If T.S. Eliot were a MySQL DBA, I think he would have been more upbeat about April.

We are gearing up for an incredible second half of April. We will be presenting three separate sessions at the Percona Live: MySQL Conference and Expo 2013, April 22-25, in Santa Clara, CA. In addition, we will be presenting at SkySQL’s MySQL & Cloud Database Solutions Day on Friday, April 26 at the same location.

Come by to see us in Booth #114, or stop by one of our sessions:

Using TokuDB: A Guided Walk Through …

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. This is essentially the same as “repeatable-read” in MySQL. Isolations of …

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 will finish execution with four rows updated and six …

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 …

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 will be deeply technical and written by the Tokutek developers.

We have a group …

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, named sbtest1 … sbtest16, each with 10 …

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.

Source: …

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 …