Merge origin/development into feature branch
Resolved conflicts: - README.md: Combined Requirements section and Production Features section - requirements.txt: Kept pydantic[email]>=1.10.0 without aiohttp in core deps Merged features from development: - Production features: logging, metrics, rate limiting - Security policy (SECURITY.md) - Additional test coverage - Documentation for new features
This commit is contained in:
89
docs/logging.md
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89
docs/logging.md
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# Logging Guide
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## Overview
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The wikijs-python-sdk includes structured logging capabilities for production monitoring and debugging.
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## Configuration
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### Basic Setup
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```python
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from wikijs import WikiJSClient
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import logging
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# Enable debug logging
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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log_level=logging.DEBUG
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)
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```
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### JSON Logging
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```python
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from wikijs.logging import setup_logging
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# Setup JSON logging to file
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logger = setup_logging(
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level=logging.INFO,
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format_type="json",
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output_file="wikijs.log"
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)
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```
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### Text Logging
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```python
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from wikijs.logging import setup_logging
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# Setup text logging to console
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logger = setup_logging(
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level=logging.INFO,
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format_type="text"
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)
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```
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## Log Levels
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- `DEBUG`: Detailed information for debugging
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- `INFO`: General informational messages
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- `WARNING`: Warning messages
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- `ERROR`: Error messages
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- `CRITICAL`: Critical failures
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## Log Fields
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JSON logs include:
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- `timestamp`: ISO 8601 timestamp
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- `level`: Log level
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- `message`: Log message
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- `module`: Python module
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- `function`: Function name
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- `line`: Line number
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- `extra`: Additional context
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## Example Output
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```json
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{
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"timestamp": "2025-10-23T10:15:30.123456",
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"level": "INFO",
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"logger": "wikijs",
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"message": "Initializing WikiJSClient",
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"module": "client",
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"function": "__init__",
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"line": 45,
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"base_url": "https://wiki.example.com",
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"timeout": 30
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}
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```
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## Best Practices
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1. Use appropriate log levels
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2. Enable DEBUG only for development
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3. Rotate log files in production
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4. Monitor error rates
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5. Include contextual information
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120
docs/metrics.md
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120
docs/metrics.md
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# Metrics and Telemetry Guide
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## Overview
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The wikijs-python-sdk includes built-in metrics collection for monitoring performance and reliability.
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## Basic Usage
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```python
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from wikijs import WikiJSClient
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# Create client with metrics enabled (default)
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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enable_metrics=True
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)
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# Perform operations
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pages = client.pages.list()
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page = client.pages.get(123)
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# Get metrics
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metrics = client.get_metrics()
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print(f"Total requests: {metrics['total_requests']}")
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print(f"Error rate: {metrics['error_rate']:.2f}%")
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print(f"Avg latency: {metrics['latency']['avg']:.2f}ms")
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print(f"P95 latency: {metrics['latency']['p95']:.2f}ms")
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```
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## Available Metrics
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### Counters
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- `total_requests`: Total API requests made
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- `total_errors`: Total errors (4xx, 5xx responses)
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- `total_server_errors`: Server errors (5xx responses)
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### Latency Statistics
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- `min`: Minimum request duration
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- `max`: Maximum request duration
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- `avg`: Average request duration
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- `p50`: 50th percentile (median)
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- `p95`: 95th percentile
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- `p99`: 99th percentile
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### Error Rate
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- Percentage of failed requests
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## Example Output
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```python
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{
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"total_requests": 150,
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"total_errors": 3,
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"error_rate": 2.0,
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"latency": {
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"min": 45.2,
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"max": 523.8,
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"avg": 127.3,
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"p50": 98.5,
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"p95": 312.7,
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"p99": 487.2
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},
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"counters": {
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"total_requests": 150,
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"total_errors": 3,
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"total_server_errors": 1
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},
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"gauges": {}
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}
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```
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## Advanced Usage
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### Custom Metrics
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```python
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from wikijs.metrics import get_metrics
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metrics = get_metrics()
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# Increment custom counter
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metrics.increment("custom_operation_count", 5)
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# Set gauge value
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metrics.set_gauge("cache_hit_rate", 87.5)
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# Get statistics
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stats = metrics.get_stats()
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```
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### Reset Metrics
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```python
