enhancement(core): add support for transient messages to dataspaces

[tobz]

Summary

This PR adds support for emitting "transient" messages to dataspaces.

Currently, dataspaces (aka DataspaceRegistry) allow for assertion/retraction of "facts": values that are set (and sometimes updated) over the lifecycle of a process but are always attached to that particular process from which they were asserted, automatically removed when the process stops. In some cases, however, we want to communicate values that can live beyond the lifecycle of a process without abusing the assertion/retraction pattern... otherwise, callers would have to try and figure out when they should pay attention to retractions vs when they don't need to, which is something we'd rather not do at all.

This PR adds support for emitting "transient" messages to dataspaces to support this lifecycle-less model of communication. In Syndicated Actors, where the concept of dataspaces originates from, this is actually an officially supported pattern, so really we're just bringing our implementation of dataspaces up to parity. 😅

In no specific order:

• we changed AssertionUpdate to DataspaceUpdate since there's now a new enum variant for messages that is unrelated to assertions
• there's a new method, DataspaceRegistry::send, for sending transient messages
• transient messages have the same properties -- strongly typed, carries an identifier -- as assertions, but aren't tied to a process
• messages come in alongside assertions/retractions on a given subscription
• messages are only sent to active subscribers: they're never stored or replayed (hence "transient")

We're not optimizing for performance here since there's no use case (yet?) that requires this to act as a high-performance event bus.

Change Type

• Bug fix
• New feature
• Non-functional (chore, refactoring, docs)
• Performance

How did you test this PR?

New and existing tests.

References

DADP-2

[tobz]

• enhancement(core): add unified emitter for diagnostics #2096
• enhancement(core): add support for transient messages to dataspaces #2095 👈 (View in Graphite)
• main

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[pr-commenter]

Binary Size Analysis (Agent Data Plane)

Baseline: c480462 · Comparison: 9e4891a · diff
Analysis Configuration: stripped binaries · Pass/Fail Threshold: +5%
Sizes: 41.57 MiB (baseline) vs 41.60 MiB (comparison)
Size Change: +28.23 KiB (+0.07%)

✅ Binary size difference within threshold

Changes by Module

Module	File Size	Symbols
tokio	+12.09 KiB	479
anon.616682f45207a0b0c5ca668317e18b01.38.llvm.4933407896587571666	+10.33 KiB	1
anon.84466f5689dfb83e51567a6b56d70585.11.llvm.4191814962711865494	-10.33 KiB	1
anon.5f2ad6b78d80e06a5486a3aa9b588cac.0.llvm.1198158128097889638	+9.50 KiB	1
anon.067697e7467f0fe52fe342a4045d2b4c.82.llvm.8321187945284259587	-9.50 KiB	1
serde_json	-8.85 KiB	21
core	-6.44 KiB	925
crossbeam_channel	+5.40 KiB	22
&mut serde_json	+4.83 KiB	1
anon.061ad657bd5d2f931b857fc07cfe4e39.2.llvm.11063963451910516310	+4.38 KiB	1
anon.08d6aa4a5b49601fe6872e18f7d05cdc.547.llvm.5257818858558998185	-4.38 KiB	1
saluki_core::runtime::state	+3.91 KiB	12
axum	+3.82 KiB	36
anyhow	-3.67 KiB	98
anon.4b96425527629dd46c8e30f667721d3d.215.llvm.10522313277786391035	+3.37 KiB	1
anon.7b1f3bc0f707c4b3c51155b8c4fa2d7c.215.llvm.5193126962255660412	-3.37 KiB	1
anon.064e082a154a7d883e9212745d52ca87.133.llvm.6972503068399417237	-3.36 KiB	1
anon.98e0a99a2b01d69d90e0b26cac49e7ed.1616.llvm.8367001703044527826	+3.29 KiB	1
anon.4b96425527629dd46c8e30f667721d3d.518.llvm.10522313277786391035	+3.27 KiB	1
anon.b4f779bdb511619f62c8ac598cbf7a0c.424.llvm.13569147439722624680	-3.20 KiB	1

