ecsctx 0.4.1

Context-aware structured logging with ECS compliance and distributed tracing

ecsctx

Context-aware structured logging with ECS compliance and W3C Trace Context distributed tracing.

Framework-agnostic core with Django, Celery, and RQ integrations.

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Table of Contents

1. What is ECS & Why It Matters
2. The Observability Pipeline
3. Architecture: Request Flow
4. Core Rules (Field Placement Reference)
5. Quick Start (Django)
6. Quick Start (FastAPI)
7. Full Django Configuration
8. Context Binding — The Core Concept
9. Service Namespace Pattern
10. Celery Integration
11. RQ Integration
12. Distributed Tracing (W3C Trace Context)
13. PII Masking & Tokenization
14. ECS Reserved Fields — The #1 Source of Bugs
15. Good vs Bad Practices (Hall of Mistake)
16. Log Levels — Decision Tree
17. Dry Run: Verifying Your Setup
18. Vector Configuration
19. Environment Variables Reference
20. API Reference
21. Log Output Example
22. Package Structure

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1. What is ECS & Why It Matters

ECS (Elastic Common Schema) is a standard field naming convention for Elasticsearch. Instead of every team inventing their own field names (user_name vs username vs user.name), ECS defines a shared vocabulary: user.id, client.ip, trace.id, error.message, etc. ecsctx outputs ECS 1.12.0 compliant JSON.

Why you should care: Elasticsearch creates index mappings from the first document it sees. If one service sends error as a string and another sends error as an object ({"message": "..."}"), Elasticsearch gets a mapping conflict — it can't store both in the same index. Mapping conflicts silently drop fields. Your logs look fine locally but are missing data in Kibana.

Data streams organize logs using the naming pattern logs-{dataset}-{namespace} (e.g., logs-myproject-production). Elasticsearch automatically manages index lifecycle (rollover, retention, deletion) through data streams. The dataset comes from PROJECT_NAME and namespace from ENVIRONMENT — both set as environment variables in your deployment.

Reference: ECS Field Reference — bookmark this. You'll need it when adding custom structured fields.

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2. The Observability Pipeline

┌─────────────────────────────────────────────────────────────┐
│                    Your Application                          │
│                                                              │
│   structlog → ecsctx processors → ECS JSON → stdout         │
│   (context injection, PII masking, ECS validation)          │
└──────────────────────┬──────────────────────────────────────┘
                       │  stdout (JSON lines)
┌──────────────────────▼──────────────────────────────────────┐
│                      Docker                                  │
│   Container labels: collect_logs=true, project=X, env=Y     │
└──────────────────────┬──────────────────────────────────────┘
                       │  docker_logs source
┌──────────────────────▼──────────────────────────────────────┐
│                      Vector                                  │
│   1. Collect from labeled containers                        │
│   2. Parse JSON (or keep raw if unparseable)                │
│   3. Ship to Elasticsearch via data stream API              │
│      → logs-{PROJECT_NAME}-{ENVIRONMENT}                    │
│      → pipeline: common-logs                                │
└──────────────────────┬──────────────────────────────────────┘
                       │  HTTPS + gzip + API key auth
┌──────────────────────▼──────────────────────────────────────┐
│               Elasticsearch                                   │
│   https://your-elasticsearch-host/                           │
│                                                              │
│   Data stream: logs-myproject-production                    │
│   Ingest pipeline: common-logs (ECS type enforcement)       │
│   → Kibana dashboards, alerts, search                       │
└─────────────────────────────────────────────────────────────┘

Key takeaway: Your app writes JSON to stdout. Vector picks it up, ships it to Elasticsearch. The field structure of that JSON determines whether it's searchable in Kibana or silently dropped due to mapping conflicts. That's why ECS compliance matters.

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3. Architecture: Request Flow

1. nginx forwards/generates traceparent header (W3C Trace Context)
   → Forward from client if present, generate if missing
                      ↓
2. CidMiddleware reads traceparent, stores in contextvar
                      ↓
3. LoggingContextMiddleware binds span_id (UUID), client IP
                      ↓
4. Auth middleware authenticates user
                      ↓
5. LoggingContextMiddleware.process_view() re-binds with user_id for authenticated requests
                      ↓
6. Your middleware/views bind domain context (merchant_id, session_id, etc.)
                      ↓
7. View executes, calls logger.info("event_name", field=value)
                      ↓
8. Processor chain:
   contextvars_injector → namespace_ecs_fields → mask_sensitive_data → ecs_validator
                      ↓
9. ECS-formatted JSON → stdout → Vector → Elasticsearch

Processor Chain (Execution Order)

# In StructlogFormatter.foreign_pre_chain:
1. structlog.contextvars.merge_contextvars     # Merge structlog contextvars
2. structlog.processors.TimeStamper(fmt="iso") # ISO 8601 timestamps
3. structlog.stdlib.add_logger_name            # Logger name (module path)
4. structlog.stdlib.PositionalArgumentsFormatter()
5. structlog.processors.CallsiteParameterAdder # func_name, lineno, pathname
6. contextvars_injector                        # ← Injects LoggingContext + trace + service
7. namespace_ecs_fields                        # ← Reshape fields + clean up flat 'level' key
8. mask_sensitive_data                         # ← PII tokenization (HMAC-SHA-256)
9. ecs_validator                               # ← Warn on ECS field violations
10. ECSFormatter                               # ← Format to ECS 1.12.0 JSON

Injection Priority

Later sources don't override earlier ones:

1. Explicit log kwargs — logger.info("event", amount=100) — highest priority
2. LoggingContext — bound via middleware, views, tasks
3. structlog contextvars — structlog.contextvars.bind_contextvars()
4. CID trace_id — W3C traceparent parsed from header
5. Service metadata — auto-detected service.name, service.version, project.name

nginx Configuration

Configure nginx to forward the traceparent header from clients or generate one if not present:

map $http_traceparent $trace_id {
    ""      "00-$request_id-$connection-01";  # Generate if missing
    default $http_traceparent;                 # Forward if present
}

server {
    location / {
        proxy_set_header traceparent $trace_id;
        proxy_pass http://upstream;
    }
}

---

4. Core Rules (Field Placement Reference)

This is the single source of truth for where fields end up in the final log output. The namespace_ecs_fields processor enforces these rules.

