Tag - disk seek

Indexing Big Data – NSF Workshop on Research Directions in the Principles of Parallel Computation

I attended the NSF Workshop on Research Directions in the Principles of Parallel Computation in Pittsburgh on 6/27/12. The workshop brought together researchers from academia and industry to explore visions for the future of parallel computing. I was one of 17 invited speakers. We were asked to address the question: “what are three big research […]

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OldSQL Tricks or NewSQL Treats

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 […]

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On “Replace Into”, “Insert Ignore”, Triggers, and Row Based Replication

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 […]

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On “Replace Into”, “Insert Ignore”, and Secondary Keys

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 […]

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Why “insert … on duplicate key update” May Be Slow, by Incurring Disk Seeks

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 […]

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Making “Insert Ignore” Fast, by Avoiding Disk Seeks

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, […]

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Making “Replace Into” Fast, by Avoiding Disk Seeks

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 […]

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