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Showing entries 1 to 10 of 284 10 Older Entries

Displaying posts with tag: TokuView (reset)

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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Announcing TokuDB v7.5: Read Free Replication
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Today we released TokuDB® v7.5, the latest version of Tokutek’s storage engine for MySQL and MariaDB.

I’ll be publishing two blogs next week to go into more details about our new “Read Free Replication”, but here are high level descriptions of the most important new features.

Read Free Replication
TokuDB replication slaves can now be configured to process the binary logs with virtually no read IO. This is accomplished via two new server parameters: one to allow the skipping of uniqueness checks (for inserts and updates), the other to eliminate read-before-write behavior (for updates and …
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An Updated Description of Clustering Keys for TokuDB
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Covering indexes can result in orders of magnitude performance improvements for queries. Bradley’s presentation on covering indexes describes what a covering index is, how it can effect performance, and why it works. However, the definition of a covering index can get cumbersome since MySQL limits the number of columns in a key to 16 (32 on MariaDB).

Tokutek introduced multiple clustering indexes into MySQL to address these problems. Zardosht describes the …

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Best Practices for Partitioned Collections and Tables in TokuDB and TokuMX
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In my last post, I gave a technical explanation of the performance characteristics of partitioned collections in TokuMX 1.5 (which is right around the corner) and partitioned tables in relational …

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Understanding the Performance Characteristics of Partitioned Collections
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In TokuMX 1.5 that is right around the corner, the big feature will be partitioned collections. This feature is similar to partitioned tables in Oracle, MySQL, SQL Server, and Postgres. A question many have is “why should I use partitioned tables?” In short, it’s complicated. The answer depends on your workload, your schema, and your database of choice. For example, this …

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Thoughts on Small Datum – Part 3
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Background: If you did not read my first blog post about why I am sharing my thoughts on the benchmarks published by Mark Callaghan on Small Datum you may want to skim through it now for a little context:Thoughts on Small Datum – Part 1”

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Last time, in Thoughts on Small Datum – Part 2 I shared my cliff notes and a graph on Mark …

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Maybe You Should Try Taking a Walk in My Shoes
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The title of this post should really be, “Maybe He Should Try Taking a Walk in Your Shoes.”

The he I’m referring to is economist and author, Tim Harford. The you is the people who use NewSQL and NoSQL approaches to mine big data with database platforms like MySQL and MongoDB (or, preferably, our high-performance distributions of them, …

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Thoughts on Small Datum – Part 2
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If you did not read my first blog post about Mark Callaghan’s (@markcallaghan) benchmarks as documented in his blog, Small Datum, you may want to skim through it now for a little context.

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On March 11th, Mark, a former Google and now Facebook database guru, published an insertion rate benchmark comparing MySQL outfitted with the …

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Showing entries 1 to 10 of 284 10 Older Entries

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