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Previous 30 Newer Entries Showing entries 31 to 60 of 67 Next 7 Older Entries

Displaying posts with tag: fractal tree indexes (reset)

Move over Marcia: Top Ten for 2012
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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

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Packing for the Holidays
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Every time I visit my family for the holidays, as the date approaches, I find myself filled with dread. It’s nothing sinister, my family’s great, and the season is nice. The reason is simple:

I hate packing.

In fact, I hate both kinds of packing: trip packing, and bit packing. Let me tell you a story about bit packing.

Bit packing

If you’ve ever browsed around the available type attributes in GCC, you may have noticed the entry for “packed”. It seems straightforward enough, and if you’re trying to cram a lot of data in a system (like we do), it can be pretty attractive.

There are plenty

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Yule Blog
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Tired of the same old holiday tunes year after year? Join us as we Rock around the Fractal Tree here at Tokutek.

We’ve fiddled with the chorus; won’t you join us?

Rockin’ around the Fractal Tree
(sung to the tune of Rockin’ around the Christmas Tree)

Rockin’ around the Fractal Tree
As the bits begin to hop
Leaves are full on trees that are not B
Our indexes cannot be stopped

Rockin’ around the Fractal Tree
Let the MySQL spirit ring
Time to compress all into an SSD
And watch out for the Merciless Ming

You will get a sensational feeling when you hear voices singing
“No, not a fable; online changes done with alter table!”
Rockin’ around the Fractal Tree










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Fractal Tree Indexing Overview
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We get a lot of questions about how Fractal Tree indexes work. It’s a write-optimized index with fast queries, but which write-optimized indexing structure is it?

In this ~15 minute video (which uses these slides), I give a quick overview of how they work and what they are good for.

Webinar: Best Practices for a Successful TokuDB Evaluation
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In this webinar we will show step by step how to install, configure, and test TokuDB for a typical performance evaluation. We’ll also be flagging potential pitfalls that can ruin the eval results. It will describe the differences between installing from scratch and replacing an existing MySQL / MariaDB installation. It will also review the most common issues that may arise when running TokuDB binaries.

Date: December 11th
Time: 2 PM EST / 11 AM PST
REGISTER TODAY

Topics will include:

  • Memory allocation
  • Initial data load
  • Indexing, including clustering indexes
  • compression algorithms

We look forward to having you join



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532x Multikey Index Insertion Performance Increase for MongoDB with Fractal Tree Indexes
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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

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Webinar: MongoDB and Fractal Tree Indexes
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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



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Presenting at tomorrow’s NoVA MySQL Meetup (DC Area)
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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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My Talk Next Week at HighLoad++
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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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Presenting “MongoDB and Fractal Tree Indexes” at MongoDB Boston 2012
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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.

Report on XLDB Tutorial on Data Structures and Algorithms
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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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First of New NSF Big Data Grants Go to Tokutek Founders
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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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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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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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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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FROSCON and VLDB
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Next week I (Bradley) will be traveling to FROSCON near Bonn, Germany, and then on to VLDB in Istanbul.

At FROSCON I’ll be talking about fast data structures for maintaining indexes. The talk will share some content with my upcoming MySQL Connect talk.

At VLDB, Dzejla Medjedovic will be presenting a talk on our paper on SSD-friendly Bloom-filter-like data structures. The paper is

Michael A. Bender, Martin Farach-Colton, Rob Johnson, Russell Kraner, Bradley C. Kuszmaul, Dzejla Medjedovic, Pablo Montes, Pradeep Shetty, Richard P. Spillane, and Erez Zadok.
Don’t Thrash: How to Cache Your Hash on Flash. PVLDB 5(11):1627-1637, 2012.

An earlier version of the paper appeared at


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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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Percona Live Slides and Video Available: The Right Read Optimization is Actually Write Optimization
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In April, I got to give a talk at Percona Live, about why The Right Read Optimization is Actually Write Optimization. It was my first industry talk, so I was delighted when someone in the audience said “I feel like I just earned a college credit.”

