Oracle sql updating multiple rows

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So, remember: stop doing row-by-row (slow-by-slow) operations when you could run the same operation in bulk, in a single SQL statement.

Hint: Always know what your ORM (if you're using one) is doing, because the ORM can help you with automatic batching/bulking in many cases.

Something that has been said many times, but needs constant repeating until every developer is aware of its importance, is the performance difference between row-by-row updating and bulk updating. Let me ask this differently: The best way to find out is to benchmark.

If you cannot guess which one will be much faster, remember that row-by-row kinda rhymes with "slow-by-slow" (hint, hint). I'm doing two benchmarks for this: Run 1, Statement 1 : .01457 (avg : .0098) Run 1, Statement 2 : .0133 (avg : .01291) Run 1, Statement 3 : .02351 (avg : .02519) Run 2, Statement 1 : .00882 (avg : .0098) Run 2, Statement 2 : .01159 (avg : .01291) Run 2, Statement 3 : .02348 (avg : .02519) Run 3, Statement 1 : .01012 (avg : .0098) Run 3, Statement 2 : .01453 (avg : .01291) Run 3, Statement 3 : .02544 (avg : .02519) Run 4, Statement 1 : .00799 (avg : .0098) Run 4, Statement 2 : .01346 (avg : .01291) Run 4, Statement 3 : .02958 (avg : .02519) Run 5, Statement 1 : .00749 (avg : .0098) Run 5, Statement 2 : .01166 (avg : .01291) Run 5, Statement 3 : .02396 (avg : .02519) The difference between Statement 1 and 3 is a factor of 2.5x.

The past few chapters have focused on basic query techniques, all centered around the task of getting data out of a database.

This chapter turns the tables, and focuses on the following three topic areas: For ease in finding them when you need them, recipes in this chapter have been grouped by topic: all the insertion recipes come first, followed by the update recipes, and finally recipes for deleting data. It begins with the simple problem of inserting a single row.

The INSERT statement allows you to create new rows in database tables.This cost is much more severe if we unnecessarily create many server roundtrips for a task that could be done in a single roundtrip, namely by using a SQL bulk . If doing everything in a single statement isn't possible due to the limitations of SQL, we can still save roundtrips by grouping statements in a block, either by using an anonymous block in databases that support them: (You can easily send these anonymous blocks over JDBC, as well! The difference is much more drastic if each call to the SQL engine has to be done over the network from another process.Again, the benchmark code is available from a gist, and I will paste it to the end of this blog post as well.

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