Compare commits

...
Author SHA1 Message Date
rsp2k b34f5683a1 Merge feature/retention-sampling: retention, exclusion and sampling
Tests / test (==2.12.2) (push) Canceled after 0s
Tests / test (==2.14.3) (push) Canceled after 0s
Tests / test (>=3.3,<4) (push) Canceled after 0s
Tests / test (>=4,<5) (push) Canceled after 0s
Tests / canary (push) Canceled after 0s
2026-09-28 22:13:07 -06:00
rsp2k 4bf2b6f906 Release 2026.09.28: retention, exclusion and sampling
Tests / test (==2.12.2) (push) Canceled after 0s
Tests / test (==2.14.3) (push) Canceled after 0s
Tests / test (>=3.3,<4) (push) Canceled after 0s
Tests / test (>=4,<5) (push) Canceled after 0s
Tests / canary (push) Canceled after 0s
2026-09-28 22:13:07 -06:00
rsp2k 25a1ca5ecd Add per-tool exclusion and sampling, and retention pruning 2026-09-28 22:11:20 -06:00
rsp2k 3abd5c6311 Merge feature/soft-errors: classify error results returned as data
Tests / test (==2.12.2) (push) Canceled after 0s
Tests / test (==2.14.3) (push) Canceled after 0s
Tests / test (>=3.3,<4) (push) Canceled after 0s
Tests / test (>=4,<5) (push) Canceled after 0s
Tests / canary (push) Canceled after 0s
2026-09-27 19:34:30 -06:00
9 changed files with 666 additions and 23 deletions
+34
View File
@@ -5,6 +5,40 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Calendar Versioning](https://calver.org/) (YYYY.MM.DD).
## [2026.09.28] - 2026-09-28
Keep the tool-call table small: leave out or sample busy tools, and prune old
rows.
### Added
- `InstrumentationMiddleware(..., exclude_tools=...)`: tools that are never
recorded and never kept for feedback links. The calls themselves are
untouched.
- `InstrumentationMiddleware(..., sample_rates={"tool": 0.01})`: record that
fraction of a tool's `ok` calls. **Failures are always recorded**: calls with
outcome `error` or `soft_error` are kept whatever the rate. Rates outside
(0, 1] raise `ValueError`. Calls sampled out skip the hooks and redaction and
are not kept for feedback links. `random=` takes a `() -> float` for tests.
- Records have a `sample_rate`: the configured rate on sampled-in `ok` rows,
None on everything recorded unconditionally (including failures of sampled
tools). `SUM(1.0 / COALESCE(sample_rate, 1))` estimates the true call count.
- `DatabaseSink(..., retention=timedelta(days=30), prune_interval=...,
prune_batch=5000)`. With `retention` set, the background writer prunes after
an insert at most once per `prune_interval` (one hour by default). A failing
prune is logged and never stops the insert.
- `DatabaseSink.prune(older_than=None) -> int` deletes older rows in batches
of `prune_batch`, one transaction each, on SQLite and Postgres. **Calls
linked to feedback are never pruned**, and links are never deleted.
### Migration
- New nullable `sample_rate` column (`Float`) on `{prefix}tool_calls`. Hosts
that manage the schema with Alembic need a migration adding it.
`create_tables=True` does not alter a table that already exists, so add it by
hand there (`ALTER TABLE ffb_tool_calls ADD COLUMN sample_rate FLOAT`). Until
the column exists, `DatabaseSink` writes fail and those records are dropped
(logged). Hosts still on 2026.09.27.3 or earlier also need the `outcome`
column from 2026.09.27.4.
## [2026.09.27.4] - 2026-09-27
Count errors that tools return as data instead of raising.
+74
View File
@@ -336,6 +336,80 @@ Code and FastMCP 4's own `Client`, do not open sessions over HTTP. Their calls
link by user only, so configure an `identity_resolver` for HTTP servers. Stdio
servers link by session with every client.
