headroom/tests/test_compression_cache.py

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"""Tests for CompressionCache with LRU eviction."""
from __future__ import annotations
import pytest
from headroom.cache.compression_cache import CompressionCache
@pytest.fixture
def cache() -> CompressionCache:
return CompressionCache()
@pytest.fixture
def small_cache() -> CompressionCache:
return CompressionCache(max_entries=3)
class TestCompressionCache:
def test_cache_miss_returns_none(self, cache: CompressionCache) -> None:
h = CompressionCache.content_hash("some content")
assert cache.get_compressed(h) is None
def test_store_and_retrieve(self, cache: CompressionCache) -> None:
content = "hello world this is a long message"
h = CompressionCache.content_hash(content)
cache.store_compressed(h, "hello world...compressed", tokens_saved=15)
assert cache.get_compressed(h) == "hello world...compressed"
def test_different_content_different_hash(self) -> None:
h1 = CompressionCache.content_hash("content A")
h2 = CompressionCache.content_hash("content B")
assert h1 != h2
def test_overwrite_same_hash(self, cache: CompressionCache) -> None:
h = CompressionCache.content_hash("some content")
cache.store_compressed(h, "v1", tokens_saved=10)
cache.store_compressed(h, "v2", tokens_saved=20)
assert cache.get_compressed(h) == "v2"
def test_stats_tracking(self, cache: CompressionCache) -> None:
h = CompressionCache.content_hash("content")
cache.store_compressed(h, "compressed", tokens_saved=5)
# One hit
cache.get_compressed(h)
# One miss
cache.get_compressed("nonexistent")
stats = cache.get_stats()
assert stats["hits"] == 1
assert stats["misses"] == 1
assert stats["entries"] == 1
assert stats["tokens_saved"] == 5
def test_eviction_at_max_entries(self, small_cache: CompressionCache) -> None:
h1 = CompressionCache.content_hash("a")
h2 = CompressionCache.content_hash("b")
h3 = CompressionCache.content_hash("c")
h4 = CompressionCache.content_hash("d")
small_cache.store_compressed(h1, "ca", tokens_saved=1)
small_cache.store_compressed(h2, "cb", tokens_saved=1)
small_cache.store_compressed(h3, "cc", tokens_saved=1)
# Adding a 4th should evict the oldest (h1)
small_cache.store_compressed(h4, "cd", tokens_saved=1)
assert small_cache.get_compressed(h1) is None
assert small_cache.get_compressed(h2) == "cb"
assert small_cache.get_compressed(h4) == "cd"
def test_access_refreshes_lru(self, small_cache: CompressionCache) -> None:
h1 = CompressionCache.content_hash("a")
h2 = CompressionCache.content_hash("b")
h3 = CompressionCache.content_hash("c")
h4 = CompressionCache.content_hash("d")
small_cache.store_compressed(h1, "ca", tokens_saved=1)
small_cache.store_compressed(h2, "cb", tokens_saved=1)
small_cache.store_compressed(h3, "cc", tokens_saved=1)
# Access h1 to refresh it
small_cache.get_compressed(h1)
# Adding h4 should evict h2 (oldest untouched), not h1
small_cache.store_compressed(h4, "cd", tokens_saved=1)
assert small_cache.get_compressed(h1) == "ca"
assert small_cache.get_compressed(h2) is None
assert small_cache.get_compressed(h4) == "cd"
def test_content_hash_list_content(self) -> None:
"""content_hash handles Anthropic-format list content."""
list_content = [
{"type": "text", "text": "hello"},
{"type": "text", "text": "world"},
]
h = CompressionCache.content_hash(list_content)
assert isinstance(h, str)
assert len(h) == 16
# Same content produces same hash
assert CompressionCache.content_hash(list_content) == h
def test_content_hash_string_length(self) -> None:
h = CompressionCache.content_hash("test")
assert len(h) == 16
class TestCompressionCacheFrozenCount:
def test_empty_cache_returns_zero(self, cache: CompressionCache) -> None:
assert cache.compute_frozen_count([]) == 0
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
def test_user_assistant_stable_with_live_zone_cap(self, cache: CompressionCache) -> None:
"""Plain user/assistant turns are individually stable, but the
trailing message is reserved as the live zone the new turn
cannot be in any provider prefix cache. See docstring on
``CompressionCache.compute_frozen_count``."""
