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0ebdd48f9d
Self-review after the #51714 feedback found the reviewer's dead-table finding was not isolated — the schema advertised far more than the code populates, and our own tests hid it by hand-feeding fields production never sends. Make the surface honest by subtraction. Schema (10 tel_* tables -> 5): - Delete tel_gateway_events, tel_cron_events, tel_skill_events, tel_memory_events, tel_feedback_events — declared, never written, never read. - Drop columns nothing populates: tel_runs.{profile_id,estimated_cost_usd, cost_status}; tel_model_calls.{ttft_ms,estimated_cost_usd,cost_status, cost_source,end_reason,retry_count}; tel_tool_calls.{backend,retry_count, approval}; tel_spans.attrs_json. Cost duplicated the existing sessions billing columns and was always NULL here. - events.py / emitter _TABLE_COLUMNS / OTLP _span_attrs / rollup / preview display all trimmed to match. Correctness: - end_reason no longer hardcodes "completed". Production finalize callers pass `reason` (shutdown/session_expired/session_reset); _coarse_end_reason now reads it and maps accordingly. - Fix a latent bug the trim exposed: the model_call hook passed end_reason= to ModelCallEvent, which the @_safe wrapper was silently swallowing — so tel_model_calls dropped every row in real runs. Now writes correctly. Tests: - Stop hand-feeding estimated_cost_usd / turn_exit_reason that no production call site sends. Finalize is now driven with the real `reason` kwarg, and assertions cover only fields that are actually populated. This is what let the model_call drop hide — the suite graded on a fictional contract. Net: a smaller system that does what it says. Verified end-to-end over the real dispatch path (runs + connected span tree + model/tool rows populate; dead tables gone). 160 telemetry/state/insights tests green.
111 lines
3.1 KiB
Python
111 lines
3.1 KiB
Python
"""Typed local telemetry events.
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These dataclasses are the rows written to the local JSONL log and the ``tel_*``
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SQLite tables. They record the values observed for each run — model id, provider, tool
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name, token counts, durations — and stay on the machine unless explicitly exported.
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass, field, asdict
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from typing import Any, Dict, Optional
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# ── local telemetry events (real values) ────────────────────────────────────
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def _now_ns() -> int:
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return time.time_ns()
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@dataclass(slots=True)
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class RunEvent:
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"""One top-level workflow execution (a trace root). A run spans one session."""
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run_id: str
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trace_id: str
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entrypoint: str
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session_id: Optional[str] = None
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platform: Optional[str] = None
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start_ns: int = field(default_factory=_now_ns)
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end_ns: Optional[int] = None
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end_reason: Optional[str] = None
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model_call_count: int = 0
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tool_call_count: int = 0
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error_count: int = 0
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def to_dict(self) -> Dict[str, Any]:
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return {"event": "run", **asdict(self)}
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@dataclass(slots=True)
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class ModelCallEvent:
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span_id: str
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run_id: str
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provider: Optional[str] = None # raw provider, e.g. "anthropic"
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model: Optional[str] = None # raw model id, e.g. "claude-opus-4"
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base_url: Optional[str] = None
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input_tokens: int = 0
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output_tokens: int = 0
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cache_read_tokens: int = 0
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cache_write_tokens: int = 0
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reasoning_tokens: int = 0
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latency_ms: Optional[int] = None
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def to_dict(self) -> Dict[str, Any]:
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return {"event": "model_call", **asdict(self)}
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@dataclass(slots=True)
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class ToolCallEvent:
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span_id: str
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run_id: str
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tool_name: Optional[str] = None # raw tool name, e.g. "web_search"
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duration_ms: Optional[int] = None
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result_class: Optional[str] = None
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def to_dict(self) -> Dict[str, Any]:
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return {"event": "tool_call", **asdict(self)}
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@dataclass(slots=True)
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class SpanEvent:
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"""A timed span — the timing/lineage backbone of a trace.
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One row per run (the root, ``parent_span_id=None``) and one per model/tool call
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(``parent_span_id`` = the run's root span). Detail rows in ``tel_model_calls`` /
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``tel_tool_calls`` share the ``span_id`` and are joined here for ordering and
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placement on a timeline.
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"""
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span_id: str
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trace_id: str
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run_id: str
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name: str
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kind: str # "run" | "model" | "tool"
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start_ns: int
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end_ns: Optional[int] = None
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parent_span_id: Optional[str] = None
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status: Optional[str] = None
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def to_dict(self) -> Dict[str, Any]:
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return {"event": "span", **asdict(self)}
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@dataclass(slots=True)
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class ErrorEvent:
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run_id: Optional[str]
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error_class: str
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subsystem: str
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recovery: Optional[str] = None
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ts_ns: int = field(default_factory=_now_ns)
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def to_dict(self) -> Dict[str, Any]:
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return {"event": "error", **asdict(self)}
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__all__ = [
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"RunEvent",
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"ModelCallEvent",
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"ToolCallEvent",
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"SpanEvent",
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"ErrorEvent",
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]
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