dyninst: add an id tying together chunks of a SymDB upload

[andreimatei]

Tag the different chunks (i.e. batches for packages) of a SymDB upload
with a common id so that the backend can recognize them as part of the
same upload. The backend needs to do that when trying to identify
duplicate uploads, and without this ID it has to use a heuristic based
on upload time.

Also tag the final chunk with a final bit so that the backend can
recognize the upload as complete.

[ajwerner]

LGTM -- wish there was a more elegant way to add that final bit

[piob-io]

Do we wanna be more paranoid maybe? 1) We could specify the total number of produced batches in the final one, if any get dropped. 2) We could specify sequence number of each message, if any get re-ordered (especially if the final one would get re-ordered).

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 903c99e
📊 Static Quality Gates Dashboard

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0.01}$$	$${708.13}$$ < $${708.41}$$	$${+0.03}$$	$${173.96}$$ < $${174.49}$$
✅	agent_deb_amd64_fips	$${0}$$	$${703.42}$$ < $${704.0}$$	$${+0}$$	$${172.65}$$ < $${173.75}$$
✅	agent_heroku_amd64	$${0}$$	$${328.72}$$ < $${329.53}$$	$${+0.01}$$	$${87.44}$$ < $${88.45}$$
✅	agent_msi	$${0}$$	$${570.71}$$ < $${982.08}$$	$${-0.02}$$	$${142.68}$$ < $${143.02}$$
✅	agent_rpm_amd64	$${+0.01}$$	$${708.12}$$ < $${708.38}$$	$${-0.05}$$	$${176.42}$$ < $${177.66}$$
✅	agent_rpm_amd64_fips	$${0}$$	$${703.4}$$ < $${703.99}$$	$${-0.02}$$	$${175.92}$$ < $${176.6}$$
✅	agent_rpm_arm64	$${+0}$$	$${689.64}$$ < $${693.52}$$	$${+0}$$	$${159.97}$$ < $${161.26}$$
✅	agent_rpm_arm64_fips	$${+0}$$	$${685.79}$$ < $${688.48}$$	$${-0.03}$$	$${159.44}$$ < $${160.55}$$
✅	agent_suse_amd64	$${+0.01}$$	$${708.12}$$ < $${708.38}$$	$${-0.05}$$	$${176.42}$$ < $${177.66}$$
✅	agent_suse_amd64_fips	$${0}$$	$${703.4}$$ < $${703.99}$$	$${-0.02}$$	$${175.92}$$ < $${176.6}$$
✅	agent_suse_arm64	$${+0}$$	$${689.64}$$ < $${693.52}$$	$${+0}$$	$${159.97}$$ < $${161.26}$$
✅	agent_suse_arm64_fips	$${+0}$$	$${685.79}$$ < $${688.48}$$	$${-0.03}$$	$${159.44}$$ < $${160.55}$$
✅	docker_agent_amd64	$${+0.01}$$	$${769.96}$$ < $${770.72}$$	$${-0}$$	$${261.74}$$ < $${262.45}$$
✅	docker_agent_arm64	$${+0}$$	$${776.07}$$ < $${780.2}$$	$${+0}$$	$${250.81}$$ < $${252.63}$$
✅	docker_agent_jmx_amd64	$${+0.01}$$	$${960.84}$$ < $${961.6}$$	$${-0}$$	$${330.38}$$ < $${331.08}$$
✅	docker_agent_jmx_arm64	$${+0}$$	$${955.67}$$ < $${959.8}$$	$${+0}$$	$${315.44}$$ < $${317.27}$$
✅	docker_cluster_agent_amd64	$${-0}$$	$${180.7}$$ < $${181.08}$$	$${-0}$$	$${63.85}$$ < $${64.49}$$
✅	docker_cluster_agent_arm64	$${0}$$	$${196.55}$$ < $${198.49}$$	$${+0}$$	$${60.14}$$ < $${61.17}$$
✅	docker_cws_instrumentation_amd64	$${0}$$	$${7.13}$$ < $${7.18}$$	$${-0}$$	$${2.99}$$ < $${3.33}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${-0}$$	$${2.73}$$ < $${3.09}$$
✅	docker_dogstatsd_amd64	$${0}$$	$${38.78}$$ < $${39.38}$$	$${-0}$$	$${15.01}$$ < $${15.82}$$
✅	docker_dogstatsd_arm64	$${0}$$	$${37.07}$$ < $${37.94}$$	$${+0}$$	$${14.34}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${-0}$$	$${7.94}$$ < $${8.79}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${28.15}$$ < $${29.11}$$	$${-0}$$	$${6.81}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${+0}$$	$${7.95}$$ < $${8.8}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${+0}$$	$${7.95}$$ < $${8.8}$$
✅	iot_agent_deb_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${+0}$$	$${11.26}$$ < $${12.04}$$
✅	iot_agent_deb_arm64	$${0}$$	$${40.11}$$ < $${40.92}$$	$${+0}$$	$${9.63}$$ < $${10.45}$$
✅	iot_agent_deb_armhf	$${0}$$	$${40.69}$$ < $${41.03}$$	$${-0}$$	$${9.82}$$ < $${10.62}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${-0}$$	$${11.28}$$ < $${12.06}$$
✅	iot_agent_suse_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${-0}$$	$${11.28}$$ < $${12.06}$$