messages = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi there"},
{"role": "user", "content": "how are you"},
]
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
# 3 messages structurally stable; cap clamps to len-1 = 2.
assert cache.compute_frozen_count(messages) == 2
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
def test_tool_result_with_cache_hit_capped_at_live_zone(self, cache: CompressionCache) -> None:
tool_content = "tool output data"
h = CompressionCache.content_hash(tool_content)
cache.store_compressed(h, "compressed tool output", tokens_saved=5)
messages = [
{"role": "user", "content": "do something"},
{
"role": "assistant",
"content": [{"type": "tool_use", "id": "t1", "name": "my_tool", "input": {}}],
},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": tool_content}],
},
]
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
# All 3 stable; cap clamps to len-1 = 2 (trailing tool_result is
# the live zone).
assert cache.compute_frozen_count(messages) == 2
def test_tool_result_cache_miss_stops_frozen(self, cache: CompressionCache) -> None:
messages = [
{"role": "user", "content": "hello"},
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": "uncached stuff"}
],
},
{"role": "user", "content": "follow up"},
]
assert cache.compute_frozen_count(messages) == 1
def test_frozen_count_with_dropped_messages(self, cache: CompressionCache) -> None:
cached_content = "cached tool output"
h = CompressionCache.content_hash(cached_content)
cache.store_compressed(h, "compressed", tokens_saved=3)
messages = [
{"role": "user", "content": "start"},
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": cached_content}
],
},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t2", "content": "not cached"}],
},
]
assert cache.compute_frozen_count(messages) == 2
def test_stable_hash_allows_frozen_count_past_uncached_tool_result(
self, cache: CompressionCache
) -> None:
"""Tool_results marked stable should not stop the frozen count walk."""
tool_content = "excluded Read output — big file contents"
h = CompressionCache.content_hash(tool_content)
cache.mark_stable(h)
messages = [
{"role": "user", "content": "hello"},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": tool_content}],
},
{"role": "user", "content": "follow up"},
]
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
# Without mark_stable, the walk would stop at msg[1] → frozen=1.
# With stable hash, the walk continues past msg[1]; structural
# count = 3, then capped at len-1 = 2 (live-zone reservation).
assert cache.compute_frozen_count(messages) == 2
def test_update_from_result_identical_content_marks_stable(
self, cache: CompressionCache
) -> None:
"""When orig == compressed, update_from_result marks the hash as stable."""
tool_content = "unchanged tool output"
originals = [
{"role": "user", "content": "hi"},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": tool_content}],
},
]
# Compressed is identical to originals (no compression happened)
compressed = [
{"role": "user", "content": "hi"},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": tool_content}],
},
]
cache.update_from_result(originals, compressed)
h = CompressionCache.content_hash(tool_content)
assert h in cache._stable_hashes
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
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# Frozen count walks past this tool_result (its hash is stable),
# but the trailing message is still reserved as live zone.
messages = [
{"role": "user", "content": "hello"},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": tool_content}],
},
{"role": "user", "content": "more stuff"},
]
fix: complete /v1/responses compression telemetry, multi-frame WS, frozen-count cap Builds on PR #406 (HTTP /v1/responses PyO3) and PR #410 (WS first-frame PyO3) which landed the binding for `compress_openai_responses_live_zone`. This change closes the remaining gaps so every (provider × endpoint × auth-mode × streaming) combination compresses AND surfaces in the dashboard. Telemetry: extend the PyO3 binding return tuple from `(bytes, modified)` to `(bytes, modified, tokens_saved, transforms_applied)` by adding `CompressionManifest::tokens_saved()` and `transforms_applied()` accessors on the existing manifest. The Python proxy populates request-log telemetry from the binding output instead of recounting tokens. Updates the existing 2-tuple call sites in HTTP and WS first-frame, plus the unpacks in tests. WebSocket multi-frame compression: subscription Codex users keep a long-lived WS open and send multiple `response.create` events per session. PR #410 only compressed the first frame; subsequent frames went raw. Added `_maybe_compress_response_create_frame` closure inside `_client_to_upstream` that runs the same Rust dispatcher on every client→upstream `response.create` text frame, passes other event types (response.cancel, session.update, etc.) through unchanged, and accumulates `tokens_saved` / `transforms_applied` / `ws_frames_compressed` counters across the session. Pre-existing dashboard gap: `streaming.py` and `anthropic.py` write `RequestLog` entries; the non-streaming OpenAI HTTP and WS handlers did not. Result: /transformations/feed was invisible for every Codex turn and every Cline / OpenClaude / Aider turn. Added the same wiring in `handle_openai_chat` (non-streaming), `handle_openai_responses` (non-streaming HTTP), and `handle_openai_responses_ws` (session-end). All three populate `auth_mode` + `endpoint` tags so the dashboard can break compression activity down by client class (PAYG / OAuth / Subscription) and surface (`chat_completions` / `responses_http` / `responses_ws`). The WS metric record is now unconditional — was previously gated on `tokens_saved > 0`, so first-frame