structguru

A native structured logging library with a loguru-style API.

Combines a loguru-style API — brace formatting, bind, contextualize, opt, sink management — with a native Rust renderer for maximum performance. Since v1.0, the Rust extension is the default (and only) rendering path; structlog and orjson are no longer dependencies.

Features

• Loguru-style API — logger.info("User {id} logged in", id=123)
• Structured JSON output in production (rendered natively in Rust for speed)
• Pretty colored console output in development
• Context management — bind() for persistent context, contextualize() for request-scoped context
• Sentry integration — redacted breadcrumbs/events with raw exceptions preserved for capture
• stdlib interop — logger.add() sinks can also receive third-party logging records
• RFC 5424 severity codes included in every log record
• Native Rust runtime — rendering and output run through the bundled abi3 extension
• Fully typed — PEP 561 compliant with strict mypy

Native processing:

• Redaction — mask sensitive fields (passwords, tokens) by key name or regex
• Sampling — probabilistic and rate-limited log suppression
• Metrics — extract counters/histograms from log events via callbacks
• Exception formatting — render exc_info as text or a structured frame dictionary
• Off-thread logging — native Rust writer with a bounded queue and backpressure
• OpenTelemetry — automatic trace_id/span_id injection from current span

Framework integrations (optional dependencies):

• ASGI (FastAPI, Starlette) — request ID, timing, context binding middleware
• Celery — task context binding and cross-worker context propagation via headers
• Flask — before/after request hooks with request ID tracking
• Django — logging dict config builder and request middleware
• SQLAlchemy — slow query detection and logging
• gRPC — server interceptor with per-RPC context binding
• Sentry — forward log events as breadcrumbs/events with configurable severity

Installation

pip install structguru

With optional integrations:

pip install structguru[celery,flask,sentry]  # pick what you need
pip install structguru[all]                   # everything

Available extras: otel, celery, flask, django, sqlalchemy, grpc, sentry, httpx, requests, all.

Quick start

from structguru import configure, logger

# Configure once at startup
configure(service="myapp", level="DEBUG", format="json")

# Use anywhere
logger.info("Hello {name}", name="world")
# → {"logger":"...","level":"INFO","severity":6,"timestamp":"...","service":"myapp","message":"Hello world"}

Usage

Log levels

logger.debug("Debug message")
logger.info("Info message")
logger.warning("Warning message")
logger.error("Error message")
logger.critical("Critical message")

# Aliases
logger.trace("Maps to DEBUG")
logger.success("Maps to INFO")
logger.warn("Alias for warning")
logger.fatal("Alias for critical")

Brace formatting

Arguments used in str.format placeholders are consumed by formatting (matching loguru behaviour). Extra kwargs that are not in any placeholder are forwarded as structured fields:

logger.info("User {user_id} logged in", user_id=42, ip="10.0.0.1")
# message: "User 42 logged in"
# ip: "10.0.0.1"  (extra kwarg kept as structured field)
# user_id is consumed by formatting and not duplicated

Bound context

log = logger.bind(request_id="abc-123", user="alice")
log.info("Processing request")   # includes request_id and user
log.info("Request complete")     # same context carried through

Request-scoped context

with logger.contextualize(request_id="abc-123"):
    logger.info("Handling request")   # includes request_id
    do_work()                         # any logging inside also gets request_id
# request_id removed automatically

Exception logging

try:
    risky_operation()
except Exception:
    logger.exception("Operation failed")  # logs with exc_info at ERROR level

# Or with opt():
logger.opt(exception=True).error("Something went wrong")

Sink management

# Add a file sink
handler_id = logger.add("/var/log/app.log", level="ERROR")

# Add a callable sink
logger.add(lambda msg: send_to_monitoring(msg), level="CRITICAL")

# Remove a specific sink
logger.remove(handler_id)

# Remove all added sinks
logger.remove()

On Unix, new files created by either logger.add(path) or the native rotating file sink are owner-only (0600). Existing files retain their permissions.

All sink forms receive structguru records. They are also registered with the stdlib root logger for third-party records, which arrive raw (unrendered) on that path. Install the stdlib bridge to receive them rendered and redacted instead — while it is installed the raw delivery is suspended, so a sink never sees the same record twice.

Native delivery uses the bounded callable queue and is drained on reconfiguration, shutdown(), fork, and interpreter exit. Call structguru.flush() when you need to block until buffered records have actually been written:

import structguru

logger.info("checkpoint")
structguru.flush()   # returns once the line has reached its sink

Console vs JSON output

format= selects the renderer: "json" (default, production) or "console" (colored, human-readable development output).

