chore: upgrade go-libddwaf to the latest v3

[RomainMuller]

What does this PR do?

Upgrades github.com/DataDog/go-libddwaf/v3 to the latest, which supports go1.24, and ensure some test fails if building the serverless agent with an unsupported Go release.

It also changes the startup mechanism to no longer fail if AppSec fails to start, instead logs a warning and uses a no-op httpsec.Monitorer.

Motivation

Customers rely on this feature.

[RomainMuller]

• chore: upgrade go-libddwaf to the latest v3 #37639 👈 (View in Graphite)
• main

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[cit-pr-commenter]

Go Package Import Differences

Baseline: bae621f
Comparison: 8f14e6d

binary	os	arch	change
serverless	linux	amd64	+3, -0 +github.com/DataDog/appsec-internal-go/apisec +github.com/ebitengine/purego +structs
serverless	linux	arm64	+3, -0 +github.com/DataDog/appsec-internal-go/apisec +github.com/ebitengine/purego +structs

[RomainMuller]

/merge

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[RomainMuller]

/merge

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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: a35e5e69-b355-4ffc-aaba-61e3c962cad3

Baseline: bae621f
Comparison: 8f14e6d
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+2.87	[+1.98, +3.77]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.78	[+0.72, +0.84]	1	Logs
➖	docker_containers_memory	memory utilization	+0.77	[+0.72, +0.81]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.66	[+0.54, +0.78]	1	Logs
➖	ddot_logs	memory utilization	+0.49	[+0.35, +0.63]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.35	[+0.18, +0.52]	1	Logs
➖	ddot_metrics	memory utilization	+0.18	[+0.07, +0.29]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.11	[-0.48, +0.69]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.07	[-0.54, +0.69]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.02	[-0.62, +0.66]	1	Logs
➖	file_tree	memory utilization	+0.01	[-0.13, +0.16]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.02, +0.03]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.30, +0.29]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.65, +0.61]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.02	[-0.26, +0.21]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.04	[-0.63, +0.55]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.05	[-0.62, +0.53]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.06	[-0.13, +0.00]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.06	[-0.71, +0.58]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.11	[-0.19, -0.02]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.36	[-0.42, -0.30]	1	Logs bounds checks dashboard
➖	docker_containers_cpu	% cpu utilization	-3.07	[-6.08, -0.06]	1	Logs
➖	quality_gate_logs	% cpu utilization	-4.30	[-7.02, -1.58]	1	Logs bounds checks dashboard

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	docker_containers_memory	memory_usage	0/10
✅	docker_containers_cpu	simple_check_run	10/10
✅	docker_containers_memory	simple_check_run	10/10
✅	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	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	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	bounds checks dashboard

