azure-functions-logging 0.7.2

Developer-friendly logging helpers for Azure Functions Python

Azure Functions Logging

Part of the Azure Functions Python DX Toolkit — dogfood-tested by azure-functions-cookbook-python.

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Invocation-aware observability for Azure Functions Python v2. Surfaces invocation_id, detects cold starts, warns on host.json misconfig, and outputs Application Insights-ready structured logs — without replacing Python's standard logging.

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Part of the Azure Functions Python DX Toolkit → Bring FastAPI-like developer experience to Azure Functions

Why this exists

Azure Functions Python logging has specific failure modes that generic logging libraries don't address:

Problem	What happens	This library
host.json log level conflict	Your INFO logs silently disappear in Azure	Detects and warns at startup
No invocation_id in logs	Impossible to correlate logs to a specific execution	Auto-injects from context object
Cold start invisible	No signal when a new worker instance starts	Detects automatically on first inject_context()
Noisy third-party loggers	azure-core, urllib3 flood your Application Insights	SamplingFilter / RedactionFilter
Local vs cloud output mismatch	Colorized output breaks in production pipelines	Environment-aware formatter switching
PII leaking into logs	Sensitive values accidentally logged as extra fields	RedactionFilter with key-based redaction

What it does

• Invocation context — auto-injects invocation_id, function_name, cold_start into every log
• Structured JSON output — Application Insights-ready NDJSON format for production
• Noise control — SamplingFilter rate-limits chatty third-party loggers
• PII protection — RedactionFilter masks sensitive fields before they reach log aggregation

Scope disclaimer. This package writes structured JSON to Python logging / stdout. How those fields appear in Application Insights depends on the Azure Functions host, worker, logging configuration, and ingestion pipeline. The library does not own ingestion or schema mapping — both customDimensions-parsed and raw-message shapes are valid in production.

Before / After

Without azure-functions-logging — plain print() output, no context, no structure:

import azure.functions as func

app = func.FunctionApp()


@app.route(route="orders")
def process_order(req: func.HttpRequest) -> func.HttpResponse:
    print("Processing order")        # no invocation_id, no structure
    print(f"Order: {req.get_json()}")  # PII may leak, no log level
    return func.HttpResponse("OK")

Terminal output:

Processing order
Order: {'customer': 'Alice', 'total': 99.99}

No invocation ID. No log level. Hard to correlate in Application Insights.

With azure-functions-logging — structured, queryable, production-ready:

import azure.functions as func

from azure_functions_logging import JsonFormatter, get_logger, logging_context, setup_logging

setup_logging(functions_formatter=JsonFormatter())
logger = get_logger(__name__)
app = func.FunctionApp()


@app.route(route="orders")
def process_order(req: func.HttpRequest, context: func.Context) -> func.HttpResponse:
    with logging_context(context):
        logger.info("Processing order", order_id="o-999")
        return func.HttpResponse("OK")

Local terminal output when run standalone (e.g. python app.py, color formatter):

10:30:00 INFO     function_app  Processing order  [invocation_id=abc-123-def, function_name=process_order, cold_start=true]

Production output under func start / Azure (Application Insights NDJSON, applied because functions_formatter is set):

{"timestamp": "2024-01-15T10:30:00+00:00", "level": "INFO", "logger": "function_app",
 "message": "Processing order", "invocation_id": "abc-123-def",
 "function_name": "process_order", "trace_id": null, "cold_start": true,
 "exception": null, "extra": {"order_id": "o-999"}}

Every log carries invocation_id and cold_start. Queryable in Application Insights. Zero print() statements.

Note: The exact Application Insights schema depends on your ingestion pipeline. In some deployments JSON fields are parsed into customDimensions; in others the JSON stays inside the message column. Examples for both shapes are below.

