Log Foundry

Consistent, structured (JSON) logs for every decorated function call — correlated by shared trace/span IDs, ready to ship to any of 30-plus built-in sinks (stdout by default; SQS → ELK is the headline production path).

log-foundry owns the logs pillar of observability. You decorate a function with @trace; it emits one identically-shaped JSON record when the call starts and another when it ends (with duration and status), stitched together by W3C-compatible trace and span IDs so nested calls form a tree you can query later.

• Zero runtime dependencies — the core pulls in nothing; every sink that needs a third-party client (boto3, kafka, redis, …) sits behind its own optional extra, lazily imported.
• Fully typed — mypy --strict, ships a PEP 561 py.typed marker.
• Structured, never free-form — every event is the same named-field JSON shape.
• Safe by default — never captures your arguments or return values (no accidental PII/secret leakage), and the decorator never swallows exceptions.
• Correct under threads and asyncio — context propagates via contextvars.
• Non-blocking delivery — finished spans are handed to a background worker; your code never blocks on sink I/O, and a graceful drain at exit means buffered events aren't lost.

Requirements

• Python ≥ 3.12 — the full gate (ruff, mypy, pytest) runs on 3.12 and 3.13 in CI.

Installation

Published on PyPI as log-foundry:

pip install log-foundry          # core, zero dependencies
pip install 'log-foundry[aws]'   # + boto3 for the SQS/SNS/Kinesis/Firehose sinks

Renamed in 0.2.0: log_forge → log_foundry. The import package now matches the distribution name — pip install log-foundry, then import log_foundry. If you are on 0.1.x, update your imports; there is no compatibility shim. The project was originally called log-forge, but PyPI rejects that name as too similar to the unrelated, pre-existing logforge project — its similarity check collapses separators, so log-forge and logforge count as the same name. Rather than keep a distribution and an import name that disagreed, everything is now log-foundry / log_foundry.

Migrating from 0.1.x is a find-and-replace on log_forge → log_foundry; no module moved and no public API changed. A handful of emitted defaults carry the name and shift with it: LoggingSink's default logger (logging.getLogger("log_foundry")), SyslogSink(app_name=…), SplunkHECSink(source=…), Datadog's ddsource, and Sentry's client tag. Override them explicitly if a downstream query or dashboard pins the old string.

import log_foundry

print(log_foundry.__version__)     # the installed version

To work on the library itself, install from a clone:

# the version is derived from Git tags, so clone with history (not --depth 1)
poetry self add "poetry-dynamic-versioning[plugin]"   # one-time, resolves the version locally
poetry install --with dev                             # or: pip install -e .

Optional extras

The core is dependency-free. Each sink built on a third-party client lives behind its own extra (the client is imported lazily, only when you construct that sink). All other sinks — stdout, file, SQLite, the stdlib-logging bridge, and every HTTP/socket platform sink (Elasticsearch, Loki, Logstash, Syslog, Datadog, Splunk, New Relic, Honeycomb) — need no extra.

Quickstart

import log_foundry as lf

# Call once at startup. These values are stamped onto every event.
lf.configure(service="billing-api", version="1.4.2", env="prod")

@lf.trace
def charge(order_id: str) -> int:
    return compute_tax(order_id)

@lf.trace(name="tax.compute", defaults={"component": "tax"})
def compute_tax(order_id: str) -> int:
    return 42

charge("ord_123")

With no sink configured, events are written as JSON lines to stdout. The call above emits four events — a span.start / span.end pair per function — all sharing one trace_id, with the child span pointing at its parent via parent_span_id:

{"timestamp": "2026-07-10T00:57:10.411Z", "level": "INFO", "message": "span.start", "trace_id": "8ab2add1480f8f6a52fe97cd23ae6f36", "span_id": "6aeb63c0eba85bf4", "parent_span_id": "b02197e75f40eb81", "log_id": "754adb40e10c445f9ec9e23a2f3dcbf2", "function": "tax.compute", "service": "billing-api", "version": "1.4.2", "env": "prod", "fields": {"component": "tax"}}
{"timestamp": "2026-07-10T00:57:10.411Z", "level": "INFO", "message": "span.end", "trace_id": "8ab2add1480f8f6a52fe97cd23ae6f36", "span_id": "6aeb63c0eba85bf4", "parent_span_id": "b02197e75f40eb81", "log_id": "3af73c51540848afbeaba9fdf7a9dce8", "function": "tax.compute", "service": "billing-api", "version": "1.4.2", "env": "prod", "fields": {"component": "tax"}, "duration_ms": 0.018, "status": "ok"}
{"timestamp": "2026-07-10T00:57:10.411Z", "level": "INFO", "message": "span.start", "trace_id": "8ab2add1480f8f6a52fe97cd23ae6f36", "span_id": "b02197e75f40eb81", "parent_span_id": null, "log_id": "e789f7e5268b46d8b779c9cbcdde8656", "function": "charge", "service": "billing-api", "version": "1.4.2", "env": "prod", "fields": {}}
{"timestamp": "2026-07-10T00:57:10.411Z", "level": "INFO", "message": "span.end", "trace_id": "8ab2add1480f8f6a52fe97cd23ae6f36", "span_id": "b02197e75f40eb81", "parent_span_id": null, "log_id": "8f3dbfcfcf4a45f688c738eefef882b0", "function": "charge", "service": "billing-api", "version": "1.4.2", "env": "prod", "fields": {}, "duration_ms": 0.326, "status": "ok"}

Note on ordering: the child span (tax.compute) finishes first, so its events flush before the parent's. Correlate by trace_id / parent_span_id, not by line order.

