chore(crashtracker): use weaker mem ordering for OP_COUNTERS by yannham · Pull Request #1744 · DataDog/libdatadog

yannham · 2026-03-17T10:07:57Z

What does this PR do?

This PR replaces a bunch of sequentially consistent atomic accesses on ops counters by weaker relaxed accesses, cleaning a leftover TODO.

Motivation

The motivation to use the weakest memory ordering applicable is two folds:

Performance: relaxed accesses compile to normal, non-atomic loads and stores on standard platforms (x86_64 and arm64 in particular). Whether this particular change has any performance impact is less obvious.
Readability: I think my main motivation is that I find it easier, at least as a reader, to reason about weaker orderings. For example, a relaxed access indicates that there's no other unsynchronized data that this atomic protect or interact with, which enables local reasoning (you don't have to care about what other threads might be doing). A sequentially consistent access is the converse: they lead to a global order which involves all other seqcst accesses to this atomic, which is a strong and far-reaching assumption.

Additional Notes

This atomic is a counter, which is the poster child for Relaxed ordering (you usually only need the atomicity). This counter doesn't protect or interact with unsynchronized memory, so there's no reason to use a stronger ordering.

How to test the change?

Should see no difference in behavior except maybe for performance.

pr-commenter · 2026-03-17T10:09:58Z

Benchmarks

Comparison

Benchmark execution time: 2026-03-17 13:52:49

Comparing candidate commit 58bfd52 in PR branch yannham/mem-ordering-op-counters with baseline commit bb2b2bb in branch main.

Found 0 performance improvements and 1 performance regressions! Performance is the same for 58 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:ip_address/quantize_peer_ip_address_benchmark

🟥 execution_time [+366.190ns; +384.706ns] or [+7.217%; +7.582%]

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	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	87.009µs	87.208µs ± 0.142µs	87.183µs ± 0.046µs	87.242µs	87.346µs	87.745µs	88.663µs	1.70%	6.253	56.559	0.16%	0.010µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[87.189µs; 87.228µs] or [-0.023%; +0.023%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	237.600ns	248.153ns ± 14.091ns	242.641ns ± 2.875ns	248.730ns	287.132ns	292.479ns	298.882ns	23.18%	2.162	3.563	5.66%	0.996ns	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	[246.200ns; 250.106ns] or [-0.787%; +0.787%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	159.715µs	160.553µs ± 0.360µs	160.515µs ± 0.161µs	160.688µs	160.995µs	161.201µs	164.192µs	2.29%	5.284	51.023	0.22%	0.025µs	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	[160.503µs; 160.603µs] or [-0.031%; +0.031%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	1.258µs	3.206µs ± 1.427µs	2.976µs ± 0.030µs	3.016µs	3.659µs	14.068µs	14.800µs	397.31%	7.331	54.943	44.41%	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	[3.009µs; 3.404µs] or [-6.170%; +6.170%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	2.442µs	2.468µs ± 0.010µs	2.467µs ± 0.006µs	2.474µs	2.488µs	2.493µs	2.495µs	1.15%	0.569	0.174	0.39%	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	[2.467µs; 2.470µs] or [-0.055%; +0.055%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	49.090ms	49.488ms ± 1.234ms	49.317ms ± 0.045ms	49.372ms	49.583ms	54.790ms	62.788ms	27.32%	8.958	84.064	2.49%	0.087ms	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	[49.317ms; 49.659ms] or [-0.346%; +0.346%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	4.220ms	4.224ms ± 0.003ms	4.224ms ± 0.002ms	4.225ms	4.229ms	4.237ms	4.251ms	0.65%	3.904	25.817	0.08%	0.000ms	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	[4.224ms; 4.225ms] or [-0.011%; +0.011%]	None	None	None

Group 8

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	5.367µs	5.449µs ± 0.052µs	5.439µs ± 0.012µs	5.449µs	5.571µs	5.576µs	5.577µs	2.55%	1.349	1.167	0.94%	0.004µs	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	[5.442µs; 5.456µs] or [-0.131%; +0.131%]	None	None	None

Group 9

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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/2598	execution_time	3.439ms	3.466ms ± 0.016ms	3.463ms ± 0.007ms	3.472ms	3.497ms	3.525ms	3.542ms	2.30%	1.688	4.177	0.46%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
receiver_entry_point/report/2598	execution_time	[3.464ms; 3.469ms] or [-0.065%; +0.065%]	None	None	None

