slpy-log 1.0.0

The Declarative Observability Framework for Python

slpy

The declarative observability framework for Python — the Python member of the SyntropyLog family.
Correlation IDs, PII masking by field name, per-level field control and retention — declared once at init() and enforced on every log, with an optional Rust masking engine (transparent pure-Python fallback). Failsafe by design: logging can never crash your app — and audit entries can survive backend outages and process restarts.

slpy is the Python implementation of SyntropyLog — the reference implementation for Node.js. The same declarative model exists for .NET as sl4n, built on Microsoft.Extensions.Logging. Same concepts everywhere — Logging Matrix, masking by field name, retention, durable delivery — each implemented idiomatically for its runtime.

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What's new in 1.0.0

First stable release. slpy now covers the same conceptual surface as its siblings (SyntropyLog for Node.js, sl4n for .NET) and is marked Production/Stable; from here semantic versioning applies — breaking changes only in major versions. Everything below is new since 0.3.x:

• Logging Matrix — a per-level whitelist of context fields, declared once. Section ›
• Retention policies — named compliance policies (with_retention('SOX_AUDIT_TRAIL')) stamped on every tagged entry. Section ›
• DurableFileTransport — audit delivery that survives backend outages AND process restarts, via a self-emptying disk spool. Section ›
• Masking hardened, in BOTH engines (Python + Rust) — non-string values and nested objects under a sensitive key are now masked (password=12345 and password={'hash': ...} no longer leak); bare credit_card/ssn/phone field names are now covered; lists are no longer corrupted by the masking pass; MASK_KEYS aliases + mask_pattern(); on_masking_error hook; on failure the entry degrades to a safe payload — raw metadata never leaks.
• Safety boundary — ANSI/control characters stripped from every string before transports (log-injection defense); on_log_failure hook; slpy.get_stats() health counters.
• Testing toolkit — slpy.testing.SpyTransport for asserting on emitted (and masked) entries.
• Timestamps are now ISO-8601 with milliseconds and offset (was epoch-ms) — the same format as SyntropyLog and sl4n.

Details: CHANGELOG.md.

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Quick start

pip install slpy-log

import asyncio
from slpy import slpy

async def main():
    # 1. Configure once — this is all you need.
    await slpy.init({'logger': {'level': 'info'}, 'masking': {'enable_default_rules': True}})

    # 2. Log. Sensitive fields are masked automatically, before any transport.
    logger = slpy.get_logger('payments')
    async with slpy.context(request_id='req-001'):
        logger.info('Card charged', email='[e-mail]', password='hunter2', amount=299.90)

    await slpy.shutdown()

asyncio.run(main())

What lands on the console (structured JSON — this is the real output):

{"level":"info","message":"Card charged","timestamp":"2026-07-09T15:14:14.929+00:00","service":"payments","request_id":"req-001","email":"j**[e-mail]","password":"*******","amount":299.9}

The request_id propagated automatically. The email and password were masked automatically. The service field appeared automatically. You wrote none of that explicitly — and this is the default behavior, not magic. From here everything is configurable: masking rules, which context fields each level emits, where logs go, retention. The sections below are how.

Masking is by field name. A field whose key matches a rule is masked; text you put in the message itself passes through untouched. So pass sensitive data as keyword fields — logger.info('msg', email=email) — not interpolated into the message string. Log-data quality is the caller's responsibility: masking enforces your rules on keyed fields — it can't find PII you hide in prose.

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What slpy is

Not a logger — an observability pipeline. With logging, structlog or loguru you wire correlation IDs, PII redaction, and per-level field control yourself, in every service. slpy does it for you: declare it once in init(), and it runs on every log call, in every coroutine, across every service — before the entry ever reaches the console, your database, Elasticsearch, Datadog, or wherever your executor sends it.

Every Python team building services ends up writing the same boilerplate: thread request_id through every function signature, scrub password before logging, remember extra={"service": ...} on every call, repeat the same middleware in every service.

slpy solves that declaratively. You declare the rules once at startup; the framework applies them consistently on every log call, in every coroutine, across every service.

