Table of Contents

We use Apache Spark 2.2.1 to streaming frameworks.

Hadoop Configuration

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Hadoop is used to generate the input data of the workloads. Create and edit conf/hadoop.conf：

Code Block

language	cpp

cp conf/hadoop.conf.template conf/hadoop.conf

Set the below properties properly:

Property	Meaning
hibench.hadoop.home	/root/hadoop
hibench.hadoop.executable	${hibench.hadoop.home}/bin/hadoop
hibench.hadoop.configure.dir	${hibench.hadoop.home}/etc/hadoop
hibench.hdfs.master	hdfs://wls-arm-huawei01:9000
hibench.hadoop.release	apache, cdh5, hdp

Note: For CDH and HDP users, please update hibench.hadoop.executable, hibench.hadoop.configure.dir and hibench.hadoop.release properly.

The default value is for Apache release.

Kafka Configuration

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Set the below Kafka properites in conf/hibench.conf and leave others as default.

Property	Meaning
hibench.streambench.kafka.home	/root/kafka_2.10-0.8.2.2
hibench.streambench.zkHost	wls-arm-huawei01:2181
hibench.streambench.kafka.brokerList	wls-arm-huawei01:9092
hibench.streambench.kafka.topicPartitions	Number of partitions of generated topic (default 20)

Generate the data

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Take workload identity as an example. genSeedDataset.sh generates the seed data on HDFS. dataGen.sh sends the data to Kafka.

Code Block

language	bash

bin/workloads/streaming/identity/prepare/genSeedDataset.sh
bin/workloads/streaming/identity/prepare/dataGen.sh

Run the streaming application

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While the data are being sent to the Kafka, start the streaming application. Take Spark streaming as an example.

Code Block

language	bash

bin/workloads/streaming/identity/spark/run.sh

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Generate the report

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metrics_reader.sh is used to generate the report.

Code Block

language	bash

bin/workloads/streaming/identity/common/metrics_reader.sh

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Debug for running Streaming bench

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when running the streaming application:

Code Block

language	bash

bin/workloads/streaming/identity/spark/run.sh

Then 2 kinds error occursThere are some issues as follows:

streaming identity run into ERROR CheckpointWriter: Could not submit checkpoint task to the thread pool executor

Code Block

language	bash

root@wls-arm-huawei01:~/HiBench# bin/workloads/streaming/identity/spark/run.sh
patching args=
Parsing conf: /root/HiBench/conf/hadoop.conf
Parsing conf: /root/HiBench/conf/hibench.conf
Parsing conf: /root/HiBench/conf/spark.conf
Parsing conf: /root/HiBench/conf/workloads/streaming/identity.conf
probe sleep jar: /root/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.1-tests.jar
start SparkStreamingIdentity bench
Export env: SPARKBENCH_PROPERTIES_FILES=/root/HiBench/report/identity/spark/conf/sparkbench/sparkbench.conf
Export env: HADOOP_CONF_DIR=/root/hadoop/etc/hadoop
Submit Spark job: /root/spark/bin/spark-submit  --properties-file /root/HiBench/report/identity/spark/conf/sparkbench/spark.conf --class com.intel.hibench.sparkbench.streaming.RunBench --master yarn-client --num-executors 2 --executor-cores 4 --executor-memory 6g /root/HiBench/sparkbench/assembly/target/sparkbench-assembly-7.1-SNAPSHOT-dist.jar /root/HiBench/report/identity/spark/conf/sparkbench/sparkbench.conf
Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" with specified deploy mode instead.
metrics is being written to kafka topic SPARK_identity_1_5_50_1528365200903
Reporter Topic: SPARK_identity_1_5_50_1528365200903
18/06/07 17:53:20 INFO zkclient.ZkEventThread: Starting ZkClient event thread.
18/06/07 17:53:20 INFO zookeeper.ZooKeeper: Client environment:zookeeper.version=3.4.6-1569965, built on 02/20/2014 09:09 GMT
18/06/07 17:53:20 INFO zookeeper.ZooKeeper: Client environment:host.name=wls-x86-hp05
18/06/07 17:53:20 INFO zookeeper.ZooKeeper: Client environment:java.version=1.8.0_171
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18/06/07 17:53:34 INFO cluster.YarnClientSchedulerBackend: Application application_1528275127402_0017 has started running.
18/06/07 17:53:34 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 45199.
18/06/07 17:53:34 INFO netty.NettyBlockTransferService: Server created on 10.169.41.13:45199
18/06/07 17:53:34 INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
18/06/07 17:53:34 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 10.169.41.13, 45199, None)
18/06/07 17:53:34 INFO storage.BlockManagerMasterEndpoint: Registering block manager 10.169.41.13:45199 with 3.0 GB RAM, BlockManagerId(driver, 10.169.41.13, 45199, None)
18/06/07 17:53:34 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.169.41.13, 45199, None)
18/06/07 17:53:34 INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(driver, 10.169.41.13, 45199, None)
18/06/07 17:53:35 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3fb450d7{/metrics/json,null,AVAILABLE,@Spark}
18/06/07 17:53:37 INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.169.41.13:43928) with ID 1
18/06/07 17:53:37 INFO storage.BlockManagerMasterEndpoint: Registering block manager wls-x86-hp05:44621 with 3.0 GB RAM, BlockManagerId(1, wls-x86-hp05, 44621, None)
18/06/07 17:53:52 INFO cluster.YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after waiting maxRegisteredResourcesWaitingTime: 30000(ms)
java.util.concurrent.RejectedExecutionException: Task org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler@76efdbf9 rejected from java.util.concurrent.ThreadPoolExecut...
        at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2063)
        at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:830)
        at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1379)
        at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:287)
        at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:297)
        at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:186)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
java.util.concurrent.RejectedExecutionException: Task org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler@54d5eb6a rejected from java.util.concurrent.ThreadPoolExecut...
        at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2063)
        at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:830)
        at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1379)
        at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:287)
        at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:297)
        at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:186)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
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The

