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Showing entries 1 to 30 of 248 Next 30 Older Entries

Displaying posts with tag: TokuDB (reset)

Why TokuDB hates Transparent HugePages
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If you try to install the TokuDB storage engine on a modern Linux distribution it might fail with following error message:

2014-07-17 19:02:55 13865 [ERROR] TokuDB will not run with transparent huge pages enabled.
2014-07-17 19:02:55 13865 [ERROR] Please disable them to continue.
2014-07-17 19:02:55 13865 [ERROR] (echo never > /sys/kernel/mm/transparent_hugepage/enabled)

You might be curious why TokuDB refuses to start with Transparent HugePages. Are they not a good thing… allowing smaller kernel page tables and less TLB misses when accessing data in the buffer pool? I was curious, so I asked Tim Callaghan this very question.

This problem originates with TokuDB using jemalloc memory allocator, which uses a particular trick to deal with memory fragmentation. The classical problem with memory



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Reference architecture for a write-intensive MySQL deployment
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We designed Percona Cloud Tools (both hardware and software setup) to handle a very high-intensive MySQL write workload. For example, we already observe inserts of 1bln+ datapoints per day. So I wanted to share what kind of hardware we use to achieve this result.

Let me describe what we use, and later I will explain why.

Server:

  • Chassis: Supermicro SC825TQ-R740LPB 2U Rackmount Chassis
  • Motherboard: Supermicro X9DRI-F dual socket
  • CPU: Dual Intel Xeon Ivy Bridge E5-2643v2 (6x 3.5Ghz cores, 12x HT cores, 25M L3)
  • Memory: 256GB (16x 16GB 256-bit quad-channel) ECC registered DDR3-1600
  • Raid: LSI MegaRAID 9260-4i 4-port 6G/s hardware RAID controller, 512M buffer
  • MainStorage: PCIe SSD HGST FlashMAX II 4.8TB
  • Secondary
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TokuDB tips: MySQL backups
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In my recent post, “TokuDB gotchas: slow INFORMATION_SCHEMA TABLES,” I saw a couple questions and tweets asking if we use TokuDB in production. Actually I mentioned it in that post and we also blogged about it in a couple of other recent posts:

So, yes, we are using Percona Server + TokuDB as a main storage engine in Percona Cloud Tools to store timeseries data.

And, yes, Percona

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TokuDB gotchas: slow INFORMATION_SCHEMA TABLES
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We are using Percona Server + TokuDB engine extensively in Percona Cloud Tools and getting real usage operational experience with this engine. So I want to share some findings we came across, in hope it may help someone in their work with TokuDB.

So, one problem I faced is that SELECT * FROM INFORMATION_SCHEMA.TABLES is quite slow when I have thousands tables in TokuDB. How slow? For example…

select * from information_schema.tables limit 1000;
...
1000 rows in set (18 min 31.93 sec)

This is very similar to what InnoDB faced a couple years back. InnoDB solved it by adding

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Percona Server 5.6.19-67.0 with TokuDB (GA) now available
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Percona is glad to announce the release of Percona Server 5.6.19-67.0 on July 1, 2014. Download the latest version from the Percona web site or from the Percona Software Repositories.

Based on MySQL 5.6.19, including all the bug fixes in it, Percona Server

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Percona Server with TokuDB (beta): Installation, configuration
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My previous post was an introduction to the TokuDB storage engine and aimed at explaining the basics of its design and how it differentiates from InnoDB/XtraDB. This post is all about motivating you to give it a try and have a look for yourself. Percona Server is not officially supporting TokuDB as of today, though the guys in the development team are working hard on this and the first GA release of Percona Server with TokuDB is looming on the horizon. However, there’s a beta version available now. For the installation tests in this post I’ve used the latest version of

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Getting to know TokuDB for MySQL
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During last April’s Percona Live MySQL Conference and Expo, TokuDB celebrated it’s first full-year as an open source storage engine. I still remember reading the official announcement and the expectations it created one year ago. The premises were very interesting as it had the potential of helping MySQL manage “big data” in a way InnoDB just couldn’t. It also provided additional interesting features like “hot schema changes,” all the while making our dear flash storages last longer.