It is scoped on purpose: slpy owns the log pipeline up to the moment of persistence — matrix filtering, context propagation, masking, sanitization, retention metadata. It does not manage any backend (no database, HTTP or broker clients in the core). Where the entry goes is a one-function executor you write. That keeps the framework independent of client-library versions and storage churn.

Four pillars:

• Logging Matrix — a declarative whitelist of context fields per log level. If a field isn't in the matrix for that level, it never reaches a transport. Field control by config, not by code review.
• Retention-aware audit trail with delivery guarantees — with_retention(...) travels with each entry so your transport routes it by policy. DurableFileTransport adds a self-emptying disk spool so audit entries survive backend outages and process restarts.
• Universal Adapter — one executor function sends logs to Postgres, Mongo, Elasticsearch, S3, anything. You write the executor; the framework stays agnostic of client libraries.
• Silent Observer pipeline — masking with a safe-payload fallback, ANSI/control-char sanitization, per-transport failure isolation. Logging cannot crash your app; failures surface through hooks and counters (get_stats()).

An optional Rust engine (slpy-native) accelerates masking when installed, with a transparent pure-Python fallback when not — same rules, same output, locked by a shared parity test.

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How it compares to logging, structlog & loguru

logging, structlog and loguru are loggers. slpy is a different category — an observability pipeline that does in the framework what a logger leaves to you:

	logging / structlog / loguru	slpy
Category	logger	observability pipeline (matrix → masking → sanitization → routing)
PII masking	bring your own processors	built in, by field name, at any depth — optional Rust engine
Correlation IDs	you thread them, per service	automatic via contextvars, declared once
Per-level field control	manual	declarative Logging Matrix
Retention / audit routing	DIY	first-class — with_retention + a restart-surviving durable transport
If logging throws	can bubble into your code	Silent Observer — logging never throws, can't crash your app

On speed — honestly: the only apples-to-apples comparison is minimal logging (no masking), and there slpy is competitive — see the measured numbers in Performance. With masking fully active slpy still beats stdlib logging without masking, but structlog/loguru numbers are a no-masking reference, not a race: they don't mask, correlate or filter.

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The declarative shift

With a logger, you write logging. With slpy, you declare observability.

Instead of…	You declare…	slpy does automatically
Threading request_id through every function	async with slpy.context(request_id=id)	Propagates to all logs in scope via contextvars
Scrubbing sensitive fields before logging	masking: {'enable_default_rules': True}	Masks email, password, token, card, SSN, phone on every log
Repeating extra={"service": "payments"}	slpy.get_logger('payments')	service on every log from that logger
Copying context into child functions	logger.child(order_id='123')	Bindings carried on every subsequent call
Filtering noisy context per level	logging_matrix: {'info': [...], 'error': ['*']}	Only whitelisted context fields reach a transport at each level
Routing compliance logs manually	logger.with_retention('SOX_AUDIT_TRAIL')	retention / retention_class / retention_days stamped on every entry
Writing a transport class per destination	AdapterTransport + UniversalAdapter	Your executor receives the clean entry — connect to anything
Building headers per downstream target	context.outbound: {'kafka': {...}}	get_propagation_headers('kafka') returns the right wire names

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Logging Matrix — the differentiator

A declarative whitelist deciding which context fields appear at each log level. A context field not whitelisted for a level never reaches a transport. Compliance reviews the matrix, not your codebase.

await slpy.init({
    'logging_matrix': {
        'default': ['request_id'],                        # any level not listed
        'info':    ['request_id', 'user_id', 'operation'],
        'error':   ['*'],                                 # '*' = every context field
    },
})

async with slpy.context(request_id='req-7', user_id='u-1', tenant_id='acme'):
    logger.info('Payment captured')
    # → request_id, user_id  (tenant_id dropped — not in the info whitelist)
    logger.error('Payment failed')
    # → request_id, user_id, tenant_id  ('*' lets everything through)

The matrix governs context, not per-call data. Core fields (level, message, service, timestamp), child() bindings and per-call kwargs are always emitted (and masked) — if you don't want a per-call field logged, don't pass it. The matrix exists for the auto-propagating context you can't trim at each call site.