Category	Keys	Placement	Notes
ECS field-sets	http, url, event, span, user, user_agent, client, trace, service, error, log	Root (nested objects)	Must be dicts, never flat strings
Custom namespaces	payment, project	Root (nested objects)	payment.orn, project.name
Sanctioned flat scalars	merchant_id, session_id, view	Root	Kept flat at root level
Labels	labels	Root (flat dict)	Values should be str/int/float/bool; non-scalars are coerced to strings
Payload containers	payload, headers	Root	Used in PII masking path
structlog internals	message, timestamp	Root	Set by structlog processors
ECS event staging	ecs_event	Root → renamed to event	Use ecs_event in log calls to avoid structlog's event message key conflict
Everything else	Any non-allowlisted key	extra.*	Auto-wrapped by namespace_ecs_fields

PII handling (see section 13 for full details):

• Automatic log masking: mask_sensitive_data processor applies HMAC-SHA-256 tokenization (ptok:v1:...) and key-based redaction
• Explicit encryption API: protect() encrypts (AES-256-GCM), reveal() decrypts, tokenize() produces deterministic HMAC tokens

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5. Quick Start (Django)

1. Install

pip install ecsctx                       # Core only (framework-agnostic, e.g., FastAPI)
pip install ecsctx[django]               # With Django support
pip install ecsctx[django,celery]        # With Django + Celery
pip install ecsctx[django,rq]            # With Django + RQ
pip install ecsctx[django,auditlog]      # With Django + auditlog integration

Requires Python >= 3.10.

2. Configure settings.py

from ecsctx.contrib.django import get_logging_config, setup_logging, CELERY_LOGGERS

# Logging — that's it!
LOGGING = get_logging_config(
    root_level="INFO",
    handler_level="DEBUG",
    use_cid_filter=True,
    loggers=CELERY_LOGGERS,
)
setup_logging()

# Middleware — ORDER MATTERS
MIDDLEWARE = [
    "corsheaders.middleware.CorsMiddleware",
    "cid.middleware.CidMiddleware",              # ← Early: reads traceparent
    # ... security, session, auth middleware ...
    "ecsctx.contrib.django.LoggingContextMiddleware",  # ← AFTER auth middleware
    # ... your app middleware (can bind_logging_context here too) ...
]

# django-cid for trace correlation
INSTALLED_APPS = [
    "cid.apps.CidAppConfig",
    # ... your apps
]
CID_GENERATE = True
CID_HEADER = "HTTP_TRACEPARENT"

# PII is auto-configured from PII_TOKEN_KEYSET_PATH env var

3. Use in your code

import structlog

logger = structlog.get_logger(__name__)

def my_view(request):
    logger.info("payment_processed", amount=100, currency="KWD")
    # Output includes: trace.id, span.id, user.id, client.ip, service.name, etc.

---

6. Quick Start (FastAPI)

For non-Django projects, use the core processors directly:

import structlog
from ecsctx import (
    ECSFormatter,
    ecs_validator,
    contextvars_injector,
    mask_sensitive_data,
    namespace_ecs_fields,
)

structlog.configure(
    processors=[
        structlog.contextvars.merge_contextvars,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        contextvars_injector,
        namespace_ecs_fields,
        mask_sensitive_data,
        ecs_validator,
        structlog.stdlib.ProcessorFormatter.wrap_for_formatter,
    ],
    logger_factory=structlog.stdlib.LoggerFactory(),
    cache_logger_on_first_use=True,
)

For FastAPI, you'll need to manage LoggingContext yourself (no middleware auto-injection):

from ecsctx import bind_logging_context, logging_context, LoggingContext
import uuid

# Option 1: FastAPI middleware
@app.middleware("http")
async def logging_middleware(request: Request, call_next):
    bind_logging_context(
        span_id=str(uuid.uuid4()),
        ip=request.client.host,
    )
    response = await call_next(request)
    return response

# Option 2: Dependency injection
async def inject_logging_context(request: Request):
    bind_logging_context(
        span_id=str(uuid.uuid4()),
        ip=request.client.host,
    )

@app.post("/payments", dependencies=[Depends(inject_logging_context)])
async def create_payment():
    logger.info("payment_created")

---

7. Full Django Configuration

get_logging_config()

Returns a complete Django LOGGING dict with structlog integration, ECS formatting, and all processors wired up.

from ecsctx.contrib.django import get_logging_config

LOGGING = get_logging_config(
    root_level="INFO",       # Root logger level (default: INFO)
    handler_level="DEBUG",   # Console handler level (default: DEBUG)
    use_cid_filter=True,     # Add CID correlation filter (default: True)
    loggers=None,            # Additional loggers to merge (dict)
)

Logger Presets

from ecsctx.contrib.django import (
    RQ_LOGGERS,           # RQ at WARNING level
    RQ_LOGGERS_DEBUG,     # RQ at INFO level (development)
    CELERY_LOGGERS,       # Celery at WARNING level
    CELERY_LOGGERS_DEBUG, # Celery at INFO level (development)
)

# Production with Celery
LOGGING = get_logging_config(loggers=CELERY_LOGGERS)

# Development with RQ (verbose)
LOGGING = get_logging_config(loggers=RQ_LOGGERS_DEBUG)

# Multiple presets + custom loggers
LOGGING = get_logging_config(loggers={
    **CELERY_LOGGERS,
    "myapp": {"level": "DEBUG", "propagate": True},
})

Middleware Ordering

This is critical. Get the order wrong and you'll have missing context in logs.

MIDDLEWARE = [
    # 1. CidMiddleware — EARLY (reads W3C traceparent header)
    "cid.middleware.CidMiddleware",

    # 2. Auth middleware — BEFORE LoggingContextMiddleware
    "django.contrib.auth.middleware.AuthenticationMiddleware",

    # 3. LoggingContextMiddleware — AFTER auth (needs request.user)
    "ecsctx.contrib.django.LoggingContextMiddleware",

    # 4. Your app middleware — CAN use bind_logging_context() here
    "utils.middleware.TenantMiddleware",  # e.g., bind merchant_id
]

Why this order?

