Ideas, releases, practical guides, and perspectives from the people building with MySQL.
In my three previous MongoDB blogs I wrote about our implementation of Fractal Tree(R) indexes on MongoDB, showing a 10x insertion performance increase, a 268x query performance increase, and a comparison of covered indexes and clustered indexes. These benchmarks show the difference that rich and efficient indexing can make to your MongoDB workload.
Given the high performance of Fractal Tree Indexes, we’ve created a new benchmark to test our ability to handle indexing large …
[Read more]Two cons against NoSQL data stores read like this: 1. It’s very hard to move data out from one NoSQL to some other system, even other NoSQL. There is a very hard lock in when it comes to NoSQL. If you ever have to move to another database, you have basically to re-implement a lot [...]
This webinar covers the basics of B-trees and Fractal Tree Indexes, the benchmarks we’ve run so far, and the development road map going forward.
Date: November 13th
Time: 2 PM EST / 11 AM PST
REGISTER TODAY
Topics will include:
We look forward to having you join the webinar. We also hope that by sharing these results with the community we will be able to elicit people’s …
[Read more]I’ll be presenting “MongoDB and Fractal Tree Indexes” at MongoDB Boston 2012 on October 24th. My presentation covers the basics of B-trees and Fractal Tree Indexes, the benchmarks we’ve run so far, and the development road map going forward.
I’ve been to this one day conference twice now and both times came away with a better understanding of MongoDB’s capabilities, use-cases, and many questions answered via their deep technical dives. I highly recommend current MongoDB users and anyone considering a MongoDB project attend – it appears that seats are still available.
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 …
[Read more]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 …
[Read more]Last week I wrote about our 10x insertion performance increase with MongoDB. We’ve continued our experimental integration of Fractal Tree® Indexes into MongoDB, adding support for clustered indexes. A clustered index stores all non-index fields as the “value” portion of the index, as opposed to a standard MongoDB index that stores a pointer to the document data. The benefit is that indexed lookups can immediately return any requested values instead of needing to do an additional lookup (and potential disk IOs) for the requested fields.
To create a clustered index you just need to add “clustering:true” as in the following example (note that version 2 indexes are Fractal Tree Indexes): …
[Read more]
I wrote about the performance gains with
libmysqld a few days ago but I had just too many things in my
head to do a proper comparison with the MySQL Cluster / Server
protocol. Yes, libmysqld is faster, but not as much faster as I
thought, and blogged about. What happened was that I had another
thing to try which I had forgotten about, which was to test using
the Client / Server protocol without the dreaded CLIENT_COMPRESS
flag (see more on this here).
Without CLIENT_COMPRESS, I could see NDB performance improve by
some 25 - 30 %. But with InnoDB, which I just tested, I achieved
some 98 k row reads per second! Yikes, I should have tested that
one before comparing with libmysqld (in which case I got 115 k …
The challenge of handling massive data processing workloads has spawned many new innovations and techniques in the database world, from indexing innovations like our Fractal Tree® technology to a myriad of “NoSQL” solutions (here is our Chief Scientist’s perspective). Among the most popular and widely adopted NoSQL solutions is MongoDB and we became curious if our Fractal Tree indexing could offer some advantage when combined with it. The answer seems to be a strong “yes”.
Earlier in the summer we kicked off a small side project and here’s what we did: we implemented a “version 2” IndexInterface as a Fractal Tree index and ran some benchmarks. Note that our integration only affects MongoDB’s secondary indexes; primary indexes continue to rely on MongoDB’s indexing code. All the changes we made to the MongoDB source …
[Read more]NSFW (audio) “…pipe your data to /dev/null – it will be very fast.” “Does /dev/null support sharding?” NSFW (audio) “…the only thing constructive we could have used their source files for was as random keys for SSL certs.” NSFW (audio) “PHP reeks … Continue reading →