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Posted to issues@spark.apache.org by "Gabor Somogyi (JIRA)" <ji...@apache.org> on 2018/12/12 08:50:00 UTC
[jira] [Commented] (SPARK-23636) [SPARK 2.2] | Kafka Consumer |
KafkaUtils.createRDD throws Exception -
java.util.ConcurrentModificationException: KafkaConsumer is not safe for
multi-threaded access
[ https://issues.apache.org/jira/browse/SPARK-23636?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16718622#comment-16718622 ]
Gabor Somogyi commented on SPARK-23636:
---------------------------------------
[~mcdeepak] It should be resolved in SPARK-19185 with 2.4.0 (even if it's different API). Can you re-test it please?
> [SPARK 2.2] | Kafka Consumer | KafkaUtils.createRDD throws Exception - java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-23636
> URL: https://issues.apache.org/jira/browse/SPARK-23636
> Project: Spark
> Issue Type: Bug
> Components: DStreams
> Affects Versions: 2.1.1, 2.2.0
> Reporter: Deepak
> Priority: Major
> Labels: performance
>
> h2.
> h2. Summary
>
> While using the KafkaUtils.createRDD API - we receive below listed error, specifically when 1 executor connects to 1 kafka topic-partition, but with more than 1 core & fetches an Array(OffsetRanges)
>
> _I've tagged this issue to "Structured Streaming" - as I could not find a more appropriate component_
>
> ----
> h2. Error Faced
> {noformat}
> java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access{noformat}
> Stack Trace
> {noformat}
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 1.0 failed 4 times, most recent failure: Lost task 5.3 in stage 1.0 (TID 17, host, executor 16): java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access
> at org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:1629)
> at org.apache.kafka.clients.consumer.KafkaConsumer.close(KafkaConsumer.java:1528)
> at org.apache.kafka.clients.consumer.KafkaConsumer.close(KafkaConsumer.java:1508)
> at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.close(CachedKafkaConsumer.scala:59)
> at org.apache.spark.streaming.kafka010.CachedKafkaConsumer$.remove(CachedKafkaConsumer.scala:185)
> at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.<init>(KafkaRDD.scala:204)
> at org.apache.spark.streaming.kafka010.KafkaRDD.compute(KafkaRDD.scala:181)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323){noformat}
>
> ----
> h2. Config Used to simulate the error
> A session with :
> * Executors - 1
> * Cores - 2 or More
> * Kafka Topic - has only 1 partition
> * While fetching - More than one Array of Offset Range , Example
> {noformat}
> Array(OffsetRange("kafka_topic",0,608954201,608954202),
> OffsetRange("kafka_topic",0,608954202,608954203)
> ){noformat}
>
> ----
> h2. Was this approach working before?
>
> This was working in spark 1.6.2
> However, from spark 2.1 onwards - the approach throws exception
>
> ----
> h2. Why are we fetching from kafka as mentioned above.
>
> This gives us the capability to establish a connection to Kafka Broker for every spark executor's core, thus each core can fetch/process its own set of messages based on the specified (offset ranges).
>
>
> ----
> h2. Sample Code
>
> {quote}scala snippet - on versions spark 2.2.0 or 2.1.0
> // Bunch of imports
> import kafka.serializer.\{DefaultDecoder, StringDecoder}
> import org.apache.avro.generic.GenericRecord
> import org.apache.kafka.clients.consumer.ConsumerRecord
> import org.apache.kafka.common.serialization._
> import org.apache.spark.rdd.RDD
> import org.apache.spark.sql.\{DataFrame, Row, SQLContext}
> import org.apache.spark.sql.Row
> import org.apache.spark.sql.hive.HiveContext
> import org.apache.spark.sql.types.\{StringType, StructField, StructType}
> import org.apache.spark.streaming.kafka010._
> import org.apache.spark.streaming.kafka010.KafkaUtils._
> {quote}
> {quote}// This forces two connections - from a single executor - to topic-partition <kafka_topic-0>.
> // And with 2 cores assigned to 1 executor : each core has a task - pulling respective offsets : OffsetRange("kafka_topic",0,1,2) & OffsetRange("kafka_topic",0,2,3)
> val parallelizedRanges = Array(OffsetRange("kafka_topic",0,1,2), // Fetching sample 2 records
> OffsetRange("kafka_topic",0,2,3) // Fetching sample 2 records
> )
>
> // Initiate kafka properties
> val kafkaParams1: java.util.Map[String, Object] = new java.util.HashMap()
> // kafkaParams1.put("key","val") add all the parameters such as broker, topic.... Not listing every property here.
>
> // Create RDD
> val rDDConsumerRec: RDD[ConsumerRecord[String, String]] =
> createRDD[String, String](sparkContext
> , kafkaParams1, parallelizedRanges, LocationStrategies.PreferConsistent)
>
> // Map Function
> val data: RDD[Row] = rDDConsumerRec.map \{ x => Row(x.topic().toString, x.partition().toString, x.offset().toString, x.timestamp().toString, x.value() ) }
>
> // Create a DataFrame
> val df = sqlContext.createDataFrame(data, StructType(
> Seq(
> StructField("topic", StringType),
> StructField("partition", StringType),
> StructField("offset", StringType),
> StructField("timestamp", StringType),
> StructField("value", BinaryType)
> )))
>
> df.show() // You will see the error reported.
> {quote}
>
> ----
>
> h2. Similar Issue reported earlier, but on a different API
>
> A similar issue reported for DirectStream is
> https://issues.apache.org/jira/browse/SPARK-19185
>
>
>
> ----
> h2. What is the impact - if a fix is not available for this problem?
>
>
> We have a lot of Spark Applications that are running in production, making parallel connections to the 1 topic-partition from each spark-executor: so parallelism is directly proportional to the num-cores in each executor.
> With spark 2.1 onwards : we are not allowed to make concurrent connections from 1 executor to 1 topic-partition. Only workaround is to start our applications with executor-cores = 1, with dynamic resource allocation enabled.
> With above configuration - for every offset range we ask kafka - a new executor is spawned to run the fetch task.
> Downside of Workaround -
> Above approach is not allowing us to leverage more than 1 spark-core per spark-executor.
> And asking for an executor - for each offset range - is costly : in terms of scheduling and allocation.
>
>
>
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