feat(tagger): implement remote OriginInfo resolution #31836
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Go Package Import DifferencesBaseline: 5b903ef
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 5b903ef ❌ Experiments with missing or malformed dataThis is a critical error. No usable optimization goal data was produced by the listed experiments. This may be a result of misconfiguration. Ping #single-machine-performance and we can help out.
Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | otel_to_otel_logs | ingress throughput | +0.90 | [+0.25, +1.55] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.75 | [+0.68, +0.82] | 1 | Logs |
| ➖ | quality_gate_logs | % cpu utilization | +0.56 | [-2.45, +3.56] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.21 | [-0.57, +1.00] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.10 | [-0.68, +0.87] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | +0.08 | [-0.02, +0.18] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | +0.04 | [-0.58, +0.66] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.04 | [-0.79, +0.87] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.00 | [-0.79, +0.79] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.00 | [-0.80, +0.80] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.12, +0.10] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.02 | [-0.86, +0.82] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.60 | [-1.07, -0.14] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.97 | [-1.01, -0.93] | 1 | Logs bounds checks dashboard |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -1.73 | [-2.46, -1.00] | 1 | Logs |
Bounds Checks: ✅ Passed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ✅ | 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 | 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.
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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.
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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.
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Uncompressed package size comparisonComparison with ancestor Diff per package
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Signed-off-by: Wassim DHIF <wassim.dhif@datadoghq.com>
Signed-off-by: Wassim DHIF <wassim.dhif@datadoghq.com>
Signed-off-by: Wassim DHIF <wassim.dhif@datadoghq.com>
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Signed-off-by: Wassim DHIF <wassim.dhif@datadoghq.com>
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What does this PR do?
Implements External Data resolution for the Remote Tagger by introducing a new gRPC call,
TaggerGenerateContainerIDFromOriginInfo. This call is used by theremote_taggerto generate a Container ID from External Data.Once the Container ID is generated and retrieved, it can be used for standard tagging operations by other Agents using the Remote Tagger.
The only part of the Origin Info that is used to generate the container ID is the External Data but the gRPC call still accept the whole Origin Info for forward compatibility.
Motivation
This implementation is necessary to fully support the new Origin Detection specification, specifically the External Data resolution for APM and the Trace Agent.
Describe how you validated your changes
Deploy the Agent with a dummy deployment:
In another Agent using the Remote Tagger such as the Trace Agent, run the following command (with the correct data):
Make sure that the Container ID the same one as your dummy deployment:
Possible Drawbacks / Trade-offs
For now, I have decided to duplicate the
OriginInfoandExternalDatastructs fororigindetectionwhile keeping them intaggertypes, as they are already used for DogStatsD Origin Detection. This approach avoids further complicating the PR.We will need to transition the current DogStatsD Origin Detection from
taggertypestoorigindetection.Additionally, caching in the remote tagger has not yet been implemented, as there is currently no client capable of using External Data resolution for APM. This will need to be addressed in a subsequent PR.
Additional Notes