Skip to main content

MRQL - a SQL on Hadoop Miracle

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. K-Means Clustering Using MR and BSP Modes for 10 steps.
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.


Popular posts from this blog

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort:Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban:A batch job scheduling system with a friendly UI, Azkab…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…