• CidMiddleware must run first to extract trace.id from the traceparent header
• Auth middleware must run before LoggingContextMiddleware because process_view() reads request.user.is_authenticated to bind user_id
• Your app middleware runs after and can add domain context (merchant_id, tenant info)

Note: The middleware binds user_id (integer) only. To log full User details (username, email), pass the User object directly in log kwargs: logger.info("event", user=request.user) — the Django-aware processor will serialize it to ECS format.

@api_logging Decorator

For Public DRF/Django views, automatically logs inbound requests and outbound responses:

from ecsctx.contrib.django.decorators import api_logging

@api_logging
class PaymentViewSet(ViewSet):
    # Logs: INBOUND POST /api/v1/payments/ (with headers, body, client IP)
    # Logs: OUTBOUND POST /api/v1/payments/ (201) (with response body, headers)

    logging_ignore_response_keys = ["sensitive_field"]  # Exclude from response logs

---

8. Context Binding — The Core Concept

Context binding is the mechanism that attaches structured metadata to every log statement within a request's journey. It can happen at any layer — middleware, views, serializers, tasks, utility functions — wherever important debug information becomes available.

The key insight: you bind_logging_context() once, and every subsequent log.* call in that request automatically includes those fields. No need to pass them around or repeat them.

Where Context Gets Bound (Real Examples)

# Layer 1: Middleware — merchant identified from request host/headers
# (e.g., TenantMiddleware identifies which merchant this request belongs to)
class TenantMiddleware:
    def process_request(self, request):
        merchant = get_merchant_from_request(request)
        bind_logging_context(extra={"merchant_id": merchant.name})
        # Every log from here onwards has merchant_id

# Layer 2: View — domain-specific IDs from the request payload
class WebhookView(APIView):
    def post(self, request):
        bind_logging_context(
            session_id=request.data.get("session_id"),
            extra={
                settings.APP_NAME: {
                    "enterprise_id": request.data["enterprise_id"],
                    "store_id": request.data["store_id"],
                }
            }
        )
        log.info("webhook_received")  # Has: merchant_id + session_id + app-specific IDs

# Layer 3: Task — additional info discovered during processing
@app.task
def process_webhook(self, enterprise_id, store_id):
    # Context from view is auto-propagated (Celery hooks)
    merchant = Merchant.objects.filter(...).first()
    bind_logging_context(extra={"merchant_id": merchant.name})  # NEW info
    log.info("task_started")  # Has everything from view + merchant_id

Two Binding Mechanisms

from ecsctx import bind_logging_context, logging_context

# 1. Direct bind (most common) — middleware handles cleanup at request end
bind_logging_context(session_id="abc123", extra={"merchant_id": "acme"})

# 2. Context manager — auto-restores previous context on exit (scoped)
with logging_context(session_id="abc123"):
    log.info("scoped_event")   # has session_id
log.info("outer_event")        # session_id gone

The extra Parameter

extra={} contents from LoggingContext get merged to root before the processor chain runs. The namespace_ecs_fields processor then reshapes the event: allowlisted keys stay at root, while all non-allowlisted keys (scalars, lists, and dicts) are wrapped into an extra object in the final output.

See the Core Rules table for the complete allowlist.

bind_logging_context(extra={"merchant_id": "acme"})
# "merchant_id" stays at root (allowlisted flat ID)

Deep Merge Behavior

Successive calls merge into existing context, not replace:

bind_logging_context(extra={"merchant_id": "acme"})
bind_logging_context(extra={"myapp": {"store_id": "s1"}})
# Context now has both: merchant_id stays at root (allowlisted), myapp goes to extra.myapp

Three Iron Rules

1. bind_logging_context() BEFORE the first log.* call. Always. If you log before binding, that log line won't have context.
2. Event name is a static string ("payment_created"), never an f-string. Static names are searchable and aggregatable in Kibana.
3. Dynamic data goes in kwargs or context, never in the message string.

# WRONG — first log has no context
log.info("webhook_received")
bind_logging_context(session_id=session_id)

# CORRECT — bind first, then log
bind_logging_context(session_id=session_id)
log.info("webhook_received")

Don't Re-state Context in Log Calls

If a field is already bound, don't pass it again:

bind_logging_context(extra={"merchant_id": "acme"})

# WRONG — merchant_id already in context, this is redundant noise
log.info("payment_created", merchant_id="acme")

# CORRECT — it's already there
log.info("payment_created")

---

9. Service Namespace Pattern

Each service (keyloop, amadeus, shopify, opera) has its own domain-specific IDs (store_id, enterprise_id, shop, reference). To avoid cross-service field collisions in Elasticsearch, namespace service-specific fields under the app name.

The Pattern

# Use a settings constant as the namespace key
bind_logging_context(extra={
    settings.KEYLOOP_APP_NAME: {
        "enterprise_id": enterprise_id,
        "store_id": store_id,
        "payment_id": payment_id,
    }
})

What Goes Where

See the Core Rules table for the complete root allowlist. Service-specific fields should be namespaced under the app name to avoid ES mapping conflicts:

Location	Fields	Why
Service namespace	enterprise_id, store_id (keyloop), shop, reference (shopify)	Avoids ES mapping conflicts between services

In Log Kwargs (Dynamic Key)

# Use ** unpacking when the namespace key is a variable
log.info("event_started", **{
    settings.SHOPIFY_APP_NAME: {
        "shop": shop_domain,
        "reference": reference,
    }
})

---

10. Celery Integration

Signal-based context propagation — no decorators needed on individual tasks.

Setup (Two Lines)

# In your celery app config or a utils/celery.py module
from ecsctx.contrib.celery import install_celery_hooks

install_celery_hooks()

How It Works

install_celery_hooks() registers three Celery signals:

Signal	When	What
before_task_publish	View calls task.apply_async()	Snapshots current LoggingContext into task headers
task_prerun	Worker picks up task	Restores context + generates new span_id + adds celery_task metadata
task_postrun	Task finishes	Resets context (prevents leakage to next task)

Key insight: trace.id is preserved across the entire chain (same distributed trace). span_id is unique per task execution (different process boundary).