The exact rules (identical across SyntropyLog, sl4n and slpy):

1. No logging_matrix configured → every context field passes (nothing changes).
2. Level listed → only those fields pass.
3. Level not listed → the default entry applies.
4. Neither the level nor default exist → all context is dropped for that level. Strict whitelist — always define default.
5. A list containing '*' → every context field passes at that level.

Change it at runtime — no restart. Security boundary: only context-field visibility changes; masking, levels and transports stay as set at init():

slpy.reconfigure_logging_matrix({'default': ['request_id'], 'error': ['*']})

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Named loggers and the fluent API

Each component gets its own named logger. child() binds context once — every log from that instance carries it automatically. Bindings are immutable and composable; child() never mutates the parent.

logger = slpy.get_logger('order-service').child(order_id=order_id, user_id=user_id)

logger.info('Processing')                          # carries order_id, user_id
logger.info('Calculated', total=299.90, items=3)   # carries order_id, user_id

payment = logger.child(step='payment')             # adds step, keeps the rest
payment.info('Charging card')

Builder	Binds to every log	Notes
get_logger('name')	service: 'name'	cached singleton per name
child(**fields)	the given fields	immutable, composable
with_meta(payload)	meta: {...}	free-form structured metadata
with_retention(name | dict)	retention fields	registry lookup by name, or inline {'days','class'}

audit(...) always emits, regardless of the configured level — use it for compliance events:

await slpy.init({'logger': {'level': 'error'}})
logger.info('hidden')    # not emitted
logger.audit('visible')  # always emitted

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Context propagation

slpy uses Python's native contextvars — the same mechanism as AsyncLocalStorage in Node.js. Context propagates correctly across asyncio.gather(), asyncio.create_task(), and thread-pool executors. Concurrent requests are fully isolated.

async def handle_request(request_id: str, user_id: str) -> None:
    async with slpy.context(request_id=request_id, user_id=user_id):
        logger.info('Request received')            # request_id, user_id here
        await fetch_from_db()                      # ...and here, no arguments threaded

async def fetch_from_db() -> None:
    logger.debug('Running query')                  # request_id, user_id propagated

FastAPI / ASGI middleware

One line wires automatic context extraction into every request (pip install slpy-log[fastapi]). Declare which inbound headers map to which conceptual fields, per source, and which wire names each outbound target uses:

'context': {
    'inbound':  {'http': {'request_id': 'X-Request-ID'}},
    'outbound': {
        'http':  {'request_id': 'X-Request-ID'},      # default target
        'kafka': {'request_id': 'requestId'},
    },
}

Propagation headers

get_propagation_headers(target) returns the current context translated to the wire names of that target — HTTP headers, Kafka headers, S3 metadata, anything:

await httpx.get(url, headers=slpy.get_propagation_headers())        # http names
await kafka.send(topic, headers=slpy.get_propagation_headers('kafka'))

Every call also emits an OutboundEvent on a separate observability channel (never masked, own transports) — a traceability record of what context left the service, to which target, and when. Route it to Jaeger, Datadog, or disable with 'observability': {'enabled': False}.

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Data masking

Masking runs automatically on every entry before it reaches any transport — identically in the Rust engine and the Python fallback (one rule set, asserted equal by a shared parity test). Rules match field names at any depth; once a key matches, everything beneath it is masked — nested dicts, lists, and non-string scalars included.