Group 10

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	186.154µs	186.523µs ± 0.200µs	186.503µs ± 0.151µs	186.656µs	186.886µs	187.022µs	187.033µs	0.28%	0.409	-0.482	0.11%	0.014µs	1	200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	5346660.776op/s	5361274.795op/s ± 5744.918op/s	5361831.468op/s ± 4354.789op/s	5366025.127op/s	5369876.524op/s	5371263.355op/s	5371899.273op/s	0.19%	-0.405	-0.486	0.11%	406.227op/s	1	200
normalization/normalize_name/normalize_name/bad-name	execution_time	17.917µs	18.053µs ± 0.038µs	18.055µs ± 0.022µs	18.076µs	18.113µs	18.161µs	18.172µs	0.65%	0.012	1.204	0.21%	0.003µs	1	200
normalization/normalize_name/normalize_name/bad-name	throughput	55030555.832op/s	55391824.996op/s ± 115474.635op/s	55385572.013op/s ± 66941.342op/s	55459026.579op/s	55573678.013op/s	55674185.316op/s	55812496.277op/s	0.77%	0.008	1.207	0.21%	8165.290op/s	1	200
normalization/normalize_name/normalize_name/good	execution_time	10.144µs	10.320µs ± 0.087µs	10.327µs ± 0.030µs	10.351µs	10.387µs	10.430µs	11.286µs	9.28%	6.568	73.073	0.84%	0.006µs	1	200
normalization/normalize_name/normalize_name/good	throughput	88604638.050op/s	96909091.781op/s ± 780055.475op/s	96830488.862op/s ± 277927.330op/s	97196524.583op/s	98019673.014op/s	98228997.362op/s	98575877.288op/s	1.80%	-5.788	62.577	0.80%	55158.252op/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	[186.495µs; 186.551µs] or [-0.015%; +0.015%]	None	None	None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	[5360478.604op/s; 5362070.985op/s] or [-0.015%; +0.015%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	execution_time	[18.048µs; 18.058µs] or [-0.029%; +0.029%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	throughput	[55375821.322op/s; 55407828.670op/s] or [-0.029%; +0.029%]	None	None	None
normalization/normalize_name/normalize_name/good	execution_time	[10.308µs; 10.332µs] or [-0.117%; +0.117%]	None	None	None
normalization/normalize_name/normalize_name/good	throughput	[96800983.594op/s; 97017199.967op/s] or [-0.112%; +0.112%]	None	None	None

Group 11

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	14.210ms	14.253ms ± 0.026ms	14.249ms ± 0.011ms	14.262ms	14.284ms	14.344ms	14.442ms	1.36%	2.961	16.108	0.18%	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	[14.249ms; 14.256ms] or [-0.025%; +0.025%]	None	None	None

