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7c80b79bb0
Addresses the review on #51714: the trace/span layer was declared but unwired — tel_spans was never written, call rows had no timestamp, and nothing set parent lineage, so the store was metrics-only and couldn't reconstruct a trace. Wire the span layer (keeping the praised star-schema shape): - New SpanEvent (span_id/trace_id/run_id/parent_span_id/name/kind/start_ns/end_ns) mapped into tel_spans via the emitter's _TABLE_COLUMNS. - The plugin mints a root span per run and, on each model/tool call, emits a SpanEvent (timing + parent = the run's root) keyed by the SAME span_id as the detail row, so tel_model_calls / tel_tool_calls JOIN to their span. - Call hooks fire on completion, so end_ns = now and start_ns is reconstructed from the measured latency/duration. The run's root span is emitted at finalize with the true run start/end. Result: tel_spans is a connected, single-trace_id, run -> calls tree a desktop waterfall (or any reader) can render directly, ordered by start_ns. Existing metrics rows (tel_runs/model_calls/tool_calls) are unchanged. OTLP: spans now flow to the exporter with their trace/parent/timing attributes. The exporter still emits one OTel span per event rather than reconstructing OTel SpanContexts into a connected trace tree; that projection is left for a follow-up and the module docstring now says so plainly instead of over-claiming. Adds test_spans_trace.py (connected-tree + detail-row JOIN) over the real dispatch path. Accurate (pre-hook) start times, real OTLP SpanContexts, and subagent cross-run lineage remain follow-ups.
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
"""Trace/span layer: tel_spans is populated as a connected run -> calls tree.
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Drives the real dispatch chain (discover_plugins -> invoke_hook) and asserts the
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timing/lineage backbone in tel_spans:
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- one root span per run (kind="run", parent_span_id NULL),
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- one child span per model/tool call parented to the root,
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- a single trace_id across the run,
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- call detail rows (tel_model_calls / tel_tool_calls) JOIN to their span by span_id,
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- reconstructed durations match the reported latency/duration.
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This is the regression guard for the waterfall a desktop trace viewer renders.
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"""
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from __future__ import annotations
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import sqlite3
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import time
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import pytest
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import hermes_state
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@pytest.fixture
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def runtime(tmp_path, monkeypatch):
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monkeypatch.setenv("HERMES_HOME", str(tmp_path))
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db = tmp_path / "state.db"
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hermes_state.SessionDB(db_path=db)
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import hermes_cli.plugins as plugins_mod
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monkeypatch.setattr(plugins_mod, "_plugin_manager", None, raising=False)
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from agent.telemetry import emitter as emitter_mod
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emitter_mod.reset_emitter_for_tests(None)
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import plugins.telemetry as plug
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plug._runs.clear()
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yield db, plugins_mod, emitter_mod
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try:
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emitter_mod.get_emitter().flush()
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except Exception:
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pass
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emitter_mod.reset_emitter_for_tests(None)
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monkeypatch.setattr(plugins_mod, "_plugin_manager", None, raising=False)
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def _one_turn(invoke_hook):
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invoke_hook("on_session_start", session_id="s1",
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model="anthropic/claude-opus-4", platform="cli")
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invoke_hook("post_api_request", session_id="s1", platform="cli",
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provider="anthropic", model="claude-opus-4", api_duration=0.9,
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usage={"input_tokens": 1000, "output_tokens": 120})
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invoke_hook("post_tool_call", session_id="s1", platform="cli",
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function_name="web_search", duration_ms=210, result='{"data": "ok"}')
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invoke_hook("on_session_finalize", session_id="s1", platform="cli",
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turn_exit_reason="completed", estimated_cost_usd=0.01, cost_status="known")
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def test_tel_spans_forms_connected_trace(runtime):
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db, plugins_mod, emitter_mod = runtime
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plugins_mod.discover_plugins(force=True)
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_one_turn(plugins_mod.invoke_hook)
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time.sleep(0.5)
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emitter_mod.get_emitter().flush()
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conn = sqlite3.connect(db)
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conn.row_factory = sqlite3.Row
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spans = conn.execute(
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"SELECT span_id, parent_span_id, kind, name, start_ns, end_ns, status, trace_id "
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"FROM tel_spans"
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).fetchall()
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# root + model + tool
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assert len(spans) == 3
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roots = [s for s in spans if s["parent_span_id"] is None]
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children = [s for s in spans if s["parent_span_id"] is not None]
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assert len(roots) == 1
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assert roots[0]["kind"] == "run"
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assert len(children) == 2
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# single trace, all children parented to the root
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assert len({s["trace_id"] for s in spans}) == 1
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assert all(c["parent_span_id"] == roots[0]["span_id"] for c in children)
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# spans are time-ordered and carry real durations
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by_kind = {s["kind"]: s for s in spans}
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assert (by_kind["model"]["end_ns"] - by_kind["model"]["start_ns"]) == 900 * 1_000_000
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assert (by_kind["tool"]["end_ns"] - by_kind["tool"]["start_ns"]) == 210 * 1_000_000
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assert by_kind["run"]["end_ns"] >= by_kind["run"]["start_ns"]
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def test_detail_rows_join_to_spans(runtime):
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db, plugins_mod, emitter_mod = runtime
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plugins_mod.discover_plugins(force=True)
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_one_turn(plugins_mod.invoke_hook)
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time.sleep(0.5)
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emitter_mod.get_emitter().flush()
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conn = sqlite3.connect(db)
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conn.row_factory = sqlite3.Row
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mc = conn.execute(
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"SELECT m.model, s.kind, s.trace_id FROM tel_model_calls m "
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"JOIN tel_spans s ON m.span_id = s.span_id"
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).fetchone()
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assert mc is not None and mc["model"] == "claude-opus-4" and mc["kind"] == "model"
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tc = conn.execute(
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"SELECT t.tool_name, s.kind FROM tel_tool_calls t "
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"JOIN tel_spans s ON t.span_id = s.span_id"
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).fetchone()
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assert tc is not None and tc["tool_name"] == "web_search" and tc["kind"] == "tool"
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conn.close()
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