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## Description A low prompt-cache hit rate is hard to act on without knowing *why* turns miss. Two very different causes need very different responses: - **TTL lapse** — the session went idle longer than the provider's cache lifetime, so the entry expired. The fix is a longer TTL (e.g. Anthropic's 1h breakpoint instead of the 5m default). - **Prefix change** — the cacheable message prefix shifted, so the new request couldn't match the cached key. A longer TTL won't help here at all. Right now those look identical from the dashboard (just "cache_read was 0"). This adds the attribution so a user can actually decide 5m vs 1h. Closes #1313 ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [x] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made `PrefixCacheTracker` already kept the previous turn's forwarded messages and a per-turn activity timestamp, so the signal was already there — it just wasn't being read. - **`prefix_tracker.py`** — `classify_cache_miss()`: when a turn expected a cached prefix (non-zero cached tokens last turn) but read 0 this turn, returns `ttl_expiry` if the idle gap exceeded the provider cache TTL, else `prefix_change` if the forwarded prefix differs from last turn's, else `unknown`. **TTL wins ties** — once the entry lapsed, a coincident content change is moot, and the 5m-vs-1h decision is exactly what the TTL signal answers. A 1h-breakpoint session can widen the window via `PrefixFreezeConfig.cache_ttl_seconds`. Cold starts and hits return `is_miss=False`. - **Anthropic handlers (streaming + non-streaming)** — classify BEFORE `update_from_response` overwrites the last-turn state the classifier reads, then record the reason. - **`prometheus_metrics.py`** — a per-provider/per-reason counter, `record_cache_miss_attribution()`, reset handling, and a `headroom_cache_miss_attribution_total{provider,reason}` export series. - **`cost.py`** — `build_prefix_cache_stats()` aggregates a `miss_attribution` block (per-provider + totals, with the ttl/prefix split as a % of *attributed* misses, so `unknown` doesn't dilute the headline). - **dashboard** — a "Cache Miss Attribution" panel (TTL expiry / prefix change / unknown / total) with a "mostly TTL lapse" vs "mostly prefix change" headline. Scoped to Anthropic for this first cut (where the tracker is fully wired); OpenAI/Gemini can follow once the shape is proven. ## Testing - [x] Unit tests pass (`pytest`) - [ ] Linting passes (`ruff check .`) - [ ] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [ ] Manual testing performed ### Test Output ```text $ python -m pytest tests/test_cache/test_prefix_tracker.py -q 38 passed # 29 existing + 9 new classifier tests (TestClassifyCacheMiss). $ python -m pytest tests/test_proxy_cache_ttl_metrics.py -k "miss_attribution or reset_runtime_clears" -q 5 passed, 8 deselected # new: counter bucketing, stats aggregation, empty case, /metrics export, reset. ``` The full `test_proxy_cache_ttl_metrics.py` / `test_proxy_dashboard_stats_cache.py` files have some failures in this sandbox (`test_stats_endpoint_*`, streaming-parser, reset-counters) — those spin up the proxy server / Rust `_core` extension, which isn't built here. I confirmed via `git stash` that they fail identically on `main` without my changes, so they're pre-existing and unrelated. My additions to the stats dict are purely additive and don't break any passing assertion. ## Real Behavior Proof - Environment: Windows 11, Python 3.10. The Rust `_core` extension and a live proxy aren't available in this checkout. - Exact command / steps: drove `classify_cache_miss()` through every branch with a faithful warm-then-miss sequence; drove `record_cache_miss_attribution()` → `build_prefix_cache_stats()` → `export()` end to end. - Observed result: classifier returns `cold_start`/`hit`/`ttl_expiry`/`prefix_change`/`unknown` correctly, TTL wins the tie when both signals fire, a growing (append-only) prefix is treated as stable, and the 1h override widens the window. The stats builder produces `miss_attribution.totals` (`ttl_expiry`/`prefix_change`/`unknown`/`total` + `ttl_expiry_pct`/`prefix_change_pct` over attributed misses) and `by_provider`; `/metrics` emits `headroom_cache_miss_attribution_total{provider="anthropic",reason="ttl_expiry"}`. - Not tested: a live Anthropic session through the running proxy with a real idle-then-resume to confirm the handler wiring fires end-to-end. I verified the handler integration by reading scope/order (classify before `update_from_response`, `provider_name`/`self.metrics` in scope) and unit-tested every layer it calls, but didn't exercise the actual server loop. ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [ ] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Additional Notes - The classifier is intentionally pure (takes the cache-read result + current forwarded messages + an optional idle override) so it's order-independent and unit-testable without a live tracker clock. - No README/docs change yet — this surfaces in the dashboard and `/metrics`, which are self-describing; happy to add a docs page if you'd like one. - CHANGELOG.md isn't touched — release-please generates it from the `feat(cache):` commit subject. - Follow-ups if useful: extend to OpenAI/Gemini handlers, and add a per-provider breakdown row in the dashboard panel (the stats already carry `by_provider`).