Box offered to host everyone’s slides from the conference here (mine is here). A big thanks from me to Sheeri Cabral, for

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TokuDB v6.0: Getting Rid of Slave Lag
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Master/slave replication is an important tool that gets used in many ways: distributing read loads among many slaves for performance, using a slave for backups so the master can handle live load, geographically distributed disaster recovery, etc. The Achilles’ Heal of slave performance is that slave workloads are single-threaded. The master can have many clients inserting, updating, querying, whereas the slave has only one insertion client: the master. InnoDB single-client performance is much slower than its multi-client performance, which means that the bottleneck in a master/slave system is often the rate at which a slave can keep up.

If the master has an average transactions per second (tps) that is higher than what the slave can handle, the slave will fall further and further behind. If the slaves are being used to distribute read workload, for example, the

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MySQL Conference and Expo Talk on Benchmarking
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I’ll be speaking on April 11th at 4:30 pm in Room 4 in at the Percona Conference and Expo Talk. The topic will be “Creating a Benchmark Infrastructure That Just Works.

Throughout my career I’ve been involved with maintaining the performance of database applications and therefore created many benchmark frameworks. At Tokutek, an important part of my role is measuring the performance of our storage engine over time and versus competing solutions. There is nothing proprietary about

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1 Billion Insertions – The Wait is Over!
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iiBench measures the rate at which a database can insert new rows while maintaining several secondary indexes. We ran this for 1 billion rows with TokuDB and InnoDB starting last week, right after we launched TokuDB v5.2. While TokuDB completed it in 15 hours, InnoDB took 7 days.

The results are shown below. At the end of the test, TokuDB’s insertion rate remained at 17,028 inserts/second whereas InnoDB had dropped to 1,050 inserts/second. That is a difference of over 16x. Our complete set of benchmarks for TokuDB v5.2 can be found here.

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Announcing TokuDB v5.2: Improved Multi-Client Scaling and Faster Queries
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TokuDB® v5.2, the latest version of Tokutek’s flagship storage engine for MySQL and MariaDB, is now available.

This version offers performance enhancements over previous releases, especially for multi-client scale up and point queries, and extends the cases where ALTER TABLE is non-blocking, in particular adding Hot Column Rename.

TokuDB v5.2 maintains all our established advantages: fast trickle load, fast bulk load, fast range queries through clustering indexes, hot schema changes, great compression, no fragmentation, and full MySQL compatibility for ease of installation. See our benchmark page for details.

Multi-client workloads

In TokuDB v5.2, we have reworked our locking scheme to better support multi-client workloads, and as

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FictionPress Selects TokuDB for Consistent Performance and Fast Disaster Recovery
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FictionPress

Issues addressed:

  • Support complex and efficient indexes at 100+ million rows.
  • Predicable and consistent performance regardless of data size growth.
  • Fast recovery.

Ensuring Predictable Performance at Scale

The Company:  FictionPress operates both FictionPress.com and FanFiction.net and is home to over 6 million works of fiction, with millions of writers/readers

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Top Ten for 2011
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It’s almost the end of the year – that means holiday cards, shopping, cooking, parties, and the inevitable year-end top lists (including gems like this one).

In the spirit of end of year list making, we fed our 60+ blogs this year through Google Analytics to find out what our own top ten blogs were (outside of product announcements). So if you missed an episode of the View (TokuView that is) we’ve got a Tokutek Top Ten for you (spoiler alert – they are mostly technical):

10. Cage Match: OldSQL, NoSQL and NewSQL – References to

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Limelight Networks Chooses TokuDB for New Cloud Storage Service
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Limelight Networks

Issue addressed: Managing metadata at exabyte scale

Delivering Agile Storage in the Cloud with Billions of Assets

The Company: Founded in 2001, Limelight Networks, Inc (NASDAQ: LLNW) is an Internet platform and services company that integrates the most business-critical parts of the online content value chain. Limelight’s cloud-based services enable customers to profit from the shift of content and advertising to the online world, from the explosive growth of mobile and connected devices, and from the migration of IT applications and

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Previous 30 Newer Entries Showing entries 31 to 60 of 67 Next 7 Older Entries

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