[
https://issues.apache.org/jira/browse/SPARK-18819?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael Kamprath updated SPARK-18819:
-------------------------------------
Description:
When I create a data frame in PySpark with a small row count (less than number
executors), then write it to a parquet file, then load that parquet file into a
new data frame, and finally do any sort of read against the loaded new data
frame, Spark fails with an {{ExecutorLostFailure}}.
Example code to replicate this issue:
{code}
from pyspark.sql.types import *
rdd = sc.parallelize([('row1',1,4.33,'name'),('row2',2,3.14,'string')])
my_schema = StructType([
StructField("id", StringType(), True),
StructField("value1", IntegerType(), True),
StructField("value2", DoubleType(), True),
StructField("name",StringType(), True)
])
df = spark.createDataFrame( rdd, schema=my_schema)
df.write.parquet('hdfs://master:9000/user/michael/test_data',mode='overwrite')
newdf = spark.read.parquet('hdfs://master:9000/user/michael/test_data/')
newdf.take(1)
{code}
The error I get when the {{take}} step runs is:
{code}
Py4JJavaError: An error occurred while calling o54.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID
8, 10.10.10.4): ExecutorLostFailure (executor 0 exited caused by one of the
running tasks) Reason: Remote RPC client disassociated. Likely due to
containers exceeding thresholds, or network issues. Check driver logs for WARN
messages.
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:1873)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2526)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2546)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2523)
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)
{code}
I have tested this against HDFS 2.7, local file system, and QFS 1.2. All have
the same results.
I generally discovered this when processing larger files that have individual
parquet part files with a single row in them. The same problem manifested then.
was:
When I create a data frame in PySpark with a small row count (less than number
executors), then write it to a parquet file, then load that parquet file into a
new data frame, and finally do any sort of read against the loaded new data
frame, Spark fails with an {{ExecutorLostFailure}}.
Example code to replicate this issue:
{code}
from pyspark.sql.types import *
rdd = sc.parallelize([('row1',1,4.33,'name'),('row2',2,3.14,'string')])
my_schema = StructType([
StructField("id", StringType(), True),
StructField("value1", IntegerType(), True),
StructField("value2", DoubleType(), True),
StructField("name",StringType(), True)
])
df = spark.createDataFrame( rdd, schema=my_schema)
df.write.parquet('hdfs://master:9000/user/michael/test_data',mode='overwrite')
newdf = spark.read.parquet('hdfs://master:9000/user/michael/test_data/')
newdf.take(1)
{code}
The error I get is:
{code}
Py4JJavaError: An error occurred while calling o54.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID
8, 10.10.10.4): ExecutorLostFailure (executor 0 exited caused by one of the
running tasks) Reason: Remote RPC client disassociated. Likely due to
containers exceeding thresholds, or network issues. Check driver logs for WARN
messages.
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$$anon$1.run(EventLoop.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1873)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2526)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
at
org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2546)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2523)
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)
{code}
I have tested this against HDFS 2.7, local file system, and QFS 1.2. All have
the same results.
I generally discovered this when processing larger files that have individual
parquet part files with a single row in them. The same problem manifested then.
> Failure to read single-row Parquet files
> ----------------------------------------
>
> Key: SPARK-18819
> URL: https://issues.apache.org/jira/browse/SPARK-18819
> Project: Spark
> Issue Type: Bug
> Components: Input/Output, PySpark
> Affects Versions: 2.0.2
> Environment: Ubuntu 14.04 LTS on ARM 7.1
> Reporter: Michael Kamprath
> Priority: Critical
>
> When I create a data frame in PySpark with a small row count (less than
> number executors), then write it to a parquet file, then load that parquet
> file into a new data frame, and finally do any sort of read against the
> loaded new data frame, Spark fails with an {{ExecutorLostFailure}}.
> Example code to replicate this issue:
> {code}
> from pyspark.sql.types import *
> rdd = sc.parallelize([('row1',1,4.33,'name'),('row2',2,3.14,'string')])
> my_schema = StructType([
> StructField("id", StringType(), True),
> StructField("value1", IntegerType(), True),
> StructField("value2", DoubleType(), True),
> StructField("name",StringType(), True)
> ])
> df = spark.createDataFrame( rdd, schema=my_schema)
> df.write.parquet('hdfs://master:9000/user/michael/test_data',mode='overwrite')
> newdf = spark.read.parquet('hdfs://master:9000/user/michael/test_data/')
> newdf.take(1)
> {code}
> The error I get when the {{take}} step runs is:
> {code}
> Py4JJavaError: An error occurred while calling o54.collectToPython.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0
> (TID 8, 10.10.10.4): ExecutorLostFailure (executor 0 exited caused by one of
> the running tasks) Reason: Remote RPC client disassociated. Likely due to
> containers exceeding thresholds, or network issues. Check driver logs for
> WARN messages.
> 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:1873)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2526)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2523)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2546)
> at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2523)
> 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)
> {code}
> I have tested this against HDFS 2.7, local file system, and QFS 1.2. All have
> the same results.
> I generally discovered this when processing larger files that have individual
> parquet part files with a single row in them. The same problem manifested
> then.
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