Query in Application Insights

When JSON fields are parsed into customDimensions

traces
| where customDimensions.invocation_id == "abc-123-def"
| project timestamp, message, customDimensions.cold_start, customDimensions.function_name
| order by timestamp asc

Find all cold starts in the last hour:

traces
| where customDimensions.cold_start == "true"
| where timestamp > ago(1h)
| summarize count() by bin(timestamp, 5m)

When JSON remains in the message column

traces
| extend payload = parse_json(message)
| where tostring(payload.invocation_id) == "abc-123-def"
| project timestamp, tostring(payload.message), tostring(payload.cold_start), tostring(payload.function_name)
| order by timestamp asc

Find all cold starts in the last hour:

traces
| extend payload = parse_json(message)
| where tostring(payload.cold_start) == "true"
| where timestamp > ago(1h)
| summarize count() by bin(timestamp, 5m)

What this package does not do

This package does not own:

• Replacing stdlib logging — it wraps and enriches Python's standard logging, never replaces it
• Distributed tracing — use OpenTelemetry or Application Insights SDK for end-to-end trace correlation
• API documentation — use azure-functions-openapi for API documentation and spec generation

Installation

pip install azure-functions-logging

Quick Start

import azure.functions as func
from azure_functions_logging import get_logger, logging_context, setup_logging

setup_logging()
logger = get_logger(__name__)

app = func.FunctionApp()

@app.route(route="hello")
def hello(req: func.HttpRequest, context: func.Context) -> func.HttpResponse:
    with logging_context(context):  # binds invocation_id, function_name, cold_start; restores previous context on exit
        logger.info("Request received")
        # log record now carries invocation_id, function_name, cold_start

        return func.HttpResponse("OK")

logging_context is the recommended primary pattern: it injects context on enter and always restores the previous context on exit (even when the handler raises), which prevents stale context from leaking into the next invocation on a reused worker.

For lower-level control or when integrating with custom middleware, use token-based restore:

from azure_functions_logging import inject_context, restore_context

# Assumes `logger` and `context` are in scope (see Quick Start).
tokens = inject_context(context)
try:
    logger.info("Request received")
finally:
    restore_context(tokens)

Use reset_context() only when you intentionally want to clear all context (e.g. test teardown).

Start the Functions host locally (using the e2e example app):

func start --script-root examples/e2e_app

Verify locally and on Azure

After deploying (see docs/deployment.md), the same request produces the same response in both environments.

Local

curl -s http://localhost:7071/api/logme?correlation_id=demo-123

{"logged": true, "correlation_id": "demo-123"}

Azure

curl -s "https://<your-app>.azurewebsites.net/api/logme?correlation_id=demo-123"

{"logged": true, "correlation_id": "demo-123"}

Verified against a temporary Azure Functions deployment in koreacentral (Python 3.12, Consumption plan). Response captured and URL anonymized.

Invocation Context

Use logging_context() to bind invocation context for the duration of a handler. It sets:

• invocation_id — unique per execution, correlates all logs for one request
• function_name — the Azure Functions function name
• trace_id — trace context from the platform; extracted only from valid W3C traceparent headers (strict validation, invalid values are ignored)
• cold_start — True on first invocation of this worker process

cold_start semantics. cold_start=True means the first invocation observed by this Python worker process after module load. It is not a platform-level cold start metric and does not correspond to App Service plan / instance allocation cold starts reported by Azure Functions metrics. Subsequent invocations on the same worker emit cold_start=False until the worker is recycled.

def my_function(req, context):
    with logging_context(context):
        logger.info("handler started")
        # every log from here carries invocation_id and cold_start

For lower-level control (e.g. middleware), use inject_context() with restore_context():

tokens = inject_context(context)
try:
    logger.info("handler started")
finally:
    restore_context(tokens)

Without context injection, these fields are None in every log line.

with_context Decorator

For less boilerplate, use the with_context decorator instead of calling inject_context() manually:

import azure.functions as func
from azure_functions_logging import get_logger, setup_logging, with_context

setup_logging()
logger = get_logger(__name__)

app = func.FunctionApp()

@app.route(route="hello")
@with_context
def hello(req: func.HttpRequest, context: func.Context) -> func.HttpResponse:
    logger.info("Request received")
    return func.HttpResponse("OK")

The decorator finds the context parameter by name, calls inject_context() before your handler runs, and restores the previous context in finally after it returns.

Custom parameter name:

@with_context(param="ctx")
def hello(req: func.HttpRequest, ctx: func.Context) -> func.HttpResponse:
    ...

Both sync and async handlers are supported.

Global LogRecordFactory (opt-in)

For applications where handlers may be added after setup_logging(), or where you want invocation context on every LogRecord regardless of handler/filter configuration, install the global context factory once at startup:

from azure_functions_logging import install_context_factory, setup_logging

install_context_factory()  # injects context at record creation time
setup_logging()

When enabled, invocation_id, function_name, trace_id, and cold_start become reserved LogRecord attributes. Passing them via stdlib extra= will raise KeyError. Use FunctionLogger (which sanitizes keys automatically) or choose different key names.

