WIP: OTLP trace export by rachelyangdog · Pull Request #1641 · DataDog/libdatadog

rachelyangdog · 2026-03-02T21:42:48Z

What does this PR do?

Summary

This PR adds an OTLP HTTP/JSON trace export path to TraceExporter. When OTEL_TRACES_EXPORTER=otlp is set, the exporter converts Datadog msgpack trace payloads to OTLP and POSTs them to the configured OTLP endpoint instead of the Datadog agent. The caller (e.g. dd-trace-py) sends traces in the same format as before — no changes are required on the tracer side.

Not in scope

http/protobuf and grpc protocols (they are parsed from env but not yet implemented; http/json is the default and only supported format). This is currently a POC so we will add additional support later.

Motivation

We are seeing an increasing number of scenarios where users have applications instrumented with the OTel SDK sending data to OTel collectors, and they would like to get additional features offered by the DD SDK without needing to update their OTel collector deployments. Although there will be follow-up work, this provides the ability for users to write vendor-neutral API instrumentation and emit vendor-neutral telemetry data so DD SDK users don't have to feel locked in when setting up Datadog APM.

Additional Notes

Anything else we should know when reviewing?

How to test the change?

Describe here in detail how the change can be validated.

github-actions · 2026-03-02T21:44:54Z

📚 Documentation Check Results

⚠️ 863 documentation warning(s) found

📦 `libdd-data-pipeline` - 863 warning(s)

Updated: 2026-03-06 19:49:51 UTC | Commit: 4462c76 | missing-docs job results

github-actions · 2026-03-02T21:46:36Z

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

Base Branch: origin/main
PR Branch: origin/rachel.yang/OTLP-trace-export

Summary by Rule

Rule	Base Branch	PR Branch	Change
unwrap_used	2	2	No change (0%)
Total	2	2	No change (0%)

Annotation Counts by File

File	Base Branch	PR Branch	Change
`libdd-data-pipeline/src/trace_exporter/mod.rs`	2	2	No change (0%)

Annotation Stats by Crate

Crate	Base Branch	PR Branch	Change
`clippy-annotation-reporter`	5	5	No change (0%)
`datadog-ffe-ffi`	1	1	No change (0%)
`datadog-ipc`	28	27	✅ -1 (-3.6%)
`datadog-live-debugger`	6	6	No change (0%)
`datadog-live-debugger-ffi`	10	10	No change (0%)
`datadog-profiling-replayer`	4	4	No change (0%)
`datadog-remote-config`	3	3	No change (0%)
`datadog-sidecar`	59	59	No change (0%)
`libdd-common`	10	10	No change (0%)
`libdd-common-ffi`	12	12	No change (0%)
`libdd-crashtracker`	12	12	No change (0%)
`libdd-data-pipeline`	5	5	No change (0%)
`libdd-ddsketch`	2	2	No change (0%)
`libdd-dogstatsd-client`	1	1	No change (0%)
`libdd-profiling`	13	13	No change (0%)
`libdd-telemetry`	19	19	No change (0%)
`libdd-tinybytes`	4	4	No change (0%)
`libdd-trace-normalization`	2	2	No change (0%)
`libdd-trace-obfuscation`	9	9	No change (0%)
`libdd-trace-utils`	15	15	No change (0%)
Total	220	219	✅ -1 (-0.5%)

About This Report

This report tracks Clippy allow annotations for specific rules, showing how they've changed in this PR. Decreasing the number of these annotations generally improves code quality.

github-actions · 2026-03-02T21:48:01Z

🔒 Cargo Deny Results

⚠️ 1 issue(s) found, showing only errors (advisories, bans, sources)

📦 `libdd-data-pipeline` - 1 error(s)

Show output

error[vulnerability]: Denial of Service via Stack Exhaustion
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:291:1
    │
291 │ time 0.3.41 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0009
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0009
    ├ ## Impact
      
      When user-provided input is provided to any type that parses with the RFC 2822 format, a denial of
      service attack via stack exhaustion is possible. The attack relies on formally deprecated and
      rarely-used features that are part of the RFC 2822 format used in a malicious manner. Ordinary,
      non-malicious input will never encounter this scenario.
      
      ## Patches
      
      A limit to the depth of recursion was added in v0.3.47. From this version, an error will be returned
      rather than exhausting the stack.
      
