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Displaying posts with tag: indexing (reset)
Using FORCE INDEX: When Your Query Optimizer Gets It Wrong

A Guest Post from Jordan Raine, Clio

Jordan Raine is a staff software developer at Clio, the leading provider of legal practice management, client relationship management, and client intake software. Clio is dedicated to helping lawyers meet client needs and run efficient, profitable practices. Jordan wrote this post to help mySQL developers save time/effort and ultimately improve their customers' experience. 

Inspecting Index Usage In MySQL, PostgreSQL, and MongoDB

In my recent post about the Left-Prefix Index Rule, I explained how queries can use all or part of a compound (multi-column) index. Knowing what makes an index fully usable by a query is important, but it's also important to know how to figure out how much of an index a query is able to use. In this article I'll show you how to do this by reading the query's explain plan. This article covers MySQL, Postgres, and MongoDB.

The Left-Prefix Index Rule

There's an important heuristic in database indexing that I call the left-prefix rule. It helps you understand whether the database will be able to make the best use of a multi-column index to speed up a query. It's one of a small handful of really important things to understand about indexes!


The left-prefix rule can be confusing, but once you grok it, it's simple and easy to use. It applies to MySQL, PostgreSQL, MongoDB, and many other databases. In this post I'll explain what the left-prefix rule means and how it works.

Covering Indexes in MySQL, PostgreSQL, and MongoDB

A covering index is a fantastic query performance optimization. An index covers a query when the index has all the data needed to execute the query, so the server can retrieve the query’s data without reading any rows or documents. Covered queries are usually SELECT queries, but in addition to reads, indexes can cover portions of what a write query needs to do its work.

In this article I’ll explain what a covering index is in a bit more detail; the technical implementation and caveats in MySQL, PostgreSQL, and MongoDB; and how to check that a query is using a covering index.

The High Cost and Low Benefit of Unused Index Advice

You have to be careful what you wish for. The signal:noise ratio of finding out which indexes are “unused” is low. This isn’t obvious at first glance, but upon deeper inspection, there’s often not much to be gained, and it can even harm you. Let’s look at why.

 

How to Find Unused Indexes

The typical way to figure out which indexes aren’t used is to examine index-usage counters over a period of time. The database itself can provide these statistics: PostgreSQL has built-in views that can expose them, and in MySQL there’s similar views as well as community patches that originally came from Google. There’s a way to get the stats in MongoDB too.

The Advice

What advice do people want about unused indexes? It typically boils down to:

  • Tell me indexes that don’t seem to have any activity according to the statistics.
  • Prioritize these indexes by the size or …
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Find Queries That Need Indexes With VividCortex

Queries can be slow or resource-heavy for many reasons, but one of the most common is that the table doesn't have a good index for the query. This is true for MySQL, PostgreSQL, and especially for MongoDB. Adding an index where it's missing is often a night-and-day improvement, as shown in this screenshot of a query's performance. The index was added midway through the time range, and the query got dramatically faster:

(That image is from Rocio Delgado's Query Performance At Scale presentation about process and tooling at GitHub).

Discovering queries that can benefit from adding indexes is typically a task for an expert, but at VividCortex we've made it possible for anyone, even a novice, to figure out which queries to examine. Our smart algorithms prioritize which queries can benefit most from speedups, and then look at which of those …

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MySQL: a few observations on the JSON type

MySQL 5.7 comes with built-in JSON support, comprising two major features:

Despite being added rather recently (in MySQL 5.7.8 to be precise - one point release number before the 5.7.9 GA version), I feel the JSON support so far looks rather useful. Improvements are certainly possible, but compared to for example XML support (added in 5.1 and 5.5), the JSON feature set added to 5.7.8 is reasonably complete, coherent and standards-compliant.

(We can of course also phrase …

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Log Buffer #426: A Carnival of the Vanities for DBAs

This Log Buffer edition transcends beyond ordinary and loop in few of the very good blog posts from Oracle, SQL Server and MySQL.


Oracle:

  • Variable selection also known as feature or attribute selection is an important technique for data mining and predictive analytics.
  • The Oracle Utilities SDK V4.3.0.0.2 has been released and is available from My Oracle Support for download.
  • This article provides a high level list of the new features that exist in HFM 11.1.2.4 and details the changes/differences between HFM 11.1.2.4 and previous releases.
  • In recent …
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7 quick MySQL performance tips for the small business

We’ve heard lots in recent years about Big Data and the alternative models of data management and processing, like Hadoop and NoSQL. But truth be told, relational databases are still the workhorses of most of today’s small and medium sized businesses. Relational DBs date back over 40 years and SQL skills are fairly common, and they’re known to be highly secure.

 

MySQL is the world’s second most popular relational database management system (RDMS) and is the most popular open-source version of the database. It’s easily accessible and is often known to be part of the LAMP web development stack, standing for the ‘M’ in the acronym of popular tools, along with Linux, Apache, and PHP/Perl/Python. The fact that MySQL is free, easy to setup and scales fast are some of the main reasons why it’s the best match for many SMBs.

 

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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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