feat(profiling): upload compression config

[morrisonlevi]

What does this PR do?

Adds upload compression configuration. For now, the options are Off,
On, and Lz4:

• Off: no compression.
• On: default, and which algorithm it chooses can change.
• Lz4: lz4 frame encoded. Currently what On uses.

Also adds some conversions between FFI Result types and
std::result::Result and exposes some more public members, so that
they can be used in examples.

Motivation

This is being done in preparation for having Zstd as an option, and
possibly the new default.

Additional Notes

We may want roundtrip_to_pprof to be configurable on what
compression algorithm it uses. None would speed up tests,
but once we have zstd, we may wish to test each one.

How to test the change?

There's nothing to change unless you want to test out the different
compression algorithms. In that case, call
ddog_prof_Profile_set_upload_compression before uploading.

[pr-commenter]

Benchmarks

Comparison

Benchmark execution time: 2025-05-29 18:00:47

Comparing candidate commit 35933df in PR branch PROF-11830-Add-upload-compression-configuration-to-libdatadog with baseline commit 8b4ff00 in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 52 metrics, 2 unstable metrics.

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	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	4.964µs	5.028µs ± 0.040µs	5.025µs ± 0.036µs	5.070µs	5.087µs	5.091µs	5.142µs	2.32%	0.324	-1.228	0.79%	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	[5.022µs; 5.033µs] or [-0.110%; +0.110%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	437.478µs	438.318µs ± 0.581µs	438.200µs ± 0.278µs	438.552µs	439.041µs	441.143µs	441.459µs	0.74%	2.658	10.472	0.13%	0.041µs	1	200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	2265215.509op/s	2281452.527op/s ± 3012.681op/s	2282061.683op/s ± 1449.073op/s	2283269.165op/s	2284519.417op/s	2285268.312op/s	2285831.582op/s	0.17%	-2.637	10.336	0.13%	213.029op/s	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	452.801µs	453.605µs ± 0.378µs	453.576µs ± 0.232µs	453.816µs	454.349µs	454.521µs	454.682µs	0.24%	0.435	0.104	0.08%	0.027µs	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	2199339.077op/s	2204561.859op/s ± 1836.655op/s	2204701.095op/s ± 1127.195op/s	2205797.547op/s	2207325.948op/s	2208379.372op/s	2208476.420op/s	0.17%	-0.430	0.099	0.08%	129.871op/s	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	178.533µs	179.134µs ± 0.349µs	179.048µs ± 0.259µs	179.411µs	179.741µs	179.851µs	179.893µs	0.47%	0.445	-0.968	0.19%	0.025µs	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	5558854.839op/s	5582429.184op/s ± 10868.366op/s	5585080.025op/s ± 8072.169op/s	5591201.315op/s	5596815.897op/s	5598672.715op/s	5601191.187op/s	0.29%	-0.440	-0.973	0.19%	768.510op/s	1	200
normalization/normalize_service/normalize_service/[empty string]	execution_time	38.955µs	39.053µs ± 0.055µs	39.042µs ± 0.030µs	39.082µs	39.159µs	39.224µs	39.320µs	0.71%	1.294	2.917	0.14%	0.004µs	1	200
normalization/normalize_service/normalize_service/[empty string]	throughput	25432190.108op/s	25606336.844op/s ± 36001.512op/s	25613381.610op/s ± 20009.920op/s	25630269.879op/s	25650810.092op/s	25667722.269op/s	25670365.363op/s	0.22%	-1.281	2.856	0.14%	2545.691op/s	1	200
normalization/normalize_service/normalize_service/test_ASCII	execution_time	46.964µs	47.092µs ± 0.056µs	47.086µs ± 0.034µs	47.121µs	47.191µs	47.239µs	47.336µs	0.53%	0.715	1.333	0.12%	0.004µs	1	200