Group 12

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	3.896µs	3.913µs ± 0.003µs	3.913µs ± 0.002µs	3.915µs	3.918µs	3.919µs	3.921µs	0.20%	-0.691	4.392	0.07%	0.000µs	1	200
credit_card/is_card_number/	throughput	255043037.016op/s	255564137.974op/s ± 186914.389op/s	255562905.045op/s ± 124555.366op/s	255680699.222op/s	255821777.029op/s	255880877.377op/s	256646579.085op/s	0.42%	0.704	4.466	0.07%	13216.843op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	80.189µs	80.771µs ± 0.264µs	80.761µs ± 0.179µs	80.927µs	81.225µs	81.417µs	81.866µs	1.37%	0.558	0.714	0.33%	0.019µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12215054.306op/s	12380796.283op/s ± 40390.458op/s	12382246.954op/s ± 27368.202op/s	12411590.257op/s	12437807.523op/s	12455285.451op/s	12470513.270op/s	0.71%	-0.535	0.650	0.33%	2856.037op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	73.018µs	73.597µs ± 0.327µs	73.547µs ± 0.212µs	73.765µs	74.197µs	74.427µs	74.574µs	1.40%	0.620	-0.113	0.44%	0.023µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	13409587.875op/s	13587743.881op/s ± 60256.775op/s	13596682.410op/s ± 39266.368op/s	13633933.589op/s	13671407.543op/s	13683962.962op/s	13695235.297op/s	0.72%	-0.600	-0.147	0.44%	4260.797op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.889µs	3.911µs ± 0.011µs	3.913µs ± 0.002µs	3.914µs	3.917µs	3.934µs	3.994µs	2.08%	3.052	24.216	0.28%	0.001µs	1	200
credit_card/is_card_number/37828224631	throughput	250385006.131op/s	255720721.351op/s ± 714640.216op/s	255588817.989op/s ± 114715.288op/s	255719836.719op/s	256889465.674op/s	257022258.557op/s	257104601.840op/s	0.59%	-2.912	23.106	0.28%	50532.694op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	70.054µs	70.589µs ± 0.238µs	70.570µs ± 0.144µs	70.717µs	71.046µs	71.224µs	71.414µs	1.20%	0.651	0.513	0.34%	0.017µs	1	200
credit_card/is_card_number/378282246310005	throughput	14002910.436op/s	14166637.504op/s ± 47736.859op/s	14170381.784op/s ± 28952.269op/s	14198292.675op/s	14234090.505op/s	14258768.057op/s	14274616.274op/s	0.74%	-0.629	0.478	0.34%	3375.506op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	53.051µs	53.147µs ± 0.040µs	53.146µs ± 0.029µs	53.172µs	53.217µs	53.236µs	53.262µs	0.22%	0.155	-0.249	0.08%	0.003µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	18775225.484op/s	18815666.099op/s ± 14291.423op/s	18816246.066op/s ± 10103.537op/s	18826937.832op/s	18837867.477op/s	18845200.927op/s	18849626.259op/s	0.18%	-0.151	-0.251	0.08%	1010.556op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	6.429µs	6.439µs ± 0.006µs	6.439µs ± 0.003µs	6.442µs	6.450µs	6.455µs	6.460µs	0.32%	0.662	0.772	0.09%	0.000µs	1	200
credit_card/is_card_number/x371413321323331	throughput	154810743.866op/s	155293694.445op/s ± 136195.971op/s	155299094.413op/s ± 79265.829op/s	155377380.974op/s	155502350.093op/s	155534870.666op/s	155547474.561op/s	0.16%	-0.656	0.759	0.09%	9630.509op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.896µs	3.912µs ± 0.003µs	3.912µs ± 0.001µs	3.914µs	3.916µs	3.919µs	3.928µs	0.41%	0.175	11.406	0.07%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	254551894.590op/s	255592385.915op/s ± 177307.610op/s	255604710.636op/s ± 94739.610op/s	255696298.797op/s	255806203.061op/s	255860145.937op/s	256678046.027op/s	0.42%	-0.148	11.429	0.07%	12537.541op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	64.881µs	65.152µs ± 0.120µs	65.132µs ± 0.072µs	65.222µs	65.373µs	65.467µs	65.637µs	0.78%	0.800	0.954	0.18%	0.008µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	15235270.165op/s	15348739.180op/s ± 28253.100op/s	15353347.319op/s ± 17085.457op/s	15367219.416op/s	15384986.562op/s	15402398.138op/s	15412767.937op/s	0.39%	-0.787	0.920	0.18%	1997.796op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	58.188µs	58.416µs ± 0.125µs	58.390µs ± 0.066µs	58.467µs	58.691µs	58.790µs	58.871µs	0.82%	1.213	1.586	0.21%	0.009µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	16986334.001op/s	17118599.828op/s ± 36471.914op/s	17126210.014op/s ± 19391.855op/s	17143856.869op/s	17162264.987op/s	17177483.892op/s	17185672.305op/s	0.35%	-1.200	1.545	0.21%	2578.954op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.891µs	3.914µs ± 0.003µs	3.915µs ± 0.002µs	3.917µs	3.919µs	3.922µs	3.928µs	0.35%	-1.052	8.902	0.09%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	254573001.597op/s	255471295.121op/s ± 228351.334op/s	255454327.192op/s ± 154027.679op/s	255626489.311op/s	255765398.368op/s	255874885.402op/s	256978819.858op/s	0.60%	1.079	9.057	0.09%	16146.878op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	55.186µs	55.576µs ± 0.177µs	55.542µs ± 0.097µs	55.658µs	55.900µs	56.003µs	56.441µs	1.62%	0.968	2.107	0.32%	0.013µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	17717628.767op/s	17993623.393op/s ± 57266.752op/s	18004270.433op/s ± 31599.426op/s	18028336.462op/s	18065493.828op/s	18101572.629op/s	18120577.728op/s	0.65%	-0.938	1.982	0.32%	4049.371op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	53.060µs	53.143µs ± 0.038µs	53.142µs ± 0.027µs	53.167µs	53.203µs	53.220µs	53.347µs	0.39%	0.753	3.004	0.07%	0.003µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	18745057.107op/s	18817298.052op/s ± 13486.199op/s	18817443.173op/s ± 9511.920op/s	18827384.191op/s	18838443.408op/s	18842373.449op/s	18846519.232op/s	0.15%	-0.744	2.954	0.07%	953.618op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	6.429µs	6.439µs ± 0.005µs	6.439µs ± 0.003µs	6.442µs	6.447µs	6.450µs	6.471µs	0.50%	1.105	4.846	0.08%	0.000µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	154540833.609op/s	155303056.032op/s ± 129350.864op/s	155313411.314op/s ± 73201.308op/s	155378687.653op/s	155498787.318op/s	155525002.236op/s	155551931.909op/s	0.15%	-1.091	4.755	0.08%	9146.487op/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	[3.913µs; 3.913µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/	throughput	[255538233.437op/s; 255590042.510op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[80.735µs; 80.808µs] or [-0.045%; +0.045%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12375198.554op/s; 12386394.012op/s] or [-0.045%; +0.045%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[73.552µs; 73.643µs] or [-0.062%; +0.062%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[13579392.872op/s; 13596094.891op/s] or [-0.061%; +0.061%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.909µs; 3.912µs] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[255621679.090op/s; 255819763.612op/s] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[70.556µs; 70.622µs] or [-0.047%; +0.047%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[14160021.634op/s; 14173253.373op/s] or [-0.047%; +0.047%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[53.142µs; 53.153µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[18813685.445op/s; 18817646.753op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[6.439µs; 6.440µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[155274818.993op/s; 155312569.897op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.912µs; 3.913µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[255567812.785op/s; 255616959.044op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[65.136µs; 65.169µs] or [-0.026%; +0.026%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15344823.572op/s; 15352654.788op/s] or [-0.026%; +0.026%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[58.399µs; 58.434µs] or [-0.030%; +0.030%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[17113545.172op/s; 17123654.484op/s] or [-0.030%; +0.030%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.914µs; 3.915µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[255439647.822op/s; 255502942.420op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[55.551µs; 55.600µs] or [-0.044%; +0.044%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[17985686.772op/s; 18001560.014op/s] or [-0.044%; +0.044%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[53.137µs; 53.148µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[18815428.995op/s; 18819167.110op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[6.438µs; 6.440µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[155285129.246op/s; 155320982.817op/s] or [-0.012%; +0.012%]	None	None	None

