This issue surfaced only because someone ran a slow suite that is
excluded by default and had not been run for eighteen days. As a column
it becomes a query: which model stopped being observable, and when.
The emitter unwraps the enum to a plain str at the single _record exit.
asyncpg makes no promise about encoding a str subclass, and a telemetry
write that fails is downgraded to one warning — it would not crash, it
would just quietly cost the Postgres path a column. Normalising at the
emitter follows what tenant_id, meta and sampling already do.
The column is appended last in COLUMNS and in both DDLs. An existing
table can only take ALTER at the end, so putting it anywhere else
forks the physical column order between a freshly built database and a
backfilled one.
The min_size=10 default survived to 1.2.4 because every PG test injected a
pool and thus skipped the pool-building path entirely. Unit tests now assert
the create_pool arguments, but "we passed min_size=0" and "the server really
opened that many backends" are two different claims, and only a real instance
can settle the second one. Count via a run-unique application_name carried on
the DSN: the instance is shared with other projects, so counting by database
or role would fold their connections into ours and make the case flaky by
construction.
Degradation is exercised through an unreachable DSN rather than by exhausting
the shared instance's connections. A refused connection lands in the same
class as exhaustion, and the fake clock lets the 60s cooldown be observed
without sleeping. retry_after_s is the signal that separates a real retry
(which renews the window) from the cheap short circuit (which does not).
Evidence: with create_pool reverted to its pre-fix form both cases go red
(observed 10 backends after a single write, and refusal surfacing at pool
creation instead of at prepare time).
The pool exhaustion in issue #15 was fatal only because min_size=10 forced
a transient error to surface at pool creation, and that step was hardcoded
to permanent death. Step is the wrong axis: it conflates "the DSN cannot
be parsed" with "someone else holds all the connections right now".
Failures are now classified by two rules. Fatal means the cause lies
entirely inside this process and cannot change, which only the
construction-time DSN satisfies. Everything else splits on whether the
failure has anything to do with this row's data: row-level failures drop
one row and keep trying, environment-level failures cool down for 60s and
then get exactly one retry, so a restarted database or a DBA creating the
table heals on its own.
42703 (missing column) is the single named exception and stays row-level
even though every row fails alike: issue #13 promised that the manual mode
trims the INSERT and exposes drift per row, and that promise outranks the
rule. Any future exception owes the same argument.
The _failed boolean is gone; the tracker is the only degradation state,
because two copies of the same fact drift apart. Closing stays outside
that state: it is the caller's own decision, not an anomaly to recover
from, so the snapshot reports it through dropped_rows and the drop reason
instead of raising the degraded flag on every clean shutdown.
The pool was the only external resource in the library that pre-allocated:
asyncpg's default min_size=10 turned pool creation into an all-or-nothing
action, so on a shared instance running low on connection budget the first
thing to fall over was the one component that must not fail silently
(4 clients x 10 = 40 idle connections just to write telemetry).
min_size=0 means "do not pre-connect" - asyncpg only builds holders - so
pool creation becomes free and never touches the database; connection
failures then land on acquire, the path that already drops one row and lets
the pool recover. max_size and the write budget become the library's
explicit statement about its own footprint, configurable through two new
keys whose defaults live in config alone (the recorder parameters are
required keyword-only, same discipline as auto_migrate).
The whole write - prepare, acquire, execute - now runs inside one
asyncio.timeout: acquire used to have no timeout at all, so a full pool
would hang forever on the caller's path. Release is explicit rather than
`async with`, because asyncpg shields release and reuses the acquire
timeout, which would let a single telemetry write consume twice the budget.
PG requires a partitioned table's unique constraints to include the
partition key, so issue #12's RANGE partitioning on created_at forces
the primary key to (call_id, created_at). The old
`ON CONFLICT (call_id) DO NOTHING` then matches no constraint and PG
rejects every row with
there is no unique or exclusion constraint matching the
ON CONFLICT specification
which the recorder swallows as a per-row warning: telemetry would go
silently dark under a partitioned deployment. The target-free form is
valid on both table shapes and is literally equivalent on a plain table
(the primary key is its only unique constraint). SQLite's
`INSERT OR IGNORE` already carries no target and is untouched.
Integration coverage on the real PG instance, both inside self-created
temp schemas: a plain table still keeps one row per call_id, and a
table partitioned by created_at now accepts writes and reads them back.
The second case was red before this change with the error above.
DDL, column order and backfill statements lived twice, once in each
recorder. A public telemetry_schema_sql() would have made three copies,
and the drift shows up downstream as "I ran the printed SQL and the
library still reports a missing column".