### Busy tools: exclusion and sampling
A polling tool can make up most of the rows. Leave it out entirely, or keep a
fraction of its successful calls:
```python
from fastmcp_feedback.instrumentation import instrument
instrument(
app,
exclude_tools={"heartbeat"}, # never recorded
sample_rates={"pending_dispatches": 0.01}, # 1% of ok calls
)
```
Excluded tools are not recorded at all and are left out of feedback links.
Sampled tools keep a random fraction of their `ok` calls, but **failures are
always recorded**: a call whose outcome is `error` or `soft_error` is kept
whatever the rate. Calls sampled out cost no hook or redaction work and are
left out of feedback links too. Rates must be in (0, 1]; anything else raises
`ValueError`. Neither setting changes what the client receives.
Sampled-in `ok` rows carry the rate in a `sample_rate` column; everything
recorded unconditionally, including failures of sampled tools, has NULL there.
So each row stands for `1 / coalesce(sample_rate, 1)` calls, and this estimates
the true number of calls per tool:
```sql
SELECT tool, SUM(1.0 / COALESCE(sample_rate, 1)) AS est_calls
FROM ffb_tool_calls GROUP BY tool;
```
### Retention
`ffb_tool_calls` grows with every call. Give the sink a retention period and it
deletes older rows as it goes:
```python
from datetime import timedelta
from fastmcp_feedback.instrumentation import DatabaseSink, instrument
sink = DatabaseSink(engine, prefix="ffb_", retention=timedelta(days=30))
mw = instrument(app, [sink])
```
Pruning runs in the background writer after an insert, at most once per
`prune_interval` (an hour by default; the first write after startup prunes).
It deletes `prune_batch` rows (5000) per transaction, so it never holds a long
lock, and a failing prune is logged without affecting the insert. **Calls linked
to feedback are never pruned**, and neither are the links, so a report keeps its
evidence. Without `retention`, nothing is deleted.
To prune on your own schedule, call it directly; it returns the rows deleted:
```python
from datetime import UTC, datetime, timedelta
deleted = await sink.prune() # now - retention
deleted = await sink.prune(older_than=datetime.now(UTC) - timedelta(days=7))
```
Automatic pruning runs inside a sink write, so it is bounded by the
dispatcher's `sink_timeout` (10 s). Batches finished before a timeout stay
deleted and the rest goes on the next run, but for a large backlog it is
quicker to run `await sink.prune()` once yourself.
**Upgrading:** `sample_rate` is a new nullable `FLOAT` column in 2026.09.28.
Hosts that manage the schema with Alembic need a migration adding it, and a
table made earlier by `create_tables=True` needs
`ALTER TABLE ffb_tool_calls ADD COLUMN sample_rate FLOAT` (`DOUBLE PRECISION`
on Postgres also works), since existing tables are not altered. Until the
column exists, database writes fail and those records are dropped (logged).
Retention needs no schema change.
### Middleware order and shutdown
FastMCP runs the first middleware added as the outermost. Call `instrument()`
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "fastmcp-feedback"
version = "2026.09.27.4" # YYYYMMDD.patch versioning - update for each release
version = "2026.09.28" # YYYYMMDD.patch versioning - update for each release
description = "Production-ready feedback collection system for FastMCP servers"
readme = "README.md"
license = "MIT"
+1 -1
View File
@@ -44,7 +44,7 @@ Quick Start:
import logging
# Version information - CalVer YYYY.MM.DD
__version__ = "2026.09.27.4"
__version__ = "2026.09.28"
__author__ = "Ryan Malloy"
__email__ = "ryan@supported.systems"
@@ -5,10 +5,11 @@ import inspect
import json
import logging
import os
import random as _random
import time
import uuid
from collections import deque
from collections.abc import Awaitable, Callable, Iterable, Sequence
from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence
from datetime import UTC, datetime, timedelta
from typing import Any
@@ -83,6 +84,10 @@ def session_key(ctx: Any) -> str | None:
return _attr(ctx, "session_id")
def _tool_name(context: MiddlewareContext) -> str:
return getattr(context.message, "name", None) or "unknown"
def _result_payload(result: Any) -> Any:
structured = getattr(result, "structured_content", None)
if structured is not None:
@@ -137,6 +142,14 @@ class InstrumentationMiddleware(Middleware):
error (``outcome="soft_error"``, ``ok=False``) and becomes the
error message. Defaults to ``DEFAULT_RESULT_CLASSIFIER``; None
turns it off. If it raises, the call is recorded as ok.