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 1571ca38-cfc1-462d-8eee-0f24a9270053

Baseline: a952ae5
Comparison: b1c4e01
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	-1.57	[-4.45, +1.32]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+3.17	[+1.66, +4.68]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+1.14	[+1.07, +1.22]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.37	[+0.27, +0.48]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	+0.26	[+0.06, +0.47]	1	Logs bounds checks dashboard
➖	docker_containers_memory	memory utilization	+0.12	[+0.05, +0.19]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.08	[+0.03, +0.14]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.07	[-0.31, +0.45]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.37, +0.46]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.01	[-0.13, +0.14]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.12, +0.12]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.08, +0.07]	1	Logs
➖	ddot_logs	memory utilization	-0.01	[-0.08, +0.06]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.04	[-0.44, +0.37]	1	Logs
➖	otlp_ingest_metrics	memory utilization	-0.06	[-0.21, +0.09]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.09	[-0.13, -0.05]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	-0.10	[-0.33, +0.13]	1	Logs
➖	quality_gate_idle	memory utilization	-0.16	[-0.20, -0.11]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_cumulative	memory utilization	-0.17	[-0.33, -0.01]	1	Logs
➖	ddot_metrics	memory utilization	-0.29	[-0.53, -0.06]	1	Logs
➖	file_tree	memory utilization	-0.37	[-0.42, -0.33]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.47	[-0.51, -0.44]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_delta	memory utilization	-0.62	[-0.83, -0.42]	1	Logs
➖	docker_containers_cpu	% cpu utilization	-1.57	[-4.45, +1.32]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	docker_containers_cpu	simple_check_run	10/10
✅	docker_containers_memory	memory_usage	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_1000ms_latency	lost_bytes	10/10
✅	file_to_blackhole_1000ms_latency	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_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
✅	quality_gate_metrics_logs	cpu_usage	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_metrics_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_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.
• 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_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_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.

[andreimatei]

Do we wanna be more paranoid maybe? 1) We could specify the total number of produced batches in the final one, if any get dropped. 2) We could specify sequence number of each message, if any get re-ordered (especially if the final one would get re-ordered).

I've added a batch number to every chunk, good idea. We don't need to unmarshal it on the backend necessarily or do anything with it, but it can't hurt.

[andreimatei]

/merge

[dd-devflow-routing-codex]

View all feedbacks in Devflow UI.

2026-01-06 22:34:30 UTC ℹ️ Start processing command /merge

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2026-01-06 22:34:34 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 1h (p90).

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2026-01-06 23:15:25 UTC ℹ️ MergeQueue: This merge request was merged