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Displaying posts with tag: indexing (reset)
Advanced JSON for MySQL: indexing and aggregation for highly complex JSON documents

What is JSON
JSON is an text based, human readable format for transmitting data between systems, for serializing objects and for storing document store data for documents that have different attributes/schema for each document. Popular document store databases use JSON (and the related BSON) for storing and transmitting data.

Problems with JSON in MySQL
It is difficult to inter-operate between MySQL and MongoDB (or other document databases) because JSON has traditionally been very difficult to work with. Up until recently, JSON is just a TEXT document. I said up until recently, so what has changed? The biggest thing is that there are new JSON UDF by Sveta Smirnova, which are part of the MySQL 5.7 Labs releases. Currently the JSON UDF are up to version 0.0.4. While these new UDF are a welcome edition to the MySQL database, they don't solve the really tough …

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Introducing ‘MySQL 101,’ a 2-day intensive educational track at Percona Live this April 15-16

Talking with Percona Live attendees last year I heard a couple of common themes. First, people told me that there is a lot of great advanced content at Percona Live but there is not much for people just starting to learn the ropes with MySQL. Second, they would like us to find a way to make such basic content less expensive.

I’m pleased to say we’re able to accommodate both of these wishes this year at Percona Live! We have created a two-day intensive track called “MySQL 101” that runs April 15-16. MySQL 101 is designed for developers, system administrators and DBAs familiar with other databases but not with MySQL. And of course it’s ideal for anyone else who would like to expand their professional experience to include MySQL. The sessions are designed to lay a solid foundation on many aspects of MySQL development, design and …

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Why Unique Indexes are Bad

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? …

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532x Multikey Index Insertion Performance Increase for MongoDB with Fractal Tree Indexes

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 benchmark to test our ability to handle indexing large …

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My Talk Next Week at HighLoad++

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 database. I’ll be reviewing the basic strategies to define the right indexes. I will also cover TokuDB’s Fractal Tree® and Cluster …

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Looking for MongoDB users to test Fractal Tree Indexing

In my three previous blogs I wrote about our implementation of Fractal Tree Indexes on MongoDB, showing a 10x insertion performance increase, a 268x query performance increase, and a comparison of covered indexes and clustered indexes. The benchmarks show the difference that rich and efficient indexing can make to your MongoDB workload.

It’s one thing for us to benchmark MongoDB + TokuDB and another to measure real world performance. If you are looking for a way to improve the performance or scalability of your MongoDB deployment, we can help …

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MongoDB Index Shootout: Covered Indexes vs. Clustered Fractal Tree Indexes

In my two previous blogs I wrote about our implementation of Fractal Tree Indexes on MongoDB, showing a 10x insertion performance increase and a 268x query performance increase. MongoDB’s covered indexes can provide some performance benefits over a regular MongoDB index, as they reduce the amount of IO required to satisfy certain queries.  In essence, when all of the fields you are requesting are present in the index key, then MongoDB does not have to go back to the main storage heap to retrieve anything.  My benchmark results are …

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Webinar: Understanding Indexing

Three rules on making indexes around queries to provide good performance

Application performance often depends on how fast a query can respond and query performance almost always depends on good indexing. So one of the quickest and least expensive ways to increase application performance is to optimize the indexes. This talk presents three simple and effective rules on how to construct indexes around queries that result in good performance.


Time: 2PM EDT / 11AM PDT

This webinar is a general discussion applicable to all databases using indexes and is not specific to any particular MySQL® storage engine (e.g., InnoDB, TokuDB®, etc.). The rules are explained using a simple model that does NOT rely on understanding B-trees, Fractal Tree® indexing, …

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Fractal Tree Indexing and Filesystems – HotStorage 2012

Modern file systems are well equipped to deal with large writes. One area that remains challenging however is to efficiently write out “microdata”, such as metadata and small portions of large files, while showing good I/O utilization when the data is read back. This challenge is evident with mount options like “noatime” which disables updating file access time on reads. This kind of solution avoids the problem altogether. Another approach, delayed allocation, is meant to coalesce small writes in memory as long as possible before writing it out to disk. Filesystems like ext4 and Btrfs use delayed allocation to make a best-effort at reducing fragmentation and random I/O.

Isn’t there a way to fundamentally solve filesystem fragmentation and random I/O?

This week, I’ll be speaking at HotStorage 2012 in Boston. My talk …

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Multi Range Read (MRR) in MySQL 5.6 and MariaDB 5.5

I have written a second blog post in the series of blog posts leading up to the talk comparing the optimizer enhancements in MySQL 5.6 and MariaDB 5.5. This blog post is aimed at the optimizer enhancement Multi Range Read (MRR). Its available in both MySQL 5.6 and MariaDB 5.5

The post Multi Range Read (MRR) in MySQL 5.6 and MariaDB 5.5 appeared first on ovais.tariq.

Showing entries 11 to 20 of 48
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