normalization/normalize_service/normalize_service/test_ASCII	throughput	21125400.608op/s	21235147.182op/s ± 25197.622op/s	21237943.573op/s ± 15220.551op/s	21252820.258op/s	21272226.190op/s	21280962.435op/s	21293057.977op/s	0.26%	-0.705	1.302	0.12%	1781.741op/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	[438.238µs; 438.399µs] or [-0.018%; +0.018%]	None	None	None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	[2281034.998op/s; 2281870.055op/s] or [-0.018%; +0.018%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	[453.553µs; 453.658µs] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	[2204307.316op/s; 2204816.401op/s] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	[179.086µs; 179.183µs] or [-0.027%; +0.027%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	[5580922.933op/s; 5583935.435op/s] or [-0.027%; +0.027%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	execution_time	[39.045µs; 39.061µs] or [-0.020%; +0.020%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	throughput	[25601347.381op/s; 25611326.307op/s] or [-0.019%; +0.019%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	execution_time	[47.084µs; 47.100µs] or [-0.016%; +0.016%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	throughput	[21231655.034op/s; 21238639.330op/s] or [-0.016%; +0.016%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	3.889µs	3.911µs ± 0.003µs	3.911µs ± 0.002µs	3.913µs	3.917µs	3.920µs	3.920µs	0.23%	-1.171	11.371	0.08%	0.000µs	1	200
credit_card/is_card_number/	throughput	255087031.746op/s	255663937.039op/s ± 211001.076op/s	255684660.651op/s ± 121101.393op/s	255791021.752op/s	255898437.741op/s	255966682.182op/s	257157586.330op/s	0.58%	1.201	11.592	0.08%	14920.029op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	76.767µs	77.543µs ± 0.406µs	77.526µs ± 0.296µs	77.789µs	78.284µs	78.485µs	78.685µs	1.50%	0.436	-0.497	0.52%	0.029µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12708934.890op/s	12896382.620op/s ± 67318.416op/s	12898957.967op/s ± 49176.645op/s	12951523.419op/s	12992729.918op/s	13010105.197op/s	13026400.606op/s	0.99%	-0.416	-0.522	0.52%	4760.131op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	70.800µs	71.355µs ± 0.238µs	71.319µs ± 0.145µs	71.488µs	71.809µs	71.975µs	72.114µs	1.12%	0.686	0.373	0.33%	0.017µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	13866881.089op/s	14014570.503op/s ± 46672.810op/s	14021605.903op/s ± 28570.934op/s	14048955.928op/s	14074137.088op/s	14114266.746op/s	14124306.519op/s	0.73%	-0.667	0.347	0.33%	3300.266op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.890µs	3.912µs ± 0.003µs	3.911µs ± 0.002µs	3.914µs	3.917µs	3.919µs	3.921µs	0.25%	-0.955	7.404	0.09%	0.000µs	1	200
credit_card/is_card_number/37828224631	throughput	255039600.459op/s	255647125.644op/s ± 219633.832op/s	255671147.528op/s ± 141904.913op/s	255782726.530op/s	255932685.040op/s	256027829.299op/s	257060776.929op/s	0.54%	0.977	7.548	0.09%	15530.457op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	67.090µs	67.876µs ± 0.401µs	67.824µs ± 0.259µs	68.096µs	68.575µs	69.082µs	69.623µs	2.65%	0.864	1.649	0.59%	0.028µs	1	200
credit_card/is_card_number/378282246310005	throughput	14363146.340op/s	14733298.805op/s ± 86638.424op/s	14744135.944op/s ± 56359.882op/s	14799092.451op/s	14849850.928op/s	14897010.032op/s	14905253.447op/s	1.09%	-0.814	1.482	0.59%	6126.262op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	51.742µs	51.801µs ± 0.034µs	51.798µs ± 0.025µs	51.823µs	51.867µs	51.885µs	51.900µs	0.20%	0.561	-0.239	0.07%	0.002µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	19267711.745op/s	19304722.232op/s ± 12659.469op/s	19305862.683op/s ± 9398.825op/s	19315448.075op/s	19321464.911op/s	19325301.410op/s	19326726.582op/s	0.11%	-0.558	-0.244	0.07%	895.160op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	6.029µs	6.229µs ± 0.063µs	6.230µs ± 0.044µs	6.271µs	6.316µs	6.380µs	6.401µs	2.75%	-0.038	-0.019	1.00%	0.004µs	1	200
