Skip to main content

DBMS within Hadoop nodes for a high performing architecture

Blurring the lines between RDBMS and pure play Hadoop systems, Hadapt offers Hadapt Adaptive Analytic Platform™ which combines the best of both worlds.

It has its roots in a Yale research project, named HadoopDB published in HadoopDB: An ArchitecturalHybrid of MapReduce and DBMS Technologies for Analytical Workloads. Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel J. Abadi, Avi Silberschatz, Alex Rasin. In Proceedings of VLDB, 2009.  With 9.5 million $ Series A funding, today Hadapt is rapidly making strides with certain key initiatives like Cloudera partnership.

The key benefits that Hadapt claims include:
-          All in One Multi Structured data analytics which means that it has one system for all structured and unstructured data. It claims that the connectors approach to integrating database with Hadoop can introduce performance latency, delay and lead to higher cost.
-          Universal SQL support which implies all data can be queried using SQL
-          Significant performance improvements where it claims to have a  huge edge over similar queries in Hadoop+Hive while not giving up fault tolerance and scalability.

Hadapt claims to have the capability to combine the job scheduler, task coordination, and parallelization layer of Hadoop, with the storage layer of the parallel DBMS. They have claimed to have added Database technology on top of a MapReduce framework provided by Hadoop instead of vice versa. The reason attributed to is it of course the lack of open source (parallel) database technology which could have been taken as base and MapReduce capability built on database by the community.

A look into the architecture for HadoopDB reveals the key components which they have introduced to make this hybrid architecture work. In the figure shown at top of this page, besides the blue colored single node database that you see merged in Hadoop nodes, there are other key components that we need to pay attention to:
1. Database Connector that allows Hadoop jobs to access multiple database systems - Theoretically, it is claimed all JDBC interfaced DB can be connected though in the original version of Hadoop DB, they have demonstrated results with PostgreSQL. In fact, a HadoopDB paper recommends connecting to single columnar database for better performance.

2. Data Loader that hash-partitions and splits data into smaller chunks and coordinates their parallel load into the database systems.
“The Data Loader consists of two main components: Global Hasher and Local Hasher. The Global Hasher executes a custom made MapReduce job over Hadoop that reads in raw data files stored in HDFS and repartitions them into as many parts as the number of nodes in the cluster. The repartitioning job does not incur the sorting overhead of typical MapReduce jobs.
The Local Hasher then copies a partition from HDFS into the local file system of each node and secondarily partitions the file into smaller sized chunks based on the maximum chunk size setting.”

3. Catalog which contains both metadata about the location of database chunks stored in the cluster and statistics about the data.

4. Query Interface which allows queries to be submitted via a MapReduce API or SQL – Initially an extension of Hive QL, it has evolved much more today to give a seamless interface for database querying. Today it supports patent pending Split query execution between DBMS and Hadoop.
Further Hadapt today has  “…extended the Database Connector to give Hadoop access to multiple database tables within the Map phase of a single job. After repartitioning on the join key, related records are sent to the Reduce phase in which the actual join is computed. Furthermore, in order to handle even more complicated queries that include multi-stage jobs, we enabled HadoopDB to consume records from a combined input consisting of data from both database tables and HDFS files. In addition, we enhanced HadoopDB so that, at any point during processing, jobs can issue additional SQL queries via an extension we call SideDB (a database task done on the side").
Apart from the SideDB extention, all query execution in HadoopDB beyond the Map phase is carried out inside the Hadoop framework.”

With this inherent architecture, Hadapt attempts to push as much processing as possible into single-node database systems and to perform as many relational query operators as possible in each Map and Reduce task. The reason for that being that DBMS over the years have been optimized to peform much faster on indexed data. Hive, on the other hand, lacks partitioning and indexing. Every selection involves full data scan and most of the joins involve repartitioning. Hadapt however may be suspect to higher data load time which it intentionally compromises to yield faster joins.

 Quick Facts (as of writing this post): 

614 Massachusetts Ave
Cambridge, MA
Phone  617-539-6110

Employees:      40

Founded:          2010

Venture Funding: $9.5 million
Chief Software Architect and Co-Founder: Kamil Bajda-Pawlikowski

Chief Executive Officer and Co-Founder: Justin Borgman

Chief Scientist and Co-founder: Dr. Daniel Abadi

VP, Customer Solutions: Kelly Stirman

CTO: Philip Wickline

VP, Marketing: Scott Howser


Popular articles

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs. To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground. 1)      Many Eyes : Many Eyes is a data visualization experiment by IBM Research and the IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Ma

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 o

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB . Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive. Introducing Apache Gora Although