Relationship with setup_logging(): setup_logging() still installs ContextFilter on handlers by default. You can call both — they set the same values, so there is no conflict. install_context_factory() ensures coverage even on handlers added later or loggers that bypass the filter chain.

Structured JSON Output (Production)

Use JSON format when logs feed Application Insights or any aggregation system:

Note: The format parameter only affects handlers created by this library (local development). In Azure Functions, the host manages handlers. Use functions_formatter=JsonFormatter() to set JSON output on host-managed handlers. Passing format="json" in Azure emits a warning.

For standalone local development or CI output:

setup_logging(format="json")

For Azure Functions / Core Tools, the host owns the handlers. To force JSON formatting on existing host-managed handlers:

from azure_functions_logging import JsonFormatter, setup_logging

setup_logging(functions_formatter=JsonFormatter())

Output per log line (NDJSON — one JSON object per line):

{"timestamp": "2024-01-15T10:30:00+00:00", "level": "INFO", "logger": "my_module",
 "message": "order accepted", "invocation_id": "abc-123", "function_name": "OrderHandler",
 "cold_start": false, "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736", "exception": null,
 "extra": {"order_id": "o-999"}}

Extra fields appear under extra in the emitted JSON. Whether they are directly indexable in Application Insights depends on your ingestion pipeline: when JSON is parsed into customDimensions they are queryable directly; when the JSON stays in the message column you need parse_json(message) first.

logger.info("order accepted", order_id="o-999", tenant_id="t-1")

host.json Conflict Detection

If your host.json suppresses log levels that your app emits, you get this warning at startup:

host.json logLevel for default is set to 'Warning' which is more restrictive than the configured level 'INFO'. Logs below 'Warning' will be suppressed by the Azure Functions host.

Recommended host.json baseline:

{
  "version": "2.0",
  "logging": {
    "logLevel": {
      "default": "Information",
      "Function": "Information"
    }
  }
}

Discovery order

host.json is located by walking up from the current working directory:

1. cwd/host.json
2. Each parent directory, up to 5 levels deep.

The first existing file wins. To bypass auto-discovery (e.g. in tests or non-standard layouts), pass an explicit path:

from pathlib import Path
from azure_functions_logging import setup_logging

setup_logging(host_json_path=Path("/site/wwwroot/host.json"))

Noise Control

Suppress chatty third-party loggers without removing them:

from azure_functions_logging import SamplingFilter, setup_logging
import logging

setup_logging()

# Sample noisy azure.* loggers: keep up to 10 records per 1-second window.
# Filters attached to a logger don't run for records propagated from
# descendants, so attach to the root handlers and scope by logger name.
for handler in logging.getLogger().handlers:
    handler.addFilter(SamplingFilter(rate=10, name="azure"))

# Silence urllib3 completely in production
logging.getLogger("urllib3").setLevel(logging.WARNING)

PII Redaction

Strip sensitive fields before they reach Application Insights:

from azure_functions_logging import RedactionFilter, setup_logging
import logging

setup_logging()
root = logging.getLogger()
# Attach the filter to handlers so records from named child loggers are also redacted.
for handler in root.handlers:
    handler.addFilter(RedactionFilter(sensitive_keys=["password", "token", "secret"]))

Any log record with extra fields whose keys match a sensitive key will have those values replaced with ***.

Local vs Cloud

Environment	Format	Behavior
Local terminal	color (default)	Colorized human-readable: HH:MM:SS LEVEL logger message [context...]
Azure / Core Tools	host-managed	Installs context filters only; pass functions_formatter=JsonFormatter() to force NDJSON on host handlers
CI / pipeline	json	NDJSON, machine-parseable

setup_logging() detects FUNCTIONS_WORKER_RUNTIME to distinguish Azure Functions / Core Tools from standalone local execution. In Azure mode it installs context filters without adding handlers (avoids duplicate output from the host pipeline).

Context Binding

Attach request-scoped metadata to every log without passing it through every call:

def process_order(order_id: str) -> None:
    order_logger = logger.bind(order_id=order_id, region="eastus")
    order_logger.info("processing started")   # includes order_id + region
    order_logger.info("processing complete")  # same metadata, new message

Create bound loggers per-invocation. Do not cache them at module level.

