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 (“Monitoring” ), 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:
Most of the benchmarks show usage instructions when invoked with no arguments. For example:
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 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:
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):
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 called io_data.To run a read benchmark, use the -read argument. Note that these files must already exist (having been written by Test DFSIO -write):
Here are the results for a real run:
When you’ve finished benchmarking, you can delete all the generated files from HDFS using the -clean argument:
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) 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.random writer.maps_per_host and test.random write.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 directory called random-data:
Next we can run the Sort program:
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. Adjusting the parameters mentioned in “Tuning a Job” is a useful exercise, too.
As a final sanity check, we validate that the data in sorted-data is, in fact, correctly sorted:
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.
There are many more Hadoop benchmarks, but the following are widely used:
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.
Hadoop Related Interview Questions
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The Hadoop Distributed Filesystem
Developing A Mapreduce Application
How Mapreduce Works
Mapreduce Types And Formats
Setting Up A Hadoop Cluster
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