Detailed Symbol Changes

    FILE SIZE        VM SIZE    
 --------------  -------------- 
  +0.5% +27.6Ki  +0.1% +3.69Ki    [6256 Others]
  [NEW] +10.3Ki  [NEW]     +79    anon.616682f45207a0b0c5ca668317e18b01.38.llvm.4933407896587571666
  [NEW] +9.50Ki  [NEW]     +91    anon.5f2ad6b78d80e06a5486a3aa9b588cac.0.llvm.1198158128097889638
  +258% +5.74Ki  +271% +5.74Ki    hyper::proto::h1::conn::Conn<I,B,T>::poll_read_body::h19f77dfa67325ef9
  [NEW] +4.83Ki  [NEW] +4.64Ki    _<&mut serde_json::de::Deserializer<R> as serde_core::de::Deserializer>::deserialize_any::he783073847c1146a
  +229% +4.75Ki  +241% +4.75Ki    hyper::proto::h1::conn::Conn<I,B,T>::poll_read_body::hbf0e0380018ee30b
  [NEW] +4.38Ki  [NEW]     +74    anon.061ad657bd5d2f931b857fc07cfe4e39.2.llvm.11063963451910516310
  [NEW] +3.85Ki  [NEW] +3.73Ki    axum::routing::method_routing::MethodRouter<S,E>::merge_for_path::h80d8c445b9ef6218
  [NEW] +3.79Ki  [NEW] +3.69Ki    indexmap::map::IndexMap<K,V,S>::insert::h3c5bbc780170908e
  [NEW] +3.37Ki  [NEW] +3.28Ki    anon.4b96425527629dd46c8e30f667721d3d.215.llvm.10522313277786391035
  [NEW] +3.29Ki  [NEW]     +89    anon.98e0a99a2b01d69d90e0b26cac49e7ed.1616.llvm.8367001703044527826
  [DEL] -3.34Ki  [DEL] -3.16Ki    serde_json::value::de::_<impl serde_core::de::Deserialize for serde_json::value::Value>::deserialize::h87cc0f243fee5ce4
  [DEL] -3.36Ki  [DEL]      -2    anon.064e082a154a7d883e9212745d52ca87.133.llvm.6972503068399417237
  [DEL] -3.37Ki  [DEL] -3.28Ki    anon.7b1f3bc0f707c4b3c51155b8c4fa2d7c.215.llvm.5193126962255660412
  [DEL] -3.75Ki  [DEL] -3.65Ki    indexmap::inner::Core<K,V>::insert_full::haef698b8658cdc47
  [DEL] -4.38Ki  [DEL]     -74    anon.08d6aa4a5b49601fe6872e18f7d05cdc.547.llvm.5257818858558998185
  [DEL] -4.69Ki  [DEL] -4.61Ki    hyper::proto::h1::decode::Decoder::decode::hede0829da98fc6d9
  [DEL] -4.77Ki  [DEL] -4.59Ki    serde_json::value::de::_<impl serde_core::de::Deserialize for serde_json::value::Value>::deserialize::hc59c15a8fa69af6f
  [DEL] -5.74Ki  [DEL] -5.66Ki    hyper::proto::h1::decode::Decoder::decode::h6622434c9fd25d92
  [DEL] -9.50Ki  [DEL]     -91    anon.067697e7467f0fe52fe342a4045d2b4c.82.llvm.8321187945284259587
  [DEL] -10.3Ki  [DEL]     -79    anon.84466f5689dfb83e51567a6b56d70585.11.llvm.4191814962711865494
  +0.1% +28.2Ki  +0.0% +4.67Ki    TOTAL

[webern]

Makes sense to me. 👍

[pr-commenter]

Regression Detector (Agent Data Plane)

Run ID: 0037e60f-9d4e-4128-b1f7-85aaa68c447f
Baseline: c4804620 · Comparison: 9e4891a4 · diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment (5)

Experiments configured erratic: true are tagged (ignored) and skipped when determining which experiments regressed or improved. Experiments which are detected as erratic at runtime are tagged (erratic) to flag that the run's sample dispersion was high, but their regression / improvement signal still counts.

experiment	goal	Δ mean %	links
quality_gates_rss_dsd_low	memory	⚪ +0.40	metrics profiles logs
quality_gates_rss_idle	memory	⚪ +0.09	metrics profiles logs
quality_gates_rss_dsd_medium	memory	⚪ -0.04	metrics profiles logs
quality_gates_rss_dsd_ultraheavy	memory	⚪ -0.14	metrics profiles logs
quality_gates_rss_dsd_heavy	memory	⚪ -0.28	metrics profiles logs

Bounds Checks: ✅ Passed (5)

experiment	check	replicates	observed	links
quality_gates_rss_dsd_heavy	memory_usage	10/10	✅ 132 MiB ≤ 140 MiB	metrics profiles logs
quality_gates_rss_dsd_low	memory_usage	10/10	✅ 43.1 MiB ≤ 50 MiB	metrics profiles logs
quality_gates_rss_dsd_medium	memory_usage	10/10	✅ 64.9 MiB ≤ 75 MiB	metrics profiles logs
quality_gates_rss_dsd_ultraheavy	memory_usage	10/10	✅ 191 MiB ≤ 200 MiB	metrics profiles logs
quality_gates_rss_idle	memory_usage	10/10	✅ 28.9 MiB ≤ 40 MiB	metrics profiles logs

Explanation

A change is flagged as a regression when |Δ mean %| > 5.00% in the regressing direction for its optimization goal AND SMP marks the experiment as a regression (is_regression: true). Improvements use the matching criteria for the improving direction. Experiments configured erratic: true (tagged (ignored)) are skipped outright; experiments detected as erratic at runtime (tagged (erratic)) still count, since that flag describes sample dispersion rather than directional certainty. The Δ mean % cell is colored accordingly: 🟢 = improvement, 🔴 = regression, ⚪ = neutral. Reduction in CPU or memory is an improvement; reduction in ingress throughput is a regression.