      ## Workarounds
      
      Limiting the length of user input is the simplest way to avoid stack exhaustion, as the amount of
      the stack consumed would be at most a factor of the length of the input.
    ├ Announcement: https://github.com/time-rs/time/blob/main/CHANGELOG.md#0347-2026-02-05
    ├ Solution: Upgrade to >=0.3.47 (try `cargo update -p time`)
    ├ time v0.3.41
      └── tracing-appender v0.2.3
          └── libdd-log v1.0.0
              └── (dev) libdd-data-pipeline v2.0.0

advisories FAILED, bans ok, sources ok

Updated: 2026-03-06 19:52:56 UTC | Commit: 4462c76 | dependency-check job results

pr-commenter · 2026-03-02T22:08:49Z

Benchmarks

Comparison

Benchmark execution time: 2026-03-06 20:05:47

Comparing candidate commit 42de22c in PR branch rachel.yang/OTLP-trace-export with baseline commit bd94dd9 in branch main.

Found 0 performance improvements and 1 performance regressions! Performance is the same for 56 metrics, 2 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

🟩 = significantly better candidate vs. baseline
🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

scenario:sql/obfuscate_sql_string

🟥 execution_time [+5.417µs; +5.480µs] or [+6.299%; +6.372%]

Candidate

Candidate benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sdk_test_data/rules-based	execution_time	146.327µs	148.189µs ± 1.550µs	147.975µs ± 0.569µs	148.544µs	149.557µs	153.887µs	162.706µs	9.95%	5.579	43.914	1.04%	0.110µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sdk_test_data/rules-based	execution_time	[147.974µs; 148.403µs] or [-0.145%; +0.145%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_trace/test_trace	execution_time	241.684ns	250.077ns ± 10.991ns	245.173ns ± 2.099ns	249.902ns	276.073ns	280.808ns	283.197ns	15.51%	1.765	1.641	4.38%	0.777ns	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_trace/test_trace	execution_time	[248.554ns; 251.600ns] or [-0.609%; +0.609%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
credit_card/is_card_number/	execution_time	3.895µs	3.912µs ± 0.003µs	3.911µs ± 0.002µs	3.913µs	3.916µs	3.917µs	3.919µs	0.20%	-0.734	5.326	0.07%	0.000µs	1	200
credit_card/is_card_number/	throughput	255137498.357op/s	255646756.582op/s ± 177128.587op/s	255656870.686op/s ± 108541.318op/s	255760967.021op/s	255884578.503op/s	255953538.641op/s	256710334.865op/s	0.41%	0.748	5.410	0.07%	12524.882op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	78.980µs	79.616µs ± 0.387µs	79.550µs ± 0.252µs	79.842µs	80.357µs	80.701µs	80.819µs	1.59%	0.738	0.224	0.48%	0.027µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12373308.613op/s	12560550.444op/s ± 60840.201op/s	12570634.374op/s ± 39808.305op/s	12606096.651op/s	12646522.319op/s	12656996.838op/s	12661390.016op/s	0.72%	-0.714	0.172	0.48%	4302.052op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	72.325µs	72.861µs ± 0.321µs	72.808µs ± 0.201µs	73.038µs	73.429µs	73.857µs	73.927µs	1.54%	0.842	0.646	0.44%	0.023µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	13526942.435op/s	13724957.862op/s ± 60155.844op/s	13734796.800op/s ± 37965.503op/s	13768652.222op/s	13804470.179op/s	13815547.238op/s	13826476.320op/s	0.67%	-0.816	0.580	0.44%	4253.661op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.895µs	3.914µs ± 0.003µs	3.913µs ± 0.002µs	3.916µs	3.919µs	3.920µs	3.921µs	0.19%	-0.547	5.110	0.08%	0.000µs	1	200
credit_card/is_card_number/37828224631	throughput	255050473.540op/s	255522648.650op/s ± 207385.302op/s	255544528.645op/s ± 131921.988op/s	255665632.981op/s	255776467.507op/s	255839699.605op/s	256760024.142op/s	0.48%	0.564	5.209	0.08%	14664.355op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	69.263µs	69.761µs ± 0.342µs	69.685µs ± 0.226µs	69.956µs	70.488µs	70.664µs	71.180µs	2.15%	1.040	1.069	0.49%	0.024µs	1	200
credit_card/is_card_number/378282246310005	throughput	14048868.208op/s	14334942.177op/s ± 69843.828op/s	14350345.652op/s ± 46557.332op/s	14389704.527op/s	14419563.384op/s	14434764.562op/s	14437686.694op/s	0.61%	-1.012	0.966	0.49%	4938.704op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	52.157µs	52.242µs ± 0.039µs	52.244µs ± 0.025µs	52.265µs	52.300µs	52.351µs	52.418µs	0.33%	0.678	2.006	0.07%	0.003µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	19077558.323op/s	19141810.897op/s ± 14233.759op/s	19140891.969op/s ± 9118.365op/s	19151711.354op/s	19163417.326op/s	19167245.204op/s	19172736.369op/s	0.17%	-0.670	1.978	0.07%	1006.479op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	6.028µs	6.038µs ± 0.010µs	6.035µs ± 0.003µs	6.038µs	6.060µs	6.074µs	6.111µs	1.26%	3.887	18.836	0.16%	0.001µs	1	200
credit_card/is_card_number/x371413321323331	throughput	163631221.865op/s	165629714.404op/s ± 271426.203op/s	165694362.943op/s ± 73005.768op/s	165756552.921op/s	165825088.493op/s	165850982.302op/s	165883943.546op/s	0.11%	-3.860	18.531	0.16%	19192.731op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.895µs	3.912µs ± 0.003µs	3.912µs ± 0.002µs	3.913µs	3.916µs	3.919µs	3.920µs	0.22%	-0.776	7.081	0.07%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	255074404.515op/s	255616591.485op/s ± 173497.041op/s	255624692.965op/s ± 99663.699op/s	255732190.290op/s	255837237.491op/s	255887293.149op/s	256712705.954op/s	0.43%	0.793	7.185	0.07%	12268.093op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	64.190µs	64.445µs ± 0.139µs	64.424µs ± 0.084µs	64.528µs	64.697µs	64.857µs	65.019µs	0.92%	0.963	1.441	0.22%	0.010µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	15380183.344op/s	15517169.387op/s ± 33482.067op/s	15522091.522op/s ± 20382.673op/s	15540363.854op/s	15562602.521op/s	15575391.692op/s	15578861.043op/s	0.37%	-0.947	1.389	0.22%	2367.540op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	58.105µs	58.411µs ± 0.165µs	58.380µs ± 0.099µs	58.510µs	58.737µs	58.889µs	59.128µs	1.28%	0.999	1.557	0.28%	