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solutions:

Turn off checkpointing and it worked:

Code Block

language	bash

hibench.streambench.spark.checkpointPath false

Set "conf/spark.conf:hibench.streambench.spark.batchInterval" to a larger value and it worked.

2. process Hangs when generating data Process of generating data hangs

The

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solutions:

Modify conf/hibench.conf to make data-generating not in infinity mode:

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Arch	count	throughput(msgs/s)	max_latency(ms)	mean_latency(ms)	min_latency(ms)	stddev_latency(ms)	p50_latency(ms)	p75_latency(ms)	p95_latency(ms)	p98_latency(ms)	p99_latency(ms)	p999_latency(ms)
Arm64	2015	99	2339	338.531	54	437.862	210	307	1579	1985.32	2148	2338
X86	6115	100	2269	162.566	25	349.292	80	87	974	1687.68	1817	2220

Repartition:

Arch	count	throughput(msgs/s)	max_latency(ms)	mean_latency(ms)	min_latency(ms)	stddev_latency(ms)	p50_latency(ms)	p75_latency(ms)	p95_latency(ms)	p98_latency(ms)	p99_latency(ms)	p999_latency(ms)
Arm64	2160	99	2896	458.714	89	573.476	257	351	1961.55	2447.12	2678.02	2885.433
X86	2930	99	2720	569.22	73	828.736	132	503	2480.45	2549.38	2573.07	2674.243

Wordcount:

Arch	count	throughput(msgs/s)	max_latency(ms)	mean_latency(ms)	min_latency(ms)	stddev_latency(ms)	p50_latency(ms)	p75_latency(ms)	p95_latency(ms)	p98_latency(ms)	p99_latency(ms)	p999_latency(ms)
Arm64	4145	99	3495	967.401	48	844.909	743	1482.5	2695.7	3005.36	3134.54	3445.854
X86	5674	92	4825	3879.146	2534	428.128	3901	4171.25	4586	4682	4731.5	4818

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Versions Compared

Old Version 1

New Version 2

Key

Hadoop Configuration

Set the below properties properly:

Kafka Configuration

Generate the data

Run the streaming application

Generate the report

Debug for running Streaming bench

The

solutions:

The

solutions:

Repartition:

Wordcount:

Page Comparison

Versions Compared

Old Version 1

New Version 2

Key

Hadoop Configuration

Set the below properties properly:

Kafka Configuration

Generate the data

Run the streaming application

Generate the report

Debug for running Streaming bench

The

solutions:

The

solutions:

Repartition:

Wordcount: