Captured from feature/tracing-migrationf510940(rebased on master8284509), same Jaeger / request set / DB snapshot procedure as pre-migration. Result: names + semantic attributes preserved, traceparent intact, no orphaned spans, async/streaming paths now single traces. See post-migration/README.md. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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# Post-migration span capture + parity diff (ticket #143, map #135)
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The "after" counterpart of `../pre-migration/` (#137). It is captured the same
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way, against the same local Jaeger, with the same request set, and diffed
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against `../pre-migration/span-inventory.json`.
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## What was run
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- **Revision:** `feature/tracing-migration` @ `f510940`, rebased onto master
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`8284509` (dev/e2e server). Release build. No source patch was needed:
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master's `LOCAL_BIND_URL=` disables the secondary `:8088` listener.
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- **Exporter:** the same local Jaeger all-in-one (OTLP/gRPC `:14317`). Traces
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were filtered by capture start time, because the container still holds the
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baseline traces.
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- **DB:** a fresh `.backup` of the live `auth.db` (21,298 `image_exif` rows,
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4 libraries, max job id 379, so the agentic job is again **380**). Output
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dirs point at temp. `REEL_PREGEN_ENABLED=0`, `REEL_CACHE_SWEEP_ENABLED=0`.
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- **LLM:** the live local stack (`LLM_BACKEND=llamacpp`,
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`qwen3.8-27b-abliterated`).
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- **Requests:** the baseline table, replayed by `replay.sh`: photos, memories,
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agentic (202, polled to `completed`), chat stream (87 deltas + `done`), and
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the `traceparent: 00-1111…-2222…-01` probe.
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Two captures were taken. Run 1 found two gaps, which are fixed in `f510940`
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(see "Gaps found and fixed"). Run 2, the files here, is the capture of record.
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## Parity result
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| Check | Before (#137) | After | Verdict |
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|---|---|---|---|
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| Operation names | — | every baseline op present (`find_relevant_events_hybrid` is a model-chosen tool; run 1 hit it, run 2 didn't) | ✅ preserved |
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| Attribute keys (semantic) | per `pre-migration/span-inventory.json` | **identical** for every common op (`list_photos`' ~27 request attrs, `ai.insight.generate_agentic`, `ai.rag.*`, `generate_video_thumbnail`, `db.*`, `llm.request`) | ✅ preserved |
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| `llm.request` | kind=client; `llm.model` / `llm.provider` / `llm.operation` / `llm.tokens.*` / `http.status_code` | same, on all 12 LLM spans | ✅ |
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| `llm.backend` | absent from every baseline span (it lives on the async-turn path, which neither capture exercises) | same | ✅ no loss |
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| Incoming `traceparent` | `list_photos` CHILD_OF remote `2222…` in trace `1111…` | the `GET /photos` root span is CHILD_OF remote `2222…`, with `list_photos` + DB spans under it, all in trace `1111…` | ✅ intact |
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| DB span nesting | most DB spans were single-span traces | **no DB span is a root**; each nests under its request / feature / job span | ✅ fixed |
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| Agentic 202 → background | two traces (`948d2756…` / `9b520713…`) | one 52-span trace: `POST …/agentic` → `http.insights.generate_agentic` → `ai.insight.generate_agentic` → `ai.agentic.loop` → `llm.request` / `ai.rag.*` / DB | ✅ fixed |
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| Streaming chat | fragmented, no handler span | one trace: `POST /insights/chat/stream` → `ai.chat.turn.stream` → `llm.request (chat_stream)` + DB | ✅ fixed |
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| Thumbnail sweep | 222 `generate_video_thumbnail` in 1 trace | 414 in 1 trace, all with one parent (the `creating thumbnails` root, still open when capture stopped) | ✅ |
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**Roots in the after capture:** only request roots (`GET /photos`,
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`GET /memories`, `POST …/agentic`, `POST /insights/chat/stream`,
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`GET /insights/generation/status`) and the background root
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`watcher.initial_probe`. **No orphans.**
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## Intentional changes (vs baseline)
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- **New per-request root spans** (`GET /photos`, …) from `tracing-actix-web`
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(#139). Operation spans (`list_photos`, …) now sit one level under them.
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- **New spans:** `ai.chat.turn.stream` (#140), `watcher.initial_probe` and the
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other job roots (#142); `db.query.get_job_by_id` shows up because the
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replay now polls job status.
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- **DB tracer scope:** `db` → `image-server`. DB spans come from the
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subscriber's tracer (#141). The span names and `db.*` attributes are unchanged.
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- **Extra per-span metadata** from tracing-opentelemetry, excluded from the key
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diff above: `code.file.path` / `code.line.number` / `code.module.name`,
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`thread.id` / `thread.name`, `target`, `busy_ns` / `idle_ns`.
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- **Removed:** the zero-length `file write` span in `upload_image` (#141). It
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wasn't exercised by either capture.
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## Gaps found and fixed (run 1 → `f510940`)
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1. **Agentic background still split off.** Both insight handlers run the
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generation in a nested `tokio::task::spawn` (for panic isolation) that
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wasn't instrumented, so `ai.insight.generate_agentic` was a root.
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2. **Watcher boot probe.** `count_for_library` ran before the first tick span,
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leaving 4 root DB spans. It now runs under `watcher.initial_probe`.
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## Files
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- `traces.json` — Jaeger `/api/traces` dump for run 2.
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- `span-inventory.json` — the baseline's schema, plus `parents` / `root_count`.
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- `diff-report.txt` — the full `analyze.py` output (before/after diff, roots,
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ancestry chains, traceparent chain, per-`llm.request` attributes).
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- `analyze.py`, `replay.sh` — reproduce with
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`python3 analyze.py <dir-with traces-after.json + inventory-before.json>`.
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## Full suite on `f510940`
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`cargo build` ✅, `cargo clippy --all-targets` 0 warnings ✅, `cargo fmt --check` ✅,
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`cargo test` ✅ (690 lib + 775 integration + 5 OTel regression + 4 doc).
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@@ -0,0 +1,149 @@
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"""#143 parity analysis: after-capture Jaeger dump vs the #137 baseline.
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Writes span-inventory.json (baseline schema + nesting columns) and prints a
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before/after diff plus targeted nesting / propagation checks.
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"""
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import json
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import sys
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from collections import defaultdict
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W = sys.argv[1]
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after = json.load(open(f"{W}/traces-after.json"))["data"]
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before_inv = {r["operation"]: r for r in json.load(open(f"{W}/inventory-before.json"))}
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# Attributes tracing-opentelemetry adds to every span (location / thread /
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# timing metadata). Reported separately so the diff shows semantic changes.
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TRACING_EXTRAS = {
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"busy_ns", "idle_ns", "target", "thread.id", "thread.name",
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"code.file.path", "code.line.number", "code.module.name",
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}
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# Spans produced by my auth probes, not by the baseline request set.
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PROBE_NOISE = set()
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spans = {}
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by_trace = defaultdict(list)
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for t in after:
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for s in t["spans"]:
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s["_tags"] = {kv["key"]: kv["value"] for kv in s["tags"]}
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spans[s["spanID"]] = s
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by_trace[s["traceID"]].append(s)
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def parent(s):
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for r in s.get("references", []):
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if r["refType"] == "CHILD_OF":
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return r["spanID"], r["traceID"]
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return None, None
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def op(sid):
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return spans[sid]["operationName"] if sid in spans else None
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# Inventory in the baseline's schema, plus nesting columns.
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inv = defaultdict(lambda: {"span_count": 0, "traces": set(), "kinds": set(), "scopes": set(),
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"statuses": set(), "keys": set(), "parents": defaultdict(int),
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"orphans": 0})
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for s in spans.values():
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r = inv[s["operationName"]]
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r["span_count"] += 1
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r["traces"].add(s["traceID"])
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r["kinds"].add(s["_tags"].get("span.kind", "internal"))
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r["scopes"].add(s["_tags"].get("otel.scope.name", "?"))
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r["statuses"].add(s["_tags"].get("otel.status_code", "(none)"))
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r["keys"].update(s["_tags"])
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pid, _ = parent(s)
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if pid is None:
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r["orphans"] += 1
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else:
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r["parents"][op(pid) or "(remote)"] += 1
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inventory = []
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for name, r in sorted(inv.items(), key=lambda kv: -kv[1]["span_count"]):
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inventory.append({
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"operation": name,
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"span_count": r["span_count"],
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"trace_count": len(r["traces"]),
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"kind": "/".join(sorted(r["kinds"])),
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"scope": "/".join(sorted(r["scopes"])),
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"status": "/".join(sorted(r["statuses"])),
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"attribute_keys": sorted(r["keys"] - TRACING_EXTRAS),
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"parents": dict(r["parents"]),
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"root_count": r["orphans"],
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})
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json.dump(inventory, open(f"{W}/span-inventory.json", "w"), indent=2)
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print("== Operations: before -> after ==")
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before_ops, after_ops = set(before_inv), set(inv)
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print("only before:", sorted(before_ops - after_ops))
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print("only after: ", sorted(after_ops - before_ops))
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print()
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print("== Common operations: kind / scope / attribute-key changes ==")
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for name in sorted(before_ops & after_ops):
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b, a = before_inv[name], next(x for x in inventory if x["operation"] == name)
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bk, ak = set(b["attribute_keys"]), set(a["attribute_keys"])
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changes = []
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if b["kind"] != a["kind"]:
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changes.append(f"kind {b['kind']}->{a['kind']}")
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if b["scope"] != a["scope"]:
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changes.append(f"scope {b['scope']}->{a['scope']}")
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if bk - ak:
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changes.append(f"LOST {sorted(bk - ak)}")
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if ak - bk:
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changes.append(f"added {sorted(ak - bk)}")
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nest = f"traces {b['trace_count']}/{b['span_count']} -> {a['trace_count']}/{a['span_count']}, roots={a['root_count']}"
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print(f"{name}: {'; '.join(changes) or 'same'} | {nest}")
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print()
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print("== Roots in the after capture (span with no parent) ==")
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for s in spans.values():
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if parent(s)[0] is None:
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print(f" {s['operationName']} trace={s['traceID'][:8]} spans_in_trace={len(by_trace[s['traceID']])}")
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def chain(s):
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out = [s["operationName"]]
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pid, _ = parent(s)
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while pid:
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if pid not in spans:
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out.append(f"(remote {pid})")
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break
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out.append(spans[pid]["operationName"])
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pid, _ = parent(spans[pid])
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return " <- ".join(out)
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print()
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print("== Ancestry of key spans ==")
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seen = set()
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for s in spans.values():
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n = s["operationName"]
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key = (n, s["_tags"].get("llm.operation"))
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if n in ("llm.request", "ai.agentic.loop", "ai.insight.generate_agentic",
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"db.insert.store_insight", "db.update.update_training_messages",
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"ai.chat.turn.stream", "list_photos", "list_memories",
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"generate_video_thumbnail") and key not in seen:
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seen.add(key)
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extra = f" [llm.operation={key[1]}]" if key[1] else ""
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print(f" {chain(s)}{extra}")
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print()
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print("== traceparent probe ==")
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probe = [s for s in spans.values() if s["traceID"].startswith("11111111")]
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for s in sorted(probe, key=lambda s: s["startTime"]):
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print(f" {chain(s)} trace={s['traceID']}")
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print()
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print("== llm.request attribute presence ==")
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for s in spans.values():
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if s["operationName"] == "llm.request":
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t = s["_tags"]
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print(f" op={t.get('llm.operation')} model={t.get('llm.model')} provider={t.get('llm.provider')} "
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f"status={t.get('http.status_code')} tokens_in={t.get('llm.tokens.input')} "
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f"parent={op(parent(s)[0])}")
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print()
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print("== turn / insight spans: llm.backend + llm.model ==")
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for s in spans.values():
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if s["operationName"] in ("ai.chat.turn.stream", "ai.insight.generate_agentic"):
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t = s["_tags"]
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print(f" {s['operationName']}: llm.backend={t.get('llm.backend')} llm.model={t.get('llm.model')} backend={t.get('backend')} model={t.get('model')}")
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@@ -0,0 +1,108 @@
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== Operations: before -> after ==
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only before: ['db.query.find_relevant_events_hybrid']
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only after: ['GET /insights/generation/status', 'GET /memories', 'GET /photos', 'POST /insights/chat/stream', 'POST /insights/generate/agentic', 'ai.chat.turn.stream', 'db.query.get_job_by_id', 'watcher.initial_probe']
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== Common operations: kind / scope / attribute-key changes ==
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ai.agentic.loop: same | traces 1/1 -> 1/1, roots=0
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ai.insight.generate_agentic: same | traces 1/1 -> 1/1, roots=0
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ai.rag.search_daily_summaries: same | traces 1/1 -> 1/2, roots=0
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db.insert.create_job: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.insert.store_insight: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.insert.upsert_entity: scope db->image-server | traces 1/2 -> 1/3, roots=0
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db.insert.upsert_fact: scope db->image-server | traces 1/1 -> 1/2, roots=0
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db.insert.upsert_photo_link: scope db->image-server | traces 1/1 -> 1/2, roots=0
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db.query.count_for_library: scope db->image-server | traces 4/4 -> 1/4, roots=0
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db.query.faces_for_hash: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.query.find_similar_summaries_with_time_weight: scope db->image-server | traces 1/1 -> 1/2, roots=0
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db.query.get_active_job: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.query.get_current_insight_for_library: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.query.get_event_count: scope db->image-server | traces 2/2 -> 2/2, roots=0
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db.query.get_exif: scope db->image-server | traces 2/2 -> 1/3, roots=0
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db.query.get_links_for_photo: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.query.get_location_count: scope db->image-server | traces 2/2 -> 2/2, roots=0
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db.query.get_memories_in_window: scope db->image-server | traces 1/4 -> 1/4, roots=0
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db.query.get_persona: scope db->image-server | traces 4/4 -> 2/4, roots=0
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db.query.get_prefs: scope db->image-server | traces 4/4 -> 3/4, roots=0
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db.query.get_tag_counts_batch: scope db->db/image-server | traces 1/1 -> 2/4, roots=0
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db.query.get_tags_for_path: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.query.has_any_faces: scope db->image-server | traces 2/2 -> 2/2, roots=0
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db.query.has_any_summaries: scope db->image-server | traces 2/2 -> 2/2, roots=0
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db.query.list_entities: scope db->image-server | traces 1/2 -> 1/4, roots=0
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db.query.list_rel_paths_for_libraries: scope db->db/image-server | traces 1/1 -> 2/4, roots=0
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db.query.resolve_content_hash: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.update.cancel_active_jobs: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.update.complete_job: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.update.update_facts_insight_id: scope db->image-server | traces 1/1 -> 1/1, roots=0
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db.update.update_training_messages: scope db->image-server | traces 1/1 -> 1/1, roots=0
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generate_video_thumbnail: same | traces 1/222 -> 1/414, roots=0
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http.insights.generate_agentic: same | traces 1/1 -> 1/1, roots=0
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list_memories: same | traces 1/1 -> 1/1, roots=0
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list_photos: same | traces 1/1 -> 2/4, roots=0
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llm.request: same | traces 3/8 -> 2/12, roots=0
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== Roots in the after capture (span with no parent) ==
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GET /insights/generation/status trace=4e4c40ef spans_in_trace=2
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GET /insights/generation/status trace=9b6f5cad spans_in_trace=2
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GET /insights/generation/status trace=92fe175c spans_in_trace=2
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GET /insights/generation/status trace=a1c2c449 spans_in_trace=2
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GET /insights/generation/status trace=73ad5d54 spans_in_trace=2
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GET /insights/generation/status trace=96ba44fa spans_in_trace=2
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GET /insights/generation/status trace=63be2acf spans_in_trace=2
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POST /insights/chat/stream trace=119b1ad6 spans_in_trace=11
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GET /insights/generation/status trace=d0f13d49 spans_in_trace=2
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GET /insights/generation/status trace=01e0824c spans_in_trace=2
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watcher.initial_probe trace=09884c56 spans_in_trace=5
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GET /insights/generation/status trace=4a47ba01 spans_in_trace=2
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POST /insights/generate/agentic trace=881c56c0 spans_in_trace=52
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GET /insights/generation/status trace=87857a0b spans_in_trace=2
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GET /insights/generation/status trace=e1cf97cb spans_in_trace=2
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GET /insights/generation/status trace=5f02a109 spans_in_trace=2
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GET /insights/generation/status trace=6fb52a67 spans_in_trace=2
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GET /insights/generation/status trace=2512bbae spans_in_trace=2
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GET /photos trace=4cd7207a spans_in_trace=4
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GET /memories trace=0c26f842 spans_in_trace=7
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GET /insights/generation/status trace=6844193f spans_in_trace=2
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== Ancestry of key spans ==
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list_photos <- (remote 2222222222222222)
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llm.request <- ai.chat.turn.stream <- POST /insights/chat/stream [llm.operation=chat_stream]
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db.update.update_training_messages <- ai.chat.turn.stream <- POST /insights/chat/stream
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ai.chat.turn.stream <- POST /insights/chat/stream
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llm.request <- ai.rag.search_daily_summaries <- ai.agentic.loop <- ai.insight.generate_agentic <- http.insights.generate_agentic <- POST /insights/generate/agentic [llm.operation=embeddings]
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llm.request <- ai.agentic.loop <- ai.insight.generate_agentic <- http.insights.generate_agentic <- POST /insights/generate/agentic [llm.operation=chat]
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ai.agentic.loop <- ai.insight.generate_agentic <- http.insights.generate_agentic <- POST /insights/generate/agentic
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db.insert.store_insight <- ai.insight.generate_agentic <- http.insights.generate_agentic <- POST /insights/generate/agentic
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ai.insight.generate_agentic <- http.insights.generate_agentic <- POST /insights/generate/agentic
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list_memories <- GET /memories
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generate_video_thumbnail <- (remote 4b78d96000f31655)
|
||||
|
||||
== traceparent probe ==
|
||||
list_photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.list_rel_paths_for_libraries <- list_photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.get_tag_counts_batch <- list_photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.list_rel_paths_for_libraries <- list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.get_tag_counts_batch <- list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.list_rel_paths_for_libraries <- list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
db.query.get_tag_counts_batch <- list_photos <- GET /photos <- (remote 2222222222222222) trace=11111111111111111111111111111111
|
||||
|
||||
== llm.request attribute presence ==
|
||||
op=chat_stream model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=11148 parent=ai.chat.turn.stream
|
||||
op=embeddings model=embed provider=llamacpp status=200 tokens_in=None parent=ai.rag.search_daily_summaries
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=10289 parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=10480 parent=ai.agentic.loop
|
||||
op=embeddings model=embed provider=llamacpp status=200 tokens_in=None parent=ai.agentic.loop
|
||||
op=embeddings model=embed provider=llamacpp status=200 tokens_in=None parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=5089 parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=10672 parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=10911 parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=10340 parent=ai.agentic.loop
|
||||
op=chat model=qwen3.8-27b-abliterated provider=llamacpp status=200 tokens_in=9858 parent=ai.agentic.loop
|
||||
op=embeddings model=embed provider=llamacpp status=200 tokens_in=None parent=ai.rag.search_daily_summaries
|
||||
|
||||
== turn / insight spans: llm.backend + llm.model ==
|
||||
ai.chat.turn.stream: llm.backend=None llm.model=None backend=None model=None
|
||||
ai.insight.generate_agentic: llm.backend=None llm.model=None backend=local model=qwen3.8-27b-abliterated
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
#!/bin/bash
|
||||
# Replays the #137 baseline request set against the after-capture server.
|
||||
set -u
|
||||
W=$(dirname "$0"); B=http://127.0.0.1:18080
|
||||
T=$(python3 $W/mint_jwt.py); H="Authorization: Bearer $T"
|
||||
echo "1) photos"; curl -s -o /dev/null -w "%{http_code}\n" -H "$H" "$B/photos?path=Colleen&recursive=true&limit=20"
|
||||
echo "2) memories"; curl -s -o /dev/null -w "%{http_code}\n" -H "$H" "$B/memories?path=.&recursive=true"
|
||||
echo "3) agentic"; R=$(curl -s -H "$H" -H 'Content-Type: application/json' -d '{"file_path":"Photo06172345.jpg","backend":"local"}' "$B/insights/generate/agentic"); echo "$R"
|
||||
JOB=$(echo "$R" | jq -r '.job_id // empty')
|
||||
if [ -n "$JOB" ]; then
|
||||
for i in $(seq 1 120); do S=$(curl -s -H "$H" "$B/insights/generation/status?job_id=$JOB" | jq -r .status); [ "$S" != "running" ] && [ "$S" != "pending" ] && break; sleep 5; done
|
||||
echo " job $JOB -> $S"
|
||||
fi
|
||||
echo "4) chat stream"; curl -sN -H "$H" -H 'Content-Type: application/json' -d '{"file_path":"Photo06172345.jpg","user_message":"What was I wearing in this photo?","backend":"local"}' "$B/insights/chat/stream" > $W/chat.sse; echo " deltas=$(grep -c '^event: text' $W/chat.sse) done=$(grep -c '^event: done' $W/chat.sse) errors=$(grep -c '^event: error' $W/chat.sse)"
|
||||
echo "5) traceparent probe"; curl -s -o /dev/null -w "%{http_code}\n" -H "$H" -H 'traceparent: 00-11111111111111111111111111111111-2222222222222222-01' "$B/photos?path=Colleen&recursive=true&limit=5"
|
||||
@@ -0,0 +1,918 @@
|
||||
[
|
||||
{
|
||||
"operation": "generate_video_thumbnail",
|
||||
"span_count": 414,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "(none)",
|
||||
"attribute_keys": [
|
||||
"file-name",
|
||||
"library",
|
||||
"otel.scope.name",
|
||||
"span.kind",
|
||||
"type"
|
||||
],
|
||||
"parents": {
|
||||
"(remote)": 414
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_job_by_id",
|
||||
"span_count": 16,
|
||||
"trace_count": 16,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"GET /insights/generation/status": 16
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "GET /insights/generation/status",
|
||||
"span_count": 16,
|
||||
"trace_count": 16,
|
||||
"kind": "server",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"http.client_ip",
|
||||
"http.flavor",
|
||||
"http.host",
|
||||
"http.method",
|
||||
"http.route",
|
||||
"http.scheme",
|
||||
"http.status_code",
|
||||
"http.target",
|
||||
"http.user_agent",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"request_id",
|
||||
"span.kind",
|
||||
"trace_id"
|
||||
],
|
||||
"parents": {},
|
||||
"root_count": 16
|
||||
},
|
||||
{
|
||||
"operation": "llm.request",
|
||||
"span_count": 12,
|
||||
"trace_count": 2,
|
||||
"kind": "client",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"http.status_code",
|
||||
"llm.model",
|
||||
"llm.operation",
|
||||
"llm.provider",
|
||||
"llm.tokens.input",
|
||||
"llm.tokens.output",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"ai.rag.search_daily_summaries": 2,
|
||||
"ai.agentic.loop": 9
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_tag_counts_batch",
|
||||
"span_count": 4,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "db/image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"file_count",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"list_photos": 4
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "list_photos",
|
||||
"span_count": 4,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"camera_make",
|
||||
"camera_model",
|
||||
"date_from",
|
||||
"date_to",
|
||||
"exclude_tag_ids",
|
||||
"file_count",
|
||||
