Sorry for not writing the patch number, it's spark 1.6.1.
The relevant code is here inline.
Please have a look and let me know if there is a resource leak.
Please also let me know if you need any more details.
Thanks
Nipun
The JavaRDDKafkaWriter code is here inline:
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.VoidFunction;
import scala.Tuple2;
import java.io.Serializable;
import java.util.Iterator;
public class JavaRDDStringKafkaWriter implements Serializable,
VoidFunction<JavaRDD<String>> {
private static final long serialVersionUID = -865193912367180261L;
private final KafkaProducerPool pool;
private final String topic;
private final Boolean kafkaAsync;
public JavaRDDStringKafkaWriter(final KafkaProducerPool pool,
String topic, Boolean kafkaAsync) {
this.pool = pool;
this.topic = topic;
this.kafkaAsync = kafkaAsync;
}
@Override
public void call(JavaRDD<String> stringJavaRDD) throws Exception {
stringJavaRDD.foreachPartition(new PartitionVoidFunction(
new RDDKafkaWriter(pool,kafkaAsync), topic));
}
private class PartitionVoidFunction implements
VoidFunction<Iterator<String>> {
private static final long serialVersionUID = 8726871215617446598L;
private final RDDKafkaWriter kafkaWriter;
private final String topic;
public PartitionVoidFunction(RDDKafkaWriter kafkaWriter, String topic) {
this.kafkaWriter = kafkaWriter;
this.topic = topic;
}
@Override
public void call(Iterator<String> iterator) throws Exception {
while (iterator.hasNext()) {
kafkaWriter.writeToKafka(topic, iterator.next());
}
}
}
}
The RDDKafkaWriter is here:
import java.io.Serializable;
import java.util.concurrent.ExecutionException;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import scala.Tuple2;
public class RDDKafkaWriter implements Serializable {
private static final long serialVersionUID = 7374381310562055607L;
private final KafkaProducerPool pool;
private final Boolean async;
public RDDKafkaWriter(final KafkaProducerPool pool, Boolean async) {
this.pool = pool;
this.async = async;
}
public void writeToKafka(String topic, Tuple2<String, String> message) {
KafkaProducer<String, String> producer = pool.borrowProducer();
ProducerRecord<String, String> record = new
ProducerRecord<String, String>(
topic, message._1(), message._2());
if (async) {
producer.send(record);
} else {
try {
producer.send(record).get();
} catch (Exception e) {
e.printStackTrace();
}
}
pool.returnProducer(producer);
}
public void writeToKafka(String topic, String message) {
KafkaProducer<String, String> producer = pool.borrowProducer();
ProducerRecord<String, String> record = new
ProducerRecord<String, String>(topic, message);
if (async) {
producer.send(record);
} else {
try {
producer.send(record).get();
} catch (Exception e) {
e.printStackTrace();
}
}
pool.returnProducer(producer);
}
}
On Tue, Jan 31, 2017 at 5:20 PM Shixiong(Ryan) Zhu <shixi...@databricks.com>
wrote:
> Please also include the patch version, such as 1.6.0, 1.6.1. Could you
> also post the JAVARDDKafkaWriter codes. It's also possible that it leaks
> resources.
>
> On Tue, Jan 31, 2017 at 2:12 PM, Nipun Arora <nipunarora2...@gmail.com>
> wrote:
>
> It is spark 1.6
>
> Thanks
> Nipun
>
> On Tue, Jan 31, 2017 at 1:45 PM Shixiong(Ryan) Zhu <
> shixi...@databricks.com> wrote:
>
> Could you provide your Spark version please?
>
> On Tue, Jan 31, 2017 at 10:37 AM, Nipun Arora <nipunarora2...@gmail.com>
> wrote:
>
> Hi,
>
> I get a resource leak, where the number of file descriptors in spark
> streaming keeps increasing. We end up with a "too many file open" error
> eventually through an exception caused in:
>
> JAVARDDKafkaWriter, which is writing a spark JavaDStream<String>
>
> The exception is attached inline. Any help will be greatly appreciated.
>
> Thanks
> Nipun
>
> -------------------------------------------
> Time: 1485762530000 ms
> -------------------------------------------
>
> Exception in thread "main" org.apache.spark.SparkException: Job aborted
> due to stage failure: Task 0 in stage 85968.0 failed 1 times, most recent
> failure: Lost task 0.0 in stage 85968.0 (TID 29562, localhost):
> java.io.FileNotFoundException:
> /tmp/blockmgr-1b3ddc44-f9a4-42cd-977c-532cb962d7d3/3e/shuffle_10625_0_0.data.4651a131-6072-460b-b150-2b3080902084
> (too many open files)
> at java.io.FileOutputStream.open(Native Method)
> at java.io.FileOutputStream.<init>(FileOutputStream.java:221)
> at
> org.apache.spark.storage.DiskBlockObjectWriter.open(DiskBlockObjectWriter.scala:88)
> at
> org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:181)
> at
> org.apache.spark.util.collection.WritablePartitionedPairCollection$$anon$1.writeNext(WritablePartitionedPairCollection.scala:56)
> at
> org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:659)
> at
> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:72)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
>
> Driver stacktrace:
> at org.apache.spark.scheduler.DAGScheduler.org
> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1857)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1870)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1883)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1954)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
> at
> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
> at
> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
> at
> org.necla.ngla.kafka.JavaRDDStringKafkaWriter.call(JavaRDDStringKafkaWriter.java:25)
> at
> org.necla.ngla.kafka.JavaRDDStringKafkaWriter.call(JavaRDDStringKafkaWriter.java:10)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at scala.util.Try$.apply(Try.scala:161)
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:229)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:229)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:229)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:228)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.io.FileNotFoundException:
> /tmp/blockmgr-1b3ddc44-f9a4-42cd-977c-532cb962d7d3/3e/shuffle_10625_0_0.data.4651a131-6072-460b-b150-2b3080902084
> (too many open files)
> at java.io.FileOutputStream.open(Native Method)
> at java.io.FileOutputStream.<init>(FileOutputStream.java:221)
> at
> org.apache.spark.storage.DiskBlockObjectWriter.open(DiskBlockObjectWriter.scala:88)
> at
> org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:181)
> at
> org.apache.spark.util.collection.WritablePartitionedPairCollection$$anon$1.writeNext(WritablePartitionedPairCollection.scala:56)
> at
> org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:659)
> at
> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:72)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> ... 3 more
>
>
>
>