exclude_tools: tools that are never recorded, nor kept for
correlation. The calls themselves are untouched.
sample_rates: tool name to the fraction of its ok calls recorded, in
(0, 1]. Failures (``error`` and ``soft_error``) are always
recorded. Sampled-in ok rows carry ``sample_rate``; calls
sampled out are not kept for correlation either.
random: ``() -> float`` in [0, 1) deciding sampling; a call is kept
when it returns less than the tool's rate. For tests.
"""
def __init__(
@@ -157,6 +170,9 @@ class InstrumentationMiddleware(Middleware):
recent_calls: int = 2000,
correlation_timeout: float = 1.0,
result_classifier: ResultClassifier | None = DEFAULT_RESULT_CLASSIFIER,
exclude_tools: Iterable[str] = (),
sample_rates: Mapping[str, float] | None = None,
random: Callable[[], float] = _random.random,
):
mode = (mode or os.getenv(MODE_ENV_VAR) or "meta").lower()
if mode not in MODES:
@@ -171,6 +187,16 @@ class InstrumentationMiddleware(Middleware):
self.max_error_chars = max_error_chars
self.correlation_timeout = correlation_timeout
self.result_classifier = result_classifier
self.exclude_tools = frozenset(exclude_tools)
self.sample_rates: dict[str, float] = {}
for tool, rate in (sample_rates or {}).items():
rate = float(rate)
if not 0 < rate <= 1: # also rejects NaN
raise ValueError(
f"sample rate for {tool!r} must be in (0, 1], got {rate!r}"
)
self.sample_rates[tool] = rate
self.random = random
self.recent: deque[ToolCallRecord] = deque(maxlen=recent_calls)
self.dispatcher = RecordDispatcher(
sinks if sinks is not None else [JsonLinesSink()],
@@ -182,6 +208,8 @@ class InstrumentationMiddleware(Middleware):
async def on_call_tool(self, context: MiddlewareContext, call_next):
if self.mode == "off":
return await call_next(context)
if _tool_name(context) in self.exclude_tools:
return await call_next(context)
started_at = datetime.now(UTC)
start = time.perf_counter()
@@ -196,14 +224,35 @@ class InstrumentationMiddleware(Middleware):
finally:
duration_ms = (time.perf_counter() - start) * 1000
try:
record = await self._build_record(
context, started_at, duration_ms, result, error
)
self.recent.append(record)
self.dispatcher.submit(record)
await self._record(context, started_at, duration_ms, result, error)
except Exception:
logger.warning("Failed to record tool call", exc_info=True)
async def _record(
self,
context: MiddlewareContext,
started_at: datetime,
duration_ms: float,
result: Any,
error: BaseException | None,
) -> None:
tool = _tool_name(context)
payload = _result_payload(result) if result is not None else None
soft = None
if error is None and self.result_classifier is not None and payload is not None:
soft = self._classify(tool, result, payload)
rate = self.sample_rates.get(tool)