"gps_lat",
|
||||
"gps_lon",
|
||||
"gps_radius_km",
|
||||
"lens_model",
|
||||
"library",
|
||||
"media_type",
|
||||
"optimization.batch_tags",
|
||||
"optimization.db_sort",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"pagination.enabled",
|
||||
"pagination.limit",
|
||||
"pagination.offset",
|
||||
"path",
|
||||
"recursive",
|
||||
"returned_count",
|
||||
"sort",
|
||||
"span.kind",
|
||||
"tag_filter_mode",
|
||||
"tag_ids",
|
||||
"total_count"
|
||||
],
|
||||
"parents": {
|
||||
"(remote)": 1,
|
||||
"GET /photos": 3
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.list_rel_paths_for_libraries",
|
||||
"span_count": 4,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "db/image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"list_photos": 4
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_persona",
|
||||
"span_count": 4,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"http.insights.generate_agentic": 1,
|
||||
"ai.agentic.loop": 1,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_prefs",
|
||||
"span_count": 4,
|
||||
"trace_count": 3,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"POST /insights/chat/stream": 1,
|
||||
"ai.insight.generate_agentic": 1,
|
||||
"http.insights.generate_agentic": 1,
|
||||
"list_memories": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.count_for_library",
|
||||
"span_count": 4,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"watcher.initial_probe": 4
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.list_entities",
|
||||
"span_count": 4,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.agentic.loop": 4
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_memories_in_window",
|
||||
"span_count": 4,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"list_memories": 4
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "GET /photos",
|
||||
"span_count": 3,
|
||||
"trace_count": 2,
|
||||
"kind": "server",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"http.client_ip",
|
||||
"http.flavor",
|
||||
"http.host",
|
||||
"http.method",
|
||||
"http.route",
|
||||
"http.scheme",
|
||||
"http.status_code",
|
||||
"http.target",
|
||||
"http.user_agent",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"request_id",
|
||||
"span.kind",
|
||||
"trace_id"
|
||||
],
|
||||
"parents": {
|
||||
"(remote)": 2
|
||||
},
|
||||
"root_count": 1
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_exif",
|
||||
"span_count": 3,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.agentic.loop": 2,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.insert.upsert_entity",
|
||||
"span_count": 3,
|
||||
"trace_count": 1,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.agentic.loop": 2,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.has_any_faces",
|
||||
"span_count": 2,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_location_count",
|
||||
"span_count": 2,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.get_event_count",
|
||||
"span_count": 2,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.has_any_summaries",
|
||||
"span_count": 2,
|
||||
"trace_count": 2,
|
||||
"kind": "internal",
|
||||
"scope": "image-server",
|
||||
"status": "OK",
|
||||
"attribute_keys": [
|
||||
"db.operation",
|
||||
"db.query_type",
|
||||
"otel.scope.name",
|
||||
"otel.status_code",
|
||||
"span.kind"
|
||||
],
|
||||
"parents": {
|
||||
"ai.chat.turn.stream": 1,
|
||||
"ai.insight.generate_agentic": 1
|
||||
},
|
||||
"root_count": 0
|
||||
},
|
||||
{
|
||||
"operation": "db.query.find_similar_summaries_with_time_weight",
|
||||
"span_count": 2,
|
||||
"trace_count": 1,
|
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Reference in new issue
Block a user