While I’ve kept an eye on the evolution of TokuDB this past year, I reckon I haven’t given

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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 databases. Given those performance characteristics, in this post, I will present some best practices when using this feature in TokuMX or TokuDB. Note that these best practices are designed for TokuMX and TokuDB only, which

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Percona Server 5.6.17-66.0 is now available
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Percona is glad to announce the release of Percona Server 5.6.17-66.0 on June 11, 2014. Downloads are available here and from the Percona Software Repositories.

Based on MySQL 5.6.17, including all the bug fixes in it, Percona Server 5.6.17-66.0 is the current GA

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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 Oracle related post states “Anyone with un-partitioned databases over 500 gigabytes is courting disaster.” That’s not true for TokuDB or TokuMX. Nevertheless,

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MariaDB 10.0.11 Overview and Highlights
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MariaDB 10.0.11 was recently released, and is available for download here:

https://downloads.mariadb.org/mariadb/10.0.11/

This is the second GA release of MariaDB 10.0, and 12th overall release of MariaDB 10.0.

This is primarily a bug-fix release.

Here are the main items of note:

  • Updated TokuDB engine to version 7.1.6
  • Updated Spider storage engine to version 3.2 (now Gamma)
  • Updated XtraDB storage engine to version 5.6.17-65.0
  • Updated InnoDB storage engine to version 5.6.17
  • Updated
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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”

    ~~~~~~~~~~~~~~~~~~~~~~~~

    Last time, in Thoughts on Small Datum – Part 2 I shared my cliff notes and a graph on Mark Callaghan’s (@markcallaghan) March 11th insertion rate benchmarks using flash storage media. In those tests he compares MySQL (http://www.mysql.com/) outfitted with the

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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 (http://www.mysql.com" target="_blank) and MongoDB (or, preferably, our high-performance distributions of them, TokuDB and TokuMX).

    Why should Mr. Harford take that walk? Well, he recently

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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.

    ——————-

    On March 11th, Mark, a former Google and now Facebook database guru, published an insertion rate benchmark comparing MySQL (http://www.mysql.com) outfitted with the InnoDB storage engine with two NoSQL alternatives — basic MongoDB and TokuMX (the Tokutek high-performance

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    Percona Live 2014 Impressions
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    Three weeks ago I had the privilege of attending my first Percona Live MySQL conference, which was incredible! In particular, there were two things that I found impressive about the conference.

    First, was the amount of knowledge sharing and support that MySQL users provide each other; it truly is a community. Coming from EMC, I’ve attended several conferences in the past, but I’ve always considered them more of a marketing focused event, mostly spent doing product launches and company roadmaps and not much time fostering knowledge sharing and informal get-togethers: Percona Live was different. There were well thought out tutorials, information packed presentations, and keynotes rife with practical knowledge culled from the real world. I had many great conversations at our booth with people that have evaluated TokuDB or TokuMX or were planning to as soon as they got back into

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    Thoughts on Small Datum – Part 1
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    A little background…

    When I ventured into sales and marketing (I’m an engineer by education) I learned I would often have to interpret and simply summarize the business value that is sometimes hidden in benchmarks. Simply put, the people who approve the purchase of products like TokuDB® and TokuMX™ appreciate the executive summary.

    Therefore, I plan to publish a multipart series here on TokuView where I will share my simple summaries and thoughts on business value for the benchmarks Mark Callaghan (@markcallaghan), a former Google and now Facebook database guru, is publishing on his blog, Small Datum.

    I’m going to start with his first benchmark post and work my way forward to

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    How Tokutek uses the Random Query Generator framework to test TokuDB
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    During a typical release cycle for TokuDB at Tokutek, we spend time qualifying and hardening the product using numerous tools.  For example, we run stress and unit tests directly on the Fractal Tree indexes, MySQL Test Runner (MTR) tests on the storage engine as well as numerous performance benchmarks to prevent regressions. In addition, we have recently been implementing the Random Query Generator (RQG) framework internally here at Tokutek to more exhaustively stress TokuDB.  My name is Joel Epstein and I am a Quality Assurance Engineer here at Tokutek who has been integrating RQG into the overall test plan strategy.

    At

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    MariaDB 10.0.10 Overview and Highlights
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    MariaDB 10.0.10 was recently released, and is available for download here:

    https://downloads.mariadb.org/mariadb/10.0.10/

    This is the first GA ("Generally Availability", aka "recommended for production systems") release of MariaDB 10.0, and 11th overall release of MariaDB 10.0.

    Since this is the initial 10.0 GA release, this is primarily a bug-fix and polishing release.