Group 13

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	146.739µs	148.485µs ± 1.536µs	148.226µs ± 0.475µs	148.754µs	149.856µs	153.887µs	163.054µs	10.00%	5.710	45.427	1.03%	0.109µs	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	[148.272µs; 148.698µs] or [-0.143%; +0.143%]	None	None	None

Group 14

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	190.406ns	192.765ns ± 1.902ns	192.651ns ± 1.168ns	193.724ns	196.349ns	197.938ns	202.702ns	5.22%	1.279	2.992	0.98%	0.134ns	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	[192.502ns; 193.029ns] or [-0.137%; +0.137%]	None	None	None

Group 15

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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_timestamped_x1000	execution_time	4.207ms	4.213ms ± 0.008ms	4.212ms ± 0.002ms	4.214ms	4.216ms	4.220ms	4.323ms	2.66%	11.825	153.724	0.20%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample_timestamped_x1000	execution_time	[4.211ms; 4.214ms] or [-0.027%; +0.027%]	None	None	None

Group 16

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	33.305µs	33.970µs ± 1.178µs	33.437µs ± 0.052µs	33.526µs	36.512µs	36.577µs	37.894µs	13.33%	1.736	1.137	3.46%	0.083µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[33.807µs; 34.133µs] or [-0.481%; +0.481%]	None	None	None

Group 17

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	17.655µs	25.283µs ± 9.412µs	17.883µs ± 0.148µs	33.680µs	41.757µs	43.249µs	66.554µs	272.16%	0.960	0.623	37.13%	0.665µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
two way interface	execution_time	[23.979µs; 26.588µs] or [-5.159%; +5.159%]	None	None	None

Group 18

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	12.961ms	13.000ms ± 0.014ms	13.000ms ± 0.009ms	13.009ms	13.022ms	13.036ms	13.048ms	0.37%	0.323	0.435	0.10%	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	[12.998ms; 13.002ms] or [-0.014%; +0.014%]	None	None	None

Group 19

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	747.236µs	748.378µs ± 0.531µs	748.310µs ± 0.336µs	748.650µs	749.373µs	749.965µs	750.642µs	0.31%	1.163	2.299	0.07%	0.038µs	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	[748.304µs; 748.451µs] or [-0.010%; +0.010%]	None	None	None

Group 20

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`58bfd52`	1773754523	yannham/mem-ordering-op-counters

