SOURCE: Tokutek, Inc.

Tokutek, Inc.

June 23, 2014 09:00 ET

Tokutek® TokuMX™ v1.5 Delivers Time-Series Data Management Improvements for MongoDB Applications

Company Adds New Capabilities to TokuMX Enabling MongoDB Application Developers to More Easily Manage Large-Scale Data Sets in Discrete Time Spans

NEW YORK, NY--(Marketwired - Jun 23, 2014) - MongoDB World -- Tokutek®, delivering database performance at scale, today announced TokuMX™ v1.5 with new partitioned collection capabilities that make it easier to manage time-series data in MongoDB applications. Using TokuMX v1.5, database architects and developers can transparently break time-series data into separate physical partitions, which can be allocated to specific time spans and later ejected from the primary data store once a specific time period has elapsed.

Tokutek will be demonstrating TokuMX v1.5 at booth #109 throughout MongoDB World.

Time series data tends to be immutable, large in volume, ordered by time, and may be conveniently structured by time for easier access. Although many organizations use MongoDB to store and analyze time-series data, before TokuMX v1.5 there was no way to easily eject unwanted data without suffering a significant performance penalty.

Analogous to a partitioned table in relational databases, the partitioned collection capability in TokuMX v1.5 creates a logical collection that is broken into (or partitioned into) several physical data files based on ranges of a "partition key." Application developers can access the logical collection with normal queries, inserts, updates, and deletes without having to make complex code changes. Secondary indexes and replication work as well. Partitioned collections have two big advantages:

  • Large chunks of data can be deleted very efficiently by dropping partitions.

  • Queries that include the partition key may be isolated to individual partitions, and therefore run faster.

"While MongoDB continues to grow wildly popular as a document-oriented NoSQL database, Tokutek is pouring fuel on the fire with this new v1.5 release of TokuMX that enables MongoDB to work effectively with rolling windows of data," said Mike Matchett, big data analyst, Taneja Group. "Application architects and developers will find TokuMX not only enterprise ready, but evolving to support a wider variety of database challenges including ever larger and dynamic data sets."

"Many big data applications have extremely large sets of time-series data. If you only want to work with a rolling period of that data -- for example, just six-months' worth -- a partitioned collection is a lot easier than using a TTL index or a capped collection," said Dave Rosenlund, vice president of marketing at Tokutek. "Our testing indicates MongoDB applications of this kind will perform far better on TokuMX v1.5 than on basic MongoDB."

Since its introduction just one year ago, TokuMX has helped MongoDB users with the most challenging big data applications dramatically improve performance and significantly reduce hardware costs with unmatched database compression, concurrency, and write optimization.

To learn more about TokuMX v1.5:
Read the TokuView blog: Introducing Partitioned Collections for MongoDB Applications. Download the Tokutek Webinar, How to Build Better Time-Series Applications with MongoDB. Find out about the TokuMX™ Innovation Award program that recognizes MongoDB users who are innovating with TokuMX, the high-performance distribution of MongoDB from Tokutek.

About Tokutek Inc.
Tokutek is a performance database company that delivers Big Data capabilities to leading open source data management platforms. Its breakthrough technology lets customers build a new class of applications that handle unprecedented amounts of incoming data and scale with the data processing needs of tomorrow. Tokutek applies patented Fractal Tree™ indexing to increase MySQL performance and MongoDB performance, decrease database size, and minimize downtime. The company is headquartered in Lexington, MA, and has offices in New York, NY. For more information, visit Tokutek.com or follow us on Twitter @Tokutek.

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