no-changes never registered. compute_frozen_count over-freeze for prose-format clients: `compute_frozen_count` walked until it found an unstable `tool_result` / `role: "tool"` block. Cline / OpenClaude / Aider — clients that embed tool calls as XML inside plain text — never produce such a boundary, so the function returned `len(messages)` and the pipeline froze 100% of messages including the brand-new user turn. Live zone empty → `Transform content_router: 16414 → 16414 tokens (saved 0)`. Reported on Discord 2026-05-07 with Cline+DeepSeek. Fix: cap at `max(0, len(messages) - 1)`. Updates 3 existing test assertions whose expected values encoded the old over-freeze. Adds 6 new prose-format invariant tests. CodeQL "clear-text logging of sensitive information" fix: `tests/e2e_real_compression.py` previously stored API keys in local variables in the same scope as diagnostic prints, which CodeQL flagged via data-flow analysis. Refactored to read keys from `os.environ` inside the request helper — the credentials never enter the runner's main scope, so the taint flow never reaches the print. End-to-end verification with real keys (.env): /v1/messages (PAYG, non-stream) tok 14109 → 969 saved 13140 /v1/messages (PAYG, stream) tok 14109 → 969 saved 13140 /v1/chat/completions (PAYG, non-stream) tok 18460 → 1374 saved 17086 /v1/chat/completions (PAYG, stream) tok 18460 → 1374 saved 17086 (cache_hit=100%) /v1/responses HTTP (PAYG, non-stream) bytes 50138 → 597 saved 18391 /v1/responses WS (frame 1) bytes 46429 → 488 saved 16791 /v1/responses WS (frame 2 multi) bytes 46429 → 488 saved 16791 /v1/responses WS (response.cancel) passthrough untouched Tests: cargo workspace + pytest (4846 pass, 0 fail), make ci-precheck passed, two E2E scripts (multi-turn HTTP, WS fake-upstream) all green.
2026-05-07 14:50:03 -07:00
assert cache.compute_frozen_count(messages) == 2
def test_mark_stable_from_messages(self, cache: CompressionCache) -> None:
"""mark_stable_from_messages records hashes for tool_results."""
content_a = "tool output A"
content_b = "tool output B"
messages = [
{"role": "user", "content": "hi"},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": content_a}],
},
{
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t2", "content": content_b}],
},
]
# Mark first 2 messages (msg[0] + msg[1])
cache.mark_stable_from_messages(messages, 2)
ha = CompressionCache.content_hash(content_a)
hb = CompressionCache.content_hash(content_b)
assert ha in cache._stable_hashes
assert hb not in cache._stable_hashes # msg[2] not included
def test_should_defer_compression_new_content(self, cache: CompressionCache) -> None:
fix(proxy): restore Anthropic compression on token mode (issue #327) Three bugs combined to drive end-to-end compression on the Anthropic backend to ~0% in token mode (the default). User report #327 saw a ~9× drop in dashboard savings from one day to the next on Claude Code traffic; the dashboard headline was technically correct but the underlying compression genuinely was not running. After this change the same Claude Code-shape multi-turn conversation goes from 14987 → 14371 tokens at the request boundary on turn 1 and only recompresses the freshest tool_result on subsequent turns, with the prior turns frozen byte-identical to preserve the upstream prefix cache. Bug 1 — IntelligentContextManager inner ContentRouter has no observer PR #302 (commit cf979958, 2026-04-28) wired CompressionObserver onto the outer ContentRouter in proxy/server.py and onto SmartCrusher. The inner ContentRouter constructed lazily inside IntelligentContextManager._get_content_router (added Jan 18, 2026 in 57b2de5 alongside the COMPRESS_FIRST strategy) was missed. That inner router handles the bulk of Claude Code's tool_result-block compression, so per-strategy counters surfaced by PR #314 in v0.15.0 showed compressions_by_strategy={"text": 6} while summary.compression.total_tokens_removed=1.3M — math-impossible. Fix: add observer= parameter to IntelligentContextManager.__init__, forward it to the inner ContentRouter at intelligent_context.py:525, and pass observer=self.metrics from proxy/server.py. Bug 2 — TTL deferral marks every fresh tool_result as stable should_defer_compression in compression_cache.py returned True on first-sight (added 2026-04-07 in commit 22dad13 with the intent of batching first-time compressions near the 5-min cache TTL boundary to trade many small busts for one). The token-mode walker at anthropic.py:766-787 walks every message past frozen_message_count, calls should_defer_compression on each fresh tool_result, gets True, and advances ttl_frozen += 1 — every iteration. Result: frozen_message_count grows to len(messages), the pipeline freezes the entire request, and nothing reaches a real compressor. The defer-first-sight rationale assumes recurring content within TTL. Real Claude Code traffic produces unique content per turn, so "defer until next sight" defers forever. Compressing fresh content on first sight does not bust any prefix cache because Anthropic has not cached that byte position yet — it's a cache write either way. Fix: should_defer_compression returns False on first-sight (record the timestamp; compress now). Subsequent sightings within TTL still defer (batch window preserved for genuinely repeating content). Updated tests in test_compression_cache.py to assert the corrected semantics and verify _first_seen is recorded on first call. Bug 3 — cross-tokenizer comparison in token-mode inflation guard anthropic.py:634 sets original_tokens = tokenizer.count_messages(...) using the proxy-side EstimatingTokenCounter. The token-mode branch at line 816 set optimized_tokens = result.tokens_after from pipeline, which uses the provider-side AnthropicProvider tiktoken estimator. The