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	495.367µs	496.255µs ± 0.590µs	496.222µs ± 0.296µs	496.512µs	496.825µs	497.112µs	501.105µs	0.98%	4.326	31.708	0.12%	0.042µs	1	200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	1995588.640op/s	2015097.009op/s ± 2384.073op/s	2015226.830op/s ± 1200.398op/s	2016434.219op/s	2017812.102op/s	2018468.374op/s	2018706.456op/s	0.17%	-4.273	31.177	0.12%	168.579op/s	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	369.655µs	370.278µs ± 0.345µs	370.235µs ± 0.179µs	370.457µs	370.735µs	371.233µs	372.938µs	0.73%	2.750	17.495	0.09%	0.024µs	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	2681414.290op/s	2700673.864op/s ± 2509.699op/s	2700984.039op/s ± 1307.448op/s	2702079.573op/s	2703649.584op/s	2704918.858op/s	2705222.597op/s	0.16%	-2.717	17.180	0.09%	177.462op/s	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	169.753µs	170.181µs ± 0.145µs	170.190µs ± 0.095µs	170.275µs	170.405µs	170.476µs	170.698µs	0.30%	-0.081	0.302	0.08%	0.010µs	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	5858301.847op/s	5876085.883op/s ± 4989.838op/s	5875769.427op/s ± 3265.983op/s	5879173.483op/s	5884238.151op/s	5886567.973op/s	5890913.704op/s	0.26%	0.087	0.298	0.08%	352.835op/s	1	200
normalization/normalize_service/normalize_service/[empty string]	execution_time	36.849µs	37.055µs ± 0.113µs	37.080µs ± 0.078µs	37.142µs	37.211µs	37.285µs	37.323µs	0.65%	-0.255	-0.904	0.30%	0.008µs	1	200
normalization/normalize_service/normalize_service/[empty string]	throughput	26793284.993op/s	26987101.025op/s ± 82127.657op/s	26968542.090op/s ± 56897.264op/s	27055988.866op/s	27120116.971op/s	27134977.910op/s	27137888.750op/s	0.63%	0.264	-0.908	0.30%	5807.302op/s	1	200
normalization/normalize_service/normalize_service/test_ASCII	execution_time	46.209µs	46.336µs ± 0.197µs	46.312µs ± 0.031µs	46.342µs	46.437µs	46.489µs	48.424µs	4.56%	9.005	86.296	0.42%	0.014µs	1	200
normalization/normalize_service/normalize_service/test_ASCII	throughput	20650708.308op/s	21581824.412op/s ± 88278.322op/s	21592645.357op/s ± 14469.225op/s	21607607.148op/s	21625384.339op/s	21631681.608op/s	21641004.434op/s	0.22%	-8.913	84.933	0.41%	6242.220op/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	[496.173µs; 496.337µs] or [-0.016%; +0.016%]	None	None	None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	[2014766.600op/s; 2015427.419op/s] or [-0.016%; +0.016%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	[370.230µs; 370.326µs] or [-0.013%; +0.013%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	[2700326.044op/s; 2701021.684op/s] or [-0.013%; +0.013%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	[170.161µs; 170.201µs] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	[5875394.339op/s; 5876777.426op/s] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	execution_time	[37.039µs; 37.071µs] or [-0.042%; +0.042%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	throughput	[26975718.922op/s; 26998483.129op/s] or [-0.042%; +0.042%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	execution_time	[46.309µs; 46.363µs] or [-0.059%; +0.059%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	throughput	[21569589.885op/s; 21594058.938op/s] or [-0.057%; +0.057%]	None	None	None

Baseline

Omitted due to size.

github-actions · 2026-03-17T10:10:27Z

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

Base Branch: origin/main
PR Branch: origin/yannham/mem-ordering-op-counters

Summary by Rule

Rule	Base Branch	PR Branch	Change

Annotation Counts by File

File	Base Branch	PR Branch	Change

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	28	No change (0%)
`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-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	208	208	No change (0%)

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.

codecov-commenter · 2026-03-17T13:23:59Z

Codecov Report

❌ Patch coverage is 0% with 8 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.49%. Comparing base (6a02f01) to head (58bfd52).
⚠️ Report is 24 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1744      +/-   ##
==========================================
- Coverage   71.59%   71.49%   -0.10%     
==========================================
  Files         430      430              
  Lines       63967    63976       +9     
==========================================
- Hits        45796    45740      -56     
- Misses      18171    18236      +65

