Tokutek is known for its full-featured fast-indexing technology. MongoDB is known for its great document-based data model and ease of use. TokuMX, version 1.0, combines the best of both worlds.

• So what, exactly, is TokuMX? The simplest (but incomplete) answer is that TokuMX is MongoDB with all its storage code replaced by Tokutek’s Fractal Tree indexes.
• How do Fractal Tree indexes improve MongoDB? The direct benefits include high-performance indexing, strong compression, and performance stability – in other words, the performance stays high, even when data is larger than RAM.
• Are there any features in TokuMX that MongoDB doesn’t have? Yes. We have added support for transactions to TokuMX, so that TokuMX is ACID compliant and has MVCC. We have also added support for clustering indexes, which dramatically accelerate many types of queries.
• …

In many environments MySQL is not the only technology used to store in-process data.

Quite frequently, especially with large-scale or complicated applications, we use MySQL alongside other technologies for certain tasks of reporting, caching as well as main data-store for portions of application.

What technologies for data storage and processing do you use alongside MySQL in your environment? Please feel free to elaborate in the comments about your use case and experiences!

Note: There is a poll embedded within this post, please visit the site to participate in this post's poll.

The post What technologies are you running alongside MySQL? appeared first on …

Continuent Tungsten Replicator 2.1 is now available for download at www.continuent.com/software and http://code.google.com/p/tungsten-replicator/downloads/list. 

Tungsten Replicator is a high performance, open source, data replication engine for MySQL and Oracle, released under a GPL V2 license. Tungsten Replicator has all the features you expect from enterprise-class data replication products

Use tungsten replicator to replicate between mysql and mongodb.

Mysql tables are equivalent to collections in mongodb. The replication works by replicating inserts and updates. But all DDL statements on mysql are ignored...

Replication in detail

Tokutek created the iiBench benchmark back in 2008. The point of the benchmark is to measure the performance of indexed insertions over time. It uses an extremely simple schema, one table with a sequential insertion pattern for the primary key along with three integer fields storing random values. The table maintains 3 secondary indexes, each including several of the random integer fields. The iiBench application itself is currently maintained on Launchpad.

B-tree implementations generally require maintenance operations to update leaf nodes (an insertion is one such operation). When the entire B-tree index does not fit in RAM an IO is required, and performance drops dramatically. Fractal Tree Indexes do not exhibit this performance drop as leaf node …

Over several blog posts, Tim has presented performance results on large data sets of TokuMX, our MongoDB product with fractal tree indexes integrated, side by side with MongoDB. Results look good. We’ve shown improved throughput numbers on a sysbench benchmark, faster load times, and high compression.

So what is TokuMX, and how does it achieve this performance?

TokuMX has replaced ALL of the storage code in MongoDB with fractal trees. Every collection, every secondary index, every metadata collection is stored with fractal trees, the same technology that implements the TokuDB storage engine for MySQL. That is, all data is stored and managed with our transactional, ACID and MVCC-compliant, write-optimized storage library.

TokuMX achieves …

“Application designers need to start by thinking about what level of data integrity they need, rather than what they want, and then design their technology stack around that reality. Everyone would like a database that guarantees perfect availability, perfect consistency, instantaneous response times, and infinite throughput, but it´s not possible to create a product with [...]

Two months ago I posted a performance comparison running Sysbench on MongoDB versus MongoDB with Fractal Tree Indexes v0.0.2. The benchmark showed a 133% improvement in throughput. Nice, but our engineering team had an effort on our road-map for lock refinement that we believed would really boost our performance, which is now available in v0.1.0. The benchmark application itself is unchanged and available on GitHub.

For anyone curious about Sysbench itself, the details are available from the prior blog. The only change for this run was hardware. Our Sun x4150 server recently began rebooting itself at random times, so it has been replaced with a newer HP server. Another change is …

As you probably know, Tungsten Replicator can replicate data from MySQL to MongoDB. The installation is relatively simple and, once done, replication works very well. There was a bug in the installation procedure recently, and as I was testing that the breakage has been fixed, I wanted to share the experience of getting started with this replication.

Step 1: install a MySQL server

For this exercise, we will use a MySQL sandbox running MySQL 5.5.31.

We download the binaries from dev.mysql.com and install a sandbox, making sure that it is configured as master, and that it is used row-based-replication.

$ mkdir -p $HOME/opt/mysql
$ cd ~/downloads
$ wget …

At tomorrow’s Effective MySQL Meetup, I’ll be presenting “Fractal Tree Indexes : Theory and Practice (MySQL and MongoDB).” The meetup is at 6:30pm Tuesday, May 14, 2013, and will be held at Alley NYC in New York City.

I’ll give an overview on how Fractal Tree® indexes work, and then get into specific product features that Fractal Trees enable in MySQL and MongoDB.  Some benchmarking and customer use-cases will be discussed, but my intent is for this to be a deep technical dive.  Several Tokutek Engineers will also be on hand, so bring any questions you’ve got.

I hope to see you there!