View-Dispatched Tasks: Context is FREE

When a view calls task.apply_async(), the view's context is automatically propagated. Don't re-bind fields the view already bound.

@app.task(bind=True, max_retries=3)
def process_webhook(self, enterprise_id, store_id):
    # ✅ Context from view (session_id, app namespace) is already here
    # DON'T re-bind fields the view already set

    merchant = Merchant.objects.filter(...).first()
    if not merchant:
        log.info("merchant_not_found")  # App namespace IDs come from context
        self.retry(countdown=30)

    # ✅ Bind merchant_id AFTER lookup — this is NEW info the view didn't have
    bind_logging_context(extra={"merchant_id": merchant.name})
    log.info("task_started")

Beat-Dispatched Tasks: Start from ZERO

Celery Beat has no LoggingContext to propagate. You MUST bind everything at line 1.

@app.task(bind=True, max_retries=3)
def process_payment_inquiry(self, merchant_id, session_id):
    # ✅ Beat task — MUST bind everything, nothing is propagated
    bind_logging_context(session_id=session_id, extra={"merchant_id": merchant_id})
    log.info("inquiry_started")

Quick Reference

Trigger	Context status	Action
task.apply_async() from view/task	Auto-propagated	Only bind NEW fields
Celery Beat schedule	Empty	Bind ALL fields at line 1
self.retry()	Preserved across retries	No re-binding needed

---

11. RQ Integration

Decorator-based context propagation for RQ background jobs.

Setup

from ecsctx.contrib.rq import with_log_context

@with_log_context
def my_background_task(user_id, amount):
    logger.info("processing_payment")  # Automatically has request context

Manual Context Capture (Custom Enqueue)

If you have a custom job enqueue wrapper:

from ecsctx.contrib.rq import capture_log_context, LOG_CONTEXT_KEY

class RQHandler:
    @classmethod
    def enqueue(cls, func, **kwargs):
        # Capture logging context before enqueuing
        log_context_data = capture_log_context()
        if log_context_data:
            kwargs[LOG_CONTEXT_KEY] = log_context_data

        queue = django_rq.get_queue("default")
        return queue.enqueue(func, **kwargs)

Context Propagation Details

• Captures: LoggingContext + trace_id
• Restores: LoggingContext + new span_id + rq_job.id in extra
• Passed via: kwargs[LOG_CONTEXT_KEY]

---

12. Distributed Tracing (W3C Trace Context)

ecsctx implements W3C Trace Context for correlating logs across service boundaries.

Traceparent Format

{version}-{trace-id}-{parent-id}-{flags}
Example: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01

trace-id:  32 hex chars (links all logs in a distributed trace)
parent-id: 16 hex chars (identifies the calling span)

Inbound: Reading Trace Context

Handled automatically by CidMiddleware + LoggingContextMiddleware:

# settings.py
CID_GENERATE = True
CID_HEADER = "HTTP_TRACEPARENT"

Outbound: Propagating Trace Context

When making HTTP calls to other services, propagate the traceparent:

from ecsctx import build_traceparent

def call_external_api(url, payload):
    headers = {}
    traceparent = build_traceparent()
    if traceparent:
        headers["traceparent"] = traceparent

    response = requests.post(url, json=payload, headers=headers)
    return response

This ensures the receiving service can correlate its logs with yours under the same trace.id.

---

13. PII Masking & Tokenization

ecsctx automatically detects and protects sensitive data in logs. The mask_sensitive_data processor uses regex-based scrubbing on JSON-serialized payloads to find and tokenize PII.

Log processor path (automatic via mask_sensitive_data):

• When PII is configured (PII_PROVIDER=file|vault): detected values become deterministic HMAC-SHA-256 tokens (ptok:v1:...) for fraud correlation. Same input always produces the same token.
• When PII is not configured: detected values are replaced with [PII_REDACTED] — raw PII never appears in logs.

Explicit encryption API (standalone, NOT part of the log processor pipeline):

• protect() / reveal() use AES-256-GCM for randomized ciphertext (penc:v1:<kid>:...) when reversible encryption is needed. Requires PII_ACCESS=full.

Keys are delivered via mounted keyset files or fetched from Vault.

What Gets Detected

Type	Detection	Output
Emails	Regex: [e-mail] patterns	"ptok:v1:KeND..."
Phone numbers	Regex: 10-15 digits with +/spaces/dashes	"ptok:v1:x8Fp..."
Names	Keys containing: name, customer, payer, billing, shipping, cardholder, email, phone, mobile, contact, recipient, beneficiary, address, udf	"ptok:v1:..."
Auth headers	authorization, api-key, x-api-key keys	"Bearer ****<last4>" (masked, not tokenized)

Whitelist (NOT Masked)

These keys are safe even though they contain "name":

gateway_name, vendor_name, module_name, func_name, task_name, service_name,
app_name, project_name, class_name, method_name, view_name, username,
site_name, domain_name, bank_name, display_name, install_name,
installation_name, event_name, customer_id, id, pk

Configuration

PII supports two keyset providers: file (for Kubernetes with mounted secrets) and vault (for hosts that authenticate directly via AppRole).

All services auto-configure lazily from env vars on first PII operation. No explicit startup call is needed.

Common env vars (all providers):

PII_PROVIDER=file          # "file" or "vault"
PII_ACCESS=tokenize        # "tokenize" (HMAC only) or "full" (HMAC + AES encrypt/decrypt)
PII_ENV=prod               # Environment name for domain separation (tokens differ across envs)

File provider — keysets are mounted by infrastructure (Vault → ESO → K8s Secret):

PII_PROVIDER=file
PII_TOKEN_KEYSET_PATH=/var/run/pii/token-keyset.json
PII_REVEAL_KEYSET_PATH=/var/run/pii/reveal-keyset.json   # only if PII_ACCESS=full

Vault provider — authenticates via AppRole and fetches keysets from KV v2:

PII_PROVIDER=vault
PII_VAULT_ADDR=https://vault.example.com
PII_VAULT_ROLE_ID_PATH=/etc/pii/vault-role-id
PII_VAULT_SECRET_ID_PATH=/etc/pii/vault-secret-id
PII_VAULT_TOKEN_KEYSET_PATH=secret/data/platform/pii/token-keyset
PII_VAULT_REVEAL_KEYSET_PATH=secret/data/platform/pii/reveal-keyset  # only if PII_ACCESS=full
PII_VAULT_CACERT_PATH=/etc/pii/vault-ca.crt   # optional, for private CA
PII_REFRESH_SECONDS=300                              # keyset refresh interval
PII_VAULT_TIMEOUT=10                                 # HTTP timeout for Vault calls

PII_ACCESS=tokenize enforces least privilege: only the token keyset is loaded, and protect()/reveal() raise PIIAccessDeniedError.

