It certainly has been a tough week on the East Coast. In the Boston area, where I am located, we got grazed by Sandy – power outages, trees down, schools closed, and Halloween delayed. This though, pales in comparison to what our NY office had to endure. One of our co-founders for example is one of the first few to get his power back at his home on Long Island just yesterday. He considers himself lucky – one of the several pine trees that came down on his property was just a few degrees off from destroying his house (it ended up taking down the gutter instead). Needless to say, it’s going to be a long recovery for NY and NJ. If you are looking to help, see here.

Of course, NYC is known for its resiliency. The first post-Sandy event at Manhattan’s Javitis Conference Center, 

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At tomorrow’s NoVA MySQL October Meetup, I will give a talk: “Fractal Tree Indexes – Theoretical Overview and Customer Use Cases.” The meetup is 7 pm Tuesday, October 23, 2012, and will be held at AOL Campus HQ in Dulles VA.

Most databases employ B-trees to achieve a good tradeoff between the ability to update data quickly and to search it quickly. It turns out that B-trees are far from the optimum in this tradeoff space. This led to the development at MIT, Rutgers and Stony Brook of Fractal Tree® indexes. Fractal Tree indexes improve MySQL® scalability and query performance by allowing greater insertion rates, supporting rich indexing and offering

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Next week I’ll be visiting Moscow to talk at Highload++. The conference will take place during Monday 22nd and Tuesday 23rd at the Radisson hotel. I will be giving my personal version of an indexing talk that my colleagues have given in meetups and conferences in the US.

Highload++ conference is targeted to address the issues of complex high traffic web properties. Most of these sites depend on databases to deliver their content, record the traffic and report the application activities in real time. As I learned early in my career at MySQL, the database schema and in particular the indexing strategy, are critical to achieve the highest possible performance out of the

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Bradley and I (Michael) gave the tutorial on Data Structures and Algorithms for Big Databases at the 6th XLDB Conference last month.

The tutorial was organized as follows:

• Module 0: Tutorial overview and introductions. We describe an observed (but not necessary) tradeoff in ingestion, querying, and freshness in traditional database.
• Module 1: I/O model and cache-oblivious analysis.
• Module 2: Write-optimized data structures. We give the optimal trade-off between inserts

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The core technology behind Tokutek is based on the academic research by our founders: Michael Bender, Bradley Kuszmaul and Martin Farach-Colton.  They are all still in academia, in addition to their work at Tokutek.

Back in March, the White House kicked off a new Initiative for Big Data.  Last week, the National Science Foundation announced the first interagency grants for this.  Eight awards were given, and our own Michael Bender and Martin Farach-Colton, along with Robert Johnson of Stony Brook University, received one of

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Recently, our CTO, Martín Farach-Colton had a chance to talk about scaling MySQL and MariaDB with Roberto Zicari of ODBMS.

In the article, Martin states “While I believe that one size fits most, claims that RDBMS can no longer keep up with modern workloads come in from all directions. When people talk about performance of databases on large systems, the root cause of their concerns is often the performance of the underlying B-tree index.” He also notes how “Fractal Tree Indexes put you on a higher-performing tradeoff curve. Query-optimal write-optimized indexing is all about making general-purpose databases faster. For some of our customers’ workloads,

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Recently, our CEO, John Partridge had a chance to talk about novel database technologies for “Big Data” with Peter Cohan of Forbes.

According to the article, “Fractal Tree indexing is helping organizations analyze big data more efficiently due to its ability to improve database efficiency thanks to faster ‘database insertion speed, quicker input/output performance, operational agility, and data compression.’” As a start-up based on “the  first algorithm-based breakthrough in the database world in 40 years,” Toktuetek is following in the footsteps of firms such as Google and RSA, which also relied on novel algortithm advances as core to their technology.

To read the full article, and

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TokuDB® is a proven solution that scales MySQL® and MariaDB® from GBs to TBs with unmatched insert and query speed, compression, replication performance and online schema flexibility. Tokutek’s recently launched TokuDB v6.5 delivers all of these features and more, not just for HDDs, but also for flash memory.

