TokuDB® is a proven solution that scales MySQL® and MariaDB® from GBs to TBs with unmatched insert and query speed, compression, replication performance and online schema flexibility. Tokutek’s recently launched TokuDB v6.5 delivers all of these features and more, not just for HDDs, but also for flash memory.

Originally Aired: October 10th
AVAILABLE ON DEMAND

TokuDB v6.5:

• Stores 10x More Data – TokuDB delivers 10x compression without any performance degradation. Users can therefore take advantage of much greater amounts of available space without paying more for additional storage.

• Delivers High Insertion Speed – TokuDB Fractal Tree® indexes …

Tokutek sponsored the Percona Live MySQL / New York 2012 Conference which took place this past Monday and Tuesday.  I spent much of the time at our booth discussing TokuDB with conference attendees but also managed to attend the following presentations:

• Understanding Performance through Measurement, Benchmarking and Profiling by Rene Cannao of PalominoDB.  Rene presented 3 hours of content covering his methodologies, toolbox, and tips & tricks to help identify performance bottlenecks. Rene has obviously been optimizing systems for some time as I’ve never seen such a comprehensive list of tools and techniques.  This …

TokuDB v6.5 adds the ability to expand certain column types without downtime.  Users can now enlarge char, varchar, varbinary, and integer columns with no interruption to insert/update/delete statements on the altered table.  Prior to this feature, enlarging one of these column types required a full table rebuild.  InnoDB blocks all insert/update/delete operations to a table during column expansion as it rebuilds the table and all indexes.

We recently added SSDs to our existing servers (HP DL380 servers with internal RAID arrays).  I opted for LSI controllers (LSI 9285-8e and LSI 9280-4i4e), Samsung 830 SSDs, and a Sans Digital AccuRaid AS108X enclosure.  I was unable to locate much in the way of optimizing Linux for SSD/RAID performance and plan on blogging my findings in the future.  However, the out of the box performance is seriously fast.

I was curious to see how much downtime occurs when issuing …

Since Fractal Tree indexes turn random writes into sequential writes, it’s easy to see why they offer a big advantage for maintaining indexes on rotating disks. It turns out that that Fractal Tree indexing also offers signficant advantages on SSD. Here are three ways that Fractal Trees improve your life if you use SSDs.

Advantage 1: Index maintenence performance.

The results below show the insertion of 1 billion rows into a table while maintaining three multicolumn secondary indexes. At the end of the test, TokuDB’s insertion rate remained at 14,532 inserts/second whereas InnoDB had dropped to 1,607 inserts/second. That’s a difference of over 9x.

Even on flash, I/O performance costs something. Since TokuDB employs Fractal Tree write-optimized …

We are excited to announce TokuDB® v6.5, the latest version of Tokutek’s flagship storage engine for MySQL and MariaDB.

This version offers optimization for Flash as well as more hot schema change operations for improved agility.

We’ll be posting more details about the new features and performance, so here’s an overview of what’s in store.

Flash

TokuDB v6.5 continues the great Toku-tradition of fast insertions. On flash drives, we show an order-of-magnitude (9x) faster insertion rate than InnoDB. TokuDB’s standard compression works just as well on flash and helps you get the most out of your storage system. And TokuDB reduces wear …

On Friday our CEO John Partridge was named a “Tech Luminary” in the 17th annual Innovation All Stars award, which is given jointly by Mass High Tech (MHT) and the Boston Business Journal (BBJ). As noted in MHT by the editor, Chris McIntosh, the Luminary designations “reflect deep accomplishment in various technology-related industries.”

For more than 20 years, Tokutek CEO John Partridge has worked with startups in both the Boston area and in Silicon Valley. He joined Tokutek from StreamBase Systems which John co-founded with database pioneer Dr. Michael Stonebraker. He started his career as a software developer …

At next week’s Strange Loop conference, I will give a talk on “Understanding Indexing”. The session is 10 am Monday, September 24th, and will be held in the Midland States Room.

Application performance often depends on how fast a query can respond and query performance almost always depends on good indexing. So one of the quickest and least expensive ways to increase application performance is to optimize the indexes. This talk presents three simple and effective rules on how to construct indexes around queries that result in good performance.

This is a general discussion applicable to all databases using indexes and is not specific to any particular MySQL® storage engine (e.g., InnoDB, TokuDB®, etc.). …

In my three previous blogs I wrote about our implementation of Fractal Tree Indexes on MongoDB, showing a 10x insertion performance increase, a 268x query performance increase, and a comparison of covered indexes and clustered indexes. The benchmarks show the difference that rich and efficient indexing can make to your MongoDB workload.

It’s one thing for us to benchmark MongoDB + TokuDB and another to measure real world performance. If you are looking for a way to improve the performance or scalability of your MongoDB deployment, we can help …

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 lack of time.)

This tutorial will explore data structures and algorithms for big databases. The topics include:

• Data structures including B-trees, Log Structured Merge Trees, and Streaming B-trees.
• Approximate Query Membership data structures including Bloom filters and cascade filters.
• Algorithms for join including hash joins and Graefe’s generalized join.
• Index design, including covering indexes. …

In my two previous blogs I wrote about our implementation of Fractal Tree Indexes on MongoDB, showing a 10x insertion performance increase and a 268x query performance increase. MongoDB’s covered indexes can provide some performance benefits over a regular MongoDB index, as they reduce the amount of IO required to satisfy certain queries.  In essence, when all of the fields you are requesting are present in the index key, then MongoDB does not have to go back to the main storage heap to retrieve anything.  My benchmark results are …