How it works

A traced call travels through a small pipeline. The first four steps run on your own thread and are deliberately fast; the last two run on a background thread so your code never waits on the destination.

1. You call the code — a @trace function, or one of the debug/info/… emitters.
2. A span opens — a record of this one call. It inherits the current trace and parent (see below), or starts a fresh trace if nothing is active.
3. Events gather on the span — an automatic span.start, then any events you emit, held in memory as one bundle rather than written line by line.
4. The span closes and hands off — on return or exception, a span.end event is added (with duration and status), and the whole bundle is handed to the background worker. This hand-off is instant and never blocks; on an exception the original error is re-raised unchanged.
5. The worker batches — the worker groups bundles and flushes them together (see below).
6. The sink ships them out — StdoutSink by default, or SQSSink in production.

Supporting this path are a handful of single-concept modules: config (the process-wide service/version/env and the sink), ids (trace/span/log ids), model (assembles the one JSON shape), context (holds the current span and baggage), and console (the optional instant echo= line).

Building the trace tree

Nested calls form a tree through a stack of open spans kept in a contextvars context — the top of the stack is the "current" span. When a traced function starts, it reads the current span: if one exists, the new span copies its trace_id and records its span_id as parent_span_id; if the stack is empty, the new span starts a fresh trace with no parent. The new span is then pushed, so anything it calls sees it as the parent. On exit the span is popped by restoring the stack to its exact prior state (via a token, not a blind pop), which stays correct even when code branches into concurrent tasks. Because the stack lives in a context variable, every asyncio task gets its own isolated copy — so asyncio.gather children share their parent's trace, and baggage set in one task never leaks into a sibling. A new thread gets a fresh context rather than a copy, so nothing follows it there unless the caller copies one (as asyncio.to_thread does).

When the worker flushes

The worker is one background thread with a bounded queue in front of it. submit drops a finished span's events into the queue and returns; the worker drains the queue into a small pending pile and flushes that pile to the sink on whichever of two triggers fires first:

• By count — once ~10 span bundles have accumulated (note: that's 10 spans, and each span carries at least its start/end pair, so a flush is usually well over 10 records). All pending bundles are flattened into a single sink.emit call.
• By time — once ~1 second has passed since the last flush, so an idle app never holds logs indefinitely. (The loop advances its flush timestamp even when idle, so an empty queue sleeps quietly instead of busy-spinning.)

A failing sink.emit is retried a few times with growing backoff; past that the batch is abandoned with a counted warning and draining continues — a broken sink degrades logging but never crashes the worker or the app. If the bounded queue fills completely, new submissions are dropped (newest-first) and counted rather than blocking your code. On shutdown() the worker stops, sweeps anything still queued into one final batch, emits it, and closes the sink.

Both triggers can be pre-empted: flush() puts a marker in the queue and the worker emits the pending pile the moment it reaches it, ignoring the count and time triggers. Because the queue is FIFO, everything submitted before the call is necessarily ahead of that marker — which is exactly why the guarantee is "events submitted before this call", and why concurrent submissions from other threads may or may not be included.

Usage

configure(...)

Set process-wide settings once at startup. Every argument is keyword-only and optional; repeated calls compose (only what you pass is applied) rather than reset.

lf.configure(
    service="billing-api",     # stamped as "service" on every event
    version="1.4.2",           # stamped as "version"
    env="prod",                # stamped as "env"
    sink=MyCustomSink(),       # defaults to StdoutSink if never set
    defaults={"region": "us-east-1"},  # base fields merged into every event's "fields"
)

If you never set a sink, the first decorated call falls back to StdoutSink(), so @trace works with zero configuration.

Passing sink= after logging has started swaps the live destination. The background worker captures its sink when it is built, so a later configure(sink=...) has to do more than update what get_config() reports — otherwise the config and the behaviour disagree silently, which is what it used to do. The swap drains everything submitted so far to the previous sink, closes it, and points the worker at the new one:

lf.configure(sink=StdoutSink())
do_some_work()                       # these events go to stdout

lf.configure(sink=SQSSink(queue_url=QUEUE_URL))
do_some_more_work()                  # these go to SQS; the earlier ones were drained to stdout

The drain is bounded at 5 s. If it cannot be confirmed in that time the swap still takes effect — you asked for that sink — but the previous sink is left open rather than closed, because the drain thread may still be inside its emit, and health().incomplete_swaps records it. Passing the sink that is already live is a no-op: no drain, no close. The previous sink is closed, so do not hand it back to a later call.

The 5 s covers the drain, not the previous sink's close(), which has no timeout of its own — a destination that blocks there blocks configure(). KafkaSink.close() flushes its producer, so an unreachable broker is the case to watch. Configure the sink before the first log where you can; that path has no worker to retarget and nothing to close.

configure() is still a startup call. It is not thread-safe, and a span finishing on another thread mid-swap may land on either sink.

