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Showing entries 1 to 10 of 291 10 Older Entries

Displaying posts with tag: TokuView (reset)

Increasing Cloud Database Efficiency – Like Crows in a Closet
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In Mo’ Data, Mo’ Problems, we explored the paradox that “Big Data” projects pose to organizations and how Tokutek is taking an innovative approach to solving those problems. In this post, we’re going to talk about another hot topic in IT, “The Cloud,” and how enterprises undertaking Cloud efforts often struggle with idea of “problem trading.” Also, for some reason, databases are just given a pass as traditionally “noisy neighbors” and that there is nothing that can be done about it. Lets take a look at why we disagree.

With the birth of the information age came a coupling of business and IT. Increasingly …

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TokuDB Table Optimization Improvements
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Section I: Fractal Tree and Optimization Overview
Tokutek’s Fractal Tree® technology provides fast performance by injecting small messages into buffers inside the Fractal Tree index. This allows writes to be batched, thus eliminating I/O that is required in traditional B-tree indexes for every operation. Additional background information on how Fractal Trees operate can be found in Zardosht Kasheff’s blog entitled, TokuMX Fractal Tree Indexes, What Are They? Don’t be thrown off by the title, Fractal Tree Indexes access …

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TokuDB Hot Backup Now a MySQL Plugin
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In the recently released TokuDB 7.5.5 the implementation of TokuDB hot-backup moved from a patch to the MySQL Server, to MySQL Plugin.  Why did we make this change?

TokuDB hot backup makes a transactionally consistent copy of the TokuDB files while applications continue to read and write these files.  Christian Rober wrote a nice series of blogs about how hot backup works.  See TokuDB hot backup 1 and TokuDB hot backup 2 for details.  In …

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Mo’ Data, Mo’ Problems
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Welcome to blog #2 in a series about the benefits of the Fractal Tree. In this post, I’ll be explaining Big Data, why it poses such a problem and how Tokutek can help. Given the fact that I am a lifelong fan of both Hip-hop and Big Data, the title was a no-brainer and, given the artist, a bit of a pun.

 I am as tired as you of hearing the term “Big Data.” It’s so overused, that it ceases to have specific meaning anymore. You see, data hardly ever starts as “big” or a “problem.” Rather, it starts small and easily manageable, but gradually grows to some unimaginable size and becomes a beast in need of slaying, like …

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Fractal Tree Greatness: The Nexus
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In my recent travels, I’ve been speaking with database users at various meetups and trade shows worldwide. Very often, I got questions centering around the best use cases for our products, be it TokuDB, our MySQL storage engine, or, TokuMX, our distribution of MongoDB. Over 90% of the time, I responded Cloud, Big Data or both. You see, in the software industry we’re like kindergartners, we like things to fit into neat categories. If you know any software sales people, you’ll recognize this as a fitting analogy (at least in terms of energy and attention span), but I digress. This strategy helps allocate resources where they are most likely to make an impact, …

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Testing TokuDB’s Group Commit Algorithm Improvement
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The MySQL 5.6 Release has introduced some changes to how two phase commit works and is managed.  In particular, the commit phase of transactions to the binary log is now serialized and this behavior is something we identified fairly immediately.  We implement a group commit algorithm that needed to be altered so that TokuDB’s group commit to its recovery log would function effectively.

As part of our effort to verify the new Binary Log Group Commit functionality introduced in TokuDB 7.5.4 for Percona Server, we wanted to demonstrate the substantial increase in throughput scaling but also show the bottleneck caused by the …

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Scaling TokuDB Performance with Binlog Group Commit
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TokuDB offers high throughput for write intensive applications, and the throughput scales with the number of concurrent clients.  However, when the binary log is turned on, TokuDB 7.5.2 throughput suffers.  The throughput scaling problem is caused by a poor interaction between the binary log group commit algorithm in MySQL 5.6 and the way TokuDB commits transactions.   TokuDB 7.5.4 for Percona Server 5.6 fixes this problem, and the result is roughly an order of magnitude increase in SysBench throughput for in memory workloads.

MySQL uses two phase commit protocol to synchronize the MySQL binary log with the …

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Benchmarking Presentation at Percona Live London 2014
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In a few weeks I’m presenting “Performance Benchmarking: Tips, Tricks, and Lessons Learned” at Percona Live London 2014 (November 3-4). I continue to learn lessons and improve my benchmarking capabilities, so the content is a full upgrade from my presentation at Percona Live Santa Clara in April 2013. Anyone interested in achieving and sustaining the best performance out of their software/hardware/application should attend.

Also, …

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TokuDB Read Free Replication : Details and Use Cases
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The biggest innovation in TokuDB v7.5 is Read Free Replication (RFR). I blogged a few days ago posting a benchmark showing how much additional throughput can be achieved on a replication slave, while at the same time lowering the read IO operations to almost zero. The official documentation on the feature is available …

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TokuDB v7.5 Read Free Replication : The Benchmark
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New to TokuDB® v7.5 is a feature we’re calling “Read Free Replication” (RFR). RFR allows TokuDB replication slaves to process insert, update, and delete statements with almost no read IO. As a result, the slave can easily keep up with the master (no lag) as well as brings all the read IO capacity of the slave for read-scaling your workload.

The goal of this blog is two-fold: (1) to cover why RFR is important and how RFR works and (2) to run a simple before/after benchmark showing the impact of RFR on a well known workload. Later this week I’ll post another blog showing other interesting use-cases for RFR beyond this first benchmark. …

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Showing entries 1 to 10 of 291 10 Older Entries

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