Tag - replace into

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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TokuDB speeds up “replace” and “insert ignore” operations by relaxing the affected rows constraint

In posts on June 30 and July 6, we 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, we 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 “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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