two tokenizers disagree by ~25% on the same payload. The inflation guard at line 901 (if optimized_tokens > original_tokens: revert to originals) treats those two numbers as comparable. After a real 12% compression the provider-tokenizer figure was still higher than the proxy-tokenizer baseline, so the guard fired, optimized_messages was reset to the original input, transforms_applied was emptied, and tokens_saved went to 0. The dashboard showed no compression even when the pipeline successfully compressed. Fix: recount optimized_tokens with the proxy tokenizer right after the pipeline returns, so the guard compares apples-to-apples. The recount cost is a few ms on a 50K-token request and is dwarfed by upstream call latency. Verification * 80 targeted tests across test_compression_cache, test_compression_observability, test_proxy_anthropic_cache_stability, test_proxy_intelligent_context pass. * make ci-precheck clean. * End-to-end real-API run against api.anthropic.com via local proxy: - Turn 1 fresh: 14987 → 14371 (4.1%) on a 3-tool-round payload; smart_crusher and diff strategies fired with non-zero savings. - Turn 2 (turn 1 history + 1 new tool_result): 23161 → 21928 (5.3%); only the new tool_result compressed; older turns marked router:protected:user_message; Anthropic returned cache_creation_input_tokens > 0 confirming the prefix was not busted. Two new regression tests in test_compression_observability lock down the inner ContentRouter observer wiring so a future copy of Bug 1 fails the suite the day it lands.
2026-04-30 12:59:19 -07:00
"""First-time content should NOT be deferred — there is no
prefix-cache entry to preserve, so compression carries no bust
cost. Issue #327: prior behavior deferred first-sight, which
marked every fresh tool_result as stable and disabled
compression for typical Claude Code workloads.
"""
h = CompressionCache.content_hash("brand new content")
fix(proxy): restore Anthropic compression on token mode (issue #327) Three bugs combined to drive end-to-end compression on the Anthropic backend to ~0% in token mode (the default). User report #327 saw a ~9× drop in dashboard savings from one day to the next on Claude Code traffic; the dashboard headline was technically correct but the underlying compression genuinely was not running. After this change the same Claude Code-shape multi-turn conversation goes from 14987 → 14371 tokens at the request boundary on turn 1 and only recompresses the freshest tool_result on subsequent turns, with the prior turns frozen byte-identical to preserve the upstream prefix cache. Bug 1 — IntelligentContextManager inner ContentRouter has no observer PR #302 (commit cf979958, 2026-04-28) wired CompressionObserver onto the outer ContentRouter in proxy/server.py and onto SmartCrusher. The inner ContentRouter constructed lazily inside IntelligentContextManager._get_content_router (added Jan 18, 2026 in 57b2de5 alongside the COMPRESS_FIRST strategy) was missed. That inner router handles the bulk of Claude Code's tool_result-block compression, so per-strategy counters surfaced by PR #314 in v0.15.0 showed compressions_by_strategy={"text": 6} while summary.compression.total_tokens_removed=1.3M — math-impossible. Fix: add observer= parameter to IntelligentContextManager.__init__, forward it to the inner ContentRouter at intelligent_context.py:525, and pass observer=self.metrics from proxy/server.py. Bug 2 — TTL deferral marks every fresh tool_result as stable should_defer_compression in compression_cache.py returned True on first-sight (added 2026-04-07 in commit 22dad13 with the intent of batching first-time compressions near the 5-min cache TTL boundary to trade many small busts for one). The token-mode walker at anthropic.py:766-787 walks every message past frozen_message_count, calls should_defer_compression on each fresh tool_result, gets True, and advances ttl_frozen += 1 — every iteration. Result: frozen_message_count grows to len(messages), the pipeline freezes the entire request, and nothing reaches a real compressor. The defer-first-sight rationale assumes recurring content within TTL. Real Claude Code traffic produces unique content per turn, so "defer until next sight" defers forever. Compressing fresh content on first sight does not bust any prefix cache because Anthropic has not cached that byte position yet — it's a cache write either way. Fix: should_defer_compression returns False on first-sight (record the timestamp; compress now). Subsequent sightings within TTL still defer (batch window preserved for genuinely repeating content). Updated tests in test_compression_cache.py to assert the corrected semantics and verify _first_seen is recorded on first call. Bug 3 — cross-tokenizer comparison in token-mode inflation guard anthropic.py:634 sets original_tokens = tokenizer.count_messages(...) using the proxy-side EstimatingTokenCounter. The token-mode branch at line 816 set optimized_tokens = result.tokens_after from pipeline, which uses the provider-side AnthropicProvider tiktoken estimator. The two tokenizers disagree by ~25% on the same payload. The inflation guard at line 901 (if optimized_tokens > original_tokens: revert to originals) treats those two numbers as comparable. After a real 12% compression the provider-tokenizer figure was still higher than the proxy-tokenizer baseline, so the guard fired, optimized_messages was reset to the original input, transforms_applied was emptied, and tokens_saved went to 0. The dashboard showed no compression even when the pipeline successfully compressed. Fix: recount optimized_tokens with the proxy tokenizer right after the pipeline returns, so the guard compares apples-to-apples. The recount cost is a few ms on a 50K-token request and is dwarfed by upstream call latency. Verification * 80 targeted tests across test_compression_cache, test_compression_observability, test_proxy_anthropic_cache_stability, test_proxy_intelligent_context pass. * make ci-precheck clean. * End-to-end real-API run against api.anthropic.com via local proxy: - Turn 1 fresh: 14987 → 14371 (4.1%) on a 3-tool-round payload; smart_crusher and diff strategies fired with non-zero savings. - Turn 2 (turn 1 history + 1 new tool_result): 23161 → 21928 (5.3%); only the new tool_result compressed; older turns marked router:protected:user_message; Anthropic returned cache_creation_input_tokens > 0 confirming the prefix was not busted. Two new regression tests in test_compression_observability lock down the inner ContentRouter observer wiring so a future copy of Bug 1 fails the suite the day it lands.