@trace

Decorate any synchronous function. Usable bare or with arguments:

@lf.trace                                  # span name = func.__qualname__
def handler(): ...

@lf.trace(name="checkout", defaults={"component": "cart"})
def process(): ...

• name — override the span name (defaults to the function's __qualname__).
• defaults — per-decorator fields merged into every event this span emits.

The outermost decorated call starts a new trace; every nested decorated call becomes a child span within it. On an exception, the decorator records status="error" plus the exception type and formatted stack, then re-raises the original exception unchanged — it never swallows errors.

Async is supported. Apply @trace to an async def and it traces the coroutine's actual run — the span opens when the coroutine starts and closes when the awaits complete, not when the coroutine object is created. contextvars keeps the trace correct across await points and concurrent tasks: children awaited under one parent (e.g. via asyncio.gather) share the parent's trace_id and link to its span_id, and baggage set in one task never leaks into a sibling. A cancelled coroutine is recorded as status="error" and the CancelledError re-raised.

@lf.trace
async def fetch(user_id: int) -> dict:
    lf.info("fetching", user_id=user_id)
    return await load(user_id)

@lf.trace
async def load(user_id: int) -> dict:
    ...

await fetch(4127)   # one trace_id; load's parent_span_id == fetch's span_id

Logging inside a span

Emit your own structured events from inside a decorated call with the level functions debug / info / warning / error / critical. Each appends one event to the current span, so the whole call's logs flush together and share its trace_id / span_id. Keyword arguments land in the event's fields; the function name is captured, but arguments and return values never are.

@lf.trace
def process_payment(user_id: int) -> str:
    lf.set_baggage(request_id="req-123")     # rides every event emitted below, in this trace
    lf.info("charging card", user_id=user_id)
    lf.info("payment complete", echo=True)   # also printed to the console, immediately
    return "ok"

• set_baggage(**kv) — attach trace-scoped context that is merged into the fields of every subsequent event in the same execution flow. Precedence, lowest to highest: config defaults → span defaults → baggage → per-call fields. Trace-scoped means it ends with the trace: when the outermost @trace call returns or raises, the baggage in effect before it is restored, so one request's keys do not reach the next request's events. Nested calls do not reset — baggage set three calls deep stays visible to its parent and to the siblings after it. Set with no span open it becomes a process-level default that later traces inherit and restore to (configure(defaults=...) is the better tool for that) — and a process that logs without @trace has no root span to release anything, so it needs reset_context().
• echo=True — additionally write a human-readable LEVEL message line to the console (sys.stderr by default), synchronously, without waiting for the async flush. The event still rides the normal pipeline to the sink — echo never redirects.
• Orphan logs — a level call made with no active span is not dropped: it emits a standalone one-event span with a fresh trace_id, flushed straight to the sink.

Continuing a trace across processes

A trace stops at the process boundary: @trace mints a fresh trace_id whenever no span is open, so two processes cooperating on one logical operation produce two unrelated traces. Pass the context across and they join up. Nothing here is serverless-specific — the same two calls join an HTTP client to its server, or a Celery caller to its worker.

The producer publishes where it is:

@lf.trace
def enqueue_check(location: str) -> None:
    sqs.send_message(
        QueueUrl=QUEUE_URL,
        MessageBody=json.dumps({
            "location": location,
            "traceparent": lf.current_traceparent(),      # "00-<trace_id>-<span_id>-01"
            "baggage": lf.current_baggage_header(),       # "request_id=req-123,tenant=acme"
        }),
    )

The consumer adopts it — one line, and make it the first line:

@lf.trace
def handler(event, context):
    lf.continue_trace(event.get("traceparent"), baggage=event.get("baggage"))
    lf.info("inspecting")          # same trace_id as the producer; parent is its span
    try:
        return inspect(event)
    finally:
        lf.flush()

Details worth knowing:

• Call continue_trace() on the first line. @trace opens the handler's span before the body runs, so the call re-parents that span in place and rewrites the events it has already buffered. A child span that already finished has been handed to the worker and can no longer be moved.
• Only a root span is re-parented. A nested span already belongs to an in-process trace, and moving it would sever it from its own parent. The adopted context still applies to the next root span opened in that context.
• Your span_id is never overwritten. The adopting span keeps its own identity and takes the inbound span as its parent_span_id — otherwise two processes would share a span id.
• parent_span_id may be omitted. With only trace_id you join the trace as another root, which beats being in a fresh trace when you know the trace but not the specific parent.
• Inbound context is untrusted and validated strictly — 32/16 lowercase hex, all-zero ids rejected, higher traceparent versions accepted per the W3C forward-compatibility rule. Anything unusable is ignored with a single bounded warning on stderr and a fresh trace is minted; a malformed id never reaches the event stream. Adopting a context grants nothing — it selects a correlation id and confers no authority.
• Baggage fails independently of the trace. A malformed baggage header is skipped with a warning while the trace is still adopted: losing correlating fields is bad, losing the trace join because one field was malformed is worse. Headers over 8192 bytes are rejected. Values are percent-encoded, so , = and non-ASCII round-trip; non-string values are serialized with str(), so a dict arrives as its repr.
• An adopted context is consumed by one root span. It applies to the next root span opened and does not survive it, so the invocation after it starts a fresh trace unless it adopts again. That is what stops a warm container from logging every later invocation into the first caller's trace. A batch that fans out to several sibling root spans therefore needs one continue_trace() per item — or, better, one @trace entry point so the items are nested spans of a single trace.
• Sampling is not honoured. traceparent's flags byte is parsed and ignored, and outbound is always 01: this library records every span, so respecting another system's sampling decision would mean dropping them.

