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PostgreSQL: Question Optimization for Mere People | by Eyal Trabelsi | Dec, 2024

admin by admin
December 4, 2024
in Artificial Intelligence
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PostgreSQL: Question Optimization for Mere People | by Eyal Trabelsi | Dec, 2024
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We are going to use it for instance of a easy question: we need to depend the variety of customers that don’t have Twitter handles.

EXPLAIN ANALYZE
SELECT COUNT(*) FROM customers WHERE twitter != '';
We are able to see the execution plan returned from the EXPLAIN ANALYZE clause

It appears to be like cryptic at first, and It’s even longer than our question, and that on a small instance of real-world execution plans may be overwhelming in case you do not focus 😭.

But it surely does present helpful data. We are able to see that the question execution took 1.27 seconds, whereas the question planning took solely 0.4 milli-seconds (negligible time).

We are able to see the time the question planning and execution took

The execution plan is structured as an inverse tree. Within the subsequent determine, you may see the execution plan is split into completely different nodes every one in all which represents a special operation whether or not it is an Aggregation or a Scan.

We are able to see the time the question planning and execution took

There are various sorts of nodes operations, from Scan associated (‘Seq Scan’, ‘Index Solely Scan’, and so on…), Be part of associated( ‘Hash Be part of’, ’Nested Loop’, and so on…), Aggregation associated (‘GroupAggregate’, ’Mixture’, and so on…) and others ( ‘Restrict’, ‘Kind’, ‘materialize’, and so on..). Happily it’s worthwhile to keep in mind any of this.

Professional Tip #3 💃: Focus is essential, look solely on nodes which are problematic.

Professional Tip #4 💃: Cheat ! on the problematic nodes search what they imply within the clarify glossary.

Now, let’s drill down into how we all know which node is the problematic one.

There’s quite a lot of data we will see on every node

Let’s drill all the way down to what these metrics truly imply.

  • Precise Loops: the variety of loops the identical node executed is 1. To get the whole time and rows, the precise time and rows have to be multiplied by loops values.
  • Precise Rows: the precise variety of produced rows of the Mixture node is 1 (per-loop common and we now have loops is 1).
  • Plan Rows: the estimated variety of produced rows of the Mixture node is 1. The estimated variety of rows may be off relying on statistics.
  • Precise Startup Time: the time it took to return the primary row in milliseconds of the Mixture node is 1271.157 (aggregated and consists of earlier operations).
  • Startup Value: arbitrary items that signify the estimated time to return the primary row of the Mixture node is 845110(aggregated and consists of earlier operations).
  • Precise Complete Time: the time it took to return all of the rows in ms of the Mixture node is 1271.158 (per-loop common and we now have loops is 1 and aggregated and embrace earlier operations).
  • Complete Value: arbitrary items that signify the estimated time to return all of the rows of Mixture node is 845110 (aggregated).
  • Plan Width: the estimated common measurement of rows of the Mixture node is 8 bytes.

Professional Tip #5 💃: be cautious of loops, keep in mind to multiply loops while you care about Precise Rows and Precise Complete Time.

We are going to drill within the subsequent part on a sensible instance.

Tags: DecEyalHumansMereOptimizationPostgreSQLqueryTrabelsi
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