[tagger/entity_id] Replace SplitN with Index to avoid unnecessary allocations

[davidor]

What does this PR do?

Optimizes a couple of functions in comp/core/tagger/types/entity_id.go to avoid unnecessary allocations.

Describe how to test/QA your changes

No manual test needed. We just need to check the results in the AML team benchmarks.

[pr-commenter]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=45307187 --os-family=ubuntu

Note: This applies to commit ac6ac2c

[gabedos]

This change seems to reduce the total number of strings allocated during each run of the function from 3 to 2.

If these string allocations are really a big source of regression, it would potentially be interesting in having the defaultEntityID struct store some additional information to make this splitting more efficient down to only 1 allocation (store the length of the prefix / id, or store them as 2 fields in the struct). However, I have no idea how feasible/compatible this would be with the rest of the tagger.

[davidor]

@gabedos the difference is that splitN allocates N strings whereas the index version works with slices. An alternative would be to use strings.Cut, but it's very similar to the version with .Index.

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: c6f08860-895a-499f-a3d6-652c79fdcd16 Metrics dashboard Target profiles

Baseline: 592a9b9
Comparison: ac6ac2c

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	file_tree	memory utilization	+2.98	[+2.86, +3.09]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.04	[+0.31, +1.77]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.81	[+0.76, +0.85]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.50	[-0.31, +1.31]	1	Logs
➖	idle	memory utilization	+0.49	[+0.44, +0.55]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.06, +0.10]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	basic_py_check	% cpu utilization	-0.25	[-2.95, +2.46]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.32	[-2.84, +2.20]	1	Logs
➖	idle_all_features	memory utilization	-2.51	[-2.63, -2.40]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[davidor]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 24m.

Use /merge -c to cancel this operation!