# Failures are always kept, and their rows carry no sample_rate.
if rate is not None and (error is not None or soft is not None):
rate = None
elif rate is not None and not self.random() < rate:
return
record = await self._build_record(
context, started_at, duration_ms, payload, error, soft, rate
)
self.recent.append(record)
self.dispatcher.submit(record)
async def _call_hook(self, hook: Callable | None, *args: Any) -> dict[str, Any]:
if hook is None:
return {}
@@ -237,14 +286,14 @@ class InstrumentationMiddleware(Middleware):
context: MiddlewareContext,
started_at: datetime,
duration_ms: float,
result: Any,
payload: Any,
error: BaseException | None,
soft: str | None,
sample_rate: float | None,
) -> ToolCallRecord:
message = context.message
tool = getattr(message, "name", None) or "unknown"
args = getattr(message, "arguments", None) or {}
tool = _tool_name(context)
args = getattr(context.message, "arguments", None) or {}
ctx = context.fastmcp_context
payload = _result_payload(result) if result is not None else None
identity = await self._call_hook(self.identity_resolver, context)
extra = await self._call_hook(self.enricher, tool, args, payload, context)
@@ -266,14 +315,12 @@ class InstrumentationMiddleware(Middleware):
error_message = self.redactor.redact_text(str(cause))[
: self.max_error_chars
]
elif self.result_classifier is not None and payload is not None:
soft = self._classify(tool, result, payload)
if soft is not None:
outcome = "soft_error"
error_type = "SoftError"
error_message = (
self.redactor.redact_text(soft)[: self.max_error_chars] or None
)
elif soft is not None:
outcome = "soft_error"
error_type = "SoftError"
error_message = (
self.redactor.redact_text(soft)[: self.max_error_chars] or None
)
return ToolCallRecord(
tool=tool,
@@ -298,6 +345,7 @@ class InstrumentationMiddleware(Middleware):
if full and payload is not None
else None,
extra=extra,
sample_rate=sample_rate,
)
@property
@@ -16,6 +16,10 @@ class ToolCallRecord:
``outcome`` is ``ok``, ``error`` (the tool raised) or ``soft_error`` (it
returned a result the classifier flagged). ``ok`` is ``outcome == "ok"``;
when ``outcome`` is not given it is derived from ``ok``.
``sample_rate`` is set on ok calls of a sampled tool: each such row stands
for ``1 / sample_rate`` calls. It is None for everything recorded
unconditionally, including failures of sampled tools.
"""
tool: str
@@ -39,6 +43,7 @@ class ToolCallRecord:
args: Any = None
result: Any = None
extra: dict[str, Any] = field(default_factory=dict)
sample_rate: float | None = None
def __post_init__(self) -> None:
if self.outcome is None:
@@ -6,8 +6,10 @@ Alembic ``env.py`` (``target_metadata``) and pass their existing AsyncEngine to
"""
import asyncio
import logging
import time
from collections.abc import Sequence
from datetime import datetime
from datetime import UTC, datetime, timedelta
from typing import Any
from sqlalchemy import (
@@ -23,6 +25,7 @@ from sqlalchemy import (
Text,
and_,
delete,
exists,
or_,
select,
)
@@ -31,6 +34,8 @@ from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from .record import ToolCallRecord
logger = logging.getLogger(__name__)
DEFAULT_TABLE_PREFIX = "ffb_"
# none_as_null: a missing payload is SQL NULL, so "WHERE args IS NULL" works.
@@ -73,6 +78,9 @@ def build_metadata(
Column("args", _JSON),
Column("result", _JSON),
Column("extra", _JSON),
# Set on sampled-in ok rows; each stands for 1/sample_rate calls.
# NULL for rows recorded unconditionally and before 2026.09.28.
Column("sample_rate", Float),
)
if links not in metadata.tables:
# No foreign key to tool_calls: a link can be written before its call
@@ -100,6 +108,13 @@ class DatabaseSink:
prefix: table name prefix, so the tables cannot collide with the host's.
create_tables: create the tables on first use. Off by default; hosts
with Alembic should generate a migration from ``build_metadata``.
retention: delete tool-call rows older than this. When set, ``write``
runs ``prune()`` at most once per ``prune_interval``, after its
insert, in the background writer. None (the default) keeps rows
forever. Calls linked to feedback are never pruned.
prune_interval: minimum time between automatic prunes.
prune_batch: rows deleted per statement, so no single delete holds
locks for long.