2026-04-30 12:59:19 -07:00
assert cache.should_defer_compression(h, ttl_seconds=300, batch_window=30) is False
# Subsequent sightings within TTL should defer (batch window).
assert cache.should_defer_compression(h, ttl_seconds=300, batch_window=30) is True
fix(proxy): restore Anthropic compression on token mode (issue #327) Three bugs combined to drive end-to-end compression on the Anthropic backend to ~0% in token mode (the default). User report #327 saw a ~9× drop in dashboard savings from one day to the next on Claude Code traffic; the dashboard headline was technically correct but the underlying compression genuinely was not running. After this change the same Claude Code-shape multi-turn conversation goes from 14987 → 14371 tokens at the request boundary on turn 1 and only recompresses the freshest tool_result on subsequent turns, with the prior turns frozen byte-identical to preserve the upstream prefix cache. Bug 1 — IntelligentContextManager inner ContentRouter has no observer PR #302 (commit cf979958, 2026-04-28) wired CompressionObserver onto the outer ContentRouter in proxy/server.py and onto SmartCrusher. The inner ContentRouter constructed lazily inside IntelligentContextManager._get_content_router (added Jan 18, 2026 in 57b2de5 alongside the COMPRESS_FIRST strategy) was missed. That inner router handles the bulk of Claude Code's tool_result-block compression, so per-strategy counters surfaced by PR #314 in v0.15.0 showed compressions_by_strategy={"text": 6} while summary.compression.total_tokens_removed=1.3M — math-impossible. Fix: add observer= parameter to IntelligentContextManager.__init__, forward it to the inner ContentRouter at intelligent_context.py:525, and pass observer=self.metrics from proxy/server.py. Bug 2 — TTL deferral marks every fresh tool_result as stable should_defer_compression in compression_cache.py returned True on first-sight (added 2026-04-07 in commit 22dad13 with the intent of batching first-time compressions near the 5-min cache TTL boundary to trade many small busts for one). The token-mode walker at anthropic.py:766-787 walks every message past frozen_message_count, calls should_defer_compression on each fresh tool_result, gets True, and advances ttl_frozen += 1 — every iteration. Result: frozen_message_count grows to len(messages), the pipeline freezes the entire request, and nothing reaches a real compressor. The defer-first-sight rationale assumes recurring content within TTL. Real Claude Code traffic produces unique content per turn, so "defer until next sight" defers forever. Compressing fresh content on first sight does not bust any prefix cache because Anthropic has not cached that byte position yet — it's a cache write either way. Fix: should_defer_compression returns False on first-sight (record the timestamp; compress now). Subsequent sightings within TTL still defer (batch window preserved for genuinely repeating content). Updated tests in test_compression_cache.py to assert the corrected semantics and verify _first_seen is recorded on first call. Bug 3 — cross-tokenizer comparison in token-mode inflation guard anthropic.py:634 sets original_tokens = tokenizer.count_messages(...) using the proxy-side EstimatingTokenCounter. The token-mode branch at line 816 set optimized_tokens = result.tokens_after from pipeline, which uses the provider-side AnthropicProvider tiktoken estimator. The two tokenizers disagree by ~25% on the same payload. The inflation guard at line 901 (if optimized_tokens > original_tokens: revert to originals) treats those two numbers as comparable. After a real 12% compression the provider-tokenizer figure was still higher than the proxy-tokenizer baseline, so the guard fired, optimized_messages was reset to the original input, transforms_applied was emptied, and tokens_saved went to 0. The dashboard showed no compression even when the pipeline successfully compressed. Fix: recount optimized_tokens with the proxy tokenizer right after the pipeline returns, so the guard compares apples-to-apples. The recount cost is a few ms on a 50K-token request and is dwarfed by upstream call latency. Verification * 80 targeted tests across test_compression_cache, test_compression_observability, test_proxy_anthropic_cache_stability, test_proxy_intelligent_context pass. * make ci-precheck clean. * End-to-end real-API run against api.anthropic.com via local proxy: - Turn 1 fresh: 14987 → 14371 (4.1%) on a 3-tool-round payload; smart_crusher and diff strategies fired with non-zero savings. - Turn 2 (turn 1 history + 1 new tool_result): 23161 → 21928 (5.3%); only the new tool_result compressed; older turns marked router:protected:user_message; Anthropic returned cache_creation_input_tokens > 0 confirming the prefix was not busted. Two new regression tests in test_compression_observability lock down the inner ContentRouter observer wiring so a future copy of Bug 1 fails the suite the day it lands.