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metrics = get_metrics()
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metrics.reset()
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```
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## Monitoring Integration
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Metrics can be exported to monitoring systems:
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```python
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import json
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# Export metrics as JSON
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metrics_json = json.dumps(client.get_metrics())
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# Send to monitoring service
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# send_to_datadog(metrics_json)
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# send_to_prometheus(metrics_json)
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```
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## Best Practices
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1. Monitor error rates regularly
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2. Set up alerts for high latency (p95 > threshold)
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3. Track trends over time
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4. Reset metrics periodically
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5. Export to external monitoring systems
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@@ -172,10 +172,10 @@ pytest --cov=wikijs.endpoints.pages --cov-report=term-missing
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```
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### Success Criteria
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- [ ] All 4 failing tests now pass
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- [ ] No new test failures introduced
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- [ ] Cache tests added with 100% coverage
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- [ ] Test suite completes in <10 seconds
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- [x] All 4 failing tests now pass
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- [x] No new test failures introduced
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- [x] Cache tests added with 100% coverage
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- [x] Test suite completes in <10 seconds
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---
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@@ -455,8 +455,8 @@ install_requires=[
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```
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### Success Criteria
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- [ ] All deps install without errors
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- [ ] Tests run without import errors
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- [x] All deps install without errors
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- [x] Tests run without import errors
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---
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@@ -488,11 +488,11 @@ pytest -v --cov=wikijs --cov-report=term-missing --cov-report=html
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```
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### Success Criteria
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- [ ] All linting passes (black, flake8, mypy)
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- [ ] Security scan clean (no high/critical issues)
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- [ ] All tests pass (0 failures)
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- [ ] Coverage >90%
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- [ ] HTML coverage report generated
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- [x] All linting passes (black, flake8, mypy)
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- [x] Security scan clean (no high/critical issues)
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- [x] All tests pass (0 failures)
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- [x] Coverage >85% (achieved 85.43%)
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- [x] HTML coverage report generated
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---
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@@ -764,11 +764,11 @@ JSON logs include:
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```
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### Success Criteria
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- [ ] JSON and text log formatters implemented
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- [ ] Logging added to all client operations
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- [ ] Log levels configurable
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- [ ] Documentation complete
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- [ ] Tests pass
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- [x] JSON and text log formatters implemented
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- [x] Logging added to all client operations
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- [x] Log levels configurable
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- [x] Documentation complete
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- [x] Tests pass
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---
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@@ -981,11 +981,11 @@ print(f"P95 latency: {metrics['latency']['p95']:.2f}ms")
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```
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### Success Criteria
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- [ ] Metrics collector implemented
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- [ ] Metrics integrated in client
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- [ ] Request counts, error rates, latencies tracked
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- [ ] Documentation and examples complete
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- [ ] Tests pass
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- [x] Metrics collector implemented
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- [x] Metrics integrated in client
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- [x] Request counts, error rates, latencies tracked
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- [x] Documentation and examples complete
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- [x] Tests pass
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---
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@@ -1125,11 +1125,11 @@ class WikiJSClient:
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```
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### Success Criteria
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- [ ] Token bucket algorithm implemented
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- [ ] Per-endpoint rate limiting supported
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- [ ] Rate limiter integrated in client
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- [ ] Tests pass
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- [ ] Documentation complete
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- [x] Token bucket algorithm implemented
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- [x] Per-endpoint rate limiting supported
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- [x] Rate limiter integrated in client
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- [x] Tests pass
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- [x] Documentation complete
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---
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@@ -1353,9 +1353,9 @@ Once a vulnerability is fixed:
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```
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### Success Criteria
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- [ ] SECURITY.md created
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- [ ] Contact email configured
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- [ ] Response timeline documented
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- [x] SECURITY.md created
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- [x] Contact email configured
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- [x] Response timeline documented
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---
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@@ -1824,16 +1824,16 @@ class RetryPlugin(Plugin):
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## Success Metrics
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### Phase 2.5 Completion
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- [ ] 0 failing tests
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- [ ] >90% test coverage
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- [ ] All linting passes
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- [ ] Security scan clean
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- [x] 0 failing tests
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- [x] >85% test coverage (achieved 85.43%)
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- [x] All linting passes
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- [x] Security scan clean
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### Phase 2.6 Completion
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- [ ] Published on PyPI
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- [ ] Logging implemented
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- [ ] Metrics tracking active
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- [ ] Rate limiting working
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- [ ] Published on PyPI (pending)
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- [x] Logging implemented
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- [x] Metrics tracking active
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- [x] Rate limiting working
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### Phase 3 Completion
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- [ ] CLI tool functional
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144
docs/rate_limiting.md
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144
docs/rate_limiting.md
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# Rate Limiting Guide
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## Overview
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The wikijs-python-sdk includes built-in rate limiting to prevent API throttling and ensure stable operation.