Clearing context in a long-lived process

@trace releases both baggage and the adopted context when the outermost decorated call returns or raises, so most callers never need reset_context(). It exists for the two cases where no root-span exit releases them in your context:

lf.reset_context()      # clears baggage *and* any adopted trace context

• You use the emitters without @trace. An orphan log opens no span, so nothing releases what set_baggage() or continue_trace() set. In a process that reuses one thread across requests — the main thread, a pooled worker, a warm Lambda container — that state reaches the next request. Call reset_context() when a unit of work ends. (An orphan log never joins an adopted trace either: it mints its own trace_id, and the adoption simply waits to claim the next root span, whenever one happens to run.)
• You adopt outside the span and dispatch into a task. The release runs in whichever context the root span's finally runs in, so continue_trace() here followed by asyncio.run(main()) clears the adoption in the task's copy of the context while this one keeps it. contextvars has no way to write back to a parent context, so clear it yourself. Adopting on the entry point's first line — the documented placement — is inside the span and needs nothing.

It clears rather than restores: a process-level baggage default set before any span is erased too — permanently when you call it outside a span. Prefer that. Called inside a span it also empties the span.start / span.end events of baggage, because those are stamped with the span's final baggage at close, and that span's exit then restores the pre-span baggage anyway, undoing the erasure. It never raises.

Sinks

A sink is the swappable output transport — any object satisfying the Sink protocol. It receives already-built, batched event dicts and knows nothing about spans or context:

class Sink(Protocol):
    def emit(self, batch: list[dict[str, object]]) -> None: ...
    def close(self) -> None: ...

Wire one up by passing an instance to configure(sink=...); if you never do, the first decorated call falls back to StdoutSink(). Sinks are not re-exported at the top level — import each from its own module, e.g. from log_foundry.sinks.sqs import SQSSink.

A few conventions hold across every sink below:

• Extras. The core is dependency-free. A sink built on a third-party client sits behind the optional extra named in its table (blank = zero-dependency, stdlib only); the client is imported lazily, so import log_foundry.sinks.<x> never fails for a missing dependency — only constructing the sink without an injected client does. See Optional extras.
• Injection. Sinks backed by an external resource accept an injected client/connection/stream (client=, connection=, producer=, stream=, opener=) for testing or bespoke configuration. The tables show the destination-defining arguments only; sinks that retry also take max_retries.
• Ownership. A resource the sink opens itself is closed on shutdown(); an injected one is left open for you to manage.
• Never crashes the app. A broken destination degrades logging and nothing more. A sink that delivered part of a batch counts what it lost (.failed, .dropped_oversized, .dropped_unadjudicated, …) and returns, since retrying would re-deliver what already landed. A sink that delivered none of it raises instead, so the worker's bounded retry engages and health().failed_batches records the loss — there is nothing downstream to duplicate. Three cases are excepted, each because a retry would be wrong rather than merely futile: an oversized event (it can never fit), a response the sink could not adjudicate (it cannot prove nothing landed), and an SQS sender fault (a byte-identical re-send can only fail again). Either way the exception never reaches your code — inside a span the worker catches it, and on the orphan path (log_foundry.info(...) outside any span, which emits synchronously) the emitter does.

Built-in, zero-dependency

from log_foundry.sinks.stdout import StdoutSink
lf.configure(sink=StdoutSink())          # explicit; also the zero-config default

Composition & adapters (zero-dependency)

configure(sink=...) takes a single sink, so compose these to filter, reshape, fan out, or bridge to a plain callable.

from log_foundry.sinks.multi import MultiSink
from log_foundry.sinks.filtering import FilteringSink
from log_foundry.sinks.stdout import StdoutSink
from log_foundry.sinks.sqs import SQSSink

lf.configure(sink=MultiSink(
    StdoutSink(),                                                    # echo everything locally
    FilteringSink(SQSSink(queue_url="…"), min_level="WARNING"),      # only WARNING+ to SQS
))

min_level is one of DEBUG/INFO/WARNING/ERROR/CRITICAL (case-insensitive); an event whose level is unknown or missing fails open (is forwarded).

Standard-library logging bridge (zero-dependency)

Hands every event to a logging.Logger (default logging.getLogger("log_foundry")) so your existing handlers, formatters, and logging.config apply. Identity fields and the nested fields are attached to each record; the sink never configures or tears down logging itself.