Components	Coverage Δ
libdd-crashtracker	`63.92% <0.00%> (+0.03%)`	⬆️
libdd-crashtracker-ffi	`18.19% <ø> (+0.46%)`	⬆️
libdd-alloc	`98.77% <ø> (ø)`
libdd-data-pipeline	`87.94% <ø> (ø)`
libdd-data-pipeline-ffi	`74.85% <ø> (ø)`
libdd-common	`79.73% <ø> (ø)`
libdd-common-ffi	`73.40% <ø> (ø)`
libdd-telemetry	`62.48% <ø> (+0.03%)`	⬆️
libdd-telemetry-ffi	`16.75% <ø> (ø)`
libdd-dogstatsd-client	`82.64% <ø> (ø)`
datadog-ipc	`80.35% <ø> (ø)`
libdd-profiling	`81.60% <ø> (+0.01%)`	⬆️
libdd-profiling-ffi	`63.65% <ø> (ø)`
datadog-sidecar	`33.51% <ø> (-1.02%)`	⬇️
datdog-sidecar-ffi	`12.56% <ø> (-4.45%)`	⬇️
spawn-worker	`54.69% <ø> (ø)`
libdd-tinybytes	`93.16% <ø> (ø)`
libdd-trace-normalization	`81.71% <ø> (ø)`
libdd-trace-obfuscation	`91.80% <ø> (ø)`
libdd-trace-protobuf	`68.25% <ø> (ø)`
libdd-trace-utils	`88.98% <ø> (ø)`
datadog-tracer-flare	`90.45% <ø> (ø)`
libdd-log	`74.69% <ø> (ø)`

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gyuheon0h · 2026-03-17T14:01:41Z

@@ -58,9 +57,7 @@ static OP_COUNTERS: [AtomicI64; OpTypes::SIZE as usize] = [ATOMIC_ZERO; OpTypes:
 /// ATOMICITY:
 ///     This function is atomic.
 pub fn begin_op(op: OpTypes) -> Result<(), CounterError> {


I wonder if this is a bug (even before these changes.)

For begin_op

If old == i64::MAX - 1, the new value becomes i64::MAX, which is still not overflow.

If old == i64::MAX, fetch_add(1, ...) wraps and stores i64::MIN before we can report an error.

For end_op
If old == 0, we return an error, but the counter has already been decremented to -1

But since we use these as diagnostic, and not critical for synchronization I think its not life-threatening?

Hmm, you're onto something. The first one is a classical problem with counters; IIRC there's specific handling in e.g. the implementation Arc (even if you do it right there could theoretically be concurrent fetch_adds between your fetch_add and the test, which would overflow). One possibility is to keep a "buffer zone": instead of checking for overflow tightly, you could for example test for old > i64::MAX / 2, and maybe reset to old upon overflow (it's almost impossible in practice that there are i64::MAX / 2 concurrent increments before the test). A clean fix requires an initial load and a compare-exchange, I fear, which is more costly for 99.99% of the code paths where you don't actually overflow.

I'm not sure the second is an issue here though, since the atomic is i64 and the check is old <= 0, it's probably ok for the counter to go far in the negative values - it's considered the same as being 0? Except maybe upon reading/reporting? Though, similarly, you could fix it with a more complex read-then-compare-exchange loop.

By the way, what do you think is a reasonable range for those counters in practice?

Yeah, Arc is basically pulling the MAX / 2 trick as well: https://doc.rust-lang.org/src/alloc/sync.rs.html#2390-2407

Oh, its a non issue for crashtracking. Each op will ever only be 0 or 1 technically. I was just thinking about the underlying logic.

Well, I guess the upper bound is the # of threads doing the same op at the same time. Mostly profiling operations.

I see. Then indeed overflow is very theoretical, in fact straight up impossible, given each thread takes up some space for its stack, making the max number of live threads at any point in time quite smaller than i64::MAX. But yeah in general I would say that keeping a safe range of (i64::MAX negative values for underflow, and i64::MAX / 2 of upper values for overflow) is practical way to do it without hurting the happy path. The right way ™️ would be to load first, and only update (with a compare-exchange) if it's indeed not overflowing/underflowing the counter, but this is quite more expensive.

dd-octo-sts · 2026-03-17T14:11:47Z

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	100.42 MB	100.42 MB	+0% (+16 B) 👌
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	8.70 MB	8.70 MB	0% (0 B) 👌

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	117.12 MB	117.12 MB	-0% (-80 B) 👌
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.28 MB	11.28 MB	0% (0 B) 👌

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	27.19 MB	27.19 MB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	76.61 KB	76.61 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	186.18 MB	186.16 MB	-0% (-16.00 KB) 👌
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	917.12 MB	917.12 MB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	9.94 MB	9.94 MB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	76.61 KB	76.61 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	24.80 MB	24.80 MB	0% (0 B) 👌
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	51.48 MB	51.48 MB	0% (0 B) 👌

