Benchmarking a Hadoop Cluster

Benchmarks make good tests because 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.


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 scheduled periodic jobs on a cluster, it is generally impossible to find a time when the cluster is not being used.


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 TestDFSIO 

TestFDSIO.0.0.4
Usage: TestFDSIO -read | -write | -clean [-nrFiles N] [-fileSize MB] [-resFile
resultFileName] [-bufferSize Bytes]


Benchmarking HDFS with TestDFSIO

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 related 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
-fileSize 1000


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


To run a read benchmark, use the -read argument. Note that these files must already
exist (having been written by TestDFSIO -write):

% hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -read -nrFiles 10
-fileSize 1000

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 -clean argument:

% hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -clean

Benchmarking MapReduce with Sort

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) 1 GB of random binary data, with keys and values of various sizes.



Here’s how to invoke RandomWriter (found in the example JAR file, not the test one) to write its output to a directory called random-data:

% hadoop jar $HADOOP_INSTALL/hadoop-*-examples.jar randomwriter random-data

Next, we can run the Sort program:

% 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 (http://jobtracker-host:50030/), where you can get a feel for how long each phase of the job takes.


sanity check, we validate that the data in sorted-data is, in fact, correctly sorted:

% hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar testmapredsort -sortInput random-data \
-sortOutput sorted-data

This command runs the SortValidator 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 (invoked with 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 (invoked with 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 the documentation
in the distribution for how to run Gridmix