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Previous 30 Newer Entries Showing entries 61 to 90 of 162 Next 30 Older Entries

Displaying posts with tag: Tokutek (reset)

ODBMS Interview: Scaling MySQL and MariaDB to TBs
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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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Scaling MySQL and MariaDB to TBs: Interview with Martín Farach-Colton.
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“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”– Martín Farach-Colton. Scaling MySQL and MariaDB [...]
Forbes: “Tokutek Makes Big Data Dance”
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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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Webinar: Introduction to TokuDB v6.5
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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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Percona Live New York Wrap-Up
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Tokutek sponsored the Percona Live MySQL / New York 2012 Conference which took place this past Monday and Tuesday.  I spent much of the time at our booth discussing TokuDB with conference attendees but also managed to attend the following presentations:

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Announcing TokuDB v6.5: Optimized for Flash
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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...]
Tokutek CEO Named Innovation All Star
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On Friday our CEO John Partridge was named a “Tech Luminary” in the 17th annual Innovation All Stars award, which is given jointly by Mass High Tech (MHT) and the Boston Business Journal (BBJ). As noted in MHT by the editor, Chris McIntosh, the Luminary designations “reflect deep accomplishment in various technology-related industries.”

For more than 20 years, Tokutek CEO John Partridge has worked with startups in both the Boston area and in Silicon Valley. He joined Tokutek from StreamBase Systems which John co-founded with

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Strange Loop Talk on Indexing
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At next week’s Strange Loop conference, I will give a talk on “Understanding Indexing”. The session is 10 am Monday, September 24th, and will be held in the Midland States Room.

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.

This is a general discussion applicable

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Looking for MongoDB users to test Fractal Tree Indexing
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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

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XLDB Tutorial on Data Structures and Algorithms
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Next week Michael and I (Bradley) will be travelling to Silicon Valley to present a tutorial on Data Structures and Algorithms for Big Databases at the 6th XLDB Conference.

The tutorial, which is 4 hours on Monday afternoon, aims to cover the following topics (but it’s looking like we’ll have to drop several items for lack of time.)

This tutorial will explore data structures and algorithms for big databases. The topics include:

  • Data structures including B-trees, Log Structured Merge Trees, and Streaming B-trees.
  • Approximate Query Membership data structures including Bloom filters and cascade filters.
  • Algorithms for join including hash joins and
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MongoDB Index Shootout: Covered Indexes vs. Clustered Fractal Tree Indexes
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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

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268x Query Performance Increase for MongoDB with Fractal Tree Indexes, SAY WHAT?
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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

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10x Insertion Performance Increase for MongoDB with Fractal Tree Indexes
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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;

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Webinar: Introduction to TokuDB
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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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Dagstuhl Seminar on Database Workload Management
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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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Real World Compression
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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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Webinar: Understanding Indexing
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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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Announcing TokuDB v6.1
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TokuDB v6.1 is now generally available and can be downloaded here.

New features include:

  • Added support for MariaDB 5.5 (5.5.25)
    • The TokuDB storage engine is now available with all the additional functionality of MariaDB 5.5.
  • Added HCAD support to our MySQL 5.5 version (5.5.24)
    • Hot column addition/deletion was present in TokuDB v6.0 for MySQL 5.1 and MariaDB 5.2, but not in MySQL 5.5.  This feature is now present in all MySQL and MariaDB versions of TokuDB.
  • Improved in-memory point query performance via lock/latch refinement
    • TokuDB has always been a great performer on range scans and workloads where the size of the working data set is significantly larger than RAM.  TokuDB v6.0 improved the performance of in-memory point queries at
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How We Spent a Tuesday Fixing a MySQL Replication Bug
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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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My Talks at MySQL Connect and Percona Live NYC
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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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Balada Para Un Loco – A Review of the MySQL, NoSQL, and Cloud Conference
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… Ya se que estás piantao, piantao, piantao…

For my lastest blog, a review of the MySQL, NoSQL and Cloud Conference, I’ll continue to use the tango metaphor. Balada para un loco (ballad for a crazy one) is a Piazzola classic and explains what I think of Santiago Lertora from Binlogic for single handedly putting together this event; he had to be piantao (slang for ‘crazy’) to pursue his vision to kick start the Open Source database community in South America into becoming as active as it is in the US and Europe. He was able to gather some renowned speakers such as our own Martin Farach-Colton, Sheeri Cabral from Mozilla, Max Mether and Massimo Brignoli from SkySQL, Colin Charles from Monty Program, Alejandro Kojima

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How to Stop Playing “Hop and Seek”: MySQL Cluster and TokuDB, Part 2
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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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Hot Table Optimization with MySQL
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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 – MySQL, NoSQL, and Cloud Conference Latin America
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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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How to Stop Playing “Hop and Seek”: MySQL Cluster and TokuDB
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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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Mi Buenos Aires Querido
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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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Addressing Hot Schema Changes in MySQL
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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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Fractal Tree Indexing and Filesystems – HotStorage 2012
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Modern file systems are well equipped to deal with large writes. One area that remains challenging however is to efficiently write out “microdata”, such as metadata and small portions of large files, while showing good I/O utilization when the data is read back. This challenge is evident with mount options like “noatime” which disables updating file access time on reads. This kind of solution avoids the problem altogether. Another approach, delayed allocation, is meant to coalesce small writes in memory as long as possible before writing it out to disk. Filesystems like ext4 and Btrfs use delayed allocation to make a best-effort at reducing fragmentation and random I/O.

Isn’t there a way to fundamentally solve filesystem fragmentation and random I/O?

This week, I’ll be speaking at

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Basement Nodes: Turning Big Writes into Small Reads
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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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Open Database Camp at SouthEast LinuxFest 2012
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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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