321 lines
11 KiB
Python
321 lines
11 KiB
Python
"""Tests for observed Anthropic cache TTL bucket metrics."""
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from __future__ import annotations
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import asyncio
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import pytest
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from headroom.observability import reset_headroom_tracing, reset_otel_metrics
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from headroom.proxy.cost import CostTracker, build_prefix_cache_stats
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from headroom.proxy.prometheus_metrics import PrometheusMetrics
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def test_prometheus_metrics_tracks_observed_ttl_buckets() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(
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metrics.record_request(
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provider="anthropic",
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model="claude-opus-4-6",
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input_tokens=100,
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output_tokens=20,
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tokens_saved=5,
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latency_ms=10.0,
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cache_read_tokens=40,
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cache_write_tokens=60,
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cache_write_5m_tokens=10,
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cache_write_1h_tokens=50,
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)
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)
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stats = metrics.cache_by_provider["anthropic"]
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assert stats["cache_write_5m_tokens"] == 10
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assert stats["cache_write_1h_tokens"] == 50
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assert stats["cache_write_5m_requests"] == 1
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assert stats["cache_write_1h_requests"] == 1
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def test_cost_tracker_exposes_observed_ttl_buckets_per_model() -> None:
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tracker = CostTracker()
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tracker.record_tokens(
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"claude-opus-4-6",
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tokens_saved=10,
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tokens_sent=90,
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cache_read_tokens=40,
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cache_write_tokens=60,
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cache_write_5m_tokens=10,
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cache_write_1h_tokens=50,
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uncached_tokens=20,
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)
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stats = tracker.stats()
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assert stats["cache_write_5m_tokens"] == 10
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assert stats["cache_write_1h_tokens"] == 50
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assert stats["per_model"]["claude-opus-4-6"]["cache_write_5m_tokens"] == 10
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assert stats["per_model"]["claude-opus-4-6"]["cache_write_1h_tokens"] == 50
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def test_prefix_cache_stats_include_observed_ttl_mix() -> None:
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metrics = PrometheusMetrics()
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provider_stats = metrics.cache_by_provider["anthropic"]
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provider_stats["requests"] = 2
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provider_stats["hit_requests"] = 1
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provider_stats["cache_read_tokens"] = 40
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provider_stats["cache_write_tokens"] = 60
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provider_stats["cache_write_5m_tokens"] = 15
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provider_stats["cache_write_1h_tokens"] = 45
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provider_stats["cache_write_5m_requests"] = 1
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provider_stats["cache_write_1h_requests"] = 1
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stats = build_prefix_cache_stats(metrics, None)
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anthropic = stats["by_provider"]["anthropic"]
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assert anthropic["observed_ttl_buckets"]["5m"]["tokens"] == 15
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assert anthropic["observed_ttl_buckets"]["1h"]["tokens"] == 45
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assert anthropic["observed_ttl_mix"]["5m_pct"] == 25.0
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assert anthropic["observed_ttl_mix"]["1h_pct"] == 75.0
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assert stats["totals"]["observed_ttl_buckets"]["5m"]["tokens"] == 15
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assert stats["totals"]["observed_ttl_buckets"]["1h"]["tokens"] == 45
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def test_prometheus_metrics_export_includes_extended_fields() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(
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metrics.record_request(
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provider="anthropic",
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model="claude-opus-4-6",
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input_tokens=100,
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output_tokens=20,
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tokens_saved=5,
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latency_ms=12.5,
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overhead_ms=3.0,
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ttfb_ms=9.0,
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pipeline_timing={"router": 4.5},
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waste_signals={"json_bloat": 7},
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cache_read_tokens=40,
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cache_write_tokens=60,
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cache_write_5m_tokens=10,
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cache_write_1h_tokens=50,
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uncached_input_tokens=20,
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)
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)
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asyncio.run(metrics.record_cache_bust(11))
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exported = asyncio.run(metrics.export())