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	22.99 MB	22.99 MB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	77.80 KB	77.80 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	190.39 MB	190.39 MB	0% (0 B) 👌
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	900.80 MB	900.80 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	7.54 MB	7.54 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	77.80 KB	77.80 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	26.54 MB	26.54 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	47.10 MB	47.10 MB	0% (0 B) 👌

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	87.62 MB	87.62 MB	+0% (+456 B) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	10.22 MB	10.22 MB	0% (0 B) 👌

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	109.99 MB	109.99 MB	+0% (+776 B) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.97 MB	11.97 MB	0% (0 B) 👌

…-unprocessed * 'main' of github.com:DataDog/libdatadog: feat(sidecar): add thread mode as fallback connection for restricted environments (#1447) feat(profiling-ffi): ProfilesDictionary_insert_strs (#1764) chore(release): merge release branch to main (#1760) fix(libdd-crashtracker-ffi)!: add missing fields for endpoint configuration (#1758) ci: prevent running macos tests on release branches (#1765) chore(datadog-tracer-flare): remove unnecessary features/deps (#1761) fix(profiling-ffi): Windows extern statics need __declspec(dllimport) (#1468) feat(profiling): thread id/name as well-known strs (#1757) ci: switch to ephemeral branches (#1731) chore(crashtracker): use weaker mem ordering for OP_COUNTERS (#1744) refactor(trace-utils)!: change header name type to accept dynamic values (#1722)

