Since we announced that TokuDB is now open source, there has been a lot of positive feedback (thanks!) and also some questions about the details. I want to take this opportunity to give a quick high level guide to describe what our repositories on Github are.

Here are the repositories:

• ft-index. This repository is the “magic”. It contains the Fractal Tree data structures we have been talking about for years. This is also the main piece that was previously closed source. Here are some interesting directories:
  • src: This directory is a layer that implements an API that is similar to the BDB API.
  • locktree: an in-memory data structure that maintains transactions’ row-level locks. …

I enjoyed Stewart Smith’s MySQL storage engine blog last week. In it he noted “I cannot emphasize how much more interesting TokuDB would be if it were open source.” Well, with our open source announcement yesterday, hopefully we are getting interesting.

We wanted to thank everyone for the great feedback. Here is a sampling from some of the forums where dialogue is occurring:

Reddit:

BrianAtDTS: “With this update, this puts MySQL in the upper echelon of data storage applications. You don’t have anything like this in any of the other major db’s.”

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Every few months, I get the fun job of announcing what’s new in TokuDB®, but this time is special. With Version 7, TokuDB for MySQL and MariaDB is going open source.

The free Community Edition is fully functional and fully performant. It has all the compression you’ve come to expect from TokuDB. It has hot schema changes: no-down-time column insertion, deletion, renaming, etc., as well as index creation. It has clustering secondary keys. We are also announcing an Enterprise Edition (coming soon) with additional benefits, such as a support package and advanced backup and recovery tools.

Making TokuDB open source is a natural next step for Tokutek’s involvement in the MySQL community. So far, Tokutek has been involved in the community in many ways:

• We’ve contributed a number of  …

I recently wrote about Where are they now: MySQL Storage Engines and The MERGE storage engine: not dead, just resting…. or forgotten. Today, it’s the turn of the MEMORY storage engine – otherwise known as HEAP.

This is yet another piece of the MySQL server that sits largely unmaintained and unloved. The MySQL Manual even claims that it supports encryption… with the caveat of having to use the SQL functions for encryption/decryption rather than in the engine itself (so, basically, it supports encryption about as much as every other engine does).

The only …

If T.S. Eliot were a MySQL DBA, I think he would have been more upbeat about April.

We are gearing up for an incredible second half of April. We will be presenting three separate sessions at the Percona Live: MySQL Conference and Expo 2013, April 22-25, in Santa Clara, CA. In addition, we will be presenting at SkySQL’s MySQL & Cloud Database Solutions Day on Friday, April 26 at the same location.

Come by to see us in Booth #114, or stop by one of our sessions:

Using TokuDB: A Guided Walk Through a …

MySQL 5.6 through the eyes of a custom storage engine MySQL plugin

MySQL is famous for its pluggable storage engine architecture which allows a DBA or an application developer to choose the right engine for the task. An application uses MySQL API and is isolated from all of the low-level implementation details at the storage level. As an example, the Cloud Storage Engine (ClouSE) enables existing MySQL applications to use cloud storage such as Amazon S3 or Google Cloud Storage to store its data. The application doesn’t need to be changed or even redeployed: with ClouSE, remote cloud storage will look like a better (ultra-scalable, durable, always-on) alternative to the local storage.

As you may already know, ClouSE now supports MySQL 5.6 release …

One doesn’t have to look far to see that there is strong interest in MongoDB compression. MongoDB has an open ticket from 2009 titled “Option to Store Data Compressed” with Fix Version/s planned but not scheduled. The ticket has a lot of comments, mostly from MongoDB users explaining their use-cases for the feature. For example, Khalid Salomão notes that “Compression would be very good to reduce storage cost and improve IO performance” and Andy notes that “SSD is getting more and more common for servers. They are very fast. The problems are high costs and low capacity.” There are many …

I’ve said it before, and, as is the nature of these things, I’ll almost certainly say it again: your database performance is only as good as your indexes.

That’s the grand thesis, so what does that mean? In any DB system — SQL, NoSQL, NewSQL, PostSQL, … — data gets ingested and organized. And the system answers queries. The pain point for most users is around the speed to answer queries. And the query speed (both latency and throughput, to be exact) depend on how the data is organized. In short: Good Indexes, Fast Queries; Poor Indexes, Slow Queries.

But building indexes is hard work, or at least it has been for the last several decades, because almost all indexing is done with B-trees. That’s true of commercial databases, of MySQL, and of most NoSQL solutions that do indexing. (The ones that don’t do …

In Part 1, we showed performance results of some of the work that’s gone in to TokuDB v6.6. In this post, we’ll take a closer look at how this happened, on the engineering side, and how to think about the performance characteristics in the new version.

Background

It’s easiest to think about our concurrency changes in terms of a Fractal Tree® index that has nodes like a B-tree index, and buffers on each node that batch changes for the subtree rooted at that node. We have materials that describe this available here, but we can proceed just knowing that:

1. To inject data into the tree, you need to store a message in a buffer at the root of the tree. These messages are moved down the tree, so you can find messages in all the internal …

With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: concurrency within a single index. Previously, while there could be many SQL transactions in-flight at any given moment, operations inside a single index were fairly serialized. We’ve been working on concurrency for a few versions, and things have been getting a lot better over time. Today I’ll talk about what to expect from v6.6. Next time, we’ll see why.

Summary of Results

Running multiple iiBench clients on a single MySQL instance, we see a big improvement in the cumulative insertion speed at all concurrency levels. We see a gain of 33.9% in single-threaded performance and 51.8% at 64 threads.

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