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assert "headroom_latency_ms_count 1" in exported
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assert 'headroom_transform_timing_ms_sum{transform="router"} 4.5' in exported
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assert 'headroom_waste_signal_tokens_total{signal="json_bloat"} 7' in exported
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assert 'headroom_cache_write_ttl_tokens_total{provider="anthropic",ttl="5m"} 10' in exported
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assert 'headroom_provider_cache_hit_requests_total{provider="anthropic"} 1' in exported
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assert "headroom_cache_bust_tokens_lost_total 11" in exported
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def test_streaming_parser_extracts_anthropic_ttl_bucket_usage() -> None:
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from headroom.proxy.server import HeadroomProxy, ProxyConfig
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proxy = HeadroomProxy(
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ProxyConfig(
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optimize=False,
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cache_enabled=False,
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rate_limit_enabled=False,
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cost_tracking_enabled=False,
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log_requests=False,
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ccr_inject_tool=False,
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ccr_handle_responses=False,
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ccr_context_tracking=False,
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)
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)
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chunk = (
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b'data: {"type":"message_start","message":{"usage":{"input_tokens":12,'
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b'"cache_read_input_tokens":3,"cache_creation_input_tokens":9,'
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b'"cache_creation":{"ephemeral_5m_input_tokens":4,"ephemeral_1h_input_tokens":5}}}}\n\n'
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)
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usage = proxy._parse_sse_usage(chunk, "anthropic")
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assert usage is not None
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assert usage["cache_creation_ephemeral_5m_input_tokens"] == 4
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assert usage["cache_creation_ephemeral_1h_input_tokens"] == 5
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def test_stats_endpoint_reports_observed_ttl_buckets() -> None:
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pytest.importorskip("fastapi")
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from fastapi.testclient import TestClient
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from headroom.proxy.server import ProxyConfig, create_app
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app = create_app(
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ProxyConfig(
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optimize=False,
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cache_enabled=False,
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rate_limit_enabled=False,
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cost_tracking_enabled=False,
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log_requests=False,
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ccr_inject_tool=False,
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ccr_handle_responses=False,
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ccr_context_tracking=False,
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)
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)
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proxy = app.state.proxy
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provider_stats = proxy.metrics.cache_by_provider["anthropic"]
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provider_stats["requests"] = 1
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provider_stats["hit_requests"] = 1
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provider_stats["cache_read_tokens"] = 30
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provider_stats["cache_write_tokens"] = 70
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provider_stats["cache_write_5m_tokens"] = 20
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provider_stats["cache_write_1h_tokens"] = 50
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provider_stats["cache_write_5m_requests"] = 1
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provider_stats["cache_write_1h_requests"] = 1
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with TestClient(app) as client:
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response = client.get("/stats")
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assert response.status_code == 200
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prefix_cache = response.json()["prefix_cache"]
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anthropic = prefix_cache["by_provider"]["anthropic"]
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assert anthropic["observed_ttl_buckets"]["5m"]["tokens"] == 20
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assert anthropic["observed_ttl_buckets"]["1h"]["tokens"] == 50
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assert prefix_cache["totals"]["observed_ttl_mix"]["active_buckets"] == ["5m", "1h"]
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def test_stats_endpoint_reports_otel_configuration(monkeypatch: pytest.MonkeyPatch) -> None:
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pytest.importorskip("fastapi")
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from fastapi.testclient import TestClient
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from headroom.proxy.server import ProxyConfig, create_app
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reset_otel_metrics()
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monkeypatch.setenv("HEADROOM_OTEL_METRICS_ENABLED", "1")
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monkeypatch.setenv("HEADROOM_OTEL_METRICS_EXPORTER", "console")
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app = create_app(
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ProxyConfig(
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optimize=False,
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cache_enabled=False,
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rate_limit_enabled=False,
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cost_tracking_enabled=False,
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log_requests=False,
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ccr_inject_tool=False,
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ccr_handle_responses=False,
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ccr_context_tracking=False,
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)
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)
