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Displaying posts with tag: TokuDB (reset)

Considering TokuDB as an engine for timeseries data
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I am working on a customer’s system where the requirement is to store a lot of timeseries data from different sensors.

For performance reasons we are going to use SSD, and therefore there is a list of requirements for the architecture:

  • Provide high insertion rate
  • Provide a good compression rate to store more data on expensive SSDs
  • Engine should be SSD friendly (less writes per timeperiod to help with SSD wear)
  • Provide a reasonable response time (within ~50 ms) on SELECT queries on hot recently inserted data

Looking on these requirements I actually think that TokuDB might be a good fit for this task.

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Building TokuMX and TokuDB for Production
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Recently, we’ve seen a few people ask us about building TokuMX from scratch. While it’s best if you just use the binaries you can get from us (they have all the right optimizations, we’ve tested them, and we can interpret coredumps they generate), we recognize there are other reasons you might need to do a custom build.

Since we actually build six distinct products all using the Fractal Tree indexing® library (community and enterprise versions of TokuDB for MySQL, TokuDB for MariaDB, and TokuMX), our build process is pretty complicated, compared to software packages that might, for example, just involve one source repository and link against a few standard libraries. Our TokuMX builds involve four git repositories, three

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Slides from Boston MongoDB User Group Meetup on 7/31/13
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On Wednesday night, the Boston MongoDB User group was kind enough to have me speak about TokuMX Internals. I spoke about Fractal Tree® indexes and the technical reasons behind the benefits they provide to MongoDB applications. Although the talk mostly references TokuMX and MongoDB, all the theory applies to TokuDB and MySQL as well.

My slides are on our technology overview page, along with other great content.

Opportunities to present technical material to an engaged audience asking tough questions is rare, and much appreciated. So thank you to the Boston MongoDB User group for having me present.

Comparing MongoDB, MySQL, and TokuMX Data Layout
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A lot is said about the differences in the data between MySQL (http://www.mysql.com/) and MongoDB. Things such as “MongoDB is document based”, “MySQL is relational”, “InnoDB has a clustering key”, etc.. Some may wonder how TokuDB, our MySQL storage engine, and TokuMX, our MongoDB product, fit in with these data layouts. I could not find anything describing the differences with a simple google search, so I figured I’d write a post explaining how things compare.

So who are the players here? With MySQL, users are likely familiar with two storage engines: MyISAM, the original default up until MySQL 5.5, and

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Why Unique Indexes are Bad
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Before creating a unique index in TokuMX or TokuDB, ask yourself, “does my application really depend on the database enforcing uniqueness of this key?” If the answer is ANYTHING other than yes, do not declare the index to be unique. Why? Because unique indexes may kill your write performance. In this post, I’ll explain why.

Unique indexes are a strange beast: they have no impact on standard databases that use B-Trees, such as MongoDB and MySQL, but may be horribly painful for databases that use write optimized data structures, like TokuMX’s Fractal Tree(R) indexes. How? They

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How TokuMX Gets Great Compression for MongoDB
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In my last post, I showed what a Fractal Tree® index is at a high level. Once again, the Fractal Tree index is the data structure inside TokuMX and TokuDB, our MongoDB and MySQL products. One of its strengths is the ability to get high levels of compression on the stored data. In this post, I’ll explain why that is.

At a high level, one can argue that there isn’t anything special about our compression algorithms. We basically do this: we take large chunks of data, use known compression methods (e.g. zlib,

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MariaDB Storage Engine for CCM forum
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CCM Benchmark is one of the leading forum provider on the web,  ROI is a major concern for them  and historically MyISAM was used on the forum replication cluster.  Reason is that MyISAM gave better ROI/performance on data that is hardly electable to cache mechanism.

This post is for MySQL users at scale,  if the number of servers or datacenter cost is not an issue for you, better get some more memory or flash storage and ou will found Lucifer server to demonstrate that your investment is not a lost of money or just migrate to Mongo.  

Quoting Damien Mangin, CTO at CCM "I like my data to be small, who want's to get to a post where the question is not popular and have no answer. Despite cleaning we still get more data than what commodity hardware memory can offer and storing all post in memory would be a major waste of



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TokuMX Fractal Tree(R) indexes, what are they?
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With our recent release of TokuMX 1.0, we’ve made some bold claims about how fast TokuMX can run MongoDB workloads. In this post, I want to dig into one of the big areas of improvement, write performance and reduced I/O.

One of the innovations of TokuMX is that it eliminates a long-held rule of databases: to get good write performance, the working set of your indexes should fit in memory. The standard reasoning goes along the lines of: if your indexes’ working set does not fit in memory, then your writes will induce I/O, you will become I/O bound, and performance will suffer. So, either make sure

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Announcing TokuDB v7 Enterprise Edition: Hot Backup and Support
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As promised, the Enterprise Edition of TokuDB®, Version 7, is ready. TokuDB Version 7, Enterprise Edition, introduces Hot Backup. You can now back up all your TokuDB tables directly from MySQL or MariaDB, with no down time. In addition, TokuDB Enterprise Edition comes with a support package.

TokuDB v7 Enterprise Edition maintains all our established advantages: hot schema changes, excellent compression, fast trickle load, fast bulk load, fast range queries through clustering indexes, no fragmentation, and full MySQL/MariaDB compatibility for ease of installation.

For details on pricing and supported MySQL and MariaDB versions, please see our FAQ.

To learn more about TokuDB:

  • Download executables
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Converting compressed InnoDB tables to TokuDB 7.0.1
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Or: how to make it work in TokuDB version 7.0.1. This is a follow up on a discussion on the tokudb-user group.

Background

I wanted to test TokuDB's compression. I took a staging machine of mine, with production data, and migrated it from Percona Server 5.5 To MariaDB 5.5+TokuDB 7.0.1. Migration went well, no problems.

To my surprise, when I converted tables from InnoDB to TokuDB, I saw an increase in table file size on disk. As explained by Tim Callaghan, this was due to TokuDB interpreting my compressed table's "KEY_BLOCK_SIZE=4" as an instruction for TokuDB's page size. TokuDB should be using 4MB block size, but thinks it's being instructed to use

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10 Newer Entries Showing entries 61 to 70 of 257 10 Older Entries

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