0.012µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	16912354.672op/s	17120201.992op/s ± 48364.298op/s	17129028.441op/s ± 29057.049op/s	17151907.847op/s	17185214.789op/s	17194379.646op/s	17210317.585op/s	0.47%	-0.977	1.482	0.28%	3419.872op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.891µs	3.913µs ± 0.003µs	3.913µs ± 0.002µs	3.915µs	3.916µs	3.918µs	3.920µs	0.17%	-2.082	14.305	0.08%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	255112628.684op/s	255571497.306op/s ± 192474.007op/s	255540287.337op/s ± 133481.577op/s	255699910.517op/s	255835914.120op/s	255890459.783op/s	257024240.291op/s	0.58%	2.109	14.556	0.08%	13609.968op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	54.594µs	54.941µs ± 0.281µs	54.871µs ± 0.179µs	55.107µs	55.434µs	55.862µs	55.926µs	1.92%	1.187	1.383	0.51%	0.020µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	17880821.685op/s	18201781.795op/s ± 92421.290op/s	18224539.500op/s ± 59639.200op/s	18277215.635op/s	18304681.611op/s	18313376.297op/s	18316926.672op/s	0.51%	-1.158	1.274	0.51%	6535.172op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	52.147µs	52.215µs ± 0.035µs	52.210µs ± 0.021µs	52.232µs	52.276µs	52.312µs	52.363µs	0.29%	0.979	1.327	0.07%	0.002µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	19097328.940op/s	19151658.578op/s ± 12785.978op/s	19153327.172op/s ± 7546.181op/s	19160737.254op/s	19168253.539op/s	19173819.109op/s	19176524.099op/s	0.12%	-0.974	1.311	0.07%	904.105op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	6.028µs	6.039µs ± 0.010µs	6.037µs ± 0.003µs	6.040µs	6.069µs	6.074µs	6.076µs	0.66%	2.515	6.257	0.16%	0.001µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	164571853.801op/s	165601873.146op/s ± 259541.986op/s	165658104.582op/s ± 89203.339op/s	165747121.289op/s	165833291.788op/s	165863767.678op/s	165886668.752op/s	0.14%	-2.506	6.218	0.16%	18352.390op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
credit_card/is_card_number/	execution_time	[3.911µs; 3.912µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/	throughput	[255622208.263op/s; 255671304.900op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[79.563µs; 79.670µs] or [-0.067%; +0.067%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12552118.577op/s; 12568982.311op/s] or [-0.067%; +0.067%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[72.817µs; 72.906µs] or [-0.061%; +0.061%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[13716620.840op/s; 13733294.883op/s] or [-0.061%; +0.061%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.913µs; 3.914µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[255493907.042op/s; 255551390.259op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[69.714µs; 69.809µs] or [-0.068%; +0.068%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[14325262.494op/s; 14344621.860op/s] or [-0.068%; +0.068%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[52.236µs; 52.247µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[19139838.235op/s; 19143783.560op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[6.036µs; 6.039µs] or [-0.023%; +0.023%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[165592097.343op/s; 165667331.465op/s] or [-0.023%; +0.023%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.912µs; 3.912µs] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[255592546.464op/s; 255640636.507op/s] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[64.426µs; 64.464µs] or [-0.030%; +0.030%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15512529.094op/s; 15521809.679op/s] or [-0.030%; +0.030%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[58.388µs; 58.434µs] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[17113499.165op/s; 17126904.819op/s] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.912µs; 3.913µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[255544822.260op/s; 255598172.352op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[54.902µs; 54.980µs] or [-0.071%; +0.071%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[18188973.093op/s; 18214590.497op/s] or [-0.070%; +0.070%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[52.210µs; 52.220µs] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[19149886.565op/s; 19153430.592op/s] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[6.037µs; 6.040µs] or [-0.022%; +0.022%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[165565903.123op/s; 165637843.169op/s] or [-0.022%; +0.022%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
two way interface	execution_time	18.525µs	27.288µs ± 10.213µs	19.033µs ± 0.300µs	36.150µs	45.395µs	50.719µs	73.348µs	285.38%	0.901	0.560	37.33%	0.722µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
two way interface	execution_time	[25.872µs; 28.703µs] or [-5.187%; +5.187%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
write only interface	execution_time	1.212µs	3.188µs ± 1.430µs	3.019µs ± 0.033µs	3.046µs	3.404µs	13.781µs	15.308µs	407.00%	7.513	57.178	44.75%	0.101µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
write only interface	execution_time	[2.989µs; 3.386µs] or [-6.218%; +6.218%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching string interning on wordpress profile	execution_time	162.061µs	162.765µs ± 0.329µs	162.711µs ± 0.155µs	162.877µs	163.432µs	163.975µs	164.419µs	1.05%	1.671	4.768	0.20%	0.023µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching string interning on wordpress profile	execution_time	[162.720µs; 162.811µs] or [-0.028%; +0.028%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
tags/replace_trace_tags	execution_time	2.330µs	2.392µs ± 0.017µs	2.390µs ± 0.005µs	2.403µs	2.416µs	2.422µs	2.438µs	2.04%	-1.119	3.060	0.70%	0.001µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
tags/replace_trace_tags	execution_time	[2.390µs; 2.395µs] or [-0.097%; +0.097%]	None	None	None