credit_card/is_card_number/x371413321323331	throughput	156221816.279op/s	160543408.600op/s ± 1616526.623op/s	160524583.135op/s ± 1124886.451op/s	161666206.102op/s	163069491.600op/s	163770328.718op/s	165851282.892op/s	3.32%	0.098	0.002	1.00%	114305.694op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.890µs	3.911µs ± 0.003µs	3.911µs ± 0.002µs	3.913µs	3.915µs	3.918µs	3.918µs	0.18%	-1.649	12.685	0.07%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	255200776.242op/s	255682907.985op/s ± 186452.015op/s	255664718.270op/s ± 129310.509op/s	255812595.936op/s	255911966.772op/s	255973301.686op/s	257045685.573op/s	0.54%	1.675	12.903	0.07%	13184.148op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	65.988µs	66.237µs ± 0.146µs	66.212µs ± 0.086µs	66.316µs	66.494µs	66.711µs	66.923µs	1.07%	1.221	2.809	0.22%	0.010µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	14942542.954op/s	15097424.131op/s ± 33145.908op/s	15103041.754op/s ± 19693.697op/s	15121939.666op/s	15142417.015op/s	15151107.885op/s	15154192.909op/s	0.34%	-1.200	2.715	0.22%	2343.770op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	59.384µs	59.556µs ± 0.073µs	59.554µs ± 0.048µs	59.607µs	59.663µs	59.726µs	59.888µs	0.56%	0.522	1.722	0.12%	0.005µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	16697880.771op/s	16790948.177op/s ± 20491.007op/s	16791532.870op/s ± 13582.538op/s	16803387.983op/s	16821663.359op/s	16836888.870op/s	16839628.698op/s	0.29%	-0.509	1.682	0.12%	1448.933op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.893µs	3.911µs ± 0.003µs	3.910µs ± 0.002µs	3.912µs	3.914µs	3.916µs	3.917µs	0.18%	-1.250	9.379	0.06%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	255276394.351op/s	255710836.356op/s ± 166579.101op/s	255723309.180op/s ± 117300.820op/s	255833845.885op/s	255911635.090op/s	255944667.746op/s	256850407.289op/s	0.44%	1.269	9.526	0.06%	11778.921op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	56.161µs	56.264µs ± 0.076µs	56.248µs ± 0.039µs	56.299µs	56.381µs	56.482µs	56.823µs	1.02%	2.545	13.789	0.14%	0.005µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	17598358.014op/s	17773396.656op/s ± 24005.534op/s	17778322.003op/s ± 12357.460op/s	17788162.423op/s	17801987.770op/s	17805048.889op/s	17805806.473op/s	0.15%	-2.507	13.432	0.13%	1697.448op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	51.739µs	51.800µs ± 0.038µs	51.799µs ± 0.025µs	51.818µs	51.868µs	51.914µs	51.980µs	0.35%	1.128	2.427	0.07%	0.003µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	19238229.575op/s	19305119.013op/s ± 14052.498op/s	19305527.436op/s ± 9213.800op/s	19315875.500op/s	19323257.105op/s	19326605.303op/s	19327889.577op/s	0.12%	-1.121	2.396	0.07%	993.662op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	6.073µs	6.223µs ± 0.066µs	6.227µs ± 0.043µs	6.267µs	6.334µs	6.351µs	6.414µs	3.02%	0.009	-0.413	1.05%	0.005µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	155897818.450op/s	160705226.308op/s ± 1694682.651op/s	160601588.934op/s ± 1114285.528op/s	161787059.349op/s	163593819.285op/s	164116162.448op/s	164669531.335op/s	2.53%	0.041	-0.430	1.05%	119832.159op/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 [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/	throughput	[255634694.319op/s; 255693179.759op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[77.487µs; 77.599µs] or [-0.073%; +0.073%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12887052.935op/s; 12905712.305op/s] or [-0.072%; +0.072%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[71.322µs; 71.388µs] or [-0.046%; +0.046%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[14008102.101op/s; 14021038.906op/s] or [-0.046%; +0.046%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.911µs; 3.912µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[255616686.507op/s; 255677564.781op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[67.820µs; 67.931µs] or [-0.082%; +0.082%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[14721291.552op/s; 14745306.057op/s] or [-0.081%; +0.081%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[51.796µs; 51.806µs] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[19302967.751op/s; 19306476.712op/s] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[6.221µs; 6.238µs] or [-0.139%; +0.139%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[160319373.557op/s; 160767443.642op/s] or [-0.140%; +0.140%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.911µs; 3.911µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[255657067.529op/s; 