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DataDog / saluki

An experimental toolkit for building telemetry data planes in Rust.
Apache License 2.0
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refactor to add shim for per-tenant context resolving/interning #232

Open lukesteensen opened 1 week ago

lukesteensen commented 1 week ago

The rebase on #217 was a little hairy, so there may be some bits left in a weird state.

pr-commenter[bot] commented 4 days ago

Regression Detector (Saluki)

Regression Detector Results

Run ID: fa923b72-dae4-4808-9a80-794b09d0110d

Baseline: 68c99c14d6be174797bc611488d73c958aa97a54 Comparison: c6170f36d1ea8bbcf79e0534125e5ea7b126f528

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

Significant changes in experiment optimization goals

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

perf experiment goal Δ mean % Δ mean % CI trials links
❌ dsd_uds_500mb_3k_contexts ingress throughput -13.64 [-13.72, -13.55] 1
✅ dsd_uds_100mb_3k_contexts_distributions_only memory utilization -15.39 [-15.57, -15.21] 1

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links | |------|-------------------------------------------------|--------------------|----------|------------------|--------|-------| | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +1.89 | [-1.43, +5.21] | 1 | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | +0.02 | [-0.01, +0.04] | 1 | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.01 | [-0.01, +0.03] | 1 | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.01 | [-0.04, +0.05] | 1 | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.01 | [-0.00, +0.02] | 1 | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | +0.00 | [-0.04, +0.04] | 1 | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.10 | [-0.38, +0.17] | 1 | | | ❌ | dsd_uds_500mb_3k_contexts | ingress throughput | -13.64 | [-13.72, -13.55] | 1 | | | ✅ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -15.39 | [-15.57, -15.21] | 1 | |

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".
pr-commenter[bot] commented 4 days ago

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: 2c8bb2d4-84a7-4832-8db9-2fb0bd7520fb

Baseline: 7.55.2 Comparison: 7.55.3

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

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 | |------|----------------------------------------------|--------------------|----------|----------------|--------|-------| | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | +0.57 | [+0.39, +0.74] | 1 | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.00 | [-0.04, +0.04] | 1 | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.00 | [-0.05, +0.04] | 1 | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | -0.01 | [-0.04, +0.02] | 1 | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.01 | [-0.04, +0.01] | 1 | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.02 | [-0.06, +0.03] | 1 | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.02 | [-0.04, +0.01] | 1 | |

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".
pr-commenter[bot] commented 4 days ago

Regression Detector Links

Experiment Result Links

experiment link(s)
dsd_uds_100mb_250k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_100mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_100mb_3k_contexts_distributions_only [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_10mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_50k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_50k_contexts_memlimit [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_500mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_512kb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_50mb_10k_contexts_no_inlining (ADP only) [Profiling (ADP)] [SMP Dashboard]
dsd_uds_50mb_10k_contexts_no_inlining_no_allocs (ADP only) [Profiling (ADP)] [SMP Dashboard]