Originally Aired: October 10th
AVAILABLE ON DEMAND

TokuDB v6.5:

• Stores 10x More Data – TokuDB delivers 10x compression without any performance degradation. Users can therefore take advantage of much greater amounts of available space without paying more for additional storage.

• Delivers High Insertion Speed – TokuDB

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We are excited to announce TokuDB® v6.5, the latest version of Tokutek’s flagship storage engine for MySQL and MariaDB.

This version offers optimization for Flash as well as more hot schema change operations for improved agility.

We’ll be posting more details about the new features and performance, so here’s an overview of what’s in store.

Flash TokuDB v6.5 continues the great Toku-tradition of fast insertions. On flash drives, we show an order-of-magnitude (9x) faster insertion rate than InnoDB. TokuDB’s standard compression works just as well on flash and helps you get the most out of your  [Read more...]

Businesses increasingly operate in a 24×7 environment, where complex analytics must be performed on live, continuously incoming “Big Data.” To address this, TokuDB has developed Fractal Tree®  technology, a revolutionary new indexing capability that enables SQL databases running advanced web applications to grow from gigabytes to terabytes while improving insert speed, query performance, compression, and enabling zero-downtime schema changes.

Date: September 5th
Time: 2 PM EST / 11 AM PST

REGISTER TODAY

TokuDB is used by MySQL and MariaDB customers worldwide to increase their database performance by 20x-80x on Big Data applications that conventional RDBMS’s cannot handle. Instead

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A few weeks ago Bradley Kuszmaul and I attended the Dagstuhl Seminar on Database Workload Management.

The Dagstuhl computer science research center is (remotely) located in the countryside in Saarland, Germany. The actual building is an 18th Century Manor House, first retooled as an old-age home, and then a computer science research center. Workshop participants typically spend the whole week talking and working together.

Dagstuhl Computer Science Center

Shivnath Babu (Duke University), Goetz Graefe (Hewlett Packard),

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Benchmarking is a tricky thing, especially when it comes to compression. Some data compresses quite well while other data does not compress at all. Storing jpeg images in a BLOB column produces 0% compression, but storing the string “AAAAAAAAAAAAAAAAAAAA” in a VARCHAR(20) column produces extremely high (and unrealistic) compression numbers.

This week I was assisting a TokuDB customer understand the insertion performance of TokuDB versus InnoDB and MyISAM for their actual data. The table contained a single VARCHAR(50), multiple INTEGER, one SET, one DECIMAL, and a surrogate primary key.  To support a varied query workload they needed 6 indexes.

Here is an obfuscated schema of the table:

col1 varchar(50) NOT NULL,
col2 int(40) NOT NULL DEFAULT '0',
col3 int(10) NOT NULL DEFAULT '0',
col4 int(10) NOT NULL DEFAULT '0',
col5 int(10) NOT NULL DEFAULT '0',
col6

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Three rules on making indexes around queries to provide good performance

Application performance often depends on how fast a query can respond and query performance almost always depends on good indexing. So one of the quickest and least expensive ways to increase application performance is to optimize the indexes. This talk presents three simple and effective rules on how to construct indexes around queries that result in good performance.

Time: 2PM EDT / 11AM PDT

This webinar is a general discussion applicable to all databases using indexes and is not specific to any particular MySQL® storage engine

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We found a simple XA transaction that crashes MySQL 5.5 replication. This simple transaction inserts a row into an InnoDB table and a TokuDB table. The bug was caused by a flaw in the logging code exposed by the transaction’s use of two XA storage engines (TokuDB and InnoDB). This bug was fixed in the TokuDB 6.0.1 release.

Here are some details.  Suppose that a database contains the following tables.

create table t1 (a int) engine=InnoDB
create table t2 (a int) engine=TokuDB

 The following transaction

begin
insert into t1 values (1)
insert into t2 values (2)
commit

causes the replication slave to crash.

The crash occurs when mysqld tries to dereference a NULL pointer.

#4  0x000000000088e203 in MYSQL_BIN_LOG::log_and_order (this=0x14b8640, thd=0x7f7758000af0, xid=161, all=true,

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Solving the Challenges of Big Databases with MySQL

When you’re using MySQL for big data (more than ten times as large as main memory), these challenges often arise: loading data fast; maintaining indexes under insertions deletions, and updates; adding and removing columns online; adding indexes online; preventing slave lag; and compressing data effectively.