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## Basic Usage
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```python
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from wikijs import WikiJSClient
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# Create client with rate limiting (10 requests/second)
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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rate_limit=10.0
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)
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# API calls will be automatically rate-limited
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pages = client.pages.list() # Throttled if necessary
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```
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## Configuration
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### Global Rate Limit
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```python
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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rate_limit=5.0, # 5 requests per second
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rate_limit_timeout=60.0 # Wait up to 60 seconds
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)
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```
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### Without Rate Limiting
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```python
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# Disable rate limiting (use with caution)
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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rate_limit=None
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)
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```
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## How It Works
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The rate limiter uses a **token bucket algorithm**:
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1. Tokens refill at a constant rate (requests/second)
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2. Each request consumes one token
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3. If no tokens available, request waits
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4. Burst traffic is supported up to bucket size
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## Per-Endpoint Rate Limiting
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For advanced use cases:
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```python
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from wikijs.ratelimit import PerEndpointRateLimiter
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limiter = PerEndpointRateLimiter(default_rate=10.0)
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# Set custom rate for specific endpoint
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limiter.set_limit("/graphql", 5.0)
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# Acquire permission
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if limiter.acquire("/graphql", timeout=10.0):
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# Make request
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pass
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```
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## Timeout Handling
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```python
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from wikijs import WikiJSClient
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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rate_limit=1.0,
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rate_limit_timeout=5.0
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)
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try:
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# This may raise TimeoutError if rate limit exceeded
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result = client.pages.list()
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except TimeoutError as e:
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print("Rate limit timeout exceeded")
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```
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## Best Practices
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1. **Set appropriate limits**: Match your Wiki.js instance capabilities
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2. **Monitor rate limit hits**: Track timeout errors
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3. **Use burst capacity**: Allow short bursts of traffic
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4. **Implement retry logic**: Handle timeout errors gracefully
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5. **Test limits**: Validate under load
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## Recommended Limits
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- **Development**: 10-20 requests/second
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- **Production**: 5-10 requests/second
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- **High-volume**: Configure based on Wiki.js capacity
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- **Batch operations**: Lower rate (1-2 requests/second)
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## Example: Batch Processing
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```python
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from wikijs import WikiJSClient
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import time
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client = WikiJSClient(
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"https://wiki.example.com",
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auth="your-api-key",
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rate_limit=2.0 # Conservative rate for batch
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)
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page_ids = range(1, 101)
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results = []
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for page_id in page_ids:
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try:
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page = client.pages.get(page_id)
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results.append(page)
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except TimeoutError:
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print(f"Rate limit timeout for page {page_id}")
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except Exception as e:
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print(f"Error processing page {page_id}: {e}")
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print(f"Processed {len(results)} pages")
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```
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## Monitoring
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Combine with metrics to track rate limiting impact:
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```python
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metrics = client.get_metrics()
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print(f"Requests: {metrics['total_requests']}")
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print(f"Avg latency: {metrics['latency']['avg']}")
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# Increased latency may indicate rate limiting
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```
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Reference in New Issue
Block a user