usm: Flush batched events from uprobe hook

[vitkyrka]

What does this PR do?

Flush batched events from uprobe hook in HTTP/2 and Kafka, similar to the way it is already done for HTTP. This is to ensure that the events are flushed in a timely manner even if the packet receive tracepoint does not hit on the CPU which queues the events.

Postgres is not handled in this PR since adding the flush there caused the code size limits to be exceeded on 4.14.

Motivation

To address flakiness in Kafka tests which generate a lot of events: https://datadoghq.atlassian.net/browse/USMON-1025

Additional Notes

Load test was run with kafka.tls and grpc.golang.tls.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 37953089 (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

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b Metrics dashboard Target profiles

Baseline: 1248ec4f0bece8d46a33f20ff1447c7de7be6706 Comparison: 9c64dc75bb465e8650903c63ea992fb913a37b94

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 | links | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | basic_py_check | % cpu utilization | +3.67 | [+1.08, +6.25] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +2.05 | [-10.78, +14.87] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.77 | [-3.15, +6.69] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.57 | [-0.31, +1.46] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +0.47 | [-0.34, +1.28] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | file_tree | memory utilization | -0.19 | [-0.25, -0.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) | | ➖ | idle | memory utilization | -0.24 | [-0.28, -0.21] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A0dc6b4cf-902c-4fc0-a9c8-b4720d59cf5b&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719815321000&to_ts=1719826721000&live=false) |

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".