Masking matches the field name, not the content. It masks the value of fields whose key matches a rule; it does not scan free-text strings or the log message for PII. Put sensitive data in keyed fields — log-data quality is the caller's responsibility.

await slpy.init({
    'masking': {
        'enable_default_rules': True,   # email, password, token, card, SSN, phone
        'rules': [
            {'pattern': r'internal_code', 'strategy': 'token'},   # your own, on top
        ],
    },
})

logger.info('Payment', card_number='4111-1111-1111-1234', amount=299.90)
# → card_number: "************1234"   amount: 299.9 (numbers untouched)

logger.info('Order', order={'user': {'token': 'abc123', 'id': 'USR-1'}})
# → order.user.token: "******"   order.user.id: "USR-1" (not a sensitive key)

# Non-strings and nested structures under a sensitive key are masked too:
logger.info('Login', password=12345)             # → password: "*****"
logger.info('Auth', password={'hash': 'abc'})    # → password.hash: "***"

Identifiers keep their last digits (debuggable); credentials are fully redacted. Real outputs from the default rules:

Field key (examples)	Result
email, mail	j**[e-mail]
password, pass, pwd, secret	******* (full mask)
token, key, auth, jwt, bearer, api_key	****** (full mask)
credit_card, card_number, creditCardNumber	************1234
ssn, social_security, ssn_number	*****6789
phone, mobile, tel, phone_number	*******4567

The default rules are deliberately greedy — a field name that contains a sensitive word is masked (user_password, sessionKey). Better to over-mask than to leak. For precise custom rules, build anchored patterns from the grouped aliases (no string literals — keeps secret scanners like Sonar quiet):

from slpy import MASK_KEYS, mask_pattern

'masking': {
    'rules': [
        {'pattern': mask_pattern(*MASK_KEYS['token']), 'strategy': 'token'},   # ^(token|key|auth|jwt|bearer)$
    ],
}

Failsafe. Masking never throws. If it fails, the failure is counted (get_stats().masking_failures), reported through the optional masking.on_masking_error hook (which never receives the raw payload), and the entry degrades to a safe payload — level/timestamp/message/service plus _masking_failed: True. The raw metadata never leaks. Rules only ever run against field names (capped at 256 chars), never values — bounded input, bounded regex evaluation.

---

Compliance routing — retention as data

Declare named policies once; tag loggers with with_retention(name); every tagged entry carries the policy so your transport routes and expires it by rule. slpy attaches the metadata — storage and expiry are the transport's job.

await slpy.init({
    'retention_policies': {
        'SOX_AUDIT_TRAIL': {'days': 2555, 'class': 'SOX'},   # 7 years
        'GDPR_ACCESS':     {'days': 90,   'class': 'GDPR'},
    },
})

audit = slpy.get_logger('payments').with_retention('SOX_AUDIT_TRAIL')
audit.audit('Manager override', user_id='u-1')
# → retention: "SOX_AUDIT_TRAIL"  retention_class: "SOX"  retention_days: 2555

• The stamped fields are never filtered by the Logging Matrix — they are bindings, not context.
• An unregistered name still stamps retention — compliance intent is never silently dropped.
• Inline policies work without a registry entry: with_retention({'days': 30, 'class': 'PCI'}).
• Composes with the rest of the fluent API: logger.child(...).with_retention(...).with_meta(...).

Pair it with DurableFileTransport (below) so tagged entries are also delivered with guarantees.

---

Transports

Transport	Output	Use case
ConsoleTransport	Structured JSON	Production, CI, log collectors — default
PrettyConsoleTransport	Colored human-readable	Local development (auto-falls back to JSON when stdout is not a TTY)
AdapterTransport	Any destination	Databases, HTTP APIs, queues, fanout
DurableFileTransport	Wraps any of the above	Audit logs that must survive outages and restarts

AdapterTransport + UniversalAdapter

You provide one executor function — sync or async — that receives the clean, already-masked entry and sends it anywhere. slpy handles context, masking, level filtering, and error isolation.

from slpy import slpy, AdapterTransport, UniversalAdapter, ConsoleTransport

async def my_executor(data: dict) -> None:
    await asyncio.gather(
        prisma.system_log.create(data=data),
        es.index(index='logs', body=data),
    )

db_transport = AdapterTransport(
    name='db',
    adapter=UniversalAdapter(executor=my_executor),
    # Optional formatter — adapt the entry to your schema:
    formatter=lambda e: {**e, 'timestamp': datetime.fromisoformat(e['timestamp'])},
)

await slpy.init({'logger': {'transports': [db_transport, ConsoleTransport()]}})

For full control, extend Transport and implement log(entry). Multiple transports are supported — entries are sent to all of them, each isolated from the others' failures.