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with TestClient(app) as client:
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response = client.get("/stats")
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assert response.status_code == 200
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otel = response.json()["otel"]
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assert otel["configured"] is True
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assert otel["enabled"] is True
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assert otel["service_name"] == "headroom-proxy"
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assert otel["exporter"] == "console"
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def test_stats_endpoint_reports_langfuse_configuration(monkeypatch: pytest.MonkeyPatch) -> None:
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pytest.importorskip("fastapi")
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from fastapi.testclient import TestClient
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from headroom.proxy.server import ProxyConfig, create_app
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reset_headroom_tracing()
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monkeypatch.setenv("HEADROOM_LANGFUSE_ENABLED", "1")
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monkeypatch.setenv("LANGFUSE_PUBLIC_KEY", "pk-lf-test")
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monkeypatch.setenv("LANGFUSE_SECRET_KEY", "sk-lf-test")
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monkeypatch.setenv("LANGFUSE_BASE_URL", "https://cloud.langfuse.com")
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app = create_app(
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ProxyConfig(
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optimize=False,
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cache_enabled=False,
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rate_limit_enabled=False,
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cost_tracking_enabled=False,
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log_requests=False,
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ccr_inject_tool=False,
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ccr_handle_responses=False,
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ccr_context_tracking=False,
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)
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)
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with TestClient(app) as client:
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response = client.get("/stats")
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assert response.status_code == 200
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langfuse = response.json()["langfuse"]
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assert langfuse["configured"] is True
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assert langfuse["enabled"] is True
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assert langfuse["service_name"] == "headroom-proxy"
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assert langfuse["endpoint"] == "https://cloud.langfuse.com/api/public/otel/v1/traces"
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# --- Cache-miss attribution (#1313) ---
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def test_record_cache_miss_attribution_buckets_by_provider_and_reason() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "prefix_change"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "unknown"))
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buckets = metrics.cache_miss_attribution_by_provider["anthropic"]
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assert buckets["ttl_expiry"] == 2
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assert buckets["prefix_change"] == 1
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assert buckets["unknown"] == 1
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def test_prefix_cache_stats_include_miss_attribution() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "prefix_change"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "unknown"))
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stats = build_prefix_cache_stats(metrics, None)
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ma = stats["miss_attribution"]
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assert ma["totals"]["ttl_expiry"] == 2
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assert ma["totals"]["prefix_change"] == 1
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assert ma["totals"]["unknown"] == 1
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assert ma["totals"]["total"] == 4
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# Percentages are over attributed (non-unknown) misses: 2 / 3, 1 / 3.
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assert ma["totals"]["ttl_expiry_pct"] == 66.7
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assert ma["totals"]["prefix_change_pct"] == 33.3
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assert ma["by_provider"]["anthropic"]["total"] == 4
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def test_prefix_cache_stats_miss_attribution_empty_when_no_misses() -> None:
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metrics = PrometheusMetrics()
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stats = build_prefix_cache_stats(metrics, None)
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ma = stats["miss_attribution"]
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assert ma["totals"]["total"] == 0
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assert ma["totals"]["ttl_expiry_pct"] == 0.0
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assert ma["by_provider"] == {}
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def test_prometheus_export_includes_miss_attribution() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "prefix_change"))
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exported = asyncio.run(metrics.export())
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assert (
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'headroom_cache_miss_attribution_total{provider="anthropic",reason="ttl_expiry"} 1'
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in exported
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)
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assert (
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'headroom_cache_miss_attribution_total{provider="anthropic",reason="prefix_change"} 1'
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in exported
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)
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def test_reset_runtime_clears_miss_attribution() -> None:
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metrics = PrometheusMetrics()
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asyncio.run(metrics.record_cache_miss_attribution("anthropic", "ttl_expiry"))
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asyncio.run(metrics.reset_runtime())
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assert dict(metrics.cache_miss_attribution_by_provider) == {}
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