How It Works

1. Log event dict is serialized to JSON string
2. Sensitive keys are found and their values tokenized (HMAC-SHA-256)
3. String content is scanned for email/phone patterns and tokenized
4. Auth header values are masked (truncated, not encrypted)
5. JSON is parsed back to dict
6. Input is normalized before tokenization (emails lowercased, phones to E.164)

Example Output

{
  "customer_name": "ptok:v1:KeNDkDCY0cXCg3VJU4xf...",
  "email": "ptok:v1:x8FpQm2kL9nR7vBwYzA3...",
  "amount": 100,
  "gateway_name": "knet"
}

amount is untouched (not a sensitive key). gateway_name is whitelisted. customer_name and email are tokenized.

---

14. ECS Reserved Fields — The #1 Source of Bugs

ECS reserves certain field names as objects with specific sub-fields. Passing them as flat strings/ints causes Elasticsearch mapping conflicts — fields get silently dropped.

The Rules

Field	Correct	Wrong	Why
error	error={"message": str(e)}	error=str(e)	ECS expects error.message, error.type
url	url={"full": url}	url=url	ECS expects url.full, url.domain
http	http={"request": {"method": "POST"}, "response": {"status_code": 200}}	method="POST"	ECS expects nested http.request.*
user	user={"name": "john"}	user="john"	ECS expects user.name, user.id
host	host={"name": "web-1"}	host="web-1"	ECS expects host.name, host.ip
event	ecs_event={"action": "login"}	event="login"	structlog uses event as message key; use ecs_event staging (renamed to event in output)
source	source={"ip": "1.2.3.4"}	source="1.2.3.4"	ECS expects source.ip, source.address
server	server={"address": "api.example.com"}	server="api.example.com"	ECS expects server.address

Full List of ECS Reserved Fields

These must always be dicts, never flat values:

client, user, host, span, trace, source, destination, server,
event, error, log, http, url, service, file, process, network,
observer, organization, cloud, container, agent, ecs, rule, threat

Reference: ECS Field Reference

Common Trap: The error Field

This is the most frequently broken field. Every except block tempts you:

# WRONG — will cause ES mapping conflict
except Exception as e:
    log.error("something_failed", error=str(e))

# CORRECT — ECS-compliant dict
except Exception as e:
    log.error("something_failed", error={"message": str(e)})

# EVEN BETTER — include exception type
except requests.HTTPError as e:
    log.error("api_call_failed", error={
        "message": str(e),
        "type": type(e).__name__,
    })

Custom Fields and the Root Allowlist

Only ECS reserved names need the dict treatment. The namespace_ecs_fields processor enforces a root allowlist — all non-allowlisted keys (scalars, lists, and dicts) get automatically wrapped into an extra object. See the Core Rules table for the complete allowlist.

# "merchant_id" stays at root (allowlisted)
log.info("payment_started", merchant_id="acme")

# "disclosure_pk" is not allowlisted — goes into extra.disclosure_pk
log.info("disclosure_created", disclosure_pk=42)

Elasticsearch Indexing: labels vs extra

• labels.*: Use for intentionally filterable, low-cardinality keywords (e.g., labels.env, labels.region). Elasticsearch indexes these as keyword by default under the ECS labels field.
• extra.*: Non-filterable detail data. If your Elasticsearch index should not index extra children, map it as flattened or enabled: false in your index template.

# Good: filterable metadata in labels
bind_logging_context(labels={"env": "prod", "region": "us-east-1"})

# Good: non-filterable details as bare kwargs (auto-wrapped into extra)
log.info("payment_processed", amount=100, currency="KWD")
# Output: {..., "extra": {"amount": 100, "currency": "KWD"}}

The ecs_validator processor will warn (not block) if ECS reserved fields are used as flat values. Watch your console during development.

---

15. Good vs Bad Practices (Hall of Mistake)

Common mistakes and how to avoid them.

Mistake #1: Using stdlib logging Instead of structlog

# ❌ WRONG — stdlib logger, no structlog processors, no ECS compliance
import logging
log = logging.getLogger(__name__)

# ✅ CORRECT
import structlog
log = structlog.get_logger(__name__)

stdlib logs bypass the entire structlog processor chain (context injection, ECS formatting, PII masking). They still get captured by ProcessorFormatter.foreign_pre_chain, but lose all LoggingContext data.

---

Mistake #2: f-string Log Messages

# ❌ WRONG — dynamic data in message, unsearchable, unaggregatable
log.info(f"Payment processed for merchant {merchant} amount {amount}")

# ❌ ALSO WRONG — printf-style formatting
log.error("OAuth token exchange failed: shop=%s response=%r", shop, response)

# ✅ CORRECT — static event name + structured kwargs
log.info("payment_processed", merchant=merchant, amount=amount)

Why it matters: In Kibana, you search by message: "payment_processed". With f-strings, every log line has a different message — you can't aggregate, alert, or build dashboards.

