Recently, the Apache Incubator accepted a new query engine for Hadoop and Hama, called MRQL (pronounced miracle), which was initially developed in 2011 by Leonidas Fegaras.
MRQL (MapReduce Query Language) is a query processing and optimization system for large-scale, distributed data analysis, built on top of Apache Hadoop and Hama. MRQL has some overlapping functionality with Hive, Impala and Drill, but one major difference is that it can capture many complex data analysis algorithms that can not be done easily in those systems in declarative form. So, complex data analysis tasks, such as PageRank, k-means clustering, and matrix multiplication and factorization, can be expressed as short SQL-like queries, while the MRQL system is able to evaluate these queries efficiently.
Another difference from these systems is that the MRQL system can run these queries in BSP (Bulk Synchronous Parallel) mode, in addition to the MapReduce mode. With BSP mode, it achieves lower latency and higher speed. According to MRQL team, “In near future, MRQL will also be able to process very large data effectively fast without memory limitation and significant performance degradation in the BSP mode”.
As a simple example, the MRQL query in Figure 1 calculates the k-means clustering algorithm.
|Figure 1. K-means Clustering Expressed as an MRQL Query|
Figure 2 shows the results of evaluating the K-means query using MR and BSP modes for limit (number of iterations) 10. We can see that the BSP evaluation outperforms the MR evaluation by an order of magnitude.
MRQL team also has plans to support additional distributed processing frameworks, such as Spark and OpenMPI in the future. Currently, a number of researchers and developers from various organizations, such as UT Arlington, Oracle, and Cloudera, are involved in the MRQL project. They are looking forward to your contributions.
You can find more information about MRQL at the website:
About the Author:
A creator of Apache Hama, a committer of Apache BigTop and MRQL. Currently he works at Oracle Corporation.
If you wish to write a post on Hadoop and want to share your experience/expertise, click here.