2026-04-30 12:59:19 -07:00
def test_should_defer_compression_records_first_seen(self, cache: CompressionCache) -> None:
"""First-sight call must record the timestamp so subsequent
in-window calls can defer. Without this the deferral pathway
for genuinely-repeated content stops working."""
h = CompressionCache.content_hash("seen-twice content")
cache.should_defer_compression(h) # first sight
assert h in cache._first_seen
def test_should_defer_compression_near_ttl(self, cache: CompressionCache) -> None:
"""Content near TTL boundary should NOT be deferred."""
import time
h = CompressionCache.content_hash("old content")
# Backdate first_seen to simulate age near TTL
cache._first_seen[h] = time.time() - 280 # 280s old, TTL=300, window=30
assert cache.should_defer_compression(h, ttl_seconds=300, batch_window=30) is False
class TestCompressionCacheApplyAndUpdate:
def test_apply_cached_swaps_tool_results(self, cache: CompressionCache) -> None:
original_content = "big tool output"
h = CompressionCache.content_hash(original_content)
cache.store_compressed(h, "small output", tokens_saved=5)
messages = [
{"role": "user", "content": "hi"},
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": original_content}
],
},
]
result = cache.apply_cached(messages)
assert result[1]["content"][0]["content"] == "small output"
def test_apply_cached_preserves_uncached_messages(self, cache: CompressionCache) -> None:
messages = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "world"},
]
result = cache.apply_cached(messages)
assert result[0] is messages[0]
assert result[1] is messages[1]
def test_apply_cached_never_adds_messages(self, cache: CompressionCache) -> None:
# Store something in cache that doesn't correspond to any message
cache.store_compressed("orphan_hash", "orphan_value", tokens_saved=1)
messages = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi"},
]
result = cache.apply_cached(messages)
assert len(result) == len(messages)
def test_update_from_result_caches_changes(self, cache: CompressionCache) -> None:
originals = [
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": "original output"}
],
},
]
compressed = [
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": "compressed output"}
],
},
]
cache.update_from_result(originals, compressed)
h = CompressionCache.content_hash("original output")
assert cache.get_compressed(h) == "compressed output"
def test_update_from_result_ignores_unchanged(self, cache: CompressionCache) -> None:
originals = [
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": "same content"}
],
},
]
compressed = [
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "t1", "content": "same content"}
],
},
]
cache.update_from_result(originals, compressed)
h = CompressionCache.content_hash("same content")
assert cache.get_compressed(h) is None
def test_apply_does_not_modify_original_messages(self, cache: CompressionCache) -> None:
original_content = "big tool output"
h = CompressionCache.content_hash(original_content)
cache.store_compressed(h, "small output", tokens_saved=5)
msg = {
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": "t1", "content": original_content}],
}
messages = [msg]
cache.apply_cached(messages)
# Original must be untouched
assert msg["content"][0]["content"] == original_content
def test_openai_format_tool_result(self, cache: CompressionCache) -> None:
original_content = "openai tool output"
h = CompressionCache.content_hash(original_content)
cache.store_compressed(h, "compressed openai", tokens_saved=4)
messages = [
{"role": "tool", "tool_call_id": "tc1", "content": original_content},
]
result = cache.apply_cached(messages)
assert result[0]["content"] == "compressed openai"
# Original untouched
assert messages[0]["content"] == original_content
fix(proxy): cache concurrency lock, multi-worker docs, bounded compression executor Three audit follow-ups from issue #327's deep-dive review. C1 — CompressionCache concurrency lock ====================================== `CompressionCache` instances are shared per `session_id` and accessed from async-dispatched threadpool workers. Pre-fix, concurrent requests for the same session raced on `_cache`, `_stable_hashes`, `_first_seen`, and `_total_tokens_saved` with no synchronization. Observable failures: * Lost-update on `_total_tokens_saved` (read-modify-write). * `RuntimeError: OrderedDict mutated during iteration` from `apply_cached` when a concurrent `store_compressed` evicts during the walk. * Lost stable-hash records — next-turn compute_frozen_count reads inconsistent state. May also explain part of SvenMeyer's `_cache: 0 entries / 