---

Mistake #3: error=str(e) — The ECS Violation

# ❌ WRONG — flat string breaks ECS error field mapping
log.exception("invalid_data", error=str(error))

# ✅ CORRECT — ECS-compliant dict
log.exception("invalid_data", error={"message": str(error)})

---

Mistake #4: log.exception(e) — Exception as Message

# ❌ WRONG — exception object as first arg, not a structured event name
except Exception as e:
    log.exception(e)

# ✅ CORRECT — static event name, structlog auto-captures exception info
except Exception as e:
    log.exception("payment_processing_failed")

---

Mistake #5: Logging Before Binding Context

# ❌ WRONG — first log has no merchant_id or payment context
def post(self, request, merchant_id, client_payment_id):
    log.info("acknowledgement_received",
        merchant_id=merchant_id,
        client_payment_id=client_payment_id,
    )
    bind_logging_context(...)  # too late for the log above

# ✅ CORRECT — bind first, then log
def post(self, request, merchant_id, client_payment_id):
    bind_logging_context(extra={
        "merchant_id": merchant_id,
        settings.APP_NAME: {"client_payment_id": client_payment_id},
    })
    log.info("acknowledgement_received")

---

Mistake #6: Redundant kwargs Duplicating Context

# ❌ WRONG — session_id already in context, passed again as kwarg
bind_logging_context(session_id=session_id)
log.info("notification_received", session_id=session_id)  # redundant!

# ✅ CORRECT — it's already in context
bind_logging_context(session_id=session_id)
log.info("notification_received")

---

Mistake #7: Service-Specific IDs at Root Instead of Namespaced

When multiple services share the same Elasticsearch index, putting service-specific fields at root level causes naming collisions. For example, two services might both use store_id but mean completely different things.

# ❌ WRONG — flat root fields collide across services in the same ES index
bind_logging_context(extra={
    "store_id": store_id,
    "enterprise_id": enterprise_id,
    "external_ref": external_ref,
})

# ✅ CORRECT — namespace under your app/service name
APP_NAME = "my_service"  # or settings.MY_APP_NAME

bind_logging_context(extra={
    APP_NAME: {
        "store_id": store_id,
        "enterprise_id": enterprise_id,
        "external_ref": external_ref,
    }
})
# Output: {"my_service": {"store_id": "s1", "enterprise_id": "e1", ...}}

See the Core Rules table for the complete field placement reference.

---

Mistake #8: log.error for Customer Config Issues

# ❌ WRONG — Sentry alert for missing pg_codes (customer config problem)
log.error("pg_codes_not_found")

# ✅ CORRECT — not our fault, not worth waking someone up
log.info("pg_codes_not_found")

---

Mistake #9: Re-binding Context That Was Auto-Propagated

# ❌ WRONG — view already bound these fields, Celery hooks propagated them
@app.task
def process_webhook(self, enterprise_id, store_id):
    bind_logging_context(extra={
        settings.APP_NAME: {
            "enterprise_id": enterprise_id,  # already in context!
            "store_id": store_id,            # already in context!
        }
    })

# ✅ CORRECT — only bind NEW info the view didn't have
@app.task
def process_webhook(self, enterprise_id, store_id):
    merchant = Merchant.objects.filter(...).first()
    bind_logging_context(extra={"merchant_id": merchant.name})  # NEW info

---

16. Log Levels — Decision Tree

This isn't just style — it directly affects Sentry alert volume and on-call fatigue.

Is this a system failure that needs human attention?
├── YES → log.error (triggers Sentry alert)
└── NO
    ├── Is this a customer config problem? → log.info
    ├── Will the task retry? → log.info (alert after retries exhausted)
    ├── Is this expected? (auth fail, 404) → log.info
    └── Debug/development info? → log.debug

The golden rule: log.error = "Wake someone up." If it's not worth waking someone up, it's not log.error.

Situation	Level	Reasoning
System/infra failure (DB down, API 500)	log.error	Needs Sentry alert + on-call
Business logic failure (max retries exceeded)	log.error	System failed its job
Customer config error (merchant not found)	log.info	Not our fault
Retry-able failure (temporary network blip)	log.info	Task will retry
Auth failure (invalid token, bad HMAC)	log.info	Expected, handled
Normal operations (webhook received)	log.info	Operational visibility
Verbose debugging (raw payloads)	log.debug	Filtered in production

---

17. Dry Run: Verifying Your Setup

Before deploying, verify the full pipeline locally.

Step 1: Check JSON Output Locally

Run your Django app and make a request. Check stdout for valid ECS JSON:

# Run the dev server
python manage.py runserver

# In another terminal, hit an endpoint
curl -H "traceparent: 00-abcdef1234567890abcdef1234567890-1234567890abcdef-01" \
     http://localhost:8000/api/v1/health/

You should see JSON on stdout like:

{
  "@timestamp": "2025-01-13T10:30:00.000Z",
  "ecs.version": "1.12.0",
  "message": "health_check",
  "log.level": "info",
  "log.logger": "core.views",
  "trace": {"id": "abcdef1234567890abcdef1234567890"},
  "span": {"id": "some-uuid-here"},
  "service": {"name": "app", "version": "1.0.0"},
  "project": {"name": "my-project"}
}

Step 2: Verify ECS Field Structure

Check these fields in your JSON output:

Check	Expected	If Wrong
trace.id present?	32-char hex string	Check CID_HEADER = "HTTP_TRACEPARENT" and CID_GENERATE = True
span.id present?	UUID string	Check LoggingContextMiddleware is in MIDDLEWARE
user.id present? (authenticated requests)	Integer or string	Check middleware is AFTER auth middleware
client.ip present?	IP address string	Check django-ipware is installed
service.name present?	"app", "rq", or "celery"	Check SERVICE_TYPE env var or auto-detection
ecs.version = "1.12.0"?	Exactly "1.12.0"	Check ECSFormatter is in processor chain
No flat error, user, client strings?	Always dicts	Read ECS Reserved Fields

Step 3: Verify PII Masking

# In a Django shell or view
import structlog
log = structlog.get_logger(__name__)

log.info("test_pii", customer_name="John Doe", email="[e-mail]", amount=100)

Expected stdout:

{
  "message": "test_pii",
  "customer_name": "ptok:v1:...",
  "email": "ptok:v1:...",
  "amount": 100
}

If customer_name shows "John Doe" in plain text, check that mask_sensitive_data is in the processor chain.