Group 8

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
receiver_entry_point/report/2597	execution_time	3.288ms	3.312ms ± 0.014ms	3.308ms ± 0.007ms	3.317ms	3.340ms	3.360ms	3.371ms	1.89%	1.373	2.436	0.42%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
receiver_entry_point/report/2597	execution_time	[3.310ms; 3.314ms] or [-0.059%; +0.059%]	None	None	None

Group 9

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
redis/obfuscate_redis_string	execution_time	33.406µs	33.958µs ± 0.891µs	33.559µs ± 0.055µs	33.635µs	35.853µs	35.868µs	37.243µs	10.98%	1.756	1.324	2.62%	0.063µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[33.835µs; 34.082µs] or [-0.364%; +0.364%]	None	None	None

Group 10

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching serializing traces from their internal representation to msgpack	execution_time	13.993ms	14.070ms ± 0.034ms	14.065ms ± 0.016ms	14.081ms	14.133ms	14.199ms	14.264ms	1.41%	2.073	7.455	0.24%	0.002ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching serializing traces from their internal representation to msgpack	execution_time	[14.065ms; 14.074ms] or [-0.034%; +0.034%]	None	None	None

Group 11

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
concentrator/add_spans_to_concentrator	execution_time	10.628ms	10.656ms ± 0.014ms	10.654ms ± 0.009ms	10.665ms	10.678ms	10.693ms	10.721ms	0.63%	0.896	2.095	0.13%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
concentrator/add_spans_to_concentrator	execution_time	[10.654ms; 10.658ms] or [-0.018%; +0.018%]	None	None	None

Group 12

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching deserializing traces from msgpack to their internal representation	execution_time	50.240ms	50.727ms ± 0.867ms	50.603ms ± 0.066ms	50.675ms	50.812ms	54.954ms	58.889ms	16.37%	7.694	62.840	1.71%	0.061ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching deserializing traces from msgpack to their internal representation	execution_time	[50.607ms; 50.847ms] or [-0.237%; +0.237%]	None	None	None

