Tag - LSM

Why Percona Acquired Tokutek: by Peter Zaitsev

It is my pleasure to announce that Percona has acquired Tokutek and will take over development and support for TokuDB® and TokuMX™ as well as the revolutionary Fractal Tree® indexing technology that enables those products to deliver improved performance, reliability and compression for modern Big Data applications.
At Percona we have been working with the […]

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A Tokutek Take on MongoDB v3.0

On February 3rd MongoDB, Inc. formally announced MongoDB v3.0 (the product release formerly known as v2.8).  It includes a number of important enhancements, some of them of particular interest to users of the Tokutek distribution of MongoDB, TokuMX, or anyone thinking about using TokuMX.
Most notably, MongoDB v3 includes a storage engine API that can be used to create […]

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XLDB Tutorial on Data Structures and Algorithms

Next week Michael and I (Bradley) will be travelling to Silicon Valley to present a tutorial on Data Structures and Algorithms for Big Databases at the 6th XLDB Conference.
The tutorial, which is 4 hours on Monday afternoon, aims to cover the following topics (but it’s looking like we’ll have to drop several items for […]

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Write Optimization: Myths, Comparison, Clarifications, Part 2

In my last post, we talked about the read/write tradeoff of indexing data structures, and some ways that people augment B-trees in order to get better write performance. We also talked about the significant drawbacks of each method, and I promised to show some more fundamental approaches.
We had two “workload-based” techniques: inserting in sequential […]

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Write Optimization: Myths, Comparison, Clarifications

Some indexing structures are write optimized in that they are better than B-trees at ingesting data. Other indexing structures are read optimized in that they are better than B-trees at query time. Even within B-trees, there is a tradeoff between write performance and read performance. For example, non-clustering B-trees (such as MyISAM) are typically […]

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