"""
def __init__(
@@ -108,7 +123,14 @@ class DatabaseSink:
*,
prefix: str = DEFAULT_TABLE_PREFIX,
create_tables: bool = False,
retention: timedelta | None = None,
prune_interval: timedelta = timedelta(hours=1),
prune_batch: int = 5000,
):
if retention is not None and retention <= timedelta(0):
raise ValueError(f"retention must be positive, got {retention!r}")
if prune_batch < 1:
raise ValueError(f"prune_batch must be at least 1, got {prune_batch!r}")
self._owns_engine = isinstance(engine, str)
self.engine = create_async_engine(engine) if isinstance(engine, str) else engine
self.metadata = build_metadata(prefix)
@@ -119,6 +141,11 @@ class DatabaseSink:
# The background writer and link_feedback can both reach _ready() at
# once; without the lock both run create_all and one fails.
self._tables_lock = asyncio.Lock()
self.retention = retention
self.prune_interval = prune_interval
self.prune_batch = prune_batch
# time.monotonic() of the last automatic prune; None until the first.
self._last_prune: float | None = None
async def ensure_tables(self) -> None:
"""Create the tables if missing. Only called when create_tables=True."""
@@ -138,6 +165,59 @@ class DatabaseSink:
await self._ready()
async with self.engine.begin() as conn:
await conn.execute(self.table.insert(), [r.to_row() for r in records])
await self._auto_prune()
async def _auto_prune(self) -> None:
if self.retention is None:
return
now = time.monotonic()
if (
self._last_prune is not None
and now - self._last_prune < self.prune_interval.total_seconds()
):
return
# Set before running, so a prune that fails is not retried every write.
self._last_prune = now
try:
deleted = await self.prune()
if deleted:
logger.info(
"Pruned %d tool-call rows from %s", deleted, self.table.name
)
except Exception:
logger.warning("Pruning %s failed", self.table.name, exc_info=True)
async def prune(self, older_than: datetime | None = None) -> int:
"""Delete tool-call rows that started before ``older_than``.
Defaults to now minus ``retention``; with neither, deletes nothing.
Calls linked to feedback in ``{prefix}feedback_call_links`` are kept,
and links are never deleted. Deletes ``prune_batch`` rows per
transaction until none are left. Returns the number of rows deleted.
"""
if older_than is None:
if self.retention is None:
return 0
older_than = datetime.now(UTC) - self.retention
await self._ready()
t, link = self.table, self.links
batch = (
select(t.c.id)
.where(
t.c.started_at < older_than,
~exists().where(link.c.call_id == t.c.id),
)
.limit(self.prune_batch)
)
total = 0
while True:
async with self.engine.begin() as conn:
ids = (await conn.execute(batch)).scalars().all()
if ids:
await conn.execute(delete(t).where(t.c.id.in_(ids)))
total += len(ids)
if len(ids) < self.prune_batch:
return total
async def recent_calls(
self,
+402
View File
@@ -0,0 +1,402 @@
"""Tests for per-tool exclusion, sampling and retention pruning.
Real FastMCP servers through fastmcp.Client, as in test_instrumentation.py.
"""
import logging
import time
from datetime import UTC, datetime, timedelta
import pytest
from fastmcp import Client, FastMCP
from fastmcp.exceptions import ToolError
from sqlalchemy import event, func, select
from fastmcp_feedback.instrumentation import (
DatabaseSink,
MemorySink,
ToolCallRecord,
instrument,
)
@pytest.fixture
async def db(tmp_path):
"""DatabaseSinks whose engines are always disposed, or the process hangs."""
made = []
def factory(name="calls", **kwargs):
sink = DatabaseSink(
f"sqlite+aiosqlite:///{tmp_path}/{name}.db", create_tables=True, **kwargs
)
made.append(sink)
return sink
yield factory
for sink in made:
await sink.engine.dispose()
class Rolls:
"""Stand-in for random.random that returns the given values in turn."""
def __init__(self, *values):
self.values = list(values)
self.calls = 0
def __call__(self):
self.calls += 1
return self.values.pop(0)
def make_app(sinks=None, **kwargs):
sink = MemorySink()
app = FastMCP("sampling")
@app.tool
def poll() -> dict:
return {"status": "idle"}
@app.tool
def render(status: str) -> dict:
return {"status": status, "error": f"render said {status}"}
@app.tool
def crash() -> dict:
raise RuntimeError("exploded")
@app.tool
def other() -> str:
return "fine"
mw = instrument(app, sinks if sinks is not None else [sink], **kwargs)
return app, sink, mw
async def call(app, mw, *calls):
"""Run (tool, args) pairs; return the results, None where the tool raised."""