# Release proposal for libdd-crashtracker, libdd-gotter and their dependencies This PR contains version bumps based on public API changes and commits since last release. ## libdd-gotter **Next version:** `1.0.0` **Semver bump:** `major` **Tag:** `libdd-gotter-v1.0.0` **Warning:** this is an initial release. Please verify that the version and commits included are correct. ## libdd-crashtracker **Next version:** `2.0.0` **Semver bump:** `major` **Tag:** `libdd-crashtracker-v2.0.0` ### ⚠️ major bump forced due to: - `libdd-common`: ^1.0.0 → ^5.2.0 - `libdd-telemetry`: ^1.0.0 → ^7.0.0 ### Commits - feat(crashtracking)!: retrieve c assert message for linux when `__assert_fail` is dynamically loaded (#2268) - test: skip/shorten slow miri jobs (#2331) - feat(crashtracking): send debug log when no data is received at all (#2321) - fix(crashtracking): check fields and exclude uuid for `has_data` (#2322) - refactor: migrate to workspace dependencies, phase 4 (#2296) - refactor: migrate to workspace dependencies, phase 3 (#2283) - chore: moving to workspace-level dependencies, phase 2 (#2270) - refactor: consolidate core dependencies at workspace level (phase 1) (#2253) - test: stabilize flaky tests (#2256) - feat(telemetry)!: make telemetry worker wasm-compatible for the TraceExporter (#2172) - chore(crashtracking): harden multi thread ptrace collection (#2216) - fix(sidecar): don't double-encode file:// telemetry endpoints (#2230) - fix: new clippy lints (#2219) - refactor(sidecar)!: Avoid a dedicated socket for crashtracker (#2179) - refactor(clippy): prefer core and alloc imports (#2196) - refactor(libdd-telemetry)!: avoid leaking libdd-common types in the public API (#2152) - feat(crashtracking): collect all native stacks for unhandled exception and also crashing thread (#2155) - fix(crashtracking): sanitize type and message for unhandled exceptions (#2148) - fix(crashtracking): multi thread collection centos flakes harden (#2113) - chore(crashtracking)!: remove frame count experimental field (#2114) - fix(crashtracking): authenticate peer granted socket ptrace access (#2098) - feat(crashtracking)!: add experimental frame count field (#2099) - fix(crashtracker): increase test_waitall_nohang timeout to 500ms (#2097) - fix(crashtracker): support socket based receiver for all thread collection (#2080) - fix(crashtracker): set failed thread stack collection as incomplete empty stack (#2079) - fix(crashtracking)!: flatten all threads object into a list of `ThreadData` (#2054) - refactor(crashtracker): update imports to linux only (#2036) - fix(crashtracker): move preload logger marking after recursive guard (#2023) - fix(libdd-telemetry): restore previous Cargo.toml version (#1993) - feat!: integrate obfuscation to the stats exporter [APMSP-2764] (#1819) - chore(crashtracking)!: create errorsintake crash ping directly from telemetry (#1963) - feat(telemetry)!: include dependencies and integrations in app-extended-heartbeat (#1962) - feat(crashtracking)!: collect all threads (#1878) - chore(crashtracking): bump libdatadog-libunwind to v1.0.2 (#1942) - chore(crashtracking): default errors intake crash report upload to be on (#1902) - chore(libdd-crashtracker): remove path reference for libdd-libunwind-sys (#1877) - fix(crashtracking): handle new lines in client submitted exception message (#1836) - fix(crashtracker): Fix bin_tests in gitlab (#1832) - feat(crashtracking)!: improve parity between errors intake payload and telemetry intake payload (#1823) - feat(crashtracking)!: emit ucontext registers as structured data (#1787) - fix(ci): use single threaded for all tests that mutate signal state and use non-fatal signal (#1812) - fix(crashtracking): fix SIGCHLD signal guarding while in CT signal handler (#1807) - fix(ci): use single threaded to avoid race conditions for sa guard tests (#1800) - fix(crashtracking): guard sigchld and sigpipe during crashtracker signal handler execution (#1771) - chore(crashtracking): preserve errno for crashtracker (#1767) - chore(crashtracker): use weaker mem ordering for OP_COUNTERS (#1744) - chore(crashtracking): rename target triple to runtime platform (#1747) - chore(ci): give libdd-libunwind-sys its own version (#1743) - chore(crashtracking): add tag for target triple (#1741) - refactor(libdd-crashtracker)!: avoid leaking Endpoint through the public API (#1705) - chore(cargo): bump to 29.0.0 (#1702) - fix(crashtracking): use libunwind to unwind frames (#1663) - chore(deps): bump blazesym to 0.2.3 and blazesym-c to 0.1.7 (#1654) - chore(ci): fix crashtracker receiver binary rpath setting (#1652) - chore(crashtracking): emit a best effort stacktrace for Mac (#1645) - chore(crashtracker): use default-features=false for aws-lc-sys (#1625) - feat(crashtracking): report unhandled exceptions (#1596) - refactor(ddcommon)!: remove direct dependency on hyper client everywhere in common (#1604) - feat(crashtracking): include `Kind` in crash ping and clarify requirements (#1595) - fix(crashtracking): add process_tags to application field (#1576) - ci: update nightly in CI to 2026-02-08 (#1539) - fix(telemetry)!: fix logs payload format [APMSP-2590] (#1498) - chore(crashtracking): bump os_info crate to 3.14 (#1507) - Don't bail (#1494) - feat(crashtracking)!: emit crashing thread name in crash report for linux crashes (#1485) - test(crashtracking): add minimal LD preload test for crashtracker collector (#1428) - chore: release libddcommon-v1.1.0 (#1456) - chore: prepare libdd-telemetry-v2.0.0 (#1457) - [crashtracker] Retrieve panic message when crashing (#1361) - fix(sidecar): AWS lambda also can return EACCESS for shm_open (#1446) - chore(crashtracking): add `is_crash_debug` tag to crashtracker receiver debug logs (#1445) - [crashtracker] Log errors in crashtracker receiver (#1395) - chore: add changelog for every published crate (#1396) - Fix CI (#1389) - [crashtracker] Set OS info in the crash info builder when receiving report (#1388) - crashtracker: support cxx bindings for crashinfo (#1379) [APMSP-2764]: https://datadoghq.atlassian.net/browse/APMSP-2764?atlOrigin=eyJpIjoiNWRkNTljNzYxNjVmNDY3MDlhMDU5Y2ZhYzA5YTRkZjUiLCJwIjoiZ2l0aHViLWNvbS1KU1cifQ --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: hoolioh <107922352+hoolioh@users.noreply.github.com>

yannham requested a review from a team as a code owner March 17, 2026 10:07

github-actions Bot added the crashtracker label Mar 17, 2026

yannham force-pushed the yannham/mem-ordering-op-counters branch from 5630e20 to 15a5128 Compare March 17, 2026 13:10

chore(crashtracker): use weaker mem ordering for OP_COUTERS

58bfd52

yannham force-pushed the yannham/mem-ordering-op-counters branch from 15a5128 to 58bfd52 Compare March 17, 2026 13:35

yannham requested a review from gyuheon0h March 17, 2026 13:42

gyuheon0h approved these changes Mar 17, 2026

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gyuheon0h changed the title ~~chore(crashtracker): use weaker mem ordering for OP_COUTERS~~ chore(crashtracker): use weaker mem ordering for OP_COUNTERS Mar 17, 2026

gh-worker-dd-devflow-36fce6 Bot added mergequeue-status: queued mergequeue-status: in_progress and removed mergequeue-status: queued labels Mar 18, 2026

gh-worker-dd-mergequeue-cf854d Bot merged commit fa18a2b into main Mar 18, 2026
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dd-octo-sts Bot mentioned this pull request Aug 18, 2026

chore(release): proposal for libdd-crashtracker, libdd-gotter #2364

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