Hadoop cluster benchmarks

To do bench marks of Hadoop cluster is an ongoing process as we use it inside the organization.

The main thing which we don't know when we buy new cluster is how this new power house of machine will behave for various different sets of workloads.

Intel who is also working on its own flavor of hadoop has product to do Benchmark of cluster performance against different types of workloads.

https://github.com/intel-hadoop/HiBench/blob/master/README.md

 

Micro Benchmarks:

1. Sort (sort)

   This workload sorts its text input data, which is generated using the Hadoop RandomTextWriter example.

2. WordCount (wordcount)

   This workload counts the occurrence of each word in the input data, which are generated using the Hadoop RandomTextWriter example. It is representative of another typical class of real world MapReduce jobs - extracting a small amount of interesting data from large data set.

3. TeraSort (terasort)

   TeraSort is a standard benchmark created by Jim Gray. Its input data is generated by Hadoop TeraGen example program.

   HDFS Benchmarks:

4. enhanced DFSIO (dfsioe)

   Enhanced DFSIO tests the HDFS throughput of the Hadoop cluster by generating a large number of tasks performing writes and reads simultaneously. It measures the average I/O rate of each map task, the average throughput of each map task, and the aggregated throughput of HDFS cluster.

   Web Search Benchmarks:

5. Nutch indexing (nutchindexing)

   Large-scale search indexing is one of the most significant uses of MapReduce. This workload tests the indexing sub-system in Nutch, a popular open source (Apache project) search engine. The workload uses the automatically generated Web data whose hyperlinks and words both follow the Zipfian distribution with corresponding parameters. The dict used to generate the Web page texts is the default linux dict file /usr/share/dict/linux.words.

6. PageRank (pagerank)

   The workloads contains an implementation of the PageRank algorithm on Hadoop (a search engine ranking benchmark included in pegasus 2.0). The workload uses the automatically generated Web data whose hyperlinks follow the Zipfian distribution.

   Machine Learning Benchmarks:

7. Mahout Bayesian classification (bayes)

   Large-scale machine learning is another important use of MapReduce. This workload tests the Naive Bayesian (a popular classification algorithm for knowledge discovery and data mining) trainer in Mahout 0.7, which is an open source (Apache project) machine learning library. The workload uses the automatically generated documents whose words follow the zipfian distribution. The dict used for text generation is also from the default linux file /usr/share/dict/linux.words.

8. Mahout K-means clustering (kmeans)

   This workload tests the K-means (a well-known clustering algorithm for knowledge discovery and data mining) clustering in Mahout 0.7. The input data set is generated by GenKMeansDataset based on Uniform Distribution and Guassian Distribution.

   Data Analytics Benchmarks:

9. Hive Query Benchmarks (hivebench)

   This workload is developed based on SIGMOD 09 paper "A Comparison of Approaches to Large-Scale Data Analysis" and HIVE-396. It contains Hive queries (Aggregation and Join) performing the typical OLAP queries described in the paper. Its input is also automatically generated Web data with hyperlinks following the Zipfian distribution.