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Guy.arbitman/usmon 1083 incomplete frame type #32514

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@guyarb guyarb commented Dec 25, 2024

What does this PR do?

Motivation

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

The telemetry is not used or modified in the function 'handle_first_frame', hence it is redundant and wasteful operation
The new enum represents the different options for an incomlete frame.
Either we have an incomplete frame header or an incomplete frame payload.

Having also a default state 'unknown' to avoid adding bugs of relying on the default
value of the type as either incomlete frame header or an incomplete frame payload
Introducing types incomplete frame header and incomplete frame payload types.
Those types will be later used to store the correct status of the incompletion
In a followup commit, we will need to have the last frame header for the case of incomplete frame payload.
For that matter, we save the last frame header from the function 'pktbuf_find_relevant_frames'.

In case we do have an incomplete frame payload, we need to know whether the frame is interesting (for further
processing) and how many bytes we need to read from future packets.
@github-actions github-actions bot added component/system-probe medium review PR review might take time labels Dec 25, 2024
The code was duplicated, and in the future commits we will use it further
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[Fast Unit Tests Report]

On pipeline 51793725 (CI Visibility). The following jobs did not run any unit tests:

Jobs:
  • tests_deb-arm64-py3
  • tests_deb-x64-py3
  • tests_flavor_dogstatsd_deb-x64
  • tests_flavor_heroku_deb-x64
  • tests_flavor_iot_deb-x64
  • tests_rpm-arm64-py3
  • tests_rpm-x64-py3
  • tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

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Uncompressed package size comparison

Comparison with ancestor 561fc3e38845591b0643862cdc9bd8e7160267c9

Diff per package
package diff status size ancestor threshold
datadog-agent-aarch64-rpm 0.01MB ⚠️ 944.36MB 944.35MB 140.00MB
datadog-agent-amd64-deb 0.01MB ⚠️ 1190.80MB 1190.80MB 140.00MB
datadog-agent-arm64-deb 0.01MB ⚠️ 935.09MB 935.08MB 140.00MB
datadog-agent-x86_64-rpm 0.01MB ⚠️ 1200.09MB 1200.09MB 140.00MB
datadog-agent-x86_64-suse 0.01MB ⚠️ 1200.09MB 1200.09MB 140.00MB
datadog-dogstatsd-amd64-deb 0.00MB 78.57MB 78.57MB 10.00MB
datadog-dogstatsd-x86_64-rpm 0.00MB 78.65MB 78.65MB 10.00MB
datadog-dogstatsd-x86_64-suse 0.00MB 78.65MB 78.65MB 10.00MB
datadog-dogstatsd-arm64-deb 0.00MB 55.77MB 55.77MB 10.00MB
datadog-heroku-agent-amd64-deb 0.00MB 505.21MB 505.21MB 70.00MB
datadog-iot-agent-amd64-deb 0.00MB 113.35MB 113.35MB 10.00MB
datadog-iot-agent-x86_64-rpm 0.00MB 113.42MB 113.42MB 10.00MB
datadog-iot-agent-x86_64-suse 0.00MB 113.42MB 113.42MB 10.00MB
datadog-iot-agent-arm64-deb 0.00MB 108.81MB 108.81MB 10.00MB
datadog-iot-agent-aarch64-rpm 0.00MB 108.88MB 108.88MB 10.00MB

Decision

⚠️ Warning

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Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=51793725 --os-family=ubuntu

Note: This applies to commit f93d3f5

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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: f247b3d1-c51c-44e9-b0c7-e202d8f5cd5d

Baseline: 561fc3e
Comparison: f93d3f5
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
tcp_syslog_to_blackhole ingress throughput +0.42 [+0.37, +0.47] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput +0.40 [-0.07, +0.87] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.08 [-0.70, +0.86] 1 Logs
file_to_blackhole_0ms_latency egress throughput +0.03 [-0.87, +0.93] 1 Logs
file_to_blackhole_300ms_latency egress throughput +0.02 [-0.62, +0.66] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.01 [-0.08, +0.10] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.02] 1 Logs
file_to_blackhole_0ms_latency_http1 egress throughput -0.00 [-0.85, +0.85] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.00 [-0.75, +0.74] 1 Logs
file_to_blackhole_0ms_latency_http2 egress throughput -0.01 [-0.84, +0.81] 1 Logs
quality_gate_idle memory utilization -0.18 [-0.22, -0.15] 1 Logs bounds checks dashboard
file_tree memory utilization -0.21 [-0.34, -0.08] 1 Logs
quality_gate_idle_all_features memory utilization -0.38 [-0.47, -0.30] 1 Logs bounds checks dashboard
file_to_blackhole_1000ms_latency egress throughput -0.50 [-1.30, +0.30] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization -1.27 [-1.94, -0.59] 1 Logs
quality_gate_logs % cpu utilization -2.14 [-5.29, +1.01] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
file_to_blackhole_100ms_latency lost_bytes 9/10
file_to_blackhole_0ms_latency lost_bytes 10/10
file_to_blackhole_0ms_latency memory_usage 10/10
file_to_blackhole_0ms_latency_http1 lost_bytes 10/10
file_to_blackhole_0ms_latency_http1 memory_usage 10/10
file_to_blackhole_0ms_latency_http2 lost_bytes 10/10
file_to_blackhole_0ms_latency_http2 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 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
quality_gate_logs lost_bytes 10/10
quality_gate_logs memory_usage 10/10

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

We distinguish between incomplete frame header and incomplete frame payload
Now the logic treat each of them differentaly and makes the code much clearer
Now we do support handling incomplete frame payload
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