Step 4: Verify Context Propagation (Celery/RQ)

# In a view, dispatch a task and check worker stdout
log.info("dispatching_task")
my_task.apply_async(args=[...])

# In the Celery worker output, the task log should have:
# - Same trace.id as the view
# - Different span.id (new span for the task)
# - celery_task.id and celery_task.name in the output

Step 5: Verify Vector Pipeline (Docker)

# Start your stack with Vector
docker compose -f docker-compose.yml -f docker-compose-vector.yml up

# Check Vector is collecting logs
docker compose logs vector

# Uncomment the console sink in vector.toml for debugging:
# [sinks.console]
# type = "console"
# inputs = ["parse_container_logs"]
# encoding.codec = "json"

Step 6: Verify in Kibana

1. Go to Kibana → Discover
2. Select the data stream: logs-{PROJECT_NAME}-{ENVIRONMENT}
3. Search: message: "test_pii"
4. Verify fields are nested correctly (trace.id, not flat trace_id)
5. Verify PII is tokenized (ptok:v1:..., not plain text)

---

18. Vector Configuration

vector.toml Template

# Collect logs from labeled Docker containers
[sources.docker_logs]
type = "docker_logs"
include_labels = ["collect_logs=true"]
exclude_containers = ["vector", "nginx", "certbot", "redis", "postgres", "db"]
auto_partial_merge = true

# Parse JSON output from structlog/ecsctx
[transforms.parse_container_logs]
type = "remap"
inputs = ["docker_logs"]
source = '''
parsed, err = parse_json(.message)
if err == null {
    . = parsed
} else {
    .raw_message = .message
    .parse_error = err
}
'''

# Ship to Elasticsearch
[sinks.elasticsearch]
type = "elasticsearch"
inputs = ["parse_container_logs"]
endpoints = ["${ES_URL:-https://your-elasticsearch-host/}"]
api_version = "v8"
mode = "data_stream"
compression = "gzip"
pipeline = "common-logs"

[sinks.elasticsearch.data_stream]
type = "logs"
dataset = "${PROJECT_NAME}"
namespace = "${ENVIRONMENT}"

[sinks.elasticsearch.request.headers]
Authorization = "ApiKey ${ES_API_KEY}"

[sinks.elasticsearch.tls]
verify_certificate = true

[sinks.elasticsearch.buffer]
type = "memory"
max_events = 4096

[sinks.elasticsearch.batch]
max_events = 2048
timeout_secs = 1

[sinks.elasticsearch.request]
retry_attempts = 5
retry_initial_backoff_secs = 1
retry_max_duration_secs = 300

# Uncomment for local debugging
# [sinks.console]
# type = "console"
# inputs = ["parse_container_logs"]
# encoding.codec = "json"

Docker Compose Labels

Add these labels to every container that should have its logs collected:

services:
  web:
    labels:
      collect_logs: "true"
      project: "${PROJECT_NAME}"
      service_type: "api"
      env: "${ENVIRONMENT:-dev}"

  celery_worker:
    labels:
      collect_logs: "true"
      project: "${PROJECT_NAME}"
      service_type: "celery"
      env: "${ENVIRONMENT:-dev}"

  rq_worker:
    labels:
      collect_logs: "true"
      project: "${PROJECT_NAME}"
      service_type: "rq"
      env: "${ENVIRONMENT:-dev}"

docker-compose-vector.yml

services:
  vector:
    image: timberio/vector:0.43.1-debian
    volumes:
      - ./vector.toml:/etc/vector/vector.toml:ro
      - /var/run/docker.sock:/var/run/docker.sock:ro
    environment:
      - ES_API_KEY=${ES_API_KEY}
      - ES_URL=${ES_URL:-https://your-elasticsearch-host/}
      - ENVIRONMENT=${ENVIRONMENT:-dev}
      - PROJECT_NAME=${PROJECT_NAME}
    restart: unless-stopped

Data Stream Naming

Your logs land in Elasticsearch under:

logs-{PROJECT_NAME}-{ENVIRONMENT}

Examples:

• logs-keyloop-production
• logs-event-backend-staging
• logs-checkout-dev

If you use a common-logs ingest pipeline, it can enforce ECS field types so malformed fields (e.g., flat error string) get flagged at ingest time.

---

19. Environment Variables Reference

Variable	Description	Default	Required
PII_PROVIDER	Keyset provider: file or vault	—	Yes (production)
PII_ACCESS	Access mode: tokenize (HMAC only) or full (HMAC + AES)	"tokenize"	Recommended
PII_ENV	Environment name for token domain separation	"unknown"	Recommended
PII_TOKEN_KEYSET_PATH	Path to HMAC token keyset file (file provider)	—	Yes for file
PII_REVEAL_KEYSET_PATH	Path to AES-GCM reveal keyset file (file provider)	—	Only if PII_ACCESS=full
PII_VAULT_ADDR	Vault server URL (vault provider)	—	Yes for vault
PII_VAULT_ROLE_ID_PATH	File containing AppRole role_id (vault provider)	—	Yes for vault
PII_VAULT_SECRET_ID_PATH	File containing AppRole secret_id (vault provider)	—	Yes for vault
PII_VAULT_TOKEN_KEYSET_PATH	Vault KV path for token keyset (vault provider)	—	Yes for vault
PII_VAULT_REVEAL_KEYSET_PATH	Vault KV path for reveal keyset (vault provider)	—	Only if PII_ACCESS=full
PII_VAULT_CACERT_PATH	CA cert for Vault TLS (vault provider)	System CA	No
PII_REFRESH_SECONDS	Keyset refresh interval in seconds (vault provider)	300	No
PII_VAULT_TIMEOUT	HTTP timeout for Vault requests in seconds	10	No
APP_VERSION	Application version in service.version	"0.0.0"	No
SERVICE_TYPE	Service type: app, rq, celery	Auto-detected from argv	No
PROJECT_NAME	Project name in project.name + Vector data stream	"connect"	Yes
ENVIRONMENT	Environment name for Vector data stream namespace	-	Yes
ES_URL	Elasticsearch endpoint	https://your-elasticsearch-host/	Yes (production)
ES_API_KEY	Elasticsearch API key for Vector auth	-	Yes (production)