Durable delivery — DurableFileTransport

Fire-and-forget is the right default for info/warn — and exactly wrong for audit entries an auditor will ask for. DurableFileTransport wraps any transport with a durability buffer (not an archive).

When does it write to disk? Only during an outage — these are the exact conditions:

1. Happy path (inner transport works): entries go straight through. Nothing touches disk.
2. The inner transport raises: the entry is appended to the spool file (JSONL, order preserved). Every following entry is spooled too.
3. Each new log probes recovery: when the inner transport accepts entries again, the backlog drains in order.
4. Backlog fully drained: the spool file deletes itself. No rotation, no archive, no cleanup to maintain.
5. Process crash/restart during an outage: the leftover spool is recovered when the transport is constructed — delivery resumes automatically.

from slpy import slpy, AdapterTransport, DurableFileTransport, UniversalAdapter

durable = DurableFileTransport(
    inner=AdapterTransport(
        name='audit',
        # Idempotent executor: at-least-once delivery means a crash
        # mid-delivery re-sends — upsert by a unique id you control.
        adapter=UniversalAdapter(executor=lambda e: audit_store.upsert(e['event_id'], e)),
    ),
    spool_path='/var/log/app/audit-spool.jsonl',   # LOCAL disk — the net, not the destination
)

await slpy.init({'logger': {'transports': [durable]}})
slpy.get_logger('payments').with_retention('SOX_AUDIT_TRAIL').audit(
    'Card charged', event_id='evt-1', order_id='o-1',
)

Same design as sl4n's DurableFileTransport, same goal as SyntropyLog's DurableAdapterTransport + persistPath.

---

Testing your code

slpy.testing.SpyTransport captures every emitted entry — including masking output — so tests assert on what would actually leave the process:

from slpy.testing import SpyTransport

spy = SpyTransport()
await slpy.init({'logger': {'transports': [spy]}})

# ...exercise your code...

assert spy.count == 2
assert spy.last_entry['level'] == 'audit'
assert spy.at_level('error') == []
assert spy.with_field('order_id', 'o-1')
assert spy.any_message_contains('info', 'charged')
assert spy.first_entry['password'] == '*******'   # masking already applied
spy.clear()

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Self-observability and the safety boundary

The pipeline survives every failure silently — a logging framework must never crash your app. get_stats() is how you see those survivals; read it from a health endpoint:

stats = slpy.get_stats()
stats.logs_processed      # entries emitted
stats.transport_failures  # transport.log() raised (isolated — healthy sinks unaffected)
stats.masking_failures    # masking failed (safe payload emitted instead)
stats.uptime_seconds
stats.native_active       # True when the Rust masking engine is in use

Hooks, for when counters aren't enough — both are guarded (a broken hook can't break logging):

await slpy.init({
    'logger':  {'on_log_failure': lambda exc, transport: alert(exc)},
    'masking': {'on_masking_error': lambda exc: alert(exc)},   # never receives the raw payload
})

Log-injection defense: ANSI escape sequences and control characters are stripped from every string value before transports — a crafted user_agent can't fake a log line, clear an operator's terminal, or smuggle escape codes into a log viewer.

Runtime changes without restart: slpy.set_level('debug') and slpy.reconfigure_logging_matrix({...}).

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Native engine (Rust)

The optional slpy-native addon runs the masking engine in Rust. When installed it is used automatically — no code change; when absent, the pure-Python engine runs the exact same semantics. The two engines are locked together by a shared parity fixture (tests/test_masking_parity.py) asserting byte-identical output — the regression lock the family adopted after a native/JS divergence in the Node.js sibling leaked PII (its KNOWN-ISSUES #2).

