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experiment(core): add new string type for optimizing memory usage #19525

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@tobz tobz commented Jan 5, 2024

(work in progress)

@tobz tobz requested a review from a team January 5, 2024 17:26
@tobz tobz requested a review from StephenWakely as a code owner January 5, 2024 17:26
@tobz tobz requested a review from a team January 5, 2024 17:26
@tobz tobz requested a review from a team January 5, 2024 17:26
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tobz commented Jan 5, 2024

/ci-run-regression

@github-actions github-actions bot added domain: sources Anything related to the Vector's sources domain: transforms Anything related to Vector's transform components domain: sinks Anything related to the Vector's sinks domain: core Anything related to core crates i.e. vector-core, core-common, etc labels Jan 5, 2024
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tobz commented Jan 5, 2024

/ci-run-regression

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Regression Detector Results

Run ID: f412ecd0-cb7c-47de-bc9e-8e1d6287d081
Baseline: 3525d06
Comparison: 8749547
Total CPUs: 7

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
datadog_agent_remap_datadog_logs_acks ingress throughput +2.57 [+2.48, +2.67]
syslog_log2metric_humio_metrics ingress throughput +2.43 [+2.29, +2.57]
http_text_to_http_json ingress throughput +2.31 [+2.18, +2.45]
syslog_log2metric_splunk_hec_metrics ingress throughput +2.22 [+2.09, +2.35]
syslog_humio_logs ingress throughput +2.07 [+1.96, +2.17]
datadog_agent_remap_datadog_logs ingress throughput +1.86 [+1.76, +1.96]
syslog_splunk_hec_logs ingress throughput +1.86 [+1.78, +1.94]
syslog_loki ingress throughput +1.26 [+1.19, +1.32]
splunk_hec_route_s3 ingress throughput +1.23 [+0.72, +1.73]
datadog_agent_remap_blackhole ingress throughput +1.15 [+1.03, +1.27]
datadog_agent_remap_blackhole_acks ingress throughput +0.93 [+0.84, +1.01]
fluent_elasticsearch ingress throughput +0.81 [+0.33, +1.29]
otlp_grpc_to_blackhole ingress throughput +0.67 [+0.57, +0.77]
syslog_log2metric_tag_cardinality_limit_blackhole ingress throughput +0.30 [+0.18, +0.42]
http_to_http_noack ingress throughput +0.18 [+0.08, +0.27]
http_elasticsearch ingress throughput +0.12 [+0.05, +0.19]
http_to_http_json ingress throughput +0.03 [-0.05, +0.10]
splunk_hec_to_splunk_hec_logs_acks ingress throughput -0.00 [-0.14, +0.14]
splunk_hec_indexer_ack_blackhole ingress throughput -0.00 [-0.14, +0.14]
splunk_hec_to_splunk_hec_logs_noack ingress throughput -0.03 [-0.15, +0.08]
http_to_http_acks ingress throughput -0.15 [-1.46, +1.16]
enterprise_http_to_http ingress throughput -0.18 [-0.26, -0.09]
http_to_s3 ingress throughput -0.26 [-0.53, +0.02]
file_to_blackhole egress throughput -0.87 [-3.27, +1.53]
syslog_regex_logs2metric_ddmetrics ingress throughput -1.17 [-1.29, -1.04]
otlp_http_to_blackhole ingress throughput -2.58 [-2.74, -2.43]
socket_to_socket_blackhole ingress throughput -2.89 [-2.97, -2.81]

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".

@tobz tobz marked this pull request as draft January 9, 2024 15:27
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