# JSON (production)
configure(service="myapp", format="json")
# → {"timestamp": "...", "service": "myapp", "level": "INFO", "message": "..."}

# Console (development) — colored, human-readable
configure(service="myapp", format="console")
# → 2025-01-15 12:00:00 [info     ] Hello world

Native processing

Redaction

Mask sensitive fields automatically:

from structguru import configure

configure(
    sensitive_keys=["password", "token", "ssn"],
    sensitive_patterns=[r"\b\d{3}-\d{2}-\d{4}\b"],
    pattern_replacement="***",
)

Patterns run on Rust's linear-time regex engine (no ReDoS), which rejects look-around and backreferences at configure() time. Most look-behinds rewrite as capture groups — (?<=password=)\S+ becomes password=(\S+) with pattern_replacement="$1[REDACTED]". For patterns that can't be rewritten, allow_backtracking_patterns=True opts them into a bounded backtracking engine: look-around and backreferences then work as written, at the cost of the linear-time guarantee for those patterns. If a value ever exceeds the backtrack limit, it is redacted entirely (fail-closed) rather than emitted unchecked.

configure(
    sensitive_patterns=[r"(?<=password=)\S+"],
    allow_backtracking_patterns=True,
)

Sampling & rate limiting

Suppress noisy logs:

from structguru import configure

configure(sample_rate=0.1, rate_limit_max=5, rate_limit_period=60)

Metric extraction

Derive metrics from log events:

from structguru import MetricProcessor, configure

metrics = MetricProcessor()
metrics.counter("user.login", lambda ed: login_counter.inc())
metrics.histogram("db.query", "duration_ms", lambda v, ed: query_hist.observe(v))

configure(metric_processor=metrics)

Exception formatting

Render exceptions as JSON-serializable dictionaries:

from structguru import configure

configure(structured_exceptions=True, exception_max_frames=20)

exception_max_frames=0 omits traceback frames entirely. Negative frame and local-representation limits are rejected during configuration.

OpenTelemetry correlation

Inject trace context into every log event:

from structguru import configure

configure(otel=True)  # no-op injection when opentelemetry-api is absent

Non-blocking logging

Since v1.0, log I/O is offloaded to a background thread by default. The native Rust writer uses a bounded 8192-record queue with lossless backpressure. Set overflow="drop" to favor caller latency, or explicitly pass maxsize=0 only when an unbounded queue is acceptable.

Native runtime

structguru ships a required Rust extension that renders and enqueues logging natively, off-thread. It is auto-enabled at import time. The runtime does not depend on orjson; exotic values (datetime, UUID, Enum, dataclasses) are converted natively in Rust.

import structguru

# Native mode is already on. Logger calls route through the Rust renderer.
structguru.logger.info("order {id} accepted", id=987)
# → JSON line written to stdout by a background writer thread

No configuration is required for the default JSON-to-stdout behavior. Call configure(...) to customize the renderer, filtering, or sinks.

import structguru

structguru.configure(service="myapp", level="INFO", file_path="/var/log/app.log")
structguru.logger.bind(request_id="abc").info("order {id} accepted", id=987)
# → JSON line written to /var/log/app.log by a background writer thread

The default import-time configuration also honors environment variables:

LOG_LEVEL=INFO STRUCTGURU_SERVICE=myapp python -m myapp

Invalid native environment values fail import with an actionable exception. This prevents a deployment from starting while the native-only logging path is disabled.

Public API:

Behavior notes:

• Overflow: the default maxsize=8192 uses overflow="block" for bounded, lossless backpressure. Use overflow="drop" for drop-newest behavior with metrics and rate-limited warnings. maxsize=0 explicitly opts into an unbounded queue.
• Redaction, level filtering, exceptions, and OpenTelemetry injection are supported natively; redaction covers the message and all structured string values before rendering or Sentry export. sensitive_keys overrides the default redaction keys. Rust's linear-time regex engine rejects backreferences and look-around with ValueError at configuration time.
• Sampling & rate limiting (sample_rate, rate_limit_max, rate_limit_period) are applied as native pre-render filters — dropped records cost zero rendering. sampled and rate_limited counters are distinct from the transport dropped counter. sample_max_level restricts sampling to records at or below that level; more severe records always pass.
• Metric hooks (metric_processor=...) invoke a structlog-style processor (e.g. MetricProcessor) for every kept record on the caller's thread, with (None, method, {"event": message, **fields}). Dropped records (level/sampling/rate-limit) never reach it; hook errors are swallowed.
• Fork/shutdown safe — the writer is flushed on exit and respawned in forked children (gunicorn/celery prefork). Rotating-file writers sharing a path coordinate through an owner-only .lock sidecar; distributed hosts should still prefer stdout and an external collector.
• Structured exceptions (structured_exceptions=True) render type, message, module, and frames as a dictionary, with optional redacted/truncated locals controlled by the exception_* options.
• stack_info is supported natively: the stack is captured in Python and rendered in the same position as StackInfoRenderer (stack between service and message). Unlike the standard path, the stack ends at the user's calling frame (structguru-internal frames are skipped, the way structlog skips its own).
• Console mode (format="console"): renders colored, human-readable lines instead of JSON — structguru's own stable dev format (<timestamp> [<LEVEL>] <message> k=v), with ANSI colors by default on a TTY. Override with colors=True/False.
• File sinks (file_path=...): write to a rotating file natively. Defaults mirror RotatingFileHandler (50 MB, 5 backups); configure via file_max_bytes/file_backup_count. Set also_stdout=True to mirror output to both file and stdout (e.g. container + persistent log).
• Callable sinks (callable_sinks=[fn, ...]): use a bounded queue (callable_queue_maxsize=1024). overflow="block" provides lossless backpressure; overflow="drop" reports callable_dropped metrics. Flush and lifecycle operations drain queued calls.
• Sentry integration (sentry_processor=SentryProcessor(...)): receives the already-redacted event and raw exc_info only for exception capture.
• Scope: the native renderer covers JSON and console rendering, file/stdout/callable sinks, redaction, sampling/rate limiting, metrics, exceptions, and stack information. logger.add() sinks receive native and stdlib records.

