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Phase 2.5: Fix Foundation (CRITICAL) - Fixed 4 failing tests by adding cache attribute to mock_client fixture - Created comprehensive cache tests for Pages endpoint (test_pages_cache.py) - Added missing dependencies: pydantic[email] and aiohttp to core requirements - Updated requirements.txt with proper dependency versions - Achieved 82.67% test coverage with 454 passing tests Phase 2.6: Production Essentials - Implemented structured logging (wikijs/logging.py) * JSON and text log formatters * Configurable log levels and output destinations * Integration with client operations - Implemented metrics and telemetry (wikijs/metrics.py) * Request tracking with duration, status codes, errors * Latency percentiles (min, max, avg, p50, p95, p99) * Error rate calculation * Thread-safe metrics collection - Implemented rate limiting (wikijs/ratelimit.py) * Token bucket algorithm for request throttling * Per-endpoint rate limiting support * Configurable timeout handling * Burst capacity management - Created SECURITY.md policy * Vulnerability reporting procedures * Security best practices * Response timelines * Supported versions Documentation - Added comprehensive logging guide (docs/logging.md) - Added metrics and telemetry guide (docs/metrics.md) - Added rate limiting guide (docs/rate_limiting.md) - Updated README.md with production features section - Updated IMPROVEMENT_PLAN_2.md with completed checkboxes Testing - Created test suite for logging (tests/test_logging.py) - Created test suite for metrics (tests/test_metrics.py) - Created test suite for rate limiting (tests/test_ratelimit.py) - All 454 tests passing - Test coverage: 82.67% Breaking Changes: None Dependencies Added: pydantic[email], email-validator, dnspython 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Rate Limiting Guide
Overview
The wikijs-python-sdk includes built-in rate limiting to prevent API throttling and ensure stable operation.
Basic Usage
from wikijs import WikiJSClient
# Create client with rate limiting (10 requests/second)
client = WikiJSClient(
"https://wiki.example.com",
auth="your-api-key",
rate_limit=10.0
)
# API calls will be automatically rate-limited
pages = client.pages.list() # Throttled if necessary
Configuration
Global Rate Limit
client = WikiJSClient(
"https://wiki.example.com",
auth="your-api-key",
rate_limit=5.0, # 5 requests per second
rate_limit_timeout=60.0 # Wait up to 60 seconds
)
Without Rate Limiting
# Disable rate limiting (use with caution)
client = WikiJSClient(
"https://wiki.example.com",
auth="your-api-key",
rate_limit=None
)
How It Works
The rate limiter uses a token bucket algorithm:
- Tokens refill at a constant rate (requests/second)
- Each request consumes one token
- If no tokens available, request waits
- Burst traffic is supported up to bucket size
Per-Endpoint Rate Limiting
For advanced use cases:
from wikijs.ratelimit import PerEndpointRateLimiter
limiter = PerEndpointRateLimiter(default_rate=10.0)
# Set custom rate for specific endpoint
limiter.set_limit("/graphql", 5.0)
# Acquire permission
if limiter.acquire("/graphql", timeout=10.0):
# Make request
pass
Timeout Handling
from wikijs import WikiJSClient
client = WikiJSClient(
"https://wiki.example.com",
auth="your-api-key",
rate_limit=1.0,
rate_limit_timeout=5.0
)
try:
# This may raise TimeoutError if rate limit exceeded
result = client.pages.list()
except TimeoutError as e:
print("Rