trace-bench v0.4.0

Executive summary

Each named instance simulates a SaaS system at one of five sizes. A backend-for-frontend (BFF) gateway sits in front of the backend services, and every instance also has one external service. The table below gives each instance's size, as set in configs/instances/<instance>.yaml.

instance	services	endpoints / service	backend endpoints	BFF endpoints	scenarios	clients	simulated time	where it runs
xs	3	3	9	4	2	20	1 hour	local (test fixture)
s	10	5	50	8	4	200	1 day	local
m	50	10	500	20	8	2,000	2 days	local
l	150	10	1,500	30	12	6,000	3 days	local
xl	350	12	4,200	60	20	20,000	5 days	cloud only

About

A simulated microservice trace benchmark with ground-truth causal graphs. Each named instance ships a raw heterogeneous log feed (no parent pointers), the correlated sequence views, the mechanism graph that generated the data (latent state variables flagged), its latent projection over (operation, outcome) tokens as the scoring target, the deployment call topology as a candidate structural prior, injected-fault labels and the oracle linkage that makes correlation loss a measured quantity. Every corpus is wholly synthetic and reproduces byte-identically from its configuration, seed, tool version and constants version (see each manifest.json).

Three variants per instance and seed, one simulation. latent: no state variable is observable. twin: every latent value is written onto the records it influenced, with a state-change log; its target is a DAG over event and state tokens. metrics: the latent corpus plus a method-readable channel metrics/ of 30-second categorical time series of traffic intensity, per-service load and pool state and per-endpoint health — what a real cluster exports — while cache contents and per-session network and auth state stay hidden; it is derived from the latent corpus of the same seed (manifest.derived_from), and its target is the partial projection: state tokens of the exposed groups as sources, bidirected edges only through the hidden groups. The score gap across the three isolates what each degree of observability costs a method.

Generator (MIT): https://github.com/alex-chadyuk/trace-bench — tool version 0.3.0, 0.4.0. Corpus licence: CC BY 4.0. Fitted realism constants: 1.

instance	variant	seed	exposed / hidden latent groups	alphabet (realized, train)	mechanism nodes	directed / bidirected at floor	edges on a Monte-Carlo estimate (max SE)
l	latent	0	none / all	7435	8382	73837 / 50291	0 / 23984 (0.002499367)
l	latent	1	none / all	7513	8382	73107 / 58068	0 / 23703 (0.002497846)
l	latent	2	none / all	7533	8382	72764 / 60578	0 / 24375 (0.002487043)
l	latent	3	none / all	7477	8382	68885 / 56822	0 / 24311 (0.002447562)
l	latent	4	none / all	7430	8382	71266 / 58899	0 / 24374 (0.002498424)
l	metrics	0	health, intensity, load, pool / auth, cache, net	7435	8382	96939 / 1085	9 / 4571 (0.002499367)
l	metrics	1	health, intensity, load, pool / auth, cache, net	7513	8382	96153 / 2506	9 / 3988 (0.002497846)
l	metrics	2	health, intensity, load, pool / auth, cache, net	7533	8382	96129 / 1772	9 / 3861 (0.002487043)
l	metrics	3	health, intensity, load, pool / auth, cache, net	7477	8382	92252 / 1361	9 / 4275 (0.002447562)
l	metrics	4	health, intensity, load, pool / auth, cache, net	7430	8382	94403 / 1755	9 / 4232 (0.002498424)
l	twin	0	all / none	7435	8382	98678 / 0	6 / 0 (0.002499133)
l	twin	1	all / none	7513	8382	98141 / 0	6 / 0 (0.002497846)
l	twin	2	all / none	7533	8382	97944 / 0	6 / 0 (0.002487043)
l	twin	3	all / none	7477	8382	93963 / 0	6 / 0 (0.002447562)
l	twin	4	all / none	7430	8382	96285 / 0	6 / 0 (0.002476752)
m	latent	0	none / all	2644	2942	24639 / 11878	0 / 6879 (0.002495237)
m	latent	1	none / all	2650	2942	24205 / 14597	0 / 7149 (0.001746237)
m	latent	2	none / all	2654	2942	25758 / 13387	0 / 8588 (0.002343441)
m	latent	3	none / all	2650	2942	24527 / 13172	0 / 7231 (0.001926874)
m	latent	4	none / all	2653	2942	26436 / 14993	0 / 27761 (0.002130168)
m	metrics	0	health, intensity, load, pool / auth, cache, net	2644	2942	32442 / 666	0 / 0 (0.0)
m	metrics	1	health, intensity, load, pool / auth, cache, net	2650	2942	32126 / 1010	0 / 0 (0.0)
m	metrics	2	health, intensity, load, pool / auth, cache, net	2654	2942	33703 / 470	0 / 1688 (0.0)
m	metrics	3	health, intensity, load, pool / auth, cache, net	2650	2942	32415 / 609	0 / 0 (0.0)
m	metrics	4	health, intensity, load, pool / auth, cache, net	2653	2942	34390 / 878	0 / 0 (0.0)
m	twin	0	all / none	2644	2942	33265 / 0	0 / 0 (0.0)
m	twin	1	all / none	2650	2942	32952 / 0	0 / 0 (0.0)
m	twin	2	all / none	2654	2942	34420 / 0	6 / 0 (0.0)
m	twin	3	all / none	2650	2942	33207 / 0	0 / 0 (0.0)
m	twin	4	all / none	2653	2942	35209 / 0	0 / 0 (0.0)
s	latent	0	none / all	322	430	2064 / 796	0 / 0 (0.0)
s	latent	1	none / all	322	430	2545 / 859	0 / 0 (0.0)
s	latent	2	none / all	322	430	2555 / 851	0 / 0 (0.0)
s	latent	3	none / all	322	430	2517 / 791	0 / 0 (0.0)
s	latent	4	none / all	322	430	2576 / 997	0 / 0 (0.0)
s	metrics	0	health, intensity, load, pool / auth, cache, net	322	430	2930 / 68	0 / 0 (0.0)
s	metrics	1	health, intensity, load, pool / auth, cache, net	322	430	3427 / 268	0 / 0 (0.0)
s	metrics	2	health, intensity, load, pool / auth, cache, net	322	430	3436 / 109	0 / 0 (0.0)
s	metrics	3	health, intensity, load, pool / auth, cache, net	322	430	3393 / 48	0 / 0 (0.0)
s	metrics	4	health, intensity, load, pool / auth, cache, net	322	430	3461 / 69	0 / 0 (0.0)
s	twin	0	all / none	322	430	3114 / 0	0 / 0 (0.0)
s	twin	1	all / none	322	430	3655 / 0	0 / 0 (0.0)
s	twin	2	all / none	322	430	3646 / 0	0 / 0 (0.0)
s	twin	3	all / none	322	430	3559 / 0	0 / 0 (0.0)
s	twin	4	all / none	322	430	3649 / 0	0 / 0 (0.0)
xl	latent	0	none / all	19677	22772	198148 / 181365	0 / 63115 (0.002396905)
xl	latent	1	none / all	19772	22772	197410 / 201988	0 / 64834 (0.002407846)
xl	latent	2	none / all	19873	22772	193085 / 204374	0 / 64891 (0.001854232)
xl	latent	3	none / all	19788	22772	193375 / 203924	0 / 63779 (0.002499959)
xl	latent	4	none / all	19713	22772	192821 / 208923	0 / 64139 (0.002360767)
xl	metrics	0	health, intensity, load, pool / auth, cache, net	19677	22772	263307 / 2900	9 / 10894 (0.002396905)
xl	metrics	1	health, intensity, load, pool / auth, cache, net	19772	22772	262500 / 3458	9 / 10339 (0.002407846)
xl	metrics	2	health, intensity, load, pool / auth, cache, net	19873	22772	257880 / 3672	9 / 10135 (0.001854232)
xl	metrics	3	health, intensity, load, pool / auth, cache, net	19788	22772	258758 / 3066	9 / 9943 (0.002499959)
xl	metrics	4	health, intensity, load, pool / auth, cache, net	19713	22772	257847 / 3447	9 / 9798 (0.002360767)
xl	twin	0	all / none	19677	22772	267210 / 0	6 / 0 (0.002396905)
xl	twin	1	all / none	19772	22772	266771 / 0	6 / 0 (0.002407251)
xl	twin	2	all / none	19873	22772	262139 / 0	6 / 0 (0.001852761)
xl	twin	3	all / none	19788	22772	262667 / 0	6 / 0 (0.002488808)
xl	twin	4	all / none	19713	22772	261924 / 0	6 / 0 (0.002360767)
xs	latent	0	none / all	78	128	453 / 111	0 / 0 (0.0)
xs	latent	1	none / all	77	128	479 / 107	0 / 0 (0.0)
xs	latent	2	none / all	76	128	410 / 120	0 / 0 (0.0)
xs	latent	3	none / all	79	128	241 / 185	0 / 0 (0.0)
xs	latent	4	none / all	79	128	434 / 165	0 / 0 (0.0)
xs	metrics	0	health, intensity, load, pool / auth, cache, net	78	128	654 / 6	0 / 0 (0.0)
xs	metrics	1	health, intensity, load, pool / auth, cache, net	77	128	677 / 20	0 / 0 (0.0)
xs	metrics	2	health, intensity, load, pool / auth, cache, net	76	128	601 / 14	0 / 0 (0.0)
xs	metrics	3	health, intensity, load, pool / auth, cache, net	79	128	445 / 6	0 / 0 (0.0)
xs	metrics	4	health, intensity, load, pool / auth, cache, net	79	128	644 / 43	0 / 0 (0.0)
xs	twin	0	all / none	78	128	720 / 0	0 / 0 (0.0)
xs	twin	1	all / none	77	128	743 / 0	0 / 0 (0.0)
xs	twin	2	all / none	76	128	664 / 0	0 / 0 (0.0)
xs	twin	3	all / none	79	128	506 / 0	0 / 0 (0.0)
xs	twin	4	all / none	79	128	716 / 0	0 / 0 (0.0)

To score a method: read only raw/, views/ and (metrics variant) metrics/; score against graphs/scoring-target.json (request grain) with python -m tracebench.score.

Scoring-target strengths are exact marginalisations of the mechanism, except where the exact frontier of one (source, destination) chain exceeds the tool's particle cap: those effects are counter-keyed Monte-Carlo estimates with common random numbers (the sample count and the largest standard error are recorded per edge as mc: {n, se} and per target as n_effects_mc / mc_se_max). The last column counts the request-grain edges that carry the flag; a reader who wants only exact strengths can drop them. The scorer reads strengths only.

The dataset viewer is disabled: a corpus is a tree of gzipped JSON lines and parquet files with several schemas, not one table.