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
execution between DBMS and Hadoop. Split
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
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