This session shows why some of these challenges are difficult to solve with storage engines based on B-trees, how Fractal Tree® data structures work, and why they can help solve these problems. Tokutek sells a transaction-safe Fractal Tree storage engine for MySQL, but the presentation is primarily about the underlying technology. It includes a discussion of both the theoretical and practical aspects of Fractal Tree indexes.

I have the privilege of being able to give

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In my last post, I wrote that I observed many similarities between TokuDB and MySQL Cluster. Many features that benefit TokuDB also benefit MySQL Cluster, and vice versa, with Hot Column Addition and Deletion (HCAD) being an example. Over my next few posts, I expand on some more of these possibly unexpected similarities.

Today I want to focus on optimizer support for clustering keys. Both MySQL Cluster and TokuDB can benefit from the MySQL optimizer supporting clustering keys. For TokuDB, the

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Table optimization is a necessary evil; tables sometimes need to be optimized to reclaim space or to improve query performance.  Unfortunately, MySQL blocks writes to a table while it is being optimized.  Because optimization time is proportional to the table size, writes can be blocked for a long time.  Fractal Tree indexes support online optimization; however, the MySQL metadata lock gets in the way of writing while optimizing.  We will describe a simple patch to MySQL that enables online optimization of TokuDB tables.

Why do tables need to be optimized?  Here are some reasons.

• Insertions with random keys can result in a tree with underutilized leaf blocks.  Many tree algorithms split nodes in half when they become full.  If these nodes are stored in fixed sized blocks, like many B-trees do, then there can be a lot of wasted space.  Table optimization of

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Volver,
con la frente marchita,
las nieves del tiempo
platearon mi sien…

Martin’s blog (Mi Buenos Aires Querido) was named after a tango, so I decided to follow his lead. The 4 lines of the refrain make reference to someone returning to Buenos Aires after 20 years, now looking a little bit older. I moved to the US about 15 years ago and have returned many times to visit. However, I have never gone back to participate in an event like this with so many renowned speakers from the

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As a TokuDB storage engine developer, numerous times I’ve been struck by the similarities between MySQL Cluster and TokuDB. Namely, many times where I find myself thinking, “TokuDB would benefit from this feature”, I also end up thinking “MySQL Cluster would benefit from this feature” as well.

At first glance, one may wonder why. TokuDB is a storage engine designed to work well on big data, providing compression, agility, and performance, while MySQL Cluster is a distributed database solution (http://www.mysql.com/products/cluster/ (http://www.mysql.com/products/cluster/" target="_blank)) that provides (among many other things) auto sharding and 99.999% availability. TokuDB’s innovation, Fractal Trees® indexes, are designed to drastically reduce the number of disk seeks performed, but TokuDB still operates on a hard

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Next week, a couple of us at Tokutek will be heading to Buenos Aires for the MySQL / MariaDB Conference & Expo LA. Gerry Narvaja actually grew up in Buenos Aires. My own family is Argentinian. I spent several years there as a child, and my childhood home in South Carolina was Argentinian in spirit: absolutely no English, and the only grits that entered my house were served with tomato sauce and called polenta!

As some of you get ready to visit Buenos Aires, maybe for the first time, I thought I’d share an abbreviated list of my favorite things to do.

First of all, make sure to watch the really fantastic con movie Nueve Reinas. Not only is it a great movie, but much of it takes place in the Hilton where the conference will be

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As ones data model evolves changing the database schema becomes painful, especially for big databases where the table must be taken offline. Fortunately, Tokutek introduced online schema changes starting in TokuDB v5.0.

A typical schema change involves adding or deleting a column from a table. These operations usually require the table to be rebuilt offline since the row format is different. In contrast to other storage engines however, column addition or deletion with TokuDB just inserts a broadcast update message into the fractal tree data structure, rather than rebuilding the table. This message defers changing rows from the old format to the new format and is executed after the alter table operation is long gone. The trick is to allow the storage engine to

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Executive Summary

Fast indexing requires the leaves of a Fractal Tree® Index to be big. But some queries require the leaves to be small in order to get any reasonable performance. Basements nodes are our way to achieve these conflicting goals, and here I’ll explain how.