When to use

• You need structured, queryable logs in Application Insights
• You want invocation_id correlation across all logs for a single request
• You need cold start detection without custom instrumentation
• You want PII redaction or noise control for third-party loggers
• Your host.json config silently suppresses logs and you don't know why

Documentation

• Full docs: yeongseon.github.io/azure-functions-logging-python
• Configuration reference
• Troubleshooting guide
• API reference

Ecosystem

This package is part of the Azure Functions Python DX Toolkit.

Design principle: azure-functions-logging owns structured logging and invocation-aware observability. It enriches Python's standard logging — it does not replace it. Adjacent concerns belong to azure-functions-openapi (API documentation and spec generation), azure-functions-validation (request/response validation and serialization), and azure-functions-langgraph (LangGraph runtime exposure).

Package	Role
azure-functions-openapi-python	OpenAPI spec generation and Swagger UI
azure-functions-validation-python	Request/response validation and serialization
azure-functions-db-python	Database bindings for SQL, PostgreSQL, MySQL, SQLite, and Cosmos DB
azure-functions-langgraph-python	LangGraph deployment adapter for Azure Functions
azure-functions-scaffold-python	Project scaffolding CLI
azure-functions-logging-python	Structured logging and observability
azure-functions-doctor-python	Pre-deploy diagnostic CLI
azure-functions-durable-graph-python	Manifest-first graph runtime with Durable Functions (experimental)
azure-functions-knowledge-python	Knowledge retrieval (RAG) decorators
azure-functions-cookbook-python	Dogfood examples — runnable recipes that exercise the full toolkit

For AI Coding Assistants

This package provides structured logging for Azure Functions with zero modifications to stdlib logging.

LLM-Friendly Resources:

• llms.txt — Concise API reference and quick start (repo root)
• llms-full.txt — Complete API signatures, patterns, and design principles (repo root)

Key Implementation Details for Code Generation:

1. Preserves host configuration — In Azure / Core Tools, no handlers are added and the root logger level is left to host.json; ContextFilter is installed on existing root handlers and on the root logger itself (so direct calls on the root logger carry context). For records that propagate from named child loggers to handlers attached later (e.g. by the host or third-party libraries), call install_context_factory() to guarantee context coverage. In standalone local mode, setup_logging(logger_name=None) configures the root logger (sets level, adds a StreamHandler if none exist).
2. Context injection is contextvar-based — Not thread-local, works with asyncio
3. Idempotent setup — Calling setup_logging() multiple times is safe
4. Two environments, two behaviors:
   • Azure/Core Tools: install ContextFilter on existing root handlers and on the root logger itself; do not add handlers or change the root level (respects host.json).
   • Standalone local: set the target/root logger level; add a StreamHandler (ColorFormatter or JsonFormatter) only if no handlers exist, otherwise just attach filters to existing handlers.
5. Test-friendly:
   • inject_context() accepts any object (no hard dependency on azure.functions.Context)
   • with_context decorator works with sync and async handlers
   • Use reset_context() in test teardown if needed

When generating code:

• Import only from azure_functions_logging public API (no underscores)
• Call setup_logging() at module level or handler startup (not per-request)
• Prefer with logging_context(context): in handlers; use raw inject_context(context) only with try/finally restore_context(tokens)
• Use logger.bind(key=value) for per-request fields (not direct logger.extra)
• Use with_context decorator if you prefer to inject context implicitly per-handler
• Call get_logging_metadata(func) to inspect @with_context metadata on a function (returns dict[str, Any] | None)
• Apply RedactionFilter for PII fields, SamplingFilter for high-volume logs

Example Pattern:

from azure_functions_logging import get_logger, logging_context, setup_logging

# Module level
setup_logging()
logger = get_logger(__name__)

# Per handler
def my_function(req: func.HttpRequest, context: func.Context) -> func.HttpResponse:
    with logging_context(context):
        req_logger = logger.bind(correlation_id=req.params.get("id"))
        req_logger.info("Processing")
        return func.HttpResponse("OK")

This project is an independent community project and is not affiliated with, endorsed by, or maintained by Microsoft.

Azure and Azure Functions are trademarks of Microsoft Corporation.

License

MIT