[CSPM] Avoid recomputing rule evaluation when inputs are unchanged

[jinroh]

This PR introduces some hashing of our resolver outputs to being able to avoid doing unnecessary Rego evaluation if the inputs is unchanged.

The main motivation is to avoid the heavy lifting of compiling Rego policies for nothing. This step is the one that shows up the most in our profiles, while our inputs are most of the time constants.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Reviewer's Checklist

• [ ] If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
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[pr-commenter[bot]]

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Regression Detector Results

Run ID: 6976997f-efc1-45ff-aabb-a5bebf19ce5e
Baseline: 80cc73d70612604eba19073f0d0038bde8dd3253
Comparison: 79ad92c4c289aa36331a5081b7132f95290f1094
Total datadog-agent CPUs: 7

Explanation

A regression test is an integrated performance test for datadog-agent in a repeatable rig, with varying configuration for datadog-agent. What follows is a statistical summary of a brief datadog-agent run for each configuration across SHAs given above. The goal of these tests are to determine quickly if datadog-agent performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
tcp_syslog_to_blackhole	ingress throughput	+0.91	[+0.75, +1.07]	100.00%
uds_dogstatsd_to_api_nodist_nomulti_200MiB	ingress throughput	+0.44	[+0.33, +0.56]	100.00%
otel_to_otel_logs	ingress throughput	+0.15	[-1.44, +1.75]	12.44%
trace_agent_json	ingress throughput	+0.02	[-0.10, +0.14]	18.81%
uds_dogstatsd_to_api_nodist_nomulti_100MiB	ingress throughput	+0.00	[-0.13, +0.13]	0.48%
uds_dogstatsd_to_api_nodist_100MiB	ingress throughput	+0.00	[-0.13, +0.13]	0.21%
file_to_blackhole	egress throughput	+0.00	[-1.38, +1.38]	0.00%
trace_agent_msgpack	ingress throughput	-0.00	[-0.11, +0.11]	0.87%
tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.07, +0.05]	19.78%
uds_dogstatsd_to_api	ingress throughput	-0.86	[-2.91, +1.20]	50.71%
uds_dogstatsd_to_api_nodist_200MiB	ingress throughput	-0.93	[-1.03, -0.82]	100.00%
file_tree	egress throughput	-2.90	[-5.10, -0.71]	97.04%