Group 13

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sql/obfuscate_sql_string	execution_time	91.247µs	91.449µs ± 0.197µs	91.421µs ± 0.058µs	91.488µs	91.633µs	91.902µs	93.732µs	2.53%	8.220	89.699	0.21%	0.014µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[91.421µs; 91.476µs] or [-0.030%; +0.030%]	None	None	None

Group 14

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
profile_add_sample_frames_x1000	execution_time	4.159ms	4.166ms ± 0.008ms	4.165ms ± 0.001ms	4.167ms	4.169ms	4.171ms	4.269ms	2.50%	12.331	163.381	0.18%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample_frames_x1000	execution_time	[4.165ms; 4.167ms] or [-0.026%; +0.026%]	None	None	None

Group 15

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	execution_time	534.116µs	535.679µs ± 1.449µs	535.048µs ± 0.473µs	536.466µs	538.177µs	540.519µs	542.256µs	1.35%	1.832	3.814	0.27%	0.102µs	1	200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	1844146.614op/s	1866804.882op/s ± 5024.999op/s	1868992.118op/s ± 1654.022op/s	1870096.188op/s	1871308.342op/s	1872063.986op/s	1872252.676op/s	0.17%	-1.812	3.709	0.27%	355.321op/s	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	388.652µs	389.285µs ± 0.264µs	389.254µs ± 0.176µs	389.464µs	389.731µs	389.866µs	390.226µs	0.25%	0.412	0.121	0.07%	0.019µs	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	2562620.247op/s	2568812.784op/s ± 1740.463op/s	2569017.832op/s ± 1159.478op/s	2570037.614op/s	2571311.812op/s	2572168.217op/s	2572993.787op/s	0.15%	-0.408	0.115	0.07%	123.069op/s	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	189.722µs	190.211µs ± 0.196µs	190.206µs ± 0.130µs	190.345µs	190.545µs	190.646µs	190.770µs	0.30%	0.064	-0.215	0.10%	0.014µs	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	5241923.506op/s	5257320.624op/s ± 5418.756op/s	5257458.413op/s ± 3588.915op/s	5260761.622op/s	5266493.518op/s	5268954.927op/s	5270874.996op/s	0.26%	-0.059	-0.216	0.10%	383.164op/s	1	200
normalization/normalize_service/normalize_service/[empty string]	execution_time	37.479µs	37.688µs ± 0.065µs	37.693µs ± 0.039µs	37.728µs	37.795µs	37.836µs	37.853µs	0.42%	-0.247	0.263	0.17%	0.005µs	1	200
normalization/normalize_service/normalize_service/[empty string]	throughput	26418263.701op/s	26534048.595op/s ± 45697.032op/s	26530399.383op/s ± 27636.486op/s	26560555.141op/s	26619620.502op/s	26639394.932op/s	26681324.455op/s	0.57%	0.259	0.270	0.17%	3231.268op/s	1	200
normalization/normalize_service/normalize_service/test_ASCII	execution_time	45.795µs	45.907µs ± 0.084µs	45.893µs ± 0.037µs	45.934µs	46.028µs	46.125µs	46.644µs	1.64%	3.737	27.841	0.18%	0.006µs	1	200
normalization/normalize_service/normalize_service/test_ASCII	throughput	21438881.967op/s	21783471.190op/s ± 39788.247op/s	21789951.825op/s ± 17557.665op/s	21806996.137op/s	21825881.499op/s	21832244.709op/s	21836282.703op/s	0.21%	-3.651	26.825	0.18%	2813.454op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	execution_time	[535.478µs; 535.879µs] or [-0.037%; +0.037%]	None	None	None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	[1866108.465op/s; 1867501.298op/s] or [-0.037%; +0.037%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	[389.249µs; 389.322µs] or [-0.009%; +0.009%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	[2568571.573op/s; 2569053.996op/s] or [-0.009%; +0.009%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	[190.184µs; 190.238µs] or [-0.014%; +0.014%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	[5256569.637op/s; 5258071.612op/s] or [-0.014%; +0.014%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	execution_time	[37.679µs; 37.697µs] or [-0.024%; +0.024%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	throughput	[26527715.426op/s; 26540381.764op/s] or [-0.024%; +0.024%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	execution_time	[45.895µs; 45.918µs] or [-0.025%; +0.025%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	throughput	[21777956.922op/s; 21788985.459op/s] or [-0.025%; +0.025%]	None	None	None