results = []
async with Client(app) as client:
for tool, args in calls:
try:
results.append(await client.call_tool(tool, args))
except ToolError:
results.append(None)
await mw.flush()
return results
def old_record(tool="old", days=40, **kwargs):
return ToolCallRecord(
tool=tool,
started_at=datetime.now(UTC) - timedelta(days=days),
duration_ms=1.0,
ok=True,
mode="meta",
**kwargs,
)
async def rows(sink, *columns):
t = sink.table
cols = [t.c[c] for c in columns] if columns else [t.c.tool]
async with sink.engine.connect() as conn:
return [
tuple(r)
for r in (await conn.execute(select(*cols).order_by(t.c.started_at))).all()
]
async def count(sink):
async with sink.engine.connect() as conn:
return (
await conn.execute(select(func.count()).select_from(sink.table))
).scalar()
class TestExclusion:
async def test_excluded_tool_not_recorded_nor_kept(self):
app, sink, mw = make_app(exclude_tools={"poll"})
results = await call(app, mw, ("poll", {}), ("other", {}), ("poll", {}))
assert [r.tool for r in sink.records] == ["other"]
assert [r.tool for r in mw.recent] == ["other"]
# The call itself is untouched.
assert results[0].structured_content == {"status": "idle"}
async def test_excluded_tool_failures_not_recorded_either(self):
app, sink, mw = make_app(exclude_tools=["crash"])
[result] = await call(app, mw, ("crash", {}))
assert result is None # the error still reached the client
assert sink.records == []
assert list(mw.recent) == []
async def test_excluded_tool_skips_hooks(self):
seen = []
app, sink, mw = make_app(
exclude_tools={"poll"}, identity_resolver=lambda c: seen.append(1) or {}
)
await call(app, mw, ("poll", {}))
assert seen == []
class TestSampling:
async def test_recorded_when_random_below_rate(self):
rolls = Rolls(0.29, 0.3, 0.31, 0.0)
app, sink, mw = make_app(sample_rates={"poll": 0.3}, random=rolls)
await call(app, mw, *[("poll", {})] * 4)
assert rolls.calls == 4
assert len(sink.records) == 2
assert [r.sample_rate for r in sink.records] == [0.3, 0.3]
assert all(r.outcome == "ok" for r in sink.records)
async def test_sampled_out_calls_not_in_recent(self):
app, sink, mw = make_app(sample_rates={"poll": 0.5}, random=Rolls(0.9, 0.1))
results = await call(app, mw, ("poll", {}), ("poll", {}))
assert len(sink.records) == 1
assert [r.id for r in mw.recent] == [sink.records[0].id]
# A sampled-out call still returns its result.
assert results[0].structured_content == {"status": "idle"}
async def test_sampled_out_ok_calls_skip_hooks(self):
seen = []
def who(context):
seen.append("identity")
return {}
def enrich(tool, args, result, context):
seen.append("enrich")
return {}
app, sink, mw = make_app(
sample_rates={"poll": 0.1},
random=Rolls(0.99),
identity_resolver=who,
enricher=enrich,
)
await call(app, mw, ("poll", {}))
assert sink.records == []
assert seen == []
async def test_unsampled_tools_recorded_without_rate_or_roll(self):
rolls = Rolls()
app, sink, mw = make_app(sample_rates={"poll": 0.1}, random=rolls)
await call(app, mw, ("other", {}), ("other", {}))
assert rolls.calls == 0
assert [(r.tool, r.sample_rate) for r in sink.records] == [
("other", None),
("other", None),
]
async def test_rate_of_one_records_everything_with_rate(self):
app, sink, mw = make_app(sample_rates={"poll": 1}, random=Rolls(0.999))
await call(app, mw, ("poll", {}))
[record] = sink.records
assert record.sample_rate == 1.0
async def test_exception_of_sampled_tool_always_recorded(self):