1003 misses` observation: the cache was being clobbered concurrently. Added `threading.RLock` guarding all mutating methods. `RLock` (not `Lock`) so future code can call locked methods from inside another locked method without self-deadlock. Also locked `HeadroomProxy._compression_caches` dict-of-caches access via a separate `_compression_caches_lock` so two concurrent calls for the same session_id can't each create distinct CompressionCache objects (which would split the cache state between them). The `/stats` endpoint snapshots the cache list under the dict lock before iterating to avoid eviction-during-iteration. C2 — Multi-worker CCR fragmentation: documented + startup warning ================================================================= The in-memory `InMemoryCcrStore` (Rust), `_compression_caches` (Python), `session_tracker_store` (Python), and TOIN learner state are ALL per-process. Multi-worker uvicorn round-robins requests across workers, so a session whose turn-1 lands on worker A may have turn-2 land on worker B. Worker B has zero knowledge of A's CCR markers, replay cache, or prefix-cache state. Result: `Retrieve original: hash=X` markers stay in-context as opaque directives, every fresh tool_result is recompressed from scratch, and `frozen_message_count=0` causes Anthropic prefix-cache busts on every cross-worker turn. Added a "Multi-worker deployment — CCR fragmentation" section in `RUST_DEV.md` documenting the failure modes, the supported configuration (`--workers 1`), and the sticky-session workaround for horizontal scale. The proxy emits a `WARNING`-level log line on startup if `workers > 1` is detected, pointing at the doc section. C3 — Bounded compression executor with cancel-aware metrics =========================================================== `asyncio.wait_for(asyncio.to_thread(pipeline.apply), timeout=...)` cancellation does NOT propagate into the threadpool worker that's running Rust code. Once the worker has picked up the task, `concurrent.futures.Future.cancel()` returns False and the thread runs to completion. Stuck threads accumulated invisibly on asyncio's default executor, contending with unrelated `to_thread` callers (file IO, etc.). Replaced all 7 `asyncio.to_thread` call sites for `pipeline.apply()` across `proxy/handlers/anthropic.py` (3) and `proxy/handlers/openai.py` (4) with a new `HeadroomProxy._run_compression_in_executor(fn, *, timeout)` helper that: 1. Submits to a dedicated bounded `ThreadPoolExecutor` named `headroom-compress` (configurable via `ProxyConfig.compression_max_workers`; defaults to `min(32, (cpu_count or 1) * 4)`). 2. Increments `_compression_in_flight` (gauge) when work starts and decrements when work completes; tracks `_compression_in_flight_max` as a high-water mark. 3. Detects "leaked threads" by comparing wall-clock elapsed against the timeout in the worker's `finally` block. Increments `_compression_leaked_threads` when a worker finishes after its asyncio future was cancelled. Operators can see the leaked-thread rate climbing in `/stats runtime.compression_executor` BEFORE the pool fills up. Tests ===== * `TestCompressionCacheConcurrency` (3 tests) — many threads store_compressed / apply_cached / update_from_result on a single CompressionCache; assert no exceptions, no lost updates, no partial state. * `test_get_compression_cache_returns_same_instance_under_contention` — 32 concurrent `_get_compression_cache(same_id)` calls return the identical instance (would split pre-lock). * `test_proxy_compression_executor.py` (8 tests) — pool size respects config, in-flight gauge tracks running compressions, high-water mark is monotonic, timeout propagates to awaiter, leaked-thread counter increments on post-deadline completion, `/stats` surfaces all three gauges. Verification ============ * All 123 targeted regression tests pass. * `make ci-precheck` clean. * No `Co-Authored-By` trailer; conventional `fix:` prefix; no `--no-verify`.
2026-05-01 15:25:18 -07:00
# ─── C1 (audit follow-up): concurrency regression suite ────────────────────
#
# CompressionCache must be safe under multi-threaded mutation. The proxy is
# async and dispatches multiple concurrent requests per `session_id` into
# `asyncio.to_thread` workers — a single CompressionCache instance therefore
# sees concurrent calls to `store_compressed` / `get_compressed` /
# `mark_stable_from_messages` / `apply_cached` / `update_from_result`.
# These tests provoke the race conditions that motivated adding `_lock`.
class TestCompressionCacheConcurrency:
"""Threading regression suite for the audit-followup lock."""