255708748.441op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[66.217µs; 66.257µs] or [-0.031%; +0.031%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15092830.427op/s; 15102017.835op/s] or [-0.030%; +0.030%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[59.546µs; 59.566µs] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[16788108.321op/s; 16793788.034op/s] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.910µs; 3.911µs] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[255687750.095op/s; 255733922.617op/s] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[56.253µs; 56.275µs] or [-0.019%; +0.019%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[17770069.720op/s; 17776723.592op/s] or [-0.019%; +0.019%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[51.795µs; 51.805µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[19303171.471op/s; 19307066.554op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[6.214µs; 6.232µs] or [-0.146%; +0.146%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[160470359.592op/s; 160940093.025op/s] or [-0.146%; +0.146%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	2.314µs	2.372µs ± 0.015µs	2.373µs ± 0.007µs	2.380µs	2.397µs	2.404µs	2.411µs	1.61%	-1.180	3.691	0.65%	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	[2.370µs; 2.374µs] or [-0.090%; +0.090%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	18.176µs	26.264µs ± 10.612µs	18.549µs ± 0.296µs	35.046µs	45.222µs	46.564µs	91.566µs	393.65%	1.677	5.738	40.30%	0.750µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
two way interface	execution_time	[24.794µs; 27.735µs] or [-5.600%; +5.600%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	218.118µs	218.611µs ± 0.283µs	218.622µs ± 0.224µs	218.818µs	219.090µs	219.258µs	219.379µs	0.35%	0.242	-0.772	0.13%	0.020µs	1	200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	4558313.132op/s	4574353.153op/s ± 5929.696op/s	4574112.550op/s ± 4678.862op/s	4579562.773op/s	4582926.968op/s	4584386.814op/s	4584680.957op/s	0.23%	-0.237	-0.778	0.13%	419.293op/s	1	200
normalization/normalize_name/normalize_name/bad-name	execution_time	17.578µs	17.664µs ± 0.048µs	17.659µs ± 0.027µs	17.686µs	17.749µs	17.777µs	17.983µs	1.83%	1.915	8.969	0.27%	0.003µs	1	200
normalization/normalize_name/normalize_name/bad-name	throughput	55609001.985op/s	56611348.448op/s ± 152960.385op/s	56629336.926op/s ± 85957.140op/s	56714460.418op/s	56795540.802op/s	56856037.518op/s	56887918.002op/s	0.46%	-1.856	8.509	0.27%	10815.933op/s	1	200
normalization/normalize_name/normalize_name/good	execution_time	10.306µs	10.339µs ± 0.019µs	10.335µs ± 0.011µs	10.350µs	10.372µs	10.405µs	10.406µs	0.69%	1.006	1.173	0.18%	0.001µs	1	200
normalization/normalize_name/normalize_name/good	throughput	96096148.965op/s	96719395.249op/s ± 175722.477op/s	96757622.577op/s ± 104018.328op/s	96843457.729op/s	96941108.429op/s	97009210.487op/s	97028771.623op/s	0.28%	-0.994	1.135	0.18%	12425.455op/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	[218.571µs; 218.650µs] or [-0.018%; +0.018%]	None	None	None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	[4573531.354op/s; 4575174.952op/s] or [-0.018%; +0.018%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	execution_time	[17.658µs; 17.671µs] or [-0.038%; +0.038%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	throughput	[56590149.609op/s; 56632547.286op/s] or [-0.037%; +0.037%]	None	None	None
normalization/normalize_name/normalize_name/good	execution_time	[10.337µs; 10.342µs] or [-0.025%; +0.025%]	None	None	None
normalization/normalize_name/normalize_name/good	throughput	[96695041.803op/s; 96743748.694op/s] or [-0.025%; +0.025%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	73.184ms	73.503ms ± 0.180ms	73.494ms ± 0.123ms	73.597ms	73.831ms	74.027ms	74.155ms	0.90%	0.901	0.935	0.24%	0.013ms	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	[73.478ms; 73.528ms] or [-0.034%; +0.034%]	None	None	None