Local file & embedded (zero-dependency)

RotatingFileSink's time trigger uses a when unit code — "S"/"M"/"H"/"D" — times interval (either trigger, or both, can be enabled). SQLiteSink stores each event as full JSON plus projected log_id/trace_id/span_id/timestamp/level/function columns; pass create_table=False when you provision the table yourself.

from log_foundry.sinks.file import RotatingFileSink
lf.configure(sink=RotatingFileSink("app.log.jsonl", max_bytes=10_000_000, backup_count=5))

HTTP & self-hosted platforms (zero-dependency)

All build on HTTPSink (stdlib urllib): they POST batches with bounded 429/5xx retry (honoring Retry-After) and need no extra. On the specialized sinks, **http_kwargs forwards to HTTPSink (headers=, auth=, gzip=, timeout=, max_retries=).

from log_foundry.sinks.elasticsearch import ElasticsearchSink
lf.configure(sink=ElasticsearchSink("https://es.internal:9200", index="app-logs",
                                     auth=("elastic", "…")))

SaaS platforms

Also HTTP-based. All are zero-dependency except SentrySink, which prefers the sentry-sdk (the sentry extra) and falls back to raw HTTP envelopes when it isn't installed.

With the sentry extra installed, SentrySink captures via sentry_sdk.capture_event (initialize the SDK yourself with sentry_sdk.init(...)); without it, pass dsn= and events are POSTed as Sentry envelopes over HTTP.

AWS — the durable-buffer path (aws extra)

pip install 'log-foundry[aws]' (pulls boto3). Credentials and region come from boto3's standard chain — log-foundry adds none of its own. Each re-chunks every batch to the service's hard per-request limits, retries partial failures, and drops any single event too large to ever fit (counted on .dropped_oversized).

KinesisSink and FirehoseSink learn which records failed positionally — the response carries a parallel array with no ids — so they check that it describes as many records as were sent before acting on it. A response that doesn't is not used to adjudicate any record in the chunk: the chunk is abandoned rather than re-sent (some of it almost certainly landed), counted on .dropped_unadjudicated, and named on stderr. A non-zero value there is real loss, and normally means the client isn't AWS-shaped. SQSSink and SNSSink correlate by explicit Id instead, so they can't mis-pair and have no such counter.

from log_foundry.sinks.sqs import SQSSink
lf.configure(service="payments",
             sink=SQSSink(queue_url="https://sqs.us-east-1.amazonaws.com/123456789012/logs"))

Consuming from the buffer and indexing into ELK is a separate component, outside this library.

SQSSink does not retry a message SQS rejects as a sender fault — the retry would re-send it byte-identical, so it can only fail the same way. Those are counted on .failed immediately and the SQS error code is named on stderr. Throttles and internal errors are still retried up to max_retries.

FIFO queues

A queue URL ending in .fifo switches SQSSink into FIFO mode automatically — AWS requires the suffix on every FIFO queue, so nothing needs configuring:

SQSSink(queue_url="https://sqs.us-east-1.amazonaws.com/123456789012/logs.fifo")

Each message then carries a MessageGroupId, which defaults to the event's own trace_id. SQS guarantees ordering within a group, and a trace is exactly the unit whose events should stay ordered — while separate traces land in separate groups, so the queue delivers them in parallel instead of serializing your whole process behind one group. (KinesisSink partitions on trace_id by default for the same reason.) The MessageDeduplicationId defaults to the event's log_id, already a per-event UUID, so SQS's five-minute deduplication window never collapses two distinct records.

Override the group with a constant or a callable:

# One group for the whole process — strict global ordering, capped at ~300 msg/s.
SQSSink(queue_url=FIFO_URL, message_group_id="payments")

# Group by anything on the event. Baggage lands in `fields`, so this groups by tenant
# and falls back to per-trace when unset:
SQSSink(queue_url=FIFO_URL,
        message_group_id=lambda e: str(e["fields"].get("tenant_id") or e["trace_id"]))

Pass fifo=True or fifo=False to override the URL-based detection. Standard queues are entirely unaffected — their messages carry neither parameter.

Two things worth knowing:

• Ordering is best-effort across a retry. If one message fails and a same-group message ahead of it succeeded, the retry lands after it. Holding a whole group back on a single failure would trade log delivery for ordering you can rebuild from timestamp, so the sink doesn't.
• FIFO queues cap throughput at 300 messages/second (3,000 with batching), or higher in high-throughput mode. That's queue-side configuration, not something the library sets.

Queue & stream

Each needs its own extra (lazy-imported). All publish + retry within a bound and close cleanly.

from log_foundry.sinks.kafka import KafkaSink
lf.configure(sink=KafkaSink("app-logs", bootstrap_servers="broker:9092"))

Databases

Write-only inserts (querying is the downstream tool's job); each needs its own extra.

PostgresSink / ClickHouseSink default create_table=False (you own the schema and indexes); set it True for an idempotent CREATE TABLE IF NOT EXISTS convenience.

Prefer a destination not listed here? Implement the Sink protocol yourself, or wrap any callable in CallbackSink.

Writing your own sink

Sink is two required methods, emit(batch) and close(), plus three rules about how emit fails and one about when it is called. They are not stylistic — the library's whole loss-reporting apparatus is built on them:

• Tolerate concurrent calls. emit may run on more than one thread at once, and close may be called while an emit is in flight. The worker drains on its own thread, but a level call made with no active span emits synchronously on the caller's thread — which is any thread of your application. If your sink holds mutable transport state (a stream it rebinds, a socket it reuses, a connection with transaction scope), guard it with a threading.Lock held for the whole operation that assumes exclusivity. If it holds none, you need do nothing. The library cannot serialize this for you: it does not own the calling thread.

• Raise when you delivered none of the batch, after your own retries are spent. That is the signal the worker's bounded retry and health().failed_batches depend on, and the one case where a retry cannot duplicate anything: nothing landed downstream. Raise SinkDeliveryError (from log_foundry.sinks.base) or any exception of your own — the contract is that something propagates.

• Do not raise when you delivered some of it. The worker retries whole batches, so raising on a partial success re-delivers the records that already arrived, and duplicates downstream are worse than a counted loss.

• Raise after your own close(), if close() released anything. A batch handed to a released transport has delivered nothing, so it is the first rule again by another route — and it is easy to miss, because the sink looks like it worked. Three of the shipped sinks got this wrong for four releases: one accepted a produce into a client buffer nothing would flush again, one appended a delivery future nothing would resolve, and one transparently reconnected, leaking a connection nothing would reap. Set the flag before you release, and read it in emit. If your close() releases nothing — you open a fresh connection per request, or the client is the caller's — then keep accepting: refusing a batch you would have delivered is loss you invented. emit([]) stays a no-op either way.

A sink that absorbs a total failure and returns normally is a sink the worker believes: the retry never engages, failed_batches stays at zero, and flush() returns True while every event is lost.

Optionally add losses() to report what you absorbed. It must never raise and must be safe to call while emit is running (health() is a poll) — which means the counters need their own lock, kept separate from the transport one so a poll never waits on an in-flight send:

import threading
from log_foundry.sinks.base import SinkDeliveryError, SinkLosses

class MySink:
    def __init__(self) -> None:
        self._dropped = self._failed = 0
        self._closed = False
        self._lock = threading.Lock()          # transport state
        self._counter_lock = threading.Lock()  # counters only, never held across I/O

    def emit(self, batch: list[dict[str, object]]) -> None:
        if not batch:
            return
        delivered = 0
        with self._lock:                       # your connection, socket or stream
            if self._closed:                   # refuse: nothing here can deliver it
                raise SinkDeliveryError(
                    f"MySink delivered none of {len(batch)} event(s): the sink is closed"
                )
            for chunk in self._chunks(batch):
                if self._send(chunk):          # your own bounded retry
                    delivered += len(chunk)
                else:
                    with self._counter_lock:   # transport -> counter, never the reverse
                        self._failed += len(chunk)
        if batch and not delivered:
            raise SinkDeliveryError(f"MySink delivered none of {len(batch)} event(s)")

    def losses(self) -> SinkLosses:
        with self._counter_lock:               # both fields from one instant
            return SinkLosses(dropped=self._dropped, failed=self._failed)

    def close(self) -> None:
        with self._lock:                       # never release under an active writer
            if self._closed:                   # idempotent: atexit races your own cleanup
                return
            self._closed = True                # set the flag, *then* release
            ...

losses() is optional and probed by name, so a sink written before it existed keeps working and simply contributes nothing to health().sink. emit([]) must be a no-op: an empty batch has not failed to deliver.

Flushing and shutdown

Delivery is off the hot path. When a span ends, its events are handed to a per-process background worker via a fast, non-blocking submit — your function returns without waiting on the sink. The worker batches events (by count and time), emits them on its own thread, retries a failing sink with backoff, and applies backpressure so a slow or down sink can never block or back-pressure the app: when its bounded queue is full it drops the newest submissions and counts them rather than stalling.

Those losses are deliberate, so the library gives you a way to notice them. log_foundry.health() returns a snapshot of the worker's counters:

h = log_foundry.health()
if (
    h.dropped or h.failed_batches or h.stopped_reason or h.incomplete_swaps
    or (h.sink and (h.sink.dropped or h.sink.failed))
    or (h.retired and h.submitted_after_shutdown)
):
    ...  # logs were silently lost — worth an alert

They tell you different things, and they want different responses:

h.sink is a SinkLosses(dropped, failed) or None — None when no worker exists yet, or when the configured sink reports nothing (losses() is optional). Note the two dropped fields count different things: the worker's is backpressure at its queue, the sink's is an event that never reached the wire. They are separate because the remedies do not overlap — and sink.dropped is itself two causes, which is why the diagnostic line matters. Most sinks drop only what can never fit; KafkaSink and GooglePubSubSink also count what their client refused outright, which may be backpressure one layer further out than the worker's, or may be a misconfiguration. The stderr line carries the exception type that distinguishes them.

sink.failed is an upper bound on loss, not a count of it. A sink that raises on total failure counts the attempt and hands the batch back to the worker, whose retry may then deliver it — so a transient outage leaves it non-zero with nothing actually lost. failed_batches is the record of a batch given up on for good.

stopped_reason is a type name (e.g. "SystemExit"), never the exception's message — a sink's error text can carry event data. It reads None for a healthy worker, for a process that has never logged, and after a clean shutdown(), so a plain truthiness check is safe. Without it a dead thread showed up only indirectly, as dropped climbing once the queue filled — the wrong signal, pointing at the wrong fix.

retired is deliberately not alerted on by itself. A process that shuts down and then stops logging is doing the right thing; it is the pair — retired, and still being handed events — that means every log line since the shutdown has gone nowhere. That state used to read as perfectly healthy: stopped_reason is None after a clean shutdown, and the queue simply grows.

