Last month my colleague Rich Prohaska covered the technical details of our “Fast Update” feature which we added to TokuDB in version 6.6.  The message based architecture of Fractal Tree Indexes allows us to defer certain operations while still maintaining the semantics that MySQL users require.

In the case of Fast Updates, TokuDB is avoiding the read-before-write requirement that the existing MySQL update statement imposes on storage engines.  We can simply inject an update message into the Fractal Tree Index, and apply that message at a later time.  The message is dynamically applied if a user selects that specific row, and permanently when the message buffers …

“Working with empirical genomic data and modern computational models, the laboratory addresses questions relevant to how genetics and the environment influence the frequency and severity of diseases in human populations” –Thibault de Malliard. Big Data for Genomic Sequencing. On this subject, I have interviewed Thibault de Malliard, researcher at the University of Montreal’s Philip Awadalla [...]

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 down its last mile and finally putting it in the hands of business users.

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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 greater …

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 …

With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: fast updates with TokuDB. Update intensive applications can have their throughput limited by the random read capacity of the storage system. The cause of the throughput limit is the read-modify-write algorithm that MySQL uses when processing update statements. MySQL reads a row from the storage engine, applies the updates to it, and then writes the new row to the storage engine. To address this throughput limit, TokuDB uses a different update algorithm that simply encodes the update expressions of the SQL statement into tiny programs that are stored in an update Fractal Tree® message. This update message is injected into the root of the Fractal Tree index. …

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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University of Montreal Tracks Genomic Data With Tokutek’s TokuDB.

Faster insertion rates, improved scalability and agility support lab’s fast growing research database as it grows from 100s of GBs to 1 TB and beyond.

Issue addressed: MySQL database used for genomic research must be able to quickly ingest huge amounts of incoming data – hundreds of thousands of records every day. It also must be able to retrieve data quickly in response to a diverse set of research requests.

Enabling the Hunt for New Cures for Diseases by Seamlessly Processing Billions of Mutations in Epidemiology Records

The Organization: The The Philip Awadalla Laboratory is the Medical and …