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[CWS] Introduce CWS network flow monitor events #32350
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 52f0517 Optimization Goals: ✅ No significant changes detected
|
perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | tcp_syslog_to_blackhole | ingress throughput | +2.28 | [+2.23, +2.33] | 1 | Logs |
➖ | quality_gate_logs | % cpu utilization | +0.56 | [-2.42, +3.55] | 1 | Logs |
➖ | quality_gate_idle_all_features | memory utilization | +0.43 | [+0.31, +0.55] | 1 | Logs bounds checks dashboard |
➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.39 | [-0.39, +1.16] | 1 | Logs |
➖ | quality_gate_idle | memory utilization | +0.22 | [+0.17, +0.27] | 1 | Logs bounds checks dashboard |
➖ | file_tree | memory utilization | +0.20 | [+0.06, +0.34] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | +0.13 | [-0.58, +0.83] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | +0.02 | [-0.07, +0.11] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.02, +0.01] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.02 | [-0.86, +0.83] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | -0.03 | [-0.86, +0.81] | 1 | Logs |
➖ | file_to_blackhole_500ms_latency | egress throughput | -0.05 | [-0.82, +0.73] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | -0.06 | [-0.69, +0.56] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.09 | [-0.94, +0.76] | 1 | Logs |
➖ | otel_to_otel_logs | ingress throughput | -0.13 | [-0.80, +0.54] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.34 | [-0.80, +0.12] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.42 | [-1.13, +0.30] | 1 | Logs |
Bounds Checks: ❌ Failed
perf | experiment | bounds_check_name | replicates_passed | links |
---|---|---|---|---|
❌ | file_to_blackhole_0ms_latency_http1 | 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 | 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 | lost_bytes | 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:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
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.
-
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 lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
What does this PR do?
This PR tackles 5 things:
BPF_MAP_TYPE_SK_STORAGE
map type) and are therefore gated by the running kernel version (TL;DR kernel 5.11+)network_flow_monitor
to send network flows to user space. This event exposes the full 5 tuple, along with network telemetry like packet count and data size.network_flow_monitor
events in activity dumps for further processing.2 new agent configuration parameters were added:
event_monitoring_config.network.flow_monitor.enabled
: controls if the network flow monitor should be enabled (when the kernel is recent enough to run it).event_monitoring_config.network.flow_monitor.period
: controls how often flows should be flushed to user space for long running processes.Motivation
This PR generates the events we'll use to address 2 use cases in upcoming PRs:
This PR is only a first step towards those 2 goals, and provides a way to continuously monitor network activity and stream it back to user space with performance in mind. Upcoming PRs will take care of the rest !
Describe how you validated your changes
Testing this feature in activity dumps
First, turn on the feature with:
Start the agent. Once it's running, start a new container and generate some network activity. Stop the dump and open the output
.dot
file. You should see network flows in the graph. Note that only thedot
export contain the flows for nowTesting this feature with a rule
First, turn on the feature with:
You can use a rule like:
Start the agent. Once it's running, generate some network activity. You should start seeing events.