Dear Alexey,
I have tried to use the following settings:
Map<String, Object> map = ImmutableMap.<String, Object>builder()
.put("topic", (Object)"kafkasink2")
.put("group.id", (Object)"test-consumer-group")
.put("partition.assignment.strategy",
(Object)"org.apache.kafka.clients.consumer.RangeAssignor")
.put("enable.auto.commit", (Object)"true")
.put("auto.offset.reset", (Object)"earliest")
.put("max.poll.records", (Object)"100000")
.build();
PCollection<KV<Long, String>> readKafkaData = p.apply(KafkaIO.<Long,
String>read()
.withBootstrapServers(KafkaProperties.KAFKA_SERVER_URL)
.withTopic("kafkasink2")
.updateConsumerProperties(map)
.withKeyDeserializer(LongDeserializer.class)
.withValueDeserializer(StringDeserializer.class)
.withoutMetadata()
);
The experimental results are shown as the following figure:
[cid:[email protected]]
From the figure, it doesn't work for my project.
When we use the suggestion from @Juan Carlos Garcia
“You can limit your Spark processing by passing the following option to your
beam pipeline: MaxRecordsPerBatch”
That does work for us.
Thanks for your idea.
Rick
From: Alexey Romanenko [mailto:[email protected]]
Sent: Wednesday, January 30, 2019 2:10 AM
To: [email protected]
Subject: Re: kafkaIO Consumer Rebalance with Spark Runner
Rick,
I think “spark.streaming.kafka.maxRatePerPartition” won’t work for you since,
afaik, it’s a configuration option of Spark Kafka reader and Beam KafkaIO
doesn’t use it (since it has own consumer implementation).
In the same time, if you want to set an option for Beam KafkaIO consumer config
then you should use "updateConsumerProperties()” method.
On 28 Jan 2019, at 10:56, <[email protected]<mailto:[email protected]>>
<[email protected]<mailto:[email protected]>> wrote:
Dear Raghu,
I add the line: “PCollection<Integer> reshuffled =
windowKV.apply(Reshuffle.viaRandomKey());” in my program.
I tried to control the streaming data size: 100,000/1sec to decrease the
processing time.
The following settings are used for my project.
1. One topic / 2 partitions
<image004.jpg>
2. Two workers / two executors
3. The spark-default setting is:
spark.executor.instances=2
spark.executor.cores=4
spark.executor.memory=2048m
spark.default.parallelism=200
spark.streaming.blockInterval=50ms
spark.streaming.kafka.maxRatePerPartition=50,000
spark.streaming.backpressure.enabled=true
spark.streaming.concurrentJobs = 1
spark.executor.extraJavaOptions=-XX:+UseConcMarkSweepGC
spark.executor.extraJavaOptions=-Xss100M
spark.shuffle.consolidateFiles=true
spark.streaming.unpersist=true
spark.streaming.stopGracefullyOnShutdown=true
I hope that the data size is controlled at 100,000.
Here,
<image005.jpg>
The data size is always over 100,000. The setting of
“spark.streaming.kafka.maxRatePerPartition” confused me.
That does not seem to work for me.
Rick
From: Raghu Angadi [mailto:[email protected]]
Sent: Saturday, January 26, 2019 3:06 AM
To: [email protected]<mailto:[email protected]>
Subject: Re: kafkaIO Consumer Rebalance with Spark Runner
You have 32 partitions. Reading can not be distributed to more than 32 parallel
tasks.
If you have a log of processing for each record after reading, you can
reshuffle the messages before processing them, that way the processing could be
distributed to more tasks. Search for previous threads about reshuffle in Beam
lists.
On Thu, Jan 24, 2019 at 7:23 PM
<[email protected]<mailto:[email protected]>> wrote:
Dear all,
I am using the kafkaIO sdk in my project (Beam with Spark runner).
The problem about task skew is shown as the following figure.
<image001.jpg>
My running environment is:
OS: Ubuntn 14.04.4 LTS
The version of related tools is:
java version: "1.8.0_151"
Beam version: 2.9.0 (Spark runner with Standalone mode)
Spark version: 2.3.1 Standalone mode
Execution condition:
Master/Driver node: ubuntu7
Worker nodes: ubuntu8 (4 Executors); ubuntu9 (4 Executors)
The number of executors is 8
Kafka Broker: 2.10-0.10.1.1
Broker node at ubuntu7
Kafka Client:
The topic: kafkasink32
kafkasink32 Partitions: 32
The programming of my project for kafkaIO SDK is as:
==============================================================================
Map<String, Object> map = ImmutableMap.<String, Object>builder()
.put("group.id<http://group.id/>", (Object)"test-consumer-group")
.build();
List<TopicPartition> topicPartitions = new ArrayList();
for(int i = 0; i < 32; i++) {
topicPartitions.add(new TopicPartition("kafkasink32",i));
}
PCollection<KV<Long, String>> readKafkaData = p.apply(KafkaIO.<Long,
String>read()
.withBootstrapServers("ubuntu7:9092")
.updateConsumerProperties(map)
.withKeyDeserializer(LongDeserializer.class)
.withValueDeserializer(StringDeserializer.class)
.withTopicPartitions(topicPartitions)
.withoutMetadata()
);
==============================================================================
Here I have two directions to solve this problem:
1. Using the following sdk from spark streaming
https://jaceklaskowski.gitbooks.io/spark-streaming/spark-streaming-kafka-LocationStrategy.html
LocationStrategies.PreferConsistent: Use in most cases as it consistently
distributes partitions across all executors.
If we would like to use this feature, we have not idea to set this in kafkaIO
SDK.
2. Setting the related configurations of kafka to perform the consumer
rebalance
set consumer group? Set group.id<http://group.id/>?
If we need to do No2., could someone give me some ideas to set configurations?
If anyone provides any direction to help us to overcome this problem, we would
appreciate it.
Thanks.
Sincerely yours,
Rick
--
本信件可能包含工研院機密資訊，非指定之收件者，請勿使用或揭露本信件內容，並請銷毀此信件。 This email may contain
confidential information. Please do not use or disclose it in any way and
delete it if you are not the intended recipient.
--
本信件可能包含工研院機密資訊，非指定之收件者，請勿使用或揭露本信件內容，並請銷毀此信件。 This email may contain
confidential information. Please do not use or disclose it in any way and
delete it if you are not the intended recipient.
--
本信件可能包含工研院機密資訊，非指定之收件者，請勿使用或揭露本信件內容，並請銷毀此信件。 This email may contain
confidential information. Please do not use or disclose it in any way and
delete it if you are not the intended recipient.
