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Generate synthetic manifests for performance tests #1495

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@plypaul plypaul commented Oct 30, 2024

This PR adds a few classes to generate a synthetic manifest. The synthetic manifest can then be used to profile performance and scaling behavior.

@cla-bot cla-bot bot added the cla:yes label Oct 30, 2024
@plypaul plypaul force-pushed the p--short-term-perf--35 branch from a174eb8 to 66aa80b Compare October 30, 2024 23:20
@plypaul plypaul marked this pull request as ready for review October 30, 2024 23:24
@plypaul plypaul force-pushed the p--short-term-perf--35 branch from 66aa80b to ab4a8b5 Compare October 31, 2024 17:11
@plypaul plypaul force-pushed the p--short-term-perf--34 branch from 85662b2 to 7357bb9 Compare November 1, 2024 17:38
@plypaul plypaul force-pushed the p--short-term-perf--35 branch from ab4a8b5 to 43468b2 Compare November 1, 2024 17:38
* Minimize the number of parameters required.

Notes:
* The synthetic manifest groups semantic models into two types - ones containing measures, and others containing dimensions.
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I suppose this still captures the relevant behavior from production models, so it's fine? Is there anything extra we'd able to test by relaxing this and creating "mixed" models?

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Same question for the other constraints: you can reach all measures via all dimensions etc.

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So those are simplifications to make it easier to generate semantic models and to make it easier to generate queries for metric. Mixed models would make it mimic production better, but I didn't think the added complexity would flesh out any performance cases that much better.

Base automatically changed from p--short-term-perf--34 to main November 9, 2024 01:54
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