1,3],[1.0,5.5])|
+------+-------------------+

{s.name: type(s.dataType) for s in df.schema}
->
{'features': pyspark.ml.linalg.VectorUDT,
 'labels': pyspark.sql.types.DoubleType}

df.write.format(
    "sagemaker"
).option(
    "labelColumnName", "labels"
).option(
    "featuresColumnName", "features"
).save(
    my_path
)

everything went ok and the data has been written into s3.

my suggestion is to change/update the imports that have been used as an example in that section - it may generate some confusions :)