Read a snapshot by attribute (h.dropped), as above. Health is a NamedTuple and has gained fields over time — a fourth (stopped_reason) in v0.7.0, and a fifth (sink) plus a sixth, seventh and eighth (retired, submitted_after_shutdown, incomplete_swaps) not yet in a tagged release — so unpacking it whole (queued, dropped, failed = health()) raises ValueError. Every field keeps its position when a new one is appended, so attribute and index access stay stable.

dropped counts submissions discarded because the queue filled; failed_batches counts batches abandoned after the retry budget was spent. Overflow also warns on stderr — on the first drop and every thousandth after it, since overflow is a high-rate condition and a line per drop would be its own outage. A process that has never logged has no worker, and asking after its health does not create one.

Because delivery is asynchronous, drain before the process exits. There are two drains, and which one you want depends on whether the process is about to end:

import log_foundry as lf

lf.flush()      # drain to the sink and keep going; returns True when everything landed
lf.shutdown()   # drain, close the sink, and stop for good; blocks until drained (30s cap)

Both are bounded, because both can be called somewhere with a deadline. flush(timeout=5.0) returns False if the drain did not complete; shutdown(timeout=30.0) returns having stopped what it could, and reports health().stopped_reason == "ShutdownTimeout". Passing None to either waits indefinitely, which is unsafe in any environment with an execution deadline.

What a broken destination can cost you. There is one drain thread, so a sink's backoff pauses all log delivery, and it spans shutdown(). At the defaults (max_retries=3) that is 0.7 s of backoff per batch for most sinks (per message for the socket-backed ones — ~70 s for a 100-message batch against a dead syslog host), and up to 90 s for an HTTP sink whose destination is sending Retry-After — clamped to max_retry_after=30.0 per wait, which you can lower. Every wait is cut short by a shutdown, and shutdown()'s own timeout bounds the total either way. Each sink's class docstring states its own worst case.

shutdown() is also registered via atexit, so a normal exit flushes automatically — call it explicitly when you need to be certain the tail reached the sink before a fast exit, e.g. at the end of a short script. It is idempotent.

flush(timeout=5.0) returns True when nothing was lost while the call was outstanding — the drain it forces reached the sink, and so did anything else the worker emitted while it waited its turn. It returns False on timeout, when the worker was already shut down or has died, and when any batch was abandoned inside that window. A True is evidence of delivery, not merely that a drain took place.

The window starts when you call it. A batch abandoned before that is deliberately not its business: the loss is already counted in health().failed_batches and reported on stderr, and folding it in would make every later flush() in the process report a failure it did not incur. So flush() answers "did the logs I am waiting on get out", and health() answers "has anything been lost at all" — check both, as the handler below does.

It never raises — a logging call must not be the reason your function fails. Passing timeout=None waits indefinitely, which is unsafe anywhere with an execution deadline.

Serverless / short-lived processes

In AWS Lambda (and anything else that freezes rather than exits) the rules are different, and getting them wrong is silent:

• Flush before the handler returns. Lambda freezes the execution environment the instant your handler returns, so the worker's interval-based flush stops mid-interval and whatever is still queued is lost when the container is eventually reaped. atexit does not save you — a frozen environment is killed without running exit handlers, so flush() is the only guaranteed drain there.

• Put it in a finally. A flush written as the last line of the handler body is precisely the line that does not run when the handler raises, and the invocation whose logs are most worth having is the one that failed.

• Never call shutdown() per invocation. It is terminal: the worker does not come back, so the first invocation on a warm container would log and every later one would silently log nothing. That failure reads as "works locally, broken in production".

  It is no longer silent. Logging after shutdown() is accepted and undeliverable, and health() says so: retired is True and submitted_after_shutdown is non-zero — that pair, and only that pair, is this mistake. The first such submission also writes one stderr line naming flush() as the remedy, throttled to the first and every thousandth after it. stopped_reason stays None throughout, because nothing failed; someone used the terminal drain where the repeatable one belonged.

import log_foundry as lf
from log_foundry.sinks.sqs import SQSSink

lf.configure(service="billing-api", env="prod", sink=SQSSink(queue_url=QUEUE_URL))

@lf.trace
def handler(event, context):
    lf.info("received", records=len(event["Records"]))
    try:
        return do_work(event)
    finally:
        # In `finally`: the failed invocation is the one worth logging. NEVER shutdown() here —
        # the worker does not come back, and every later invocation on this warm container
        # would log nothing.
        drained = lf.flush()
        h = lf.health()
        if not drained or h.failed_batches or h.dropped or h.stopped_reason or h.retired:
            # `drained` covers this invocation's tail; the counters cover anything the worker
            # lost earlier — a batch its own interval trigger already gave up on, for instance.
            # `h.retired` catches the mistake above: inside a handler it can only mean something
            # called shutdown(), and from here on this container logs nothing.
            # Emitting this through your platform's own logger keeps it outside the pipeline
            # that just failed.
            print(f"log-foundry: undelivered logs ({drained=}, {h=})")

By default each invocation is its own trace, so N invocations produce N trace_ids. To join them into one — a step function, a producer and its consumer — pass the context across with continue_trace().