Move both DDLs, both backfill lists and the 24 INSERT fields into
telemetry/schema.py verbatim; the recorders now import them and build
_INSERT through insert_sql(backend, COLUMNS) at import time. The
generated statements are byte-identical to the previous constants, so
runtime behaviour is unchanged (the postgres conflict target stays
bound to call_id for now).
insert_sql() validates its columns against COLUMNS: from the next task
on those names come from database probing, not from a constant, so the
subset check is the gate on the only injection surface. The new
telemetry_schema_sql() prints a paste-ready migration script; its
postgres backfill deliberately uses ADD COLUMN IF NOT EXISTS while the
library's own statements do not, because that form takes an ACCESS
EXCLUSIVE lock even when the column exists. Both variants are derived
from one declaration list so their column sets cannot drift.
The cache-key test only asserted a hit, so a key degraded to a constant
would still pass it. Adding a namespace control group that must miss
proves the key still distinguishes inputs; verified by degrading
build_cache_key to a constant and watching the case go red.
The allow_nan=False branch had no test at all. A ChatRequest built with
a nan meta value (bypassing the entry validation, i.e. a future entry
point that forgets to validate) must drop the row and not raise;
verified red by removing allow_nan=False.
Also restore the read-only file permissions in a finally block, so a
failing assertion does not get masked by a PermissionError from tmp_path
cleanup; rename the warnings fixture to captured_warnings so it stops
shadowing the stdlib module; and drop a downstream business term from a
fixture value (zero-business-assumption rule).
Both telemetry backends gain tenant_id and meta at the end of the
column list, and TelemetryEmitter fills them from the request. The two
halves ship together because the emitter is the only caller of
record_llm_call: adding the columns without filling them leaves every
row short of two keys, and the backends read those keys outside their
try block, so the KeyError degrades to a warning and the whole table
stops filling.
The columns are appended, never inserted. An old table can only gain
columns through ALTER, which puts them last; a new table built from the
DDL would put them wherever the DDL says. Anywhere but the end and the
two paths produce different physical column orders, while the INSERT
uses positional placeholders.
The two backends spell the default differently for different reasons.
SQLite refuses a NOT NULL column without a non-NULL constant default
outright, so the default is what makes the backfill legal at all. On
Postgres a non-volatile constant default is what keeps the ALTER from
rewriting the table, and NOT NULL DEFAULT '' is what keeps old rows out
of the black hole a NULL tenant_id falls into under an RLS policy.
Normalisation happens in the emitter, not the recorder, matching how
canonical_sampling_json already settles the sampling column: None
becomes the empty string, an empty mapping becomes the literal '{}'.
Keys are sorted so one set of dimensions serialises identically on
every row, and allow_nan=False is a second gate behind the entry
validation -- json.dumps would otherwise write a bare NaN, which JSONB
rejects, and the failed insert would be swallowed as a warning.
All three emit entry points read the request. Cache hits read it too,
rather than the replayed response: the dimensions answer who made this
call, not who made the one whose result is being replayed.
PostgreSQL checks the schema CREATE privilege before the IF NOT EXISTS
existence test, so an account with only table-level INSERT was denied on
CREATE TABLE IF NOT EXISTS even though the table was right there and
writable. The denial set _failed and the whole recorder went no-op for
the process lifetime, silently: 150+ calls downstream lost their latency,
token and cost rows with nothing but one warning to show for it.
The probe is the direct fix. The larger fix is the criterion: structural
degradation now means "provably cannot write" (pool creation failed, or
the table is absent and cannot be created), not "something threw during
init" -- a probe or acquire failure just skips the row and retries on the
next call.
SQLite stays as it is on purpose. Measured: it short-circuits the
statement at parse time, so it passes even under another connection's
EXCLUSIVE lock or on a read-only file. A probe there would buy nothing;
the docstring now says so to keep symmetry-minded future edits away.
Reasoning tokens are already counted inside completion_tokens, so the
cost total was never wrong -- what was missing is the attribution: how
much of a call was spent thinking rather than answering.
LLMResponse and TransportResult each gain a trailing reasoning_tokens
field, and the telemetry port grows from 21 to 22 columns with the new
column appended in both backends so fresh and migrated schemas keep the
same physical order.
None means this particular call did not report the field, not that the
source never reports it: a relay that falls back to a local tokenizer
replaces the whole usage object and drops completion_tokens_details.
Downstream checks must therefore read "in (None, 0)"; no provider was
observed reporting a literal zero.
The test passed overlay and sampling as separate objects while production
aliases them, so an in-place mutation slipped through it. Also syncs the
telemetry schema page and adds the missing postgres round-trip assertion.
The fixture used to DROP the shared llm_calls table on every run, wiping
concurrent migration-batch telemetry (and its own count assertion was
polluted in return). Assertions now filter by a per-run call_id prefix
and teardown deletes only its own rows.
iomgaa