Is the cluster set up correctly? The best way to answer this question is empirically: run some jobs and confirm that you get the expected results. Benchmarks make good tests, as you also get numbers that you can compare with other clusters as a sanity check on whether your new cluster is performing roughly as expected. And you can tune a cluster using benchmark results to squeeze the best performance out of it. This is often done with monitoring systems in place, so you can see how resources are being used across the cluster.
To get the best results, you should run benchmarks on a cluster that is not being used by others. In practice, this is just before it is put into service, and users start relying on it. Once users have periodically scheduled jobs on a cluster it is generally impossible to find a time when the cluster is not being used (unless you arrange downtime with users), so you should run benchmarks to your satisfaction before this happens.
Experience has shown that most hardware failures for new systems are hard drive failures. By running I/O intensive benchmarks—such as the ones described next—you can “burn in” the cluster before it goes live.
Hadoop comes with several benchmarks that you can run very easily with minimal setup cost. Benchmarks are packaged in the test JAR file, and you can get a list of them, with descriptions, by invoking the JAR file with no arguments:
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar
Most of the benchmarks show usage instructions when invoked with no arguments. For example:
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIOTestFDSIO.0.0.4 Usage: TestFDSIO -read | -write | -clean [-nrFiles N] [-fileSize MB] [-resFile resultFileName] [-bufferSize Bytes]
TestDFSIO tests the I/O performance of
HDFS. It does this by using a MapReduce job as a convenient way to
read or write files in parallel. Each file is read or written in a
separate map task, and the output of the map is used for collecting
statistics relating to the file just processed. The statistics are
accumulated in the reduce, to produce a summary.
The following command writes 10 files of 1,000 MB each:
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -write -nrFiles 10
At the end of the run, the results are written to the console and also recorded in a local file (which is appended to, so you can rerun the benchmark and not lose old results):
cat TestDFSIO_results.log----- TestDFSIO ----- : write Date & time: Sun Apr 12 07:14:09 EDT 2009 Number of files: 10 Total MBytes processed: 10000 Throughput mb/sec: 7.796340865378244 Average IO rate mb/sec: 7.8862199783325195 IO rate std deviation: 0.9101254683525547 Test exec time sec: 163.387
The files are written under the
/benchmarks/TestDFSIO directory by default
(this can be changed by setting the
test.build.data system property), in a directory
To run a read benchmark, use the
argument. Note that these files must already exist (having been
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -read -nrFiles 10
Here are the results for a real run:
----- TestDFSIO ----- : read Date & time: Sun Apr 12 07:24:28 EDT 2009 Number of files: 10 Total MBytes processed: 10000 Throughput mb/sec: 80.25553361904304 Average IO rate mb/sec: 98.6801528930664 IO rate std deviation: 36.63507598174921 Test exec time sec: 47.624
When you’ve finished benchmarking, you can delete all the
generated files from HDFS using the
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -clean
Hadoop comes with a MapReduce program that does a partial sort of its input. It is very useful for benchmarking the whole MapReduce system, as the full input dataset is transferred through the shuffle. The three steps are: generate some random data, perform the sort, then validate the results.
First we generate some random data using
RandomWriter. It runs a MapReduce job with 10
maps per node, and each map generates (approximately) 10 GB of
random binary data, with key and values of various sizes. You can
change these values if you like by setting the properties
test.randomwrite.bytes_per_map. There are also
settings for the size ranges of the keys and values; see
RandomWriter for details.
Here’s how to invoke
RandomWriter (found in
the example JAR file, not the test one) to write its output to a
hadoop jar $HADOOP_INSTALL/hadoop-*-examples.jar randomwriter random-data
Next we can run the
hadoop jar $HADOOP_INSTALL/hadoop-*-examples.jar sort random-data sorted-data
The overall execution time of the sort is the metric we are
interested in, but it’s instructive to watch the job’s progress via
the web UI
where you can get a feel for how long each phase of the job takes.
As a final sanity check, we validate the data in
sorted-data is, in fact, correctly
hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar testmapredsort -sortInput random-data \
This command runs the
program, which performs a series of checks on the unsorted and
sorted data to check whether the sort is accurate. It reports the
outcome to the console at the end of its run:
SUCCESS! Validated the MapReduce framework's 'sort' successfully.
mrbench) runs a small job a number of times. It acts as a good counterpoint to sort, as it checks whether small job runs are responsive.
nnbench) is useful for load testing namenode hardware.
Gridmix is a suite of benchmarks designed to model a realistic cluster workload, by mimicking a variety of data-access patterns seen in practice. See
src/benchmarks/gridmix2in the distribution for further details.
For tuning, it is best to include a few jobs that are representative of the jobs that your users run, so your cluster is tuned for these and not just for the standard benchmarks. If this is your first Hadoop cluster and you don’t have any user jobs yet, then Gridmix is a good substitute.
When running your own jobs as benchmarks you should select a dataset for your user jobs that you use each time you run the benchmarks to allow comparisons between runs. When you set up a new cluster, or upgrade a cluster, you will be able to use the same dataset to compare the performance with previous runs.
Learn more about this topic from Hadoop: The Definitive Guide.
Apache Hadoop is ideal for organizations with a growing need to process massive application datasets. Hadoop: The Definitive Guide is a comprehensive resource for using Hadoop to build reliable, scalable, distributed systems. Programmers will find details for analyzing large datasets with Hadoop, and administrators will learn how to set up and run Hadoop clusters. The book includes case studies that illustrate how Hadoop is used to solve specific problems.