Group 16

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
profile_add_sample2_frames_x1000	execution_time	721.961µs	722.916µs ± 0.556µs	722.802µs ± 0.333µs	723.216µs	723.806µs	724.504µs	726.295µs	0.48%	1.669	6.476	0.08%	0.039µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample2_frames_x1000	execution_time	[722.839µs; 722.993µs] or [-0.011%; +0.011%]	None	None	None

Group 17

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
ip_address/quantize_peer_ip_address_benchmark	execution_time	4.953µs	5.020µs ± 0.043µs	5.015µs ± 0.035µs	5.054µs	5.087µs	5.090µs	5.093µs	1.55%	0.383	-1.252	0.85%	0.003µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
ip_address/quantize_peer_ip_address_benchmark	execution_time	[5.014µs; 5.026µs] or [-0.118%; +0.118%]	None	None	None

Group 18

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
single_flag_killswitch/rules-based	execution_time	188.275ns	190.790ns ± 2.348ns	190.450ns ± 1.428ns	191.687ns	195.645ns	197.578ns	201.836ns	5.98%	1.494	2.678	1.23%	0.166ns	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
single_flag_killswitch/rules-based	execution_time	[190.464ns; 191.115ns] or [-0.171%; +0.171%]	None	None	None

Group 19

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`42de22c`	1772826505	rachel.yang/OTLP-trace-export

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	execution_time	184.918µs	185.346µs ± 0.222µs	185.318µs ± 0.164µs	185.508µs	185.721µs	185.849µs	186.122µs	0.43%	0.486	-0.256	0.12%	0.016µs	1	200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	5372813.483op/s	5395332.889op/s ± 6461.657op/s	5396136.447op/s ± 4789.599op/s	5400500.621op/s	5404295.591op/s	5406370.955op/s	5407795.797op/s	0.22%	-0.481	-0.267	0.12%	456.908op/s	1	200
normalization/normalize_name/normalize_name/bad-name	execution_time	18.030µs	18.127µs ± 0.057µs	18.123µs ± 0.025µs	18.148µs	18.196µs	18.225µs	18.715µs	3.27%	5.599	55.143	0.31%	0.004µs	1	200
normalization/normalize_name/normalize_name/bad-name	throughput	53432019.429op/s	55166019.845op/s ± 170389.921op/s	55178886.241op/s ± 77114.017op/s	55255840.351op/s	55353723.795op/s	55402328.617op/s	55462858.528op/s	0.51%	-5.361	51.929	0.31%	12048.387op/s	1	200
normalization/normalize_name/normalize_name/good	execution_time	10.318µs	10.448µs ± 0.067µs	10.441µs ± 0.053µs	10.498µs	10.557µs	10.600µs	10.661µs	2.11%	0.389	-0.226	0.64%	0.005µs	1	200
normalization/normalize_name/normalize_name/good	throughput	93801697.526op/s	95717195.242op/s ± 616267.553op/s	95776649.563op/s ± 482822.516op/s	96211209.945op/s	96623403.728op/s	96786619.789op/s	96918007.366op/s	1.19%	-0.359	-0.281	0.64%	43576.697op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	execution_time	[185.315µs; 185.376µs] or [-0.017%; +0.017%]	None	None	None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	[5394437.365op/s; 5396228.413op/s] or [-0.017%; +0.017%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	execution_time	[18.119µs; 18.135µs] or [-0.044%; +0.044%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	throughput	[55142405.441op/s; 55189634.250op/s] or [-0.043%; +0.043%]	None	None	None
normalization/normalize_name/normalize_name/good	execution_time	[10.439µs; 10.457µs] or [-0.089%; +0.089%]	None	None	None
normalization/normalize_name/normalize_name/good	throughput	[95631786.486op/s; 95802603.998op/s] or [-0.089%; +0.089%]	None	None	None

Baseline

Omitted due to size.

dd-octo-sts · 2026-03-02T22:21:44Z

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	8.51 MB	8.57 MB	+.73% (+64.21 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	97.30 MB	97.74 MB	+.45% (+448.50 KB) 🔍

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	112.92 MB	113.47 MB	+.48% (+559.65 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.12 MB	11.13 MB	+.11% (+13.01 KB) 🔍

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	27.16 MB	27.30 MB	+.51% (+143.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	76.26 KB	76.26 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	185.98 MB	186.61 MB	+.34% (+648.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	916.65 MB	920.64 MB	+.43% (+3.99 MB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	9.93 MB	10.00 MB	+.68% (+70.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	76.26 KB	76.26 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	24.76 MB	24.89 MB	+.50% (+128.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	51.43 MB	51.69 MB	+.51% (+270.58 KB) 🔍