    Here are the main items of note:

    read more

    Why TokuDB does not use the ‘uint3korr’ function
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    The ‘uint3korr’ function inside of the mysqld server extracts a 3 byte unsigned integer from a memory buffer. One use is for ‘mediumint’ columns which encode their value in 3 bytes. MySQL 5.6 and MariaDB 10.0 claims to have optimized this function for x86 and x86_64 processors. There is a big comment that says:

    Attention: Please, note, uint3korr reads 4 bytes (not 3)!
    It means, that you have to provide enough allocated space.

    The ‘uint3korr’ optimization may be fast, but it is not valgrind safe. Here is an example where valgrind detects TokuDB reading beyond the end of a buffer when it uses the ‘uint3korr’ function.

    ==3899== Thread 36:
    ==3899== Invalid read of size 4
    ==3899== at 0xB76C089: tokudb_uint3korr(unsigned char const*) (hatoku_defines.h:533)
    ==3899== by 0xB795C5E:
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    Percona Live 2014 Recap
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    The MySQL community continues to amaze me, everyone is friendly and always willing to answer questions and help others. It’s always nice to meet people in-person that I’ve met virtually in the last 12 months. Percona Live 2014 MySQL Conference and Expo in Santa Clara just wrapped up and I wanted to share my highlights of the event before I unplug for the weekend.

    • I heard several stories of people’s experiences with TokuDB, open sourcing 12 months ago has been a huge success for Tokutek. We have a growing community in our tokudb-user Google Group, please share your experiences and help others.
    • In his “Galera Cluster New Features” presentation, Sappo Jaakola of
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    Uninitialized data in the TokuDB recovery log
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    A TokuDB MySQL test run with valgrind reported an uninitialized data error when writing into the TokuDB recovery log.

    ==1032== Syscall param write(buf) points to uninitialised byte(s)
    ==1032== at 0x3EFA60E4ED: ??? (in /lib64/libpthread-2.12.so)
    ==1032== by 0xB894038: toku_os_full_write(int, void const*, unsigned long) (file.cc:249)
    ==1032== by 0xB83248A: write_outbuf_to_logfile(tokulogger*, __toku_lsn*) (logger.cc:513)
    ==1032== by 0xB83326C: toku_logger_maybe_fsync(tokulogger*, __toku_lsn, int, bool) (logger.cc:836)
    ==1032== by 0xB8327DE: toku_logger_fsync_if_lsn_not_fsynced(tokulogger*, __toku_lsn) (logger.cc:586)
    ==1032== by 0xB8493E6: toku_txn_maybe_fsync_log(tokulogger*, __toku_lsn, bool) (txn.cc:600)
    ==1032== by 0xB7B4EBB: toku_txn_commit(__toku_db_txn*, unsigned int, void (*)(__toku_txn_progress*, void*), void*, bool, bool) (ydb_txn.cc:198)
    ==1032==
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    Lock Escalation and Big Transactions in TokuDB and TokuMX
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    We have seen TokuDB lock escalation stall the execution of SQL operations for tens of seconds. To address this problem, we changed the lock escalation algorithm used by TokuDB and TokuMX so that the cost of lock escalation only affects big transactions. We also eliminated a serialization point when running lock escalation.

    Transactions in TokuDB and TokuMX accumulate locks on key ranges while they execute. These locks allow multiple transactions to run concurrently. The locks are released when the transaction commits or aborts.

    The locks are stored in an in memory data structure that contains a set of key range and transaction identifier pairs. Since the locks are stored in memory and we want to support arbitrarily large transactions, an algorithm is needed to kick in when the amount of memory used to store locks exceeds a maximum limit. The

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    Percona Server with TokuDB: Packing 15TB into local SSDs
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    Two weeks ago we released an Alpha release of Percona Server with TokuDB. Right now I am on a final stage of evaluation of TokuDB for using in our project Percona Cloud Tools and it looks promising.

    What is the most attractive in TokuDB? For me it is compression, but not just compression: TokuDB provides great performance over compressed data.

    In my synthetic tests I saw a compression ratio of 10:1 (TokuDB LZMA to InnoDB uncompressed), in the real production data it is less, 6:1, but still impressive.