Big Leaves

On many occasions, we at Tokutek have pointed out that TokuDB is write optimized, which means TokuDB indexes data much faster than a B-tree solution such as InnoDB. As with any write-optimized data structure, Fractal Tree indexes need to bundle up lots of small writes into a few big writes. Otherwise, there’d be no way to beat a B-tree. So the question is, how big do the writes have to be?

Consider how long it takes to write k bytes to a disk. First, there is the seek time s, which we can assume to be independent of k.

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I’ll be attending this year’s US based Open Database Camp from June 8-10 in Charlotte, NC. The conference is co-located with SouthEast LinuxFest 2012.

It appears that OpenSQL Camp was renamed Open Database Camp since I see many database technologies listed on their site that do not use SQL as an access method. The final schedule of presentations shows lots of MySQL content for Friday. There is one session each for MySQL, PostgreSQL, and MongoDB on Saturday. Sunday is “unconference” style, hopefully we can get more variety in those sessions.

I love attending this type of conference because I learn how real-world

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The signal-to-noise ratio in the NoSQL world has made it hard to figure out what’s going on, or even who has something new. For all the talk of performance in the NoSQL world, much of the most exciting part of what’s new is really not about performance at all.

Take for example, MongoDB, which has a really great data model and MapReduce has a very handy scripting language. These are genuine and probably long-lasting contributions. Their innovation is all about finding a new language to use for interacting with data. They are about NoSQL.

The confusion comes, for me, when we get to the performance side of the equation. I explore this in detail in an article I did for Datanami recently – http://www.datanami.com/datanami/2012-05-22/the_sound_and_the_nosql_fury.html.

TokuDB® is a proven solution that scales MySQL® and MariaDB® from GBs to TBs with unmatched insert and query speed, compression, and online schema flexibility.

Tokutek’s recently launched TokuDB v6 delivers all of these features and more, with the introduction of high performance replication for MySQL and MariaDB. TokuDB v6 eliminates the common and persistent problem of “slave lag” in which a replication server is unable to keep up with the query load borne by the master server. TokuDB v6 solves this by offering high ingestion rates at the slave.

Time: 2PM EDT / 11AM PDT

REGISTER TODAY

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Many database management tasks become difficult as you move from millions of rows and gigabytes of data to billions of rows and terabytes of data. Such tasks include ingesting data while maintaining indexes; changing schemas without downtime; and supporting connections, replication, and backup. For some scaling problems (connections and replication), MySQL® is better than most of the competition. For others, such as indexing, schema changes, and backup, MySQL has typically been harder to use. Fortunately, the tasks MySQL does well are in its core, whereas the tasks that are more difficult can be solved with storage engine plug-ins.

I recently gave a talk at

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Tackling machine data on the ground to ensure successful operations for NASA in space

Issues addressed:

• Scaling MySQL to multi-terabytes
• Insertion rates as InnoDB hit a performance wall
• Schema flexibility to handle an evolving data model

The Company:  Southwest Research Institute (SwRI) is an independent, nonprofit applied research and development organization. The staff of more than 3,000 specializes in the creation and transfer of technology in engineering and the physical sciences. Currently, SwRI is part of an international team working on the NASA

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We are excited to have Gerry Narvaja start today at Tokutek! Gerry has spent more than 25 years in the software industry, most of them working with databases for different kinds of applications, from embedded to large-scale web products. Gerry worked first at MySQL, and then Sun Microsystems supporting the Sales teams. In 2008 he transitioned into being a Senior MySQL DBA. Gerry graduated as an Electronic Engineer from I.T.B.A (Instituto Tecnológico de Buenos Aires) and has an M.B.A. from Universidad del Salvador in collaboration with S.U.N.Y.A (State University of NY at Albany).

Gerry enjoys helping users to solve complex database production issues. For almost a year he has been co-hosting the popular MySQL Community podcast, OurSQL, which was given the

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