57 lines
2.3 KiB
Markdown
57 lines
2.3 KiB
Markdown
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monasca-persister performance benchmarking
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=============
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This tool benchmarkes the Monasca Persister performance and throughput.
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It uses JMeter and Kafka plugin to initiate Kafka metric messages and
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query the Dropwizard rest api to retrieve perister processing metrics.
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Because the Monasca persister python implementation does not have
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similar internal processing metric rest api available, the support
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for python implementation performance benchmark will be added in a
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future release.
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# Install
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1. Download and install the latest Apache JMeter from
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http://jmeter.apache.org/download_jmeter.cgi.
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2. Add JMeter bin directory to the path, for example,
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PATH=$PATH:/opt/apache-jmeter/bin.
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3. Clone KafkaMeter repository: https://github.com/BrightTag/kafkameter.
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4. Run Maven package and install Kafkameter jar to $JMETER_HOME/lib/ext
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folder under the Apache JMeter installation home.
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# Configure
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1. Make a copy of the jmeter_test_plan.jmx file and modify the test plan
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to fit your goal. The available options are:
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- The number of threads(users)
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- The number of loops, i.e., the number of metric messages each thread/user
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will create)
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- The range of the random values in the metric name and dimension values.
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This controls the number of unique metric definitions the test plan will
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create.
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The total number of metrics = the number of threads(users) * loop count.
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# Run test
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1. Execute persister_perf.sh, for example,
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```
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./persister_perf.sh -t jmeter_test_plan.jmx -n 1000000 -s 192.168.1.5,192.168.1.6 -p 8091 -w 10
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-n the expected number of metric messages (equals to the number of threads(users) * loop count.
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-s a comma separated list of the Monasca Persister server hosts in the cluster
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-p Monasca Persister server port number
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-w the time to wait for before checking the processing status from the Monasca persister next time
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```
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2. For each test run, an output folder (postfixed by the timestamp) is
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created. You can monitor the log file to watch the progress of jmeter
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sending messages, Monasca persister reading messages as well as
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Persister flushing the metrics into the time series database. It
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includes the accumulated number of metrics created, read and flushed
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and snapshots of throughput in each check interval.
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3. The output file contains the summary of the performance testing result.
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