Group 8

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	1.184µs	3.183µs ± 1.437µs	2.985µs ± 0.026µs	3.007µs	3.641µs	13.888µs	14.878µs	398.49%	7.413	55.886	45.02%	0.102µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
write only interface	execution_time	[2.984µs; 3.383µs] or [-6.255%; +6.255%]	None	None	None

Group 9

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	31.450µs	32.413µs ± 1.307µs	31.624µs ± 0.101µs	34.017µs	34.583µs	34.830µs	34.923µs	10.43%	0.932	-1.062	4.02%	0.092µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[32.232µs; 32.595µs] or [-0.559%; +0.559%]	None	None	None

Group 10

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	65.775µs	65.946µs ± 0.223µs	65.907µs ± 0.037µs	65.957µs	66.098µs	66.600µs	68.442µs	3.85%	8.280	82.883	0.34%	0.016µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[65.915µs; 65.977µs] or [-0.047%; +0.047%]	None	None	None

Group 11

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	246.971ns	254.076ns ± 10.186ns	250.026ns ± 2.039ns	254.818ns	284.364ns	291.175ns	292.200ns	16.87%	2.469	5.371	4.00%	0.720ns	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	[252.665ns; 255.488ns] or [-0.556%; +0.556%]	None	None	None

Group 12

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	145.836µs	146.506µs ± 0.339µs	146.424µs ± 0.169µs	146.646µs	147.168µs	147.570µs	147.970µs	1.06%	1.557	3.002	0.23%	0.024µs	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	[146.458µs; 146.553µs] or [-0.032%; +0.032%]	None	None	None

Group 13

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	35933df	1748540890	PROF-11830-Add-upload-compression-configuration-to-libdatadog

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	5.928ms	5.942ms ± 0.006ms	5.942ms ± 0.004ms	5.946ms	5.952ms	5.959ms	5.964ms	0.37%	0.627	0.930	0.10%	0.000ms	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	[5.942ms; 5.943ms] or [-0.014%; +0.014%]	None	None	None

Baseline

Omitted due to size.

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 73.68421% with 15 lines in your changes missing coverage. Please review.

Project coverage is 70.92%. Comparing base (8b4ff00) to head (35933df).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1064      +/-   ##
==========================================
+ Coverage   70.91%   70.92%   +0.01%     
==========================================
  Files         329      329              
  Lines       49877    49916      +39     
==========================================
+ Hits        35369    35403      +34     
- Misses      14508    14513       +5     

Components	Coverage Δ
datadog-crashtracker	42.55% <ø> (ø)
datadog-crashtracker-ffi	6.03% <ø> (ø)
datadog-alloc	98.73% <ø> (ø)
data-pipeline	90.61% <ø> (ø)
data-pipeline-ffi	89.26% <ø> (ø)
ddcommon	79.93% <0.00%> (-0.15%)	⬇️
ddcommon-ffi	68.34% <0.00%> (-0.31%)	⬇️
ddtelemetry	60.56% <ø> (ø)
ddtelemetry-ffi	21.32% <ø> (ø)
dogstatsd-client	83.26% <ø> (ø)
datadog-ipc	82.68% <ø> (+0.10%)	⬆️
datadog-profiling	77.21% <80.76%> (+0.03%)	⬆️
datadog-profiling-ffi	62.71% <86.48%> (+0.59%)	⬆️
datadog-sidecar	41.40% <ø> (ø)
datdog-sidecar-ffi	3.21% <ø> (ø)
spawn-worker	55.35% <ø> (ø)
tinybytes	90.96% <ø> (ø)
datadog-trace-normalization	98.24% <ø> (ø)
datadog-trace-obfuscation	94.16% <ø> (ø)
datadog-trace-protobuf	77.10% <ø> (ø)
datadog-trace-utils	89.44% <ø> (ø)

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[r1viollet]

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	66.02 MB	66.00 MB	--.03% (-24.52 KB) 💪
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	8.03 MB	7.96 MB	--.82% (-68.00 KB) 💪
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so.debug	20.31 MB	20.30 MB	--.01% (-4.15 KB) 💪

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	8.00 MB	7.93 MB	--.82% (-68.00 KB) 💪
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so.debug	22.70 MB	22.68 MB	--.07% (-17.58 KB) 💪
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	72.62 MB	72.56 MB	--.08% (-63.10 KB) 💪

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	16.71 MB	16.69 MB	--.08% (-14.50 KB) 💪
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	62.31 KB	62.31 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	112.66 MB	112.53 MB	--.11% (-136.00 KB) 💪
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	631.82 MB	631.29 MB	--.08% (-550.74 KB) 💪
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	4.99 MB	4.99 MB	--.04% (-2.50 KB) 💪
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	62.31 KB	62.31 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	15.88 MB	15.87 MB	--.04% (-8.00 KB) 💪
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	26.43 MB	26.40 MB	--.09% (-26.26 KB) 💪