Framework integrations

ASGI (FastAPI / Starlette)

from structguru.integrations.asgi import StructguruMiddleware

app = FastAPI()
app.add_middleware(StructguruMiddleware, request_id_header="X-Request-ID")

Celery

from structguru.integrations.celery import setup_celery_logging

setup_celery_logging(propagate_context=True, context_keys=["request_id"])
# Binds task_id/task_name to context, propagates selected keys via headers

Flask

from structguru.integrations.flask import setup_flask_logging

app = Flask(__name__)
setup_flask_logging(app, request_id_header="X-Request-ID")

Django

# settings.py
from structguru.integrations.django import build_logging_config, StructguruMiddleware

LOGGING = build_logging_config(service="myapp", level="INFO", json_logs=True)
MIDDLEWARE = ["structguru.integrations.django.StructguruMiddleware", ...]

SQLAlchemy

from structguru.integrations.sqlalchemy import setup_query_logging

setup_query_logging(engine, slow_threshold_ms=100, log_all=False)

gRPC

from structguru.integrations.grpc import StructguruInterceptor

server = grpc.server(
    futures.ThreadPoolExecutor(),
    interceptors=[StructguruInterceptor()],
)

Sentry

import logging

from structguru import configure
from structguru.integrations.sentry import SentryProcessor

sentry = SentryProcessor(event_level=logging.ERROR, tag_keys=frozenset({"service"}))
configure(sentry_processor=sentry)

Stdlib bridge

Third-party libraries log through the standard logging module. Installing the bridge re-emits those records through structguru, so they share the same JSON / console formatting, redaction, and output stream as your own logs:

from structguru.integrations.stdlib import install_stdlib_bridge

bridge = install_stdlib_bridge(
    level="INFO",
    suppress_loggers=("urllib3", "botocore"),
    disable_existing_loggers=False,
)

import logging
logging.getLogger("sqlalchemy.engine").info("SELECT 1")
# → {"logger":"sqlalchemy.engine","level":"INFO",...,"message":"SELECT 1"}

While the bridge is installed, logger.add() sinks receive third-party records only through it — rendered once, never also raw. Pass the returned handler to uninstall_stdlib_bridge() to restore the previous behavior.

Installing a second bridge while one is active raises RuntimeError. When logging setup legitimately runs more than once per process (a Django manage.py that imports a Celery app module, repeated setup in test suites), pass replace=True to release the previous bridge first — last call wins:

bridge = install_stdlib_bridge(level="INFO", replace=True)

The swap is atomic for callers: a record logged by another thread during it is delivered at most once (rendered, raw, or dropped — never twice). Suppression levels applied by the earlier install are not reverted, and calling uninstall_stdlib_bridge() on the replaced handler is a no-op.

disable_existing_loggers=True disables named stdlib loggers that already exist at installation time; False re-enables them, following dictConfig semantics. When the option is omitted, install_stdlib_bridge() reads STRUCTGURU_STDLIB_DISABLE_EXISTING_LOGGERS; if the variable is also unset, existing states are preserved. Explicit Python values override the environment. An empty environment value is treated as unset.

STRUCTGURU_STDLIB_DISABLE_EXISTING_LOGGERS=false python -m myapp

To configure all bridge options from environment variables at a controlled point in application startup:

STRUCTGURU_STDLIB_LEVEL=INFO \
STRUCTGURU_STDLIB_DISABLE_EXISTING_LOGGERS=false \
python -m myapp

from structguru.integrations.stdlib import install_stdlib_bridge_from_env

bridge = install_stdlib_bridge_from_env()

Requirements

• Python 3.11+
• The compiled Rust extension (shipped as abi3 wheels for Linux/macOS/Windows)

Documentation & Examples

• Integrations Guide — Detailed instructions for setting up frameworks.
• Full-stack Example — FastAPI + Celery + SQLAlchemy in action.
• Existing-loggers Example — Configure the stdlib policy and bridge replacement from code or environment.

Development

uv sync --all-extras
uv run pytest
make bench
uv run ruff check .
uv run mypy src/

License

MIT — Copyright (c) 2025 Aleksandr Pavlov