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	22.97 MB	23.09 MB	+.55% (+130.00 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	77.44 KB	77.44 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	190.16 MB	190.83 MB	+.35% (+688.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	900.31 MB	904.32 MB	+.44% (+4.00 MB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	7.53 MB	7.58 MB	+.64% (+49.50 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	77.44 KB	77.44 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	26.52 MB	26.66 MB	+.50% (+136.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	47.06 MB	47.30 MB	+.52% (+252.02 KB) 🔍

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	85.27 MB	85.66 MB	+.46% (+402.36 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	10.04 MB	10.09 MB	+.50% (+52.21 KB) 🔍

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	105.90 MB	106.38 MB	+.45% (+492.39 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.79 MB	11.85 MB	+.50% (+60.69 KB) 🔍

codecov-commenter · 2026-03-03T01:47:43Z

Codecov Report

❌ Patch coverage is 76.74419% with 150 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.44%. Comparing base (8fb3175) to head (42de22c).
⚠️ Report is 16 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1641      +/-   ##
==========================================
+ Coverage   71.19%   71.44%   +0.25%     
==========================================
  Files         425      431       +6     
  Lines       62666    63539     +873     
==========================================
+ Hits        44615    45396     +781     
- Misses      18051    18143      +92

Components	Coverage Δ
libdd-crashtracker	`63.00% <ø> (-0.06%)`	⬇️
libdd-crashtracker-ffi	`16.56% <ø> (+0.92%)`	⬆️
libdd-alloc	`98.77% <ø> (ø)`
libdd-data-pipeline	`86.76% <76.74%> (-0.62%)`	⬇️
libdd-data-pipeline-ffi	`76.20% <ø> (+3.36%)`	⬆️
libdd-common	`79.73% <ø> (ø)`
libdd-common-ffi	`73.40% <ø> (ø)`
libdd-telemetry	`62.48% <ø> (ø)`
libdd-telemetry-ffi	`16.75% <ø> (ø)`
libdd-dogstatsd-client	`82.64% <ø> (ø)`
datadog-ipc	`80.35% <ø> (-0.39%)`	⬇️
libdd-profiling	`81.60% <ø> (-0.01%)`	⬇️
libdd-profiling-ffi	`63.65% <ø> (ø)`
datadog-sidecar	`34.48% <ø> (+1.03%)`	⬆️
datdog-sidecar-ffi	`16.55% <ø> (+4.13%)`	⬆️
spawn-worker	`54.69% <ø> (ø)`
libdd-tinybytes	`93.16% <ø> (ø)`
libdd-trace-normalization	`81.71% <ø> (ø)`
libdd-trace-obfuscation	`94.69% <ø> (+0.01%)`	⬆️
libdd-trace-protobuf	`68.00% <ø> (ø)`
libdd-trace-utils	`89.07% <ø> (+0.10%)`	⬆️
datadog-tracer-flare	`88.95% <ø> (-1.50%)`	⬇️
libdd-log	`74.69% <ø> (ø)`

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

libdd-data-pipeline-ffi/src/trace_exporter.rs

libdd-data-pipeline/src/otlp/config.rs

mabdinur · 2026-03-05T17:04:32Z

libdd-data-pipeline-ffi/src/trace_exporter.rs

+/// Sets the tracer/SDK name for OTLP resource attribute `telemetry.sdk.name`
+/// (e.g. "dd-trace-py"). When unset, OTLP uses "libdatadog".
+#[no_mangle]
+pub unsafe extern "C" fn ddog_trace_exporter_config_set_tracer_name(


Is tracer name a required field in the otlp trace payload. It would be nice if we went to with a minimal implementation where we defer unnecessary configurations. Also can the tracer name be interfered from other configurations?

mabdinur · 2026-03-05T18:03:19Z

libdd-data-pipeline/src/otlp/json_types.rs

+
+#[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]
+pub struct KeyValue {


Did we look into vendoring or adding opentelemetry-rust as a dependency to the libdd-data-pipeline so we can re-use existing functionality (ex).

At the minimum we should make it clear which components are borrowed from opentelemetry-rust and include a versioned link to the original implementation.

libdd-data-pipeline/src/otlp/json_types.rs

mabdinur · 2026-03-05T18:07:51Z

libdd-data-pipeline/src/otlp/json_types.rs

+}
+
+#[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]


DO we need the rename_all option here?

mabdinur · 2026-03-05T18:09:30Z

libdd-data-pipeline/src/otlp/json_types.rs

+}
+
+/// OTLP SpanKind enum values.
+pub mod span_kind {


Why are we using snakeCasing here? This is inconsistent with AnyValue,

mabdinur · 2026-03-05T18:15:06Z

libdd-data-pipeline/src/otlp/mapper.rs

+    }
+}
+
+fn build_resource(metadata: &TracerMetadata) -> Resource {


We have a similar function in dd-trace-rs. It would be nice if we could pull out some of this logic into a common rust crate and re-use it in both places. We can do something similar with a few other components in this PR (re-use helpers for applying otlp configurations).