Event schema

Every event is the same shape (arch §6). Boundary events add a few fields:

IDs are W3C Trace Context-compatible by design, so the logs can later correlate with distributed traces cheaply.

Field values are coerced and bounded

Every value you pass is made JSON-safe and given a size ceiling once, when the event is assembled — so no sink can be handed a payload JSON refuses, and no single field can grow without limit. Strings are clipped to max_value_bytes (8192 by default), mappings and sequences to max_keys entries and max_depth levels. datetime, UUID, Decimal, bytes and friends render as strings; anything with no JSON form becomes <unserializable: TypeName> — the type name only, never a repr, so coercion can never leak a value the library was careful not to capture.

Integers are the one case worth knowing about. They are passed through unchanged — an ID or an amount stays a number, at full precision — but an integer too long to render is replaced by <int: ~N digits>. CPython refuses to convert an integer past sys.get_int_max_str_digits() decimal digits (4300 by default) and raises, and json.dumps inherits that refusal, so with the default configuration the interpreter's limit — not max_value_bytes — is what binds. You are unlikely to meet it deliberately; int.from_bytes(blob, "big") over a couple of kilobytes gets there. Any ceiling firing sets truncated: true on the event.

max_value_bytes therefore carries two units: UTF-8 bytes for a string, rendered decimal length (sign included) for an integer. They coincide for ASCII digits, and one ceiling for "how big may a single value get" was preferred to a second config key. Note that all four ceilings bound each value — an event of many bounded values can still be large; see Known constraints.

Development

poetry install --with dev      # set up (Python 3.12+)
poetry run pytest              # test
poetry run ruff check .        # lint (line-length 100)
poetry run mypy                # typecheck (strict, over src/)

The library uses a src layout (src/log_foundry/) with a single concept per module: config, ids, model, context, decorator, api, console, worker, and the sinks/ package (the base protocol, stdout, and one module per sink family — see Sinks). Deeper design docs live in docs/ — start with docs/architecture.md.

Continuous integration

Every pull request and every push to main runs the same set of checks:

CodeQL runs as GitHub's default setup — a repository setting, not a workflow file, which is why there is no codeql.yml here (adding one would disable the default setup and silently stop the uploads). The two scanners that don't fail a build report findings to code scanning deliberately: the alert count is the verdict there, not the green check mark.

dependabot.yml opens scheduled version updates for pip and github-actions on top of the security updates GitHub raises against advisories. Both ecosystems use a cooldown so a freshly published release isn't adopted within hours of appearing, and pip uses increase-if-necessary so an update never narrows a floor this library publishes to its consumers.

Security

Please report a vulnerability through GitHub's private reporting rather than a public issue: open a draft advisory. SECURITY.md covers what to include and what to expect — an acknowledgement within 7 days, an assessment within 30. Fixes land on the latest released minor; there are no long-term support branches.

Three properties of the supply chain are worth stating, since a logging library sits inside everything it instruments:

• Zero runtime dependencies in the core. A default pip install log-foundry pulls in no third-party code at all; every sink needing a client sits behind an extra you opt into.
• Every action is pinned to a commit SHA, Dependabot maintains the pins, and every workflow declares least-privilege permissions instead of inheriting the repository default.
• Releases publish over OIDC, so no PyPI token is stored in the repository — see Releasing.
• Every release ships a CycloneDX SBOM as a release asset (latest release, log-foundry-X.Y.Z.cdx.json), describing the published wheel with every extra installed. SECURITY.md has the detail.

Scanning runs continuously rather than at release time: CodeQL over the source and the workflows, zizmor over the workflows, dependency-review on every pull request, a weekly pip-audit across all eleven extras, and OpenSSF Scorecard. Findings go to code scanning; dependency-review and pip-audit are the two that fail a build.

Releasing

The version is never hand-edited. It is derived from Git tags at build time by poetry-dynamic-versioning, so pyproject.toml carries no literal version and the published number can't drift from what Git says.

release.yml reuses the CI suite as a gate, then builds an sdist and a wheel:

Dev pre-releases keep the upload path exercised on every merge, so a real release is never the first time it runs. pip install log-foundry still resolves to the latest stable version — pip ignores pre-releases unless you pass --pre.

Cutting a release is one tag:

git tag -a v0.9.0 -m "log-foundry 0.9.0"
git push origin v0.9.0

Uploads authenticate with PyPI Trusted Publishing (OIDC) through the pypi GitHub Environment — there is no API token stored in the repository. A tagged build refuses to publish if the derived version doesn't match the tag, and the tagged job deliberately omits skip-existing so re-pushing an already-published version fails loudly.

Every action on this path is pinned to a commit SHA, pypa/gh-action-pypi-publish included — deliberately off the rolling release/v1 branch PyPA recommends, because this is the job holding id-token: write against PyPI and a mutable reference there reaches every consumer's pip install. Dependabot moves the pins, so they stay maintained rather than frozen.

License

MIT © Andrew Griffith