Enable obfuscation cache for SQL and MongoDB

[sethsamuel]

What does this PR do?

• Enables a cache for obfuscation of SQL and MongoDB queries.
• Moves a previously inactive configuration option to the correct place for users to disable this cache if desired.

Motivation

Obfuscating SQL and Mongo queries can incur a substantial CPU overhead, especially of those strings are long. They should be perfectly cacheable, as the obfuscated output should never change given the same input. The query cache was added in a previous change but was not configured correctly.

Enabling the cache on a branch build shows major CPU savings for a Mongo instance:

Describe how to test/QA your changes

Once deployed there should be a drop in CPU usage for agents monitoring databases with large queries.

Possible Drawbacks / Trade-offs

Smoke testing this change on a branch build showed no substantial change in memory usage. The cache is capped at a size that should prevent memory growth.

Additional Notes

Change was deployed to gizmo for testing. Hit rate on the cache is close to 100% very quickly and CPU usage is steady or lower compared to previous RC. Profiling confirms the ObfuscateSQLString method is no longer consuming measurable CPU time.

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: d097dddd-a26b-4dfe-bf89-ac14bf0b033f

Baseline: 6faf2e3
Comparison: c8ce7d6
Diff

Optimization Goals: ❌ Significant changes detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	basic_py_check	% cpu utilization	-5.22	[-9.12, -1.33]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	otel_to_otel_logs	ingress throughput	+0.81	[+0.12, +1.50]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.07	[-0.71, +0.85]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.06	[+0.01, +0.12]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.03	[-0.69, +0.74]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.02	[-0.60, +0.65]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.02	[-0.12, +0.08]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.03	[-0.85, +0.79]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.06	[-0.83, +0.71]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.20	[-0.66, +0.27]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.53	[-1.27, +0.20]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.84	[-4.32, +2.63]	1	Logs
➖	quality_gate_idle	memory utilization	-1.05	[-1.10, -0.99]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	-1.51	[-1.65, -1.37]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-3.14	[-3.29, -2.98]	1	Logs bounds checks dashboard
✅	basic_py_check	% cpu utilization	-5.22	[-9.12, -1.33]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard

Explanation

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

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

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[agent-platform-auto-pr]

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=50001871 --os-family=ubuntu

Note: This applies to commit c8ce7d6

[louis-cqrl]

LGTM

[ajgajg1134]

Overall looks good, only a few small comments

[ajgajg1134]

looks like this line is unfinished? Maybe it should add SQL and Mongo?

[ajgajg1134]

part of me thinks this reflection check isn't the ideal place to check this (I do recall the internal chat about this) - since technically there could exist a component that performs valid shutdown logic even on a nil pointer. I'm not super strong feeling here though, so this change isn't necessary for merging here.

[sethsamuel]

/merge

[dd-devflow]

Devflow running: /merge

View all feedbacks in Devflow UI.

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2024-11-26 18:31:59 UTC ℹ️ MergeQueue: waiting for PR to be ready

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2024-11-26 19:04:41 UTC ℹ️ MergeQueue: merge request added to the queue

The median merge time in main is 23m.