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Displaying posts with tag: indexes (reset)
Write Optimization: Myths, Comparison, Clarifications

Some indexing structures are write optimized in that they are better than B-trees at ingesting data. Other indexing structures are read optimized in that they are better than B-trees at query time. Even within B-trees, there is a tradeoff between write performance and read performance. For example, non-clustering B-trees (such as MyISAM) are typically faster at indexing than clustering B-trees (such as InnoDB), but are then slower at queries.

This post is the first of two about how to understand write optimization, what it means for overall performance, and what the difference is between different write-optimized indexing schemes. We’ll be talking about how to deal with workloads that don’t fit in memory—in particular, if we had our data in B-trees, only the internal nodes (perhaps not even all of them) would fit in memory.

As I’ve already said, there is a tradeoff between write and read …

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Database Insights from Archimedes to the Houston Rockets

Archimedes, the first DBA

According to a recent MIT Sloan Management Review study, top performing organizations use analytics 5 times more than lower performers. That’s pretty astounding. And while we all know about the ocean/lake/waves/(your favorite water analogy) of Big Data we struggle with everyday, information is not knowledge. So how can we get insight from data? Recent articles from O’Reilly and HBR offered some …

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May the Index be with you!

 

The summer’s end is rapidly approaching — in the next two weeks or so, most people will be settling back into work. Time to change your mindset, re-evaluate your skills and see if you are ready to go back from the picnic table to the database table.

With this in mind, let’s see how much folks can remember from the recent indexing talks my colleague Zardosht Kasheff gave (O’Reilly Conference, Boston, and SF MySQL Meetups). Markus Winand’s site “Use the Index, Luke!” (not to be confused with …

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Understanding B+tree Indexes and how they Impact Performance

Indexes are a very important part of databases and are used frequently to speed up access to particular data item or items. So before working with indexes, it is important to understand how indexes work behind the scene and what is the data structure that is used to store these indexes, because unless you understand the inner working of an index, you will never be able to fully harness its power.

On Covering Indexes and Their Impact on Performance

The purpose of this post is to describe what covering indexes are and how they can be used to improve the performance of queries. People mostly use indexes to filter or sort the results but not much thought is given to actually reduce the disk reads by using proper indexes. So I will show you how to reduce disk reads and hence improve the performance of queries by utilizing indexes properly.

Understanding Indexing – SF MySQL Meetup

At this week’s SF MySQL Meetup, I will give a talk: “Understanding Indexing: Three rules on making indexes around queries to provide good performance.” The meetup is 7 pm tomorrow (Wednesday, 6/22), and will be held at CBS Interactive (235 2nd St., San Francisco). Thanks to hosts Erin O’Neill and Mike Tougeron for the invitation and location.

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.

This is a general discussion applicable to all databases using indexes and is not specific to any particular MySQL storage engine (e.g., InnoDB, TokuDB, …

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Utilizing multiple indexes per MySQL table join

Historically it was considered that MySQL will generally use only one index per referenced table in a SQL query. In MySQL 5.0 the introduction of merge indexes enabled for certain conditions the possibility to utilize two indexes however this could result in worst performance then creating a better index. In MySQL 5.1 it became possible to control optimization switches with the optimizer_switch system variable.

However in explaining how to utilize the intersection, union and sort union in queries I discovered that MySQL could use three indexes for one given table.

        Extra: Using union(name,intersect(founded,type)); Using where

I was not aware of this.

Improving Performance with Better Indexes


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Learn how to use one simple advanced technique to make better MySQL indexes and improve your queries by 500% or more. Even with a highly indexed schema significant improvements in performance can be achieved by creating better indexes.

This presentation introduces the approach for correct identification and verification of problem SQL statements and then describes the means of identifying index choices for optimization. Then discussed is not only how to apply indexes to improve query performance, but how to apply better indexes and provide even greater performance gains.

This presentation includes:

  • 6 steps to successful SQL review
  • Effective …
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Tokutek’s Chief Scientist Discusses TokuDB v5.0

Running with Big Data

It’s spring here in Boston, though one could hardly tell (still barely hitting 40°F). So, for those stuck indoors working out on the treadmill, or those lucky enough to do a workout outdoors, we’ve got a great podcast. Our Chief Scientist and co-founder Martín Farach-Colton had the privilege of sitting down with Sheeri Cabral and Sarah Novotny for their weekly MySQL Database Community Podcast (OurSQL Episode 39). In it, he speaks about Tokutek and TokuDB v5.0, which was just released last week (see …

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Better Indexes $ave You Money


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Presentation

Can database performance improvements be achieved with zero code changes? Learn how to use one simple advanced technique to make better MySQL indexes and improve your queries by 500% or more. Even with a highly indexed schema as shown in our 10 table join example, significant improvements in performance can be achieved.

This presentation introduces the approach for correct identification and verification of problem SQL statements and then describes the means of identifying index choices for optimization. Then discussed is not only how to apply indexes to improve query performance, but how to apply better indexes and provide even great performance gains.

This presentation …

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Showing entries 31 to 40 of 52
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