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

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor bae621f

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0}$$	$${731.85}$$ < $${752.99}$$	$${-0.01}$$	$${181.35}$$ < $${187.44}$$
✅	agent_deb_amd64_fips	$${+0}$$	$${730.14}$$ < $${751.36}$$	$${-0.01}$$	$${180.78}$$ < $${187.06}$$
✅	agent_heroku_amd64	$${+0}$$	$${358.53}$$ < $${369.68}$$	$${-0}$$	$${96.49}$$ < $${99.55}$$
✅	agent_msi	$${+0}$$	$${959.66}$$ < $${987.01}$$	$${+0}$$	$${146.41}$$ < $${150.72}$$
✅	agent_rpm_amd64	$${+0}$$	$${731.84}$$ < $${752.98}$$	$${+0.02}$$	$${183.54}$$ < $${190.03}$$
✅	agent_rpm_amd64_fips	$${+0}$$	$${730.13}$$ < $${751.35}$$	$${+0.01}$$	$${183.67}$$ < $${189.81}$$
✅	agent_rpm_arm64	$${+0}$$	$${719.23}$$ < $${739.42}$$	$${-0.01}$$	$${165.42}$$ < $${171.23}$$
✅	agent_rpm_arm64_fips	$${+0}$$	$${717.65}$$ < $${737.91}$$	$${-0.01}$$	$${164.76}$$ < $${170.22}$$
✅	agent_suse_amd64	$${+0}$$	$${731.84}$$ < $${752.98}$$	$${+0.02}$$	$${183.54}$$ < $${190.03}$$
✅	agent_suse_amd64_fips	$${+0}$$	$${730.13}$$ < $${751.35}$$	$${+0.01}$$	$${183.67}$$ < $${189.81}$$
✅	agent_suse_arm64	$${+0}$$	$${719.23}$$ < $${739.42}$$	$${-0.01}$$	$${165.42}$$ < $${171.23}$$
✅	agent_suse_arm64_fips	$${+0}$$	$${717.65}$$ < $${737.91}$$	$${-0.01}$$	$${164.76}$$ < $${170.22}$$
✅	docker_agent_amd64	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_arm64	$${+0}$$	$${826.48}$$ < $${858.97}$$	$${-0}$$	$${263.66}$$ < $${274.36}$$
✅	docker_agent_jmx_amd64	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_jmx_arm64	$${+0}$$	$${826.48}$$ < $${858.97}$$	$${-0}$$	$${263.66}$$ < $${274.36}$$
✅	docker_agent_windows1809	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows1809_core	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows1809_core_jmx	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows1809_jmx	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows2022	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows2022_core	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows2022_core_jmx	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_agent_windows2022_jmx	$${+0}$$	$${815.64}$$ < $${849.39}$$	$${-0.01}$$	$${277.07}$$ < $${288.34}$$
✅	docker_cluster_agent_amd64	$${+0}$$	$${259.14}$$ < $${259.73}$$	$${-0}$$	$${102.86}$$ < $${103.68}$$
✅	docker_cluster_agent_arm64	$${+0}$$	$${273.57}$$ < $${274.24}$$	$${+0}$$	$${97.59}$$ < $${98.45}$$
✅	docker_cws_instrumentation_amd64	$${0}$$	$${7.08}$$ < $${7.12}$$	$${0}$$	$${2.95}$$ < $${3.29}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${+0}$$	$${2.7}$$ < $${3.07}$$
✅	docker_dogstatsd_amd64	$${0}$$	$${38.92}$$ < $${39.57}$$	$${+0}$$	$${14.95}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${0}$$	$${37.52}$$ < $${38.2}$$	$${0}$$	$${13.96}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${+0}$$	$${8.03}$$ < $${8.97}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${29.16}$$ < $${30.08}$$	$${-0}$$	$${6.97}$$ < $${7.92}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${-0}$$	$${8.04}$$ < $${8.98}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${-0}$$	$${8.04}$$ < $${8.98}$$
✅	iot_agent_deb_amd64	$${0}$$	$${50.45}$$ < $${60.17}$$	$${+0}$$	$${12.85}$$ < $${15.82}$$
✅	iot_agent_deb_arm64	$${0}$$	$${47.9}$$ < $${56.94}$$	$${-0}$$	$${11.14}$$ < $${13.86}$$
✅	iot_agent_deb_armhf	$${0}$$	$${47.49}$$ < $${56.41}$$	$${+0}$$	$${11.2}$$ < $${13.86}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${50.45}$$ < $${60.18}$$	$${-0}$$	$${12.87}$$ < $${15.84}$$
✅	iot_agent_rpm_arm64	$${0}$$	$${47.9}$$ < $${56.94}$$	$${-0}$$	$${11.16}$$ < $${13.76}$$
✅	iot_agent_suse_amd64	$${0}$$	$${50.45}$$ < $${60.18}$$	$${-0}$$	$${12.87}$$ < $${15.84}$$

[Julio-Guerra]

/merge

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/merge

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[github-actions]

Serverless Benchmark Results

BenchmarkStartEndInvocation comparison between bae621f and 76402ab.

tl;dr

Use these benchmarks as an insight tool during development.

1. Skim down the vs base column in each chart. If there is a ~, then there was no statistically significant change to the benchmark. Otherwise, ensure the estimated percent change is either negative or very small.

2. The last row of each chart is the geomean. Ensure this percentage is either negative or very small.

What is this benchmarking?

The BenchmarkStartEndInvocation compares the amount of time it takes to call the start-invocation and end-invocation endpoints. For universal instrumentation languages (Dotnet, Golang, Java, Ruby), this represents the majority of the duration overhead added by our tracing layer.

The benchmark is run using a large variety of lambda request payloads. In the charts below, there is one row for each event payload type.

How do I interpret these charts?

The charts below comes from benchstat. They represent the statistical change in duration (sec/op), memory overhead (B/op), and allocations (allocs/op).

The benchstat docs explain how to interpret these charts.

Before the comparison table, we see common file-level configuration. If there are benchmarks with different configuration (for example, from different packages), benchstat will print separate tables for each configuration.

The table then compares the two input files for each benchmark. It shows the median and 95% confidence interval summaries for each benchmark before and after the change, and an A/B comparison under "vs base". ... The p-value measures how likely it is that any differences were due to random chance (i.e., noise). The "~" means benchstat did not detect a statistically significant difference between the two inputs. ...

Note that "statistically significant" is not the same as "large": with enough low-noise data, even very small changes can be distinguished from noise and considered statistically significant. It is, of course, generally easier to distinguish large changes from noise.

Finally, the last row of the table shows the geometric mean of each column, giving an overall picture of how the benchmarks changed. Proportional changes in the geomean reflect proportional changes in the benchmarks. For example, given n benchmarks, if sec/op for one of them increases by a factor of 2, then the sec/op geomean will increase by a factor of ⁿ√2.

I need more help

First off, do not worry if the benchmarks are failing. They are not tests. The intention is for them to be a tool for you to use during development.

If you would like a hand interpreting the results come chat with us in #serverless-agent in the internal DataDog slack or in #serverless in the public DataDog slack. We're happy to help!

Benchmark stats