cc: @paullegranddc

let's live with the duplication for now

libdd-data-pipeline/src/otlp/mapper.rs

paullegranddc

To test the otlp trace export, you can add test agent snapshot tests here

paullegranddc · 2026-03-09T14:16:36Z

libdd-data-pipeline/src/otlp/config.rs

+// Copyright 2024-Present Datadog, Inc. https://www.datadoghq.com/
+// SPDX-License-Identifier: Apache-2.0


I think we should parse the configurations from the host language, and not in libdatadg.

The way we would configure the TraceExporter for agent_url for instance would be

1) language_configuration resolves agent_url from different sources 2) when the tracer wants to instantiate a TraceExporter it first creates a TraceExporterBuilder and calls setters on it (set_agent_url in this case) 3) TraceExporterBuilder::build gives a TraceExporter containing the agent_url 4) In TraceExporter::send we can just read self.agent_url

paullegranddc · 2026-03-09T14:31:11Z

libdd-data-pipeline/src/otlp/exporter.rs

+pub async fn send_otlp_traces_http(
+    client: &HttpClient,
+    config: &OtlpTraceConfig,
+    json_body: Vec<u8>,
+) -> Result<(), TraceExporterError> {


You should use libdd_trace_utils::send_with_retry::send_with_retry to send a serialized payloads to the agent, it already handles retries/telemetry (defined here)

paullegranddc · 2026-03-09T14:37:43Z

libdd-data-pipeline/src/otlp/json_types.rs

+//!
+//! The Rust implementation in opentelemetry-rust uses `prost`-generated types with an optional
+//! `with-serde` feature (`opentelemetry-proto` crate). We use hand-rolled serde structs here to
+//! avoid the `prost` + `tonic` dependency tree in this early implementation. If/when protobuf


agreed with duplicating type defs in here, and let's add opentelemetry-proto when we actually want to add http/proto export

paullegranddc · 2026-03-09T14:40:24Z

libdd-data-pipeline/src/otlp/json_types.rs

+// Copyright 2024-Present Datadog, Inc. https://www.datadoghq.com/
+// SPDX-License-Identifier: Apache-2.0
+


You should probably move both this file and mapper.rs to libdd_trace_utils in an otlp_encoder module/folder there to put the code

paullegranddc · 2026-03-09T14:44:31Z

libdd-data-pipeline/src/otlp/mapper.rs

+where
+    T::Text: Borrow<str>,


This is not necessary, T::Text: Borrow<str> is already required in the definition of TraceData and the code compiles without it

paullegranddc · 2026-03-09T14:45:30Z

libdd-data-pipeline/src/otlp/mapper.rs

+    }
+}
+
+fn build_resource(metadata: &TracerMetadata) -> Resource {


let's live with the duplication for now

paullegranddc · 2026-03-09T14:56:03Z

libdd-data-pipeline/src/otlp/json_types.rs

+pub struct AnyValue {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub string_value: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bool_value: Option<bool>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub int_value: Option<i64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub double_value: Option<f64>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bytes_value: Option<String>,
+}


Should be an enum instead

#[derive(Debug, Serialize)] #[serde(rename_all = "camelCase")] enum AnyValue { StringValue(String), BoolValue(bool), // and so on }

paullegranddc · 2026-03-09T14:59:19Z

libdd-data-pipeline/src/trace_exporter/mod.rs

+    where
+        T::Text: Borrow<str>,


same as the comment before, this trait bound is no necessary

WIP: OTLP trace export

1e86c8d

rachelyangdog requested a review from a team as a code owner March 2, 2026 21:42

rachelyangdog marked this pull request as draft March 2, 2026 21:42

github-actions bot added the data-pipeline label Mar 2, 2026

rachelyangdog added 2 commits March 2, 2026 16:48

linting

01662c2

linting on comments

91a6559

fix endpoint and mapping

293ecd8

mabdinur reviewed Mar 6, 2026

View reviewed changes

rachelyangdog marked this pull request as ready for review March 6, 2026 19:18

rachelyangdog added 2 commits March 6, 2026 14:34

implement feedback

2dd4b57

lint and private fields

42de22c

paullegranddc reviewed Mar 9, 2026

View reviewed changes

		// Copyright 2024-Present Datadog, Inc. https://www.datadoghq.com/
		// SPDX-License-Identifier: Apache-2.0

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