# Rolls would sample every call out; failures must not consult them.
rolls = Rolls(0.99, 0.99)
app, sink, mw = make_app(sample_rates={"crash": 0.01}, random=rolls)
await call(app, mw, ("crash", {}), ("crash", {}))
assert [(r.outcome, r.error_type, r.sample_rate) for r in sink.records] == [
("error", "RuntimeError", None),
("error", "RuntimeError", None),
]
assert len(mw.recent) == 2
async def test_soft_error_of_sampled_tool_always_recorded(self):
rolls = Rolls(0.99, 0.99)
app, sink, mw = make_app(sample_rates={"render": 0.01}, random=rolls)
await call(
app, mw, ("render", {"status": "failed"}), ("render", {"status": "done"})
)
assert [(r.outcome, r.sample_rate) for r in sink.records] == [
("soft_error", None)
]
assert sink.records[0].error_message == "status=failed: render said failed"
assert [r.id for r in mw.recent] == [sink.records[0].id]
async def test_sampled_in_failure_has_no_rate(self):
app, sink, mw = make_app(sample_rates={"crash": 0.5}, random=Rolls(0.0))
await call(app, mw, ("crash", {}))
[record] = sink.records
assert record.sample_rate is None
@pytest.mark.parametrize("rate", [0, -0.1, 1.01, 2, float("nan")])
def test_invalid_rates_rejected(self, rate):
with pytest.raises(ValueError, match="poll"):
make_app(sample_rates={"poll": rate})
def test_non_numeric_rate_rejected(self):
with pytest.raises((TypeError, ValueError)):
make_app(sample_rates={"poll": "often"})
async def test_estimated_count(self):
"""sum(1 / coalesce(sample_rate, 1)) estimates the true call count."""
rolls = Rolls(*[i / 10 for i in range(10)])
app, sink, mw = make_app(sample_rates={"poll": 0.2}, random=rolls)
await call(app, mw, *[("poll", {})] * 10, ("crash", {}))
estimate = sum(1 / (r.sample_rate or 1) for r in sink.records)
assert estimate == pytest.approx(11)
class TestDatabaseSinkColumn:
async def test_writes_sample_rate(self, db):
sink = db()
app, _, mw = make_app(
sinks=[sink],
sample_rates={"poll": 0.25, "crash": 0.25},
random=Rolls(0.1, 0.9),
)
await call(app, mw, ("poll", {}), ("poll", {}), ("crash", {}), ("other", {}))
assert await rows(sink, "tool", "outcome", "sample_rate") == [
("poll", "ok", 0.25),
("crash", "error", None),
("other", "ok", None),
]
class TestPrune:
async def test_deletes_old_rows_keeps_new(self, db):
sink = db()
await sink.write([old_record("a", days=40), old_record("b", days=31)])
await sink.write([old_record("c", days=29), old_record("d", days=0)])
deleted = await sink.prune(datetime.now(UTC) - timedelta(days=30))
assert deleted == 2
assert await rows(sink) == [("c",), ("d",)]
async def test_default_cutoff_uses_retention(self, db):
sink = db(retention=timedelta(days=30))
sink._last_prune = time.monotonic() # keep write() from pruning
await sink.write([old_record("a", days=40), old_record("b", days=1)])
assert await count(sink) == 2
assert await sink.prune() == 1
assert await rows(sink) == [("b",)]
async def test_no_retention_no_cutoff_is_a_no_op(self, db):
sink = db()
await sink.write([old_record(days=400)])
assert await sink.prune() == 0
assert await count(sink) == 1
async def test_keeps_linked_calls_and_links(self, db):
sink = db()
linked, plain = old_record("linked"), old_record("plain")
await sink.write([linked, plain])