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	14.15 MB	14.14 MB	--.08% (-12.00 KB) 💪
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	63.26 KB	63.26 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	114.51 MB	114.40 MB	--.10% (-120.00 KB) 💪
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	624.40 MB	624.27 MB	--.02% (-133.58 KB) 💪
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	3.78 MB	3.78 MB	--.06% (-2.50 KB) 💪
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	63.26 KB	63.26 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	16.50 MB	16.48 MB	--.09% (-16.00 KB) 💪
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	24.34 MB	24.32 MB	--.10% (-24.93 KB) 💪

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	57.50 MB	57.48 MB	--.03% (-21.75 KB) 💪
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	8.43 MB	8.42 MB	--.12% (-10.38 KB) 💪
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so.debug	18.71 MB	18.70 MB	--.01% (-2.93 KB) 💪

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	68.61 MB	68.58 MB	--.04% (-31.02 KB) 💪
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	8.43 MB	8.42 MB	--.12% (-10.38 KB) 💪
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so.debug	20.90 MB	20.89 MB	--.03% (-7.70 KB) 💪

[felixge]

Update ddog_prof_Profile_serialize to pass a UploadCompression.
You probably want DDOG_PROF_UPLOAD_COMPRESSION_ON when
migrating.

What kind of env var is this? Did you mean DD_PROFILING_UPLOAD_COMPRESSION=on?

[ivoanjo]

I wonder if for this feature it's worth implementing it in a slightly different way. E.g. the current approach of exposing the compression as a flag means:

1. Breaking API change, everyone will need to change to support this
2. Everyone will need to parse DD_PROFILING_UPLOAD_COMPRESSION, and when new values get added there (e.g. things such as "zstd-2"), everyone needs to update their string-to-enum-entry code again
3. We're considering that this feature may not live forever, and thus we'd need another breaking change if/when we remove it.

As an alternative, it would be really cool if libdatadog just looked at DD_PROFILING_UPLOAD_COMPRESSION directly and did the right thing automatically. This would mean that all libraries would instantly gain this feature with the upgrade, and as we added or removed options those would be automatically supported.

This means a bit less control for the libdatadog callers but... again in this case may be worth having the exception?

[r1viollet]

Update ddog_prof_Profile_serialize to pass a UploadCompression.
You probably want DDOG_PROF_UPLOAD_COMPRESSION_ON when
migrating.

What kind of env var is this? Did you mean DD_PROFILING_UPLOAD_COMPRESSION=on?

Aligning on env vars is a good idea, @felixge proposed a convention (you can refer to internal notebook)

[r1viollet]

...

This means a bit less control for the libdatadog callers but... again in this case may be worth having the exception?

Though do you want libdatadog APIs calling getenv ? Or would you document this as being part of the init process?
These are minor thread safety concerns, feel free to consider both approaches. I can see the testing requirements and API stability requirements as valid.

[ivoanjo]

Though do you want libdatadog APIs calling getenv ? Or would you document this as being part of the init process?

In general no, but I think this particular situation warrants doing that.

[morrisonlevi]

Runtimes need to manage the env var, partly because calling getenv in the wrong place can cause crashes. It's also partly because PHP has INI settings, and getenv obviously won't integrate with those.

Regarding DDOG_PROF_UPLOAD_COMPRESSION_ON, it is not an env var, but rather an enum value generated by the API.

I can make a parsing function like fn parse(value: &str) -> Option<UploadCompression> or similar, and FFI it. That way libraries don't have to updating parsing code as things change, but they will still have to manage the env var/INI settings.

[ivoanjo]

It's also partly because PHP has INI settings, and getenv obviously won't integrate with those.

I'm with you -- Ruby also has a config-via-code that usually we try to respect.

What I'm suggesting is -- in this case, I don't think it's worth respecting it? Since this is supposed to be for internal testing anyway, so it seems reasonable to say it can only be set via env flag.

[danielsn]

Proposal: we have a flag on the profiler object that defaults to on, with an API function to set it. Then the API still works the same way by default, but each language can override as it wishes.