    In our servers we have 4 x SSD Crucial M500 960GB combined in RAID5, which give 2877.0 GB of usable space. With TokuDB we should be


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    My Favorite MongoDB Replication Feature: Crash Safety
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    At an extremely high level, replication in MongoDB and MySQL are similar. Both databases have exactly one machine, the primary (or master), that accepts writes from clients. With a single transaction (or atomic operation, in MongoDB’s case), the tables and oplog (or binary log in MySQL) are modified to reflect the change. The log captures what the change is so other secondaries (or slaves) can read the changes and process them, making the slaves identical to the master. (Note that I am NOT talking about multi-master replication.)

    Underneath the covers, their implementations are quite different. And in peeking underneath the covers while developing TokuMX, I learned

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    Big trouble with zero-length character columns in TokuDB
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    What good is a zero-length character column in a MySQL table? A zero-length character column has type of ‘char(0)’. If it is nullable, then it can at least store one bit. If it is not nullable, then the value for this column in all rows is a null string. IMO, not very useful. However, the MySQL Reference Manual says that there are valid uses for such a column, so TokuDB should support it. Unfortunately, we recently found and fixed a bug related to zero length character columns in TokuDB.

    A Random Query Generator (RQG) trial generated a table with a ‘char(0)’ column and caused TokuDB to crash when executing an alter

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    March 20 Webinar: How to Scale MySQL for Big Data Applications
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    You may think that you have to buy, install, and get up to speed on a new database if you want to work with large amounts of data, but you can do more than you think with the MySQL you already have.
    Register Now!

    SPEAKER: Jon Tobin, Tokutek
    DATE: Thursday, March 20th
    TIME: 1pm ET

    Without having to change your application or do special tuning you can increase performance and save significant time and money when you need to scale.

    Join Tokutek’s Jon Tobin as he demonstrates how to use MySQL or MariaDB in Big Data applications by simply upgrading the storage engine with TokuDB, and how to effectively evaluate TokuDB for increased performance, compression and agility.

    During this webinar you will learn:

    • How to dramatically increase performance without having to rewrite



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    Announcing TokuDB v7.1.5
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    Yesterday we released TokuDB v7.1.5, which includes the following important features and fixes:

    • Upgraded MySQL and MariaDB to version 5.5.36.
    • Six months of performance improvements to our underlying Fractal Tree indexing.
    • Fixes for bugs, stalls, and behavioral issues reported by users.
    • Fixes for issues identified by the addition of Random Query Generator (RQG) testing.
    • Fixes for issues identified by the addition of Valgrind testing.
    • Full details on the changes in TokuDB v7.1.5 can be found in the release notes section of the TokuDB User’s Guide, available from our documentation page.

    As always, you can download the Community and Enterprise

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    Tokutek and Percona Live 2014
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    I’ve been a little behind in recent blogging efforts, and realized that in less than a month we’ll be back at Percona Live: MySQL Conference and Expo 2014, aka PLMCE. Last year’s PLMCE was my first, as well as the event where Tokutek announced the open sourcing of TokuDB.

    It’s hard to believe that a year has gone by, but the customer adoption in both enterprise and community users has been awesome. TokuDB is available from our website for both MySQL and MariaDB, and is also

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    The MySQL ARCHIVE storage engine – Alternatives
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    In my previous post I pointed out that the existing ARCHIVE storage engine in MySQL may not be the one that will satisfy your needs when it comes to effectively storing large and/or old data. But are there any good alternatives? As the primary purpose of this engine is to store rarely accessed data in disk space efficient way, I will focus here on data compression abilities rather then on performance.

    The InnoDB engine provides compressed row format, but is it’s efficiency even close to the one from that available in archive engine? You can also compress MyISAM tables by using myisampack tool, but that also means a table will be read only after such operation.

    Moreover, I don’t trust MyISAM nor Archive when it comes to data

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    The Effects of Database Heap Storage Choices in MongoDB
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    William Zola over at MongoDB gave a great talk called “The (Only) Three Reasons for Slow MongoDB Performance”. It reminded me of an interesting characteristic of updates in MongoDB. Because MongoDB’s main data store is a flat file and secondary indexes store offsets into the flat file (as I explain here), if the location of a document changes, corresponding entries in secondary indexes must also change. So, an update to an unindexed field that causes the document to move also causes modifications to every secondary index, which, as William points out, can be expensive. If a document has indexed an array, this

      [Read more...]
    Showing entries 1 to 30 of 248 Next 30 Older Entries

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