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Displaying posts with tag: disk seek (reset)

Understanding Indexing – SF MySQL Meetup
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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 …

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OldSQL Tricks or NewSQL Treats
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Why do B-trees need “Tricks” to work?

Marko Mäkelä recently posted a couple of “tips and tricks” you can use to improve InnoDB performance. Tips and tricks. A general purpose relational database like MySQL shouldn’t need “tips and tricks” to perform well, and I lay the blame on design choices that were made in the early ’70s: the B-tree data structure underlying all OldSQL databases. B-trees were designed for machines that had very different performance characteristics than the machines of today. Hardware has changed, but B-trees are the same. Tips …

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On “Replace Into”, “Insert Ignore”, Triggers, and Row Based Replication
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In posts on June 30 and July 6, I explained how implementing the commands “replace into” and “insert ignore” with TokuDB’s fractal trees data structures can be two orders of magnitude faster than implementing them with B-trees. Towards the end of each post, I hinted at that there are some caveats that complicate the story …

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On “Replace Into”, “Insert Ignore”, and Secondary Keys
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In posts on June 30 and July 6, I explained how implementing the commands “replace into” and “insert ignore” with TokuDB’s fractal trees data structures can be two orders of magnitude faster than implementing them with B-trees. Towards the end of each post, I hinted at that there are some caveats that complicate the story a little. In …

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Why “insert … on duplicate key update” May Be Slow, by Incurring Disk Seeks
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In my post on June 18th, I explained why the semantics of normal ad-hoc insertions with a primary key are expensive because they require disk seeks on large data sets. I previously explained why it would be better to use “replace into” or to use “insert ignore” over normal inserts. In this post, I explain why another alternative to normal inserts, “insert … on duplicate key update” is no better in MySQL, …

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Making “Insert Ignore” Fast, by Avoiding Disk Seeks
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In my post from three weeks ago, I explained why the semantics of normal ad-hoc insertions with a primary key are expensive because they require disk seeks on large data sets. Towards the end of the post, I claimed that it would be better to use “replace into” or “insert ignore” over normal inserts, because the semantics of these statements do NOT require disk seeks. In my post last week, I explained how the command “replace …

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Making “Replace Into” Fast, by Avoiding Disk Seeks
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In this post two weeks ago, I explained why the semantics of normal ad-hoc insertions with a primary key are expensive because they require disk seeks on large data sets. Towards the end of the post, I claimed that it would be better to use “replace into” or “insert ignore” over normal inserts, because the semantics of these statements do NOT require disk seeks. In this post, I explain how the command “replace into” can be fast with fractal trees.

The semantics of “replace into” are as follows:


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Making Updates Fast, by Avoiding Disk Seeks
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The analysis that shows how to make deletions really fast by using clustering keys and TokuDB’s fractal tree based engine also applies to make updates really fast. (I left it out of the last post to keep the story simple). As a quick example, let’s look at the following statement:

update foo set price=price+1 where product=toy;

Executing …

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Disk seeks are evil, so let’s avoid them, pt. 4
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Continuing in the theme from previous posts, I’d like to examine another case where we can eliminate all disk seeks from a MySQL operation and therefore get two orders-of-magnitude speedup. The general outline of these posts is:


  • B-trees do insertion disk seeks. While they’re at it, they piggyback some other work on the disk seeks. This piggyback work requires disk seeks regardless.
  • TokuDB’s Fractal Tree indexes don’t do insertion disk seeks. If we also get rid of the …



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Making Deletions Fast, by Avoiding Disk Seeks
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In my last post, I discussed how fractal tree data structures can be up to two orders of magnitude faster on deletions over B-trees. I focused on the deletions where the row entry is known (the storage engine API handler::delete_row), but I did not fully analyze how MySQL delete statements can be fast. In this post, I do. Here I show how one can use TokuDB, a storage engine that uses fractal tree data structures, to make MySQL deletions run fast.

Let’s take a step back and analyze the work needed to be done to execute a MySQL …

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Showing entries 1 to 10 of 11 1 Older Entries

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