• Rules with a custom_mask Python callable can't cross into Rust — the engine transparently stays in Python (parity over speed).
• Disable explicitly with the environment variable SLPY_NATIVE_DISABLE=1.
• slpy.get_stats().native_active tells you which engine is running.

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Performance

Honest framing first: the numbers below compare slpy against loggers that don't mask, correlate or filter — their columns are a no-masking reference, not a race.

Simple log             Complex object + masking
---------------------  ------------------------------------
slpy        6.2 µs  ①  structlog      10.8 µs  (no masking)
structlog   8.0 µs     slpy           18.0 µs  ② ✅ masking ON
logging    18.8 µs     logging        20.4 µs  (no masking)
loguru     58.9 µs     loguru         61.8 µs  (no masking)

pyperf, null transport, Windows 11 local — conservative numbers.

• On simple logs slpy is the fastest structured logger measured — faster than structlog, the Python performance reference.
• With masking fully active, slpy is still faster than stdlib logging without masking.
• The Rust addon reduced masking overhead from 29 µs to 12 µs. Without it, the pure-Python engine runs transparently.

Sustained throughput (GitHub Actions, Ubuntu, Python 3.12): 85k logs/sec simple, 33k logs/sec complex+masking, zero degradation from 1M to 10M calls, 0 B heap growth on child() and context scopes. Full methodology and reproduction: benchmark/README.md.

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What slpy is not

slpy is a structured logging and context propagation framework. It is not:

• A log aggregation backend (use Elasticsearch, Loki, CloudWatch)
• A distributed tracing system (use OpenTelemetry)
• A metrics collector (use Prometheus, Datadog)

It is the component that makes every log line correct, consistent, and safe before it reaches any of those systems.

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Security & supply chain

• No network I/O at runtime. slpy contacts no external URLs; the only output is what your transports produce.
• Zero runtime dependencies. The core package has no install_requires. The optional [fastapi] extra adds only starlette (already a FastAPI dependency).
• Log-injection boundary built in (ANSI/control-char stripping).
• Masking failsafe — a masking failure yields a safe payload, never the raw metadata.
• custom_mask functions in masking rules are consumer-supplied configuration, not influenced by external input.

---

What's in the box

Feature	One-liner	Where
Logging Matrix	Whitelist of context fields per level; '*' wildcard, default fallback, runtime reconfigure	logging_matrix config
MaskingEngine	By field name at any depth; non-strings and nested values under a sensitive key included; MASK_KEYS + mask_pattern()	masking config
Native engine (Rust)	Same semantics as Python, parity-locked; auto-detected	pip install slpy-native
Universal Adapter	One executor → any backend	AdapterTransport + UniversalAdapter
DurableFileTransport	Self-emptying disk spool; survives outages and restarts; at-least-once	slpy.durable
Retention policies	Named compliance policies stamped per entry, matrix-proof	retention_policies config + with_retention()
Fluent API	child, with_meta, with_retention; audit level always emits	Logger
Context propagation	contextvars scopes; per-source inbound / per-target outbound wire names	slpy.context() / context config
FastAPI / ASGI	One-line middleware extracts declared headers into context	slpy-log[fastapi]
Observability channel	OutboundEvent on every get_propagation_headers() — separate transports, never masked	observability config
Safety boundary	ANSI/control-char stripping; per-transport isolation; guarded hooks	built in
Self-observability	get_stats() — processed/failure counters, uptime, engine in use	slpy.get_stats()
Testing toolkit	SpyTransport — assert on emitted, masked entries	slpy.testing

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Documentation & examples

• Data masking · Logging matrix · Lifecycle · Architecture & design decisions
• Runnable examples (examples/01–09): basic setup, named loggers + child(), context propagation, masking, with_meta + audit, FastAPI middleware, pretty console, adapter transport fanout, observability events.
• The family: SyntropyLog (Node.js) — the reference implementation · sl4n (.NET)

git clone https://github.com/Syntropysoft/syntropylog.py
cd syntropylog.py
python examples/01_basic_setup.py

Tests: pip install slpy-log[dev] && pytest

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License

Apache 2.0 — see LICENSE.