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Previous 30 Newer Entries Showing entries 61 to 90 of 274 Next 30 Older Entries

Displaying posts with tag: TokuView (reset)

TokuDB Fast Update Benchmark
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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

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Wanted: Evaluators to Try MongoDB with Fractal Tree Indexing
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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

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Sysbench Benchmark for MongoDB
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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,

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The Last Mile for Big Data – Strata Overview with Jeff Kelly of Wikibon (Part 2)
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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

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MySQL and MongoDB – Strata Discussion with Jeff Kelly of Wikibon (Part 1)
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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

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MongoDB + Fractal Tree Indexes = High Compression
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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

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NoSQL is Great, But You Still Need Indexes
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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

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Fast Updates with TokuDB
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With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: fast updates with TokuDB. Update intensive applications can have their throughput limited by the random read capacity of the storage system. The cause of the throughput limit is the read-modify-write algorithm that MySQL uses when processing update statements. MySQL reads a row from the storage engine, applies the updates to it, and then writes the new row to the storage engine. To address this throughput limit, TokuDB uses a different update algorithm that simply encodes the update expressions of the SQL statement into tiny programs that are stored in an update Fractal Tree® message. This update message is

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Concurrency Improvements in TokuDB v6.6 (Part 2)
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In Part 1, we showed performance results of some of the work that’s gone in to TokuDB v6.6. In this post, we’ll take a closer look at how this happened, on the engineering side, and how to think about the performance characteristics in the new version.

Background

It’s easiest to think about our concurrency changes in terms of a Fractal Tree® index that has nodes like a B-tree index, and buffers on each node that batch changes for the subtree rooted at that node. We have materials that describe this available here, but we can proceed just knowing that:

  • To inject data into the tree, you need to store a message in a buffer at the root of the tree. These
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    Concurrency Improvements in TokuDB v6.6 (Part 1)
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    With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: concurrency within a single index. Previously, while there could be many SQL transactions in-flight at any given moment, operations inside a single index were fairly serialized. We’ve been working on concurrency for a few versions, and things have been getting a lot better over time. Today I’ll talk about what to expect from v6.6. Next time, we’ll see why.

    Summary of Results

    Running multiple iiBench clients on a single MySQL instance, we see a big improvement in the cumulative insertion speed at all concurrency levels. We see a gain of 33.9% in single-threaded performance and 51.8% at

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    Tracking 5.3 Billion Mutations: Using MySQL for Genomic Big Data
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    University of Montreal Tracks Genomic Data With Tokutek’s TokuDB.

    Faster insertion rates, improved scalability and agility support lab’s fast growing research database as it grows from 100s of GBs to 1 TB and beyond.

    Issue addressed: MySQL database used for genomic research must be able to quickly ingest huge amounts of incoming data – hundreds of thousands of records every day. It also must be able to retrieve data quickly in response to a diverse set of research requests.

    Enabling the Hunt for New Cures for Diseases by Seamlessly Processing Billions of Mutations  [Read more...]

    The Results Are In!
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    We wanted to take a moment to say thanks to all of our customers and to the wider MySQL and MariaDB community. Today we announced a doubling of our customer base for the year ending December 31, 2012. Significant milestones over the last year included new technology and service partnerships, several awards, rapid hiring, as well as three upgrades to TokuDB®. We even dabbled in some MongoDB benchmarks. And to fuel continued growth in 2013, we secured additional venture capital funding last November.

    Did You Hear? NASA Uses TokuDB for Big Data with MySQL!

    To read the full press release and learn more,

      [Read more...]
    Webinar: Introduction to TokuDB v6.6
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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 v6.6 delivers all of these features and more, with additional improvements in multi-client, fast SQL updates, and in-memory performance.

    Date: January 15th
    Time: 2 PM EST / 11 AM PST
    REGISTER TODAY

    Topics will include:

    • Performance – With a 10x or more improvement in insertions and indexing, TokuDB delivers faster, more complex ad hoc queries in live production systems without rewriting or tuning applications. Offering high performance even when tables are too large for memory, TokuDB scales MySQL and MariaDB


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    Announcing TokuDB v6.6: Performance Improvements
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    We are excited to announce TokuDB® v6.6, the latest version of Tokutek’s flagship storage engine for MySQL and MariaDB.

    This version offers three types of performance improvements: in-memory, multi-client and fast updates.

    Although TokuDB is optimized for large tables, which are larger than memory, many workloads consist of a mix of large and small tables. TokuDB v6.6 offers improvements on in-memory performance, with a more than 100% improvement on Sysbench at many concurrency levels and more than 200% improvement on TPC-C at many concurrency levels. Details to follow.

    We have also made improvements in multi-threaded performance. For example, single threaded trickle loads have always been fast in TokuDB. But now multi-threaded trickle loads are even faster. An

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    Marinating in 2013
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    What a flashback this week. Staring at a text terminal trying to establish a connection with a remote server, I began to fret whether I would get my homework assignment done on time. My mind raced back to college nights years ago in the Fishbowl, hunched over an Athena workstation. Would this be another late night fueled by Jolt cola in order to get my problem set done?

    Thankfully, no!

    Embarking on my first software class in quite a while was relatively painless, and I have Sheeri Cabral and her detailed guidance to thank. This week I started the

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    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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    Scalable Databases for Startups
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    One of the great things about the MassTLC unConference is the spontaneity of the ideas. In the morning I ran into an old colleague whose startup was looking at switching databases and struggling with the options. Hence, “Scalable Databases for Startups” seemed like a great topic, so I proposed it, and then was off and running full steam after lunch.

    The session brought in a wide variety of firms. While there were several vendors there – Basho, Calpont, InterSystems,

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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

      [Read more...]
    Small Data
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    There is obviously much being written these days about Big Data. While the term has many different meanings to many different folks, our MySQL and MariaDB customers tend to find their data to be uncomfortably big when the tables become too large for memory. In this case, more storage has to be acquired, performance starts to lag, and making changes to the schema becomes a challenge.

    TokuDB addresses these issues for big MySQL instances by delivering high compression rates, faster insertion and query performance, and agile

      [Read more...]
    Join us at ad:tech this week
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    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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    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

      [Read more...]
    Previous 30 Newer Entries Showing entries 61 to 90 of 274 Next 30 Older Entries

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