def test_concurrent_store_does_not_corrupt_total_tokens_saved(self) -> None:
"""Many threads each store_compressed with tokens_saved=N; the
bookkeeping field must equal SUM(N) when threads finish. Pre-lock
this races (read-modify-write of `_total_tokens_saved`)."""
import threading
cache = CompressionCache(max_entries=1_000_000)
n_threads = 32
per_thread = 100
per_thread_tokens = 7
def worker(tid: int) -> None:
for i in range(per_thread):
h = CompressionCache.content_hash(f"thread-{tid}-item-{i}")
cache.store_compressed(h, f"comp-{tid}-{i}", tokens_saved=per_thread_tokens)
threads = [threading.Thread(target=worker, args=(t,)) for t in range(n_threads)]
for t in threads:
t.start()
for t in threads:
t.join()
expected = n_threads * per_thread * per_thread_tokens
stats = cache.get_stats()
assert stats["entries"] == n_threads * per_thread
# The expected token count is exact only because each (thread, item)
# produces a unique hash → no overwrite path. Pre-lock this would be
# < expected due to lost updates.
assert stats["tokens_saved"] == expected
def test_concurrent_apply_cached_with_concurrent_store_does_not_raise(self) -> None:
"""`apply_cached` iterates `_cache` (via `get_compressed`); if a
concurrent `store_compressed` mutates the OrderedDict during the
iteration, pre-lock you'd get `RuntimeError: OrderedDict mutated
during iteration`. Locks make this a single critical section."""
import threading
cache = CompressionCache()
# Pre-populate so apply_cached has work to do.
for i in range(50):
h = CompressionCache.content_hash(f"seed-{i}")
cache.store_compressed(h, f"comp-{i}", tokens_saved=1)
msgs = [
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": f"t{i}",
"content": f"seed-{i}",
}
],
}
for i in range(50)
]
stop = threading.Event()
errors: list[Exception] = []
def reader() -> None:
try:
while not stop.is_set():
_ = cache.apply_cached(msgs)
except Exception as e: # pragma: no cover
errors.append(e)
def writer() -> None:
try:
for i in range(500):
h = CompressionCache.content_hash(f"writer-{i}")
cache.store_compressed(h, f"w-{i}", tokens_saved=1)
except Exception as e: # pragma: no cover
errors.append(e)
readers = [threading.Thread(target=reader) for _ in range(4)]
writers = [threading.Thread(target=writer) for _ in range(4)]
for t in readers + writers:
t.start()
for t in writers:
t.join()
stop.set()
for t in readers:
t.join()
assert errors == [], f"Concurrent ops raised: {errors}"
def test_concurrent_update_from_result_no_partial_state(self) -> None:
"""update_from_result must be all-or-nothing per call. With many
threads calling update_from_result in parallel on the same cache,
the final state must reflect every call's full effect (no partial
writes)."""
import threading
cache = CompressionCache()
n_threads = 16
per_thread_calls = 20
def worker(tid: int) -> None:
for i in range(per_thread_calls):
orig_text = f"orig-{tid}-{i}-" + "X" * 200
comp_text = f"comp-{tid}-{i}-" + "X" * 50
originals = [
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": f"t-{tid}-{i}",
"content": orig_text,
}
],
}
]
compressed = [
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": f"t-{tid}-{i}",
"content": comp_text,
}
],
}
]
cache.update_from_result(originals, compressed)
threads = [threading.Thread(target=worker, args=(t,)) for t in range(n_threads)]
for t in threads:
t.start()
for t in threads:
t.join()
stats = cache.get_stats()
# Each (tid, i) is a unique hash → cache entries == n_threads * per_thread_calls.
assert stats["entries"] == n_threads * per_thread_calls
assert stats["tokens_saved"] > 0
def test_get_compression_cache_returns_same_instance_under_contention() -> None:
"""`HeadroomProxy._get_compression_cache(session_id)` must return the
SAME `CompressionCache` instance for concurrent calls with the same
session_id. Pre-lock, two concurrent calls could both see "not in dict"
and each create a new instance, splitting the cache state across them.
"""
import threading
pytest.importorskip("fastapi")
from headroom.proxy.server import ProxyConfig, create_app
config = ProxyConfig(
optimize=False,
cache_enabled=False,
rate_limit_enabled=False,
cost_tracking_enabled=False,
log_requests=False,
ccr_inject_tool=False,
ccr_handle_responses=False,
ccr_context_tracking=False,
image_optimize=False,
)
app = create_app(config)
proxy = app.state.proxy
n_threads = 32
results: list[CompressionCache] = []
results_lock = threading.Lock()
def worker() -> None:
c = proxy._get_compression_cache("shared-session-id")
with results_lock:
results.append(c)
threads = [threading.Thread(target=worker) for _ in range(n_threads)]
for t in threads:
t.start()
for t in threads:
t.join()
assert len(results) == n_threads
first = results[0]
for c in results[1:]:
assert c is first, "Concurrent _get_compression_cache returned different instances"