# elk-stack

This rule sets conventions for ELK logging, including JSON output, shared fields, and trace IDs. Use it to keep application logs consistent across services.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/elk-stack.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/elk-stack

## What it does

The rule calls for structured JSON logs written to `stdout`, rather than local files. Each entry follows a shared schema with a timestamp, level, message, service name, `trace.id`, `span.id`, and context-specific fields. That gives teams consistent data to filter and correlate in Elasticsearch and Kibana.

Python examples use `structlog` and show middleware adding request IDs to the logging context. The guidance also distinguishes log levels by purpose. `DEBUG` is for diagnostics and must stay off in production; applications that rely on local log files need a different setup.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/elk-stack.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/elk-stack.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/elk-stack.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/elk-stack.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/elk-stack.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/elk-stack.mdc

## When to use it
- send structured application logs to `stdout`
- standardize log fields across services
- attach `trace.id` and `span.id` to request logs
- choose log levels for different event severity

## Example prompts
- Update the application logging to emit structured JSON to `stdout` under the `elk-stack` rule.
- Check that request logs include `trace.id` and `span.id`.
- Replace local-file logging with `stdout` and apply the rule’s log-level guidance.

## Source text · elk-stack.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f763287b89d96f00.

BEGIN THIRD-PARTY FILE f763287b89d96f00
````text
---
description: Definitive guidelines for emitting, structuring, and managing logs within the ELK stack, ensuring robust observability and efficient troubleshooting.
globs: **/*
---
# elk-stack Best Practices

The ELK (Elasticsearch-Logstash-Kibana) stack is our standard for centralized logging and observability. Adhering to these guidelines ensures our logs are consistent, actionable, and efficient, enabling rapid troubleshooting and deep insights.

## Code Organization and Structure

### 1. Standardize on Structured Logging

Always emit logs as structured JSON to `stdout`. This is the **only** acceptable method for application logging. Avoid writing to local files.

**Rationale**: `stdout` is the standard stream for containerized applications, easily captured by Elastic Agent or Filebeat. Structured JSON ensures logs are machine-readable and parsable without complex regex, making them immediately queryable in Elasticsearch.

**✅ GOOD: Python with `structlog`**

```python
# app/logging_config.py
import sys
import structlog
import os

def configure_logging():
    # Define canonical fields and processors
    shared_processors = [
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.merge_extra_context,
        # Enforce canonical fields: service_name, trace_id, span_id
        lambda logger, method_name, event_dict: event_dict.update(
            service_name=os.getenv("SERVICE_NAME", "unknown-service"),
            trace_id=os.getenv("X_B3_TRACEID", "no-trace-id"), # Example for B3 propagation
            span_id=os.getenv("X_B3_SPANID", "no-span-id"),
        ),
    ]

    if os.getenv("APP_ENV", "development") == "production":
        # Production: JSON output for log aggregators
        processors = shared_processors + [
            structlog.processors.dict_tracebacks, # Structured tracebacks
            structlog.processors.JSONRenderer(),
        ]
    else:
        # Development: Pretty printing for local readability
        processors = shared_processors + [
            structlog.dev.ConsoleRenderer(),
        ]

    structlog.configure(
        processors=processors,
        logger_factory=structlog.stdlib.LoggerFactory(),
        wrapper_class=structlog.stdlib.BoundLogger,
        cache_logger_on_first_use=True,
    )

    # Optionally, redirect standard Python logging to structlog
    # import logging
    # logging.basicConfig(handlers=[structlog.stdlib.ProcessorFormatter.wrap_for_formatter], level=os.getenv("LOG_LEVEL", "INFO").upper())
    # structlog.stdlib.ProcessorFormatter.remove_processors_from_logger(logging.getLogger())

# In your application entry point:
# from app.logging_config import configure_logging
# configure_logging()
# log = structlog.get_logger(__name__)
```

### 2. Enforce a Canonical Log Line Schema

Every log entry **must** conform to a shared schema. This enables consistent parsing, filtering, and dashboarding across all services.

**Required Fields**:
-   `@timestamp` (ISO 8601 format)
-   `log.level` (e.g., `info`, `warn`, `error`, `debug`)
-   `message` (human-readable string)
-   `service.name` (unique identifier for the service)
-   `trace.id` (correlation ID for distributed tracing)
-   `span.id` (specific operation within a trace)
-   Additional context-specific key-value pairs (e.g., `user_id`, `request.method`, `http.status_code`).

**Rationale**: A consistent schema is crucial for automated parsing and correlation in Kibana. It reduces noise and makes logs immediately useful.

## Common Patterns and Anti-patterns

### 1. Emit Structured JSON to `stdout`

**✅ GOOD: Logging structured data to `stdout`**

```python
# Using the configured structlog logger
from app.logging_config import log # Assuming log is configured as above

log.info("user_registered", user_id="abc-123", email="user@example.com", source="web_app")
````
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