Before creating a unique index in TokuMX or TokuDB, ask yourself, “does my application really depend on the database enforcing uniqueness of this key?” If the answer is ANYTHING other than yes, do not declare the index to be unique. Why? Because unique indexes may kill your write performance. In this post, I’ll explain […]Read more
In my last post, I showed what a Fractal Tree® index is at a high level. Once again, the Fractal Tree index is the data structure inside TokuMX and TokuDB, our MongoDB and MySQL products. One of its strengths is the ability to get high levels of compression on the stored data. In this […]Read more
With our recent release of TokuMX 1.0, we’ve made some bold claims about how fast TokuMX can run MongoDB workloads. In this post, I want to dig into one of the big areas of improvement, write performance and reduced I/O.
One of the innovations of TokuMX is that it eliminates a long-held rule of databases: […]
I am actually quite excited about Tokutek’s release of TokuMX. I think it is going to change the landscape of database systems and it is finally something that made me looking into NoSQL.
Why is TokuMX interesting? A few reasons:
It comes with transactions, and all that good stuff that transactions provide: a concurrent access to […]Read more
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 […]Read more
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 […]
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 […]Read more
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 […]
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 […]Read more
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 […]