[NPM] Only count evicts on adding to process cache

[hmahmood]

What does this PR do?

Only counts evicts that happen when adding an entry to the process cache in the network_tracer module. Previously, we were incrementing the counter in the evict callback, which is also be called during a remove operation on the cache.

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

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[paulcacheux]

I think this might change again in the upstream library..

• https://github.com/hashicorp/golang-lru/releases/tag/v2.0.6
• Revert #135 and add tests for eviction callback hashicorp/golang-lru#154

[hmahmood]

I think this might change again in the upstream library..

• https://github.com/hashicorp/golang-lru/releases/tag/v2.0.6
• Revert #135 and add tests for eviction callback hashicorp/golang-lru#154

We are already using 2.0.6 I believe (although this appears to be a recent change #19020). So we should have the right behavior for evicts.

[hmahmood]

Oh great @paulcacheux you're the one who bumped it :-)

[hmahmood]

The Remove behavior is still the same in 2.0.6, which is what this PR is trying to avoid.

[pr-commenter]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 2725bae0-1fb8-4efa-8940-7f0ce349aa86
Baseline: 955b682
Comparison: bfef0c1
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
otel_to_otel_logs	ingress throughput	+0.40	[-1.18, +1.98]	32.31%
file_tree	egress throughput	+0.00	[-2.04, +2.04]	0.00%
tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.07, +0.06]	12.49%
trace_agent_msgpack	ingress throughput	-0.03	[-0.14, +0.08]	35.45%
trace_agent_json	ingress throughput	-0.05	[-0.17, +0.08]	46.16%
uds_dogstatsd_to_api_nodist	ingress throughput	-0.70	[-0.81, -0.60]	100.00%
file_to_blackhole	egress throughput	-0.73	[-2.17, +0.71]	59.45%
tcp_syslog_to_blackhole	ingress throughput	-0.82	[-0.96, -0.69]	100.00%
uds_dogstatsd_to_api	ingress throughput	-0.97	[-3.09, +1.14]	55.19%
uds_dogstatsd_to_api_nomultivalue	ingress throughput	-2.47	[-4.57, -0.38]	94.79%