await sink.replace_links(
"bug-1",
[
{
"feedback_ref": "bug-1",
"call_id": linked.id,
"position": 0,
"rule": "session",
"linked_at": datetime.now(UTC),
},
{
# A link to a call that is not in this database.
"feedback_ref": "bug-1",
"call_id": "missing",
"position": 1,
"rule": "session",
"linked_at": datetime.now(UTC),
},
],
)
assert await sink.prune(datetime.now(UTC)) == 1
assert await rows(sink) == [("linked",)]
context = await sink.linked_calls("bug-1")
assert [(r["call_id"], r["tool"]) for r in context] == [
(linked.id, "linked"),
("missing", None),
]
async def test_deletes_in_batches(self, db):
sink = db(prune_batch=3)
await sink.write(
[old_record(f"t{i}") for i in range(10)] + [old_record("new", 0)]
)
deletes = []
def spy(conn, cursor, statement, params, context, executemany):
if statement.lstrip().upper().startswith("DELETE"):
deletes.append(statement)
event.listen(sink.engine.sync_engine, "before_cursor_execute", spy)
try:
assert await sink.prune(datetime.now(UTC) - timedelta(days=1)) == 10
finally:
event.remove(sink.engine.sync_engine, "before_cursor_execute", spy)
assert len(deletes) == 4 # 3 + 3 + 3 + 1
assert await rows(sink) == [("new",)]
async def test_exact_multiple_of_batch(self, db):
sink = db(prune_batch=5)
await sink.write([old_record(f"t{i}") for i in range(10)])
assert await sink.prune(datetime.now(UTC)) == 10
assert await count(sink) == 0
def test_invalid_settings_rejected(self):
with pytest.raises(ValueError):
DatabaseSink("sqlite+aiosqlite://", retention=timedelta(0))
with pytest.raises(ValueError):
DatabaseSink("sqlite+aiosqlite://", prune_batch=0)
class TestAutomaticPrune:
async def test_first_write_prunes_then_waits_for_interval(self, db):
sink = db(retention=timedelta(days=30), prune_interval=timedelta(hours=1))
await sink.write([old_record("a"), old_record("new", 0)])
assert await rows(sink) == [("new",)]
await sink.write([old_record("b")])
assert await count(sink) == 2 # inside the interval: not pruned
sink._last_prune = time.monotonic() - 3601
await sink.write([old_record("c", 0)])
assert sorted(await rows(sink)) == [("c",), ("new",)]
async def test_no_retention_never_prunes(self, db):
sink = db()
await sink.write([old_record(days=4000)])
assert await count(sink) == 1
assert sink._last_prune is None
async def test_failing_prune_does_not_stop_inserts(self, db, caplog):
sink = db(retention=timedelta(days=30))
attempts = []
async def broken(older_than=None):
attempts.append(older_than)
raise RuntimeError("prune blew up")
sink.prune = broken
with caplog.at_level(logging.WARNING):
await sink.write([old_record("a", 0)])
await sink.write([old_record("b", 0)])
assert await rows(sink) == [("a",), ("b",)]
assert len(attempts) == 1 # not retried on every write
assert "prune blew up" in caplog.text
async def test_through_the_middleware(self, db):
sink = db(retention=timedelta(days=30))
sink._last_prune = time.monotonic()
await sink.write([old_record("stale")])
sink._last_prune = None
app, _, mw = make_app(sinks=[sink])
await call(app, mw, ("other", {}))
assert mw.dispatcher.sink_errors == 0
assert await rows(sink) == [("other",)]
async def test_prune_failure_through_middleware_keeps_records(self, db):
sink = db(retention=timedelta(days=30))
async def broken(older_than=None):
raise RuntimeError("no")
sink.prune = broken
app, _, mw = make_app(sinks=[sink])
await call(app, mw, ("other", {}), ("poll", {}))
assert mw.dispatcher.sink_errors == 0
assert sorted(await rows(sink)) == [("other",), ("poll",)]
Generated
+1 -1
View File
@@ -553,7 +553,7 @@ wheels = [
[[package]]
name = "fastmcp-feedback"
version = "2026.9.27.4"
version = "2026.9.28"
source = { editable = "." }
dependencies = [
{ name = "fastmcp" },