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?
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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
Topics will include:
We look forward to having you join[Read more...]
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[Read more...]
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
Topics will include:
We look forward to having you join the webinar. We also hope that by sharing these results with[Read more...]
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[Read more...]
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[Read more...]
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.
The tutorial was organized as follows:
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[Read more...]
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,[Read more...]
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[Read more...]
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
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[Read more...]
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[Read more...]
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[Read more...]
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;[Read more...]
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[Read more...]
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[Read more...]
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.”[Read more...]
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[Read more...]
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[Read more...]
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.[Read more...]
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.
In TokuDB v5.2, we have reworked our locking scheme to better support multi-client workloads, and as[Read more...]
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[Read more...]
Issue addressed: Managing metadata at exabyte scale
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[Read more...]
At next month’s Boston MySQL Meetup, I will give a talk: “Fractal Tree Indexes – Theoretical Overview and Customer Use Cases.” The meetup is 7 pm Monday, January 9th, 2012, and will be held at MIT Building E51 Room 337e (corner of Ames & Amherst St, Cambridge, MA). Thanks to host Sheeri Cabral for the invitation.
Most databases employ B-trees to achieve a good tradeoff between the ability to update data quickly and to search it quickly. It[Read more...]
As a storage engine developer, I am excited for MySQL 5.6. Looking at http://dev.mysql.com/tech-resources/articles/whats-new-in-mysql-5.6.html, there has been plenty of work done to improve the performance of reads in MySQL for all storage engines (provided they take advantage of the new APIs).
What would be great to add is API improvements to increase the performance of writes, and more specifically, updates. For many applications that perform updates, such as applications that do click counting or impression counting, there are significant opportunities for improving write performance.
Take the following example of click counting (or impression counting). You have a website and want to save the number of times links on your website have been clicked. Your table[Read more...]
With the release of TokuDB v5.0 last March, we delivered a powerful and agile storage engine that broke through traditional MySQL scalability and performance barriers. As deployments of TokuDB have grown more varied, one request we have repeatedly heard from customers and prospects, especially in areas such as online advertising, social media, and clickstream analysis, is for improved performance for multi-client workloads.
Tokutek is now pleased to announce limited beta availability for TokuDB v5.2. The latest version of our flagship product offers a significant improvement over TokuDB v5.0 in multi-client scaling as well as performance gains in point queries, range queries, and trickle load speed. There are a host of other smaller changes and improvements that are detailed in our release notes (available to beta participants).
TokuDB continues to[Read more...]
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