.env Example

PII_PROVIDER=file
PII_ACCESS=tokenize
PII_TOKEN_KEYSET_PATH=/var/run/pii/token-keyset.json
PII_ENV=prod
APP_VERSION=1.2.3
PROJECT_NAME=keyloop
ENVIRONMENT=production
ES_URL=https://your-elasticsearch-host/
ES_API_KEY=your-api-key-here

---

20. API Reference

Core (ecsctx)

from ecsctx import (
    # Context management
    LoggingContext,          # Dataclass holding logging context
    get_logging_context,    # Get current context from contextvar
    bind_logging_context,   # Bind context (non-scoped)
    reset_logging_context,  # Reset to previous token state
    logging_context,        # Context manager for scoped binding

    # Distributed tracing
    get_trace_id,           # Extract trace_id from W3C traceparent
    build_traceparent,      # Build W3C traceparent for outbound requests

    # Formatters
    ECSFormatter,           # ECS 1.12.0 formatter

    # Processors
    contextvars_injector,   # Injects context into log events
    mask_sensitive_data,    # PII tokenization (HMAC-SHA-256)
    namespace_ecs_fields,   # Reshape fields + clean up flat ECS fields
    ecs_validator,          # Warn on ECS field violations

    # PII
    configure_pii,          # Configure PII keyset provider
    pii_configured,         # Check if PII is configured
    tokenize,               # HMAC-SHA-256 deterministic token
    protect,                # AES-256-GCM reversible encryption
    reveal,                 # Decrypt penc:vN:... values
)

Django (ecsctx.contrib.django)

from ecsctx.contrib.django import (
    # Middleware
    LoggingContextMiddleware,

    # Logging setup
    get_logging_config,     # Returns complete Django LOGGING dict
    setup_logging,          # Configures structlog + captures warnings
    configure_structlog,    # Configures structlog processor chain

    # Logger presets
    RQ_LOGGERS,             # RQ at WARNING
    RQ_LOGGERS_DEBUG,       # RQ at INFO
    CELERY_LOGGERS,         # Celery at WARNING
    CELERY_LOGGERS_DEBUG,   # Celery at INFO

    # Processors
    contextvars_injector,   # Django-aware version (serializes User objects passed in log kwargs)
)

# Decorators
from ecsctx.contrib.django.decorators import api_logging

# Auditlog (import explicitly to avoid circular imports)
from ecsctx.contrib.django.context_binder import LogContextBinder

Celery (ecsctx.contrib.celery)

from ecsctx.contrib.celery import install_celery_hooks

RQ (ecsctx.contrib.rq)

from ecsctx.contrib.rq import (
    with_log_context,       # Decorator for RQ job functions
    capture_log_context,    # Capture context for manual enqueue
    LOG_CONTEXT_KEY,        # Key used in kwargs for context data
)

LoggingContext Fields

@dataclass
class LoggingContext:
    span_id: str | None          # → span.id (UUID per request/task)
    user_id: int | None          # → user.id
    ip: str | None               # → client.ip
    session_id: str | None       # → session_id (flat)
    orn: str | None              # → payment.orn
    pg_code: str | None          # → payment.pg_code
    reference_number: str | None # → payment.reference
    extra: dict                  # → merged to root, then reshaped by namespace_ecs_fields
    labels: dict                 # → labels (flat values only: str/int/float/bool)

---

21. Log Output Example

{
  "@timestamp": "2025-01-13T10:30:00.000Z",
  "ecs.version": "1.12.0",
  "message": "payment_processed",
  "log.level": "info",
  "log.logger": "core.payment.views",
  "trace": {
    "id": "0af7651916cd43dd8448eb211c80319c"
  },
  "span": {
    "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
  },
  "user": {
    "id": 42
  },
  "client": {
    "ip": "192.168.1.1"
  },
  "service": {
    "name": "app",
    "version": "1.2.3"
  },
  "project": {
    "name": "keyloop"
  },
  "payment": {
    "orn": "ref-123",
    "pg_code": "knet"
  },
  "session_id": "sess-456",
  "merchant_id": "acme-corp",
  "labels": {
    "env": "production",
    "region": "us-east-1"
  },
  "extra": {
    "amount": 100,
    "currency": "KWD",
    "keyloop": {
      "enterprise_id": "ent-789",
      "store_id": "store-001"
    }
  }
}

Field annotations:

• trace.id — from W3C traceparent, links across services
• span.id — unique per request/task boundary
• payment.* — mapped from LoggingContext fields (pg_code, orn, reference)
• session_id — flat root field (sanctioned custom ID)
• labels.* — low-cardinality keyword metadata for Elasticsearch filtering
• extra.* — non-allowlisted keys auto-wrapped by namespace_ecs_fields, including service-namespaced fields (keyloop.*) and bare scalar kwargs

---

22. Package Structure

ecsctx/
├── __init__.py                # All public exports
├── context.py                 # LoggingContext, bind/reset/get, trace functions
├── processors.py              # contextvars_injector, mask_sensitive_data
├── formatters.py              # ECSFormatter (v1.12.0)
├── ecs_validator.py           # ECS field validation (warn on violations)
├── pii/
│   ├── __init__.py            # configure_pii, tokenize, protect, reveal
│   ├── provider.py            # KeysetProvider ABC
│   ├── crypto.py              # HMAC-SHA-256 + AES-256-GCM primitives
│   ├── keyset.py              # FileKeysetProvider (mtime-based hot-reload)
│   ├── vault.py               # VaultKeysetProvider (AppRole auth)
│   └── normalize.py           # Email/phone normalization for deterministic tokens
└── contrib/
    ├── django/
    │   ├── __init__.py        # Django exports
    │   ├── middleware.py      # LoggingContextMiddleware
    │   ├── processors.py     # Django-aware contextvars_injector
    │   ├── logging.py        # get_logging_config, setup_logging, presets
    │   ├── decorators.py     # @api_logging
    │   └── context_binder.py # LogContextBinder (auditlog, import explicitly)
    ├── celery/
    │   ├── __init__.py        # Celery exports
    │   └── log_context.py     # install_celery_hooks, signal handlers
    └── rq/
        ├── __init__.py        # RQ exports
        └── log_context.py     # @with_log_context, capture_log_context

License

MIT. See LICENSE.