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!

Earlier, I talked about the transactional semantics we are introducing to MongoDB. As I hinted at the end of the post, we are actually doing more. We are introducing multi-statement transactions. That’s right, multiple queries, updates, deletes, and inserts will be able to run inside of a single transaction. We are working on the details of the semantics as we develop our beta, but at a high level, think of it as having the same semantics as TokuDB and InnoDB’s multi-statement transactions in MySQL.

So how will it work? We introduce three new commands:

db.runCommand({"beginTransaction", "isolation": "mvcc"})

This begins a transaction with the isolation level of MVCC, which means queries will use a snapshot of the system.

People claim that MongoDB is not transactional. It actually is, and that’s a good thing.

In MongoDB 2.2, individual operations are Atomic. By having per database locks control reads and writes to collections, write operations on collections are Consistent and Isolated. With journaling on, operations may be made Durable. Put these properties together, and you have basic ACID properties for transactions.

The shortcoming with MongoDB’s implementation is that these semantics apply to individual write operations, such as an individual insert or individual update. If a MongoDB statement updates 10 rows, and something goes wrong with the fifth row, then the statement

We recently resumed our discussion around bringing Fractal Tree indexes to MongoDB.  This effort includes Tokutek’s interview with Jeff Kelly at Strata as well as my two recent tech blogs which describe the compression achieved on a generic MongoDB data set and performance improvements we measured using on our implementation of Sysbench for MongoDB.  I have a full line-up of benchmarks and blogs planned for the next few months, as our project continues.  Many of these

As we continue to test our Fractal Tree Indexing with MongoDB, I’ve been updating my benchmark infrastructure so I can compare performance, correctness, and resource utilization.  Sysbench has long been a standard for testing MySQL performance, so I created a version that is compatible with MongoDB.  You can grab my current version of Sysbench for MongoDB here.

So what exactly is Sysbench?  According to the Sysbench homepage, “Sysbench is a modular, cross-platform and multi-threaded benchmark tool for evaluating OS [Operating System] parameters that are important for a system running a database under intensive load.”

• Sysbench schema
• 16 copies of the same collection,

During the second half of our CUBE discussion with Wikibon analyst Jeff Kelly at this year’s Strata Conference in Santa Clara, we talked about the tipping point for Big Data. Strata veterans could see at a glance that this year’s conference was markedly different. No longer the exclusive domain of geeks and database administrators, this year’s Strata featured some of the biggest enterprise vendors around. With heavy weight enterprise players Intel and EMC Greenplum announcing their own Hadoop distributions, big data is clearly going mainstream. Now that we know how to capture, store, access and analyze big data, what’s the next step? Listen in to hear my conversation with Jeff Kelly about taking big data

We had the opportunity to do a CUBE interview with Wikibon analyst Jeff Kelly at last week’s Strata Conference in Santa Clara. In the first part of our conversation, we discuss how our success in integrating Tokutek’s Fractal Tree® technology into MySQL has led us to another popular database, MongoDB. We explain the results of our recent benchmarking tests with MongoDB, which indicate that adding indexing can also improve performance for this popular NoSQL database with faster insertion rates, lower query latency and

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

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

Well, it’s that time of the year again for top ten lists. There have been many versions showing up on the web the last few days, including Time Magazine’s “Top 10 Everything of 2012″ list, with 55 wide ranging lists!

Last year we started using Google Analytics to see what content for blogs was most popular on Tokutek.com and generated a 2011 top ten list, ending up with a few surprises.  This year saw spikes in some interesting areas as well, including flash performance, NASA and Big Data, and MongoDB.

Without further adieu, here is the top ten list for 2012:

10. Announcing TokuDB v6.1 –

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

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:

• What is a Fractal Tree Index?
• How to Fractal Trees compare with B-Trees
• What can a Fractal Tree do for MongoDB performance
• Benchmarks + Gotchas
• What’s next

We look forward to having you join the webinar. We also hope that by sharing these results with

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

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

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

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;