CODEWITHSUNDEEP
apache-sparkpyspark
Tronald Dump
Tronald Dump

Reputation: 1350

PySpark Simple count of records

I am trying to learn Spark.

adultsrdd = sc.textFile("dbfs:/databricks-datasets/adult/adult.data")
educationrdd =adultsrdd.map(lambda row: row.split(',')[3])
educationrdd.take(5)

Gives the following result.

Out[78]: [u' Bachelors', u' Bachelors', u' HS-grad', u' 11th', u' Bachelors']

educationrdd.count()

org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 259.0 failed 1 times, most recent failure: Lost task 1.0 in stage 259.0 (TID 859, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):

Why do I get the error on count()?

Trace:

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 1.0 failed 1 times, most recent failure: Lost task 1.0 in stage 1.0 (TID 2, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "/databricks/spark/python/pyspark/worker.py", line 172, in main process() File "/databricks/spark/python/pyspark/worker.py", line 167, in process serializer.dump_stream(func(split_index, iterator), outfile) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 317, in func return f(iterator) File "/databricks/spark/python/pyspark/rdd.py", line 1008, in return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File "/databricks/spark/python/pyspark/rdd.py", line 1008, in return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File "", line 3, in IndexError: list index out of range at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193) at org.apache.spark.api.python.PythonRunner$$anon$1.(PythonRDD.scala:234) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) at org.apache.spark.rdd.RDD.iterator(RDD.scala:283) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:314) 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$$failJobAndIndependentStages(DAGScheduler.scala:1454) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1891) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1904) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1917) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931) 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.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453) at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala) 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: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: org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "/databricks/spark/python/pyspark/worker.py", line 172, in main process() File "/databricks/spark/python/pyspark/worker.py", line 167, in process serializer.dump_stream(func(split_index, iterator), outfile) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 2371, in pipeline_func return func(split, prev_func(split, iterator)) File "/databricks/spark/python/pyspark/rdd.py", line 317, in func return f(iterator) File "/databricks/spark/python/pyspark/rdd.py", line 1008, in return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File "/databricks/spark/python/pyspark/rdd.py", line 1008, in return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File "", line 3, in IndexError: list index out of range at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193) at org.apache.spark.api.python.PythonRunner$$anon$1.(PythonRDD.scala:234) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) at org.apache.spark.rdd.RDD.iterator(RDD.scala:283) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:314) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) ... 1 more

Upvotes: 0

Views: 4665

Answers (2)

Rafael Valero
Rafael Valero

Reputation: 2816

I had a similar problem, I tried something like:

numPartitions = a number for example 10 or 100 adultsrdd = sc.textFile("dbfs:/databricks-datasets/adult/adult.data",numPartitions) Inspired by: How to repartition evenly in Spark? or here: https://databricks.gitbooks.io/databricks-spark-knowledge-base/content/performance_optimization/how_many_partitions_does_an_rdd_have.html

Upvotes: 0

Assaf Mendelson
Assaf Mendelson

Reputation: 13001

In all probability you have some line which does not have 4 elements after the split. This often happens when you have a blank line or similar issue. You can do one of two things:

1. In the map replace this case with some default value as such:

educationrdd =adultsrdd.map(lambda row: row.split(',')[3] if (row is not None and len(row.split(','))>3) else None)

2. use flatmap to have just the relevant data:

educationrdd =adultsrdd.flatMap(lambda row: [row.split(',')[3]] if (row is not None and len(row.split(','))>3) else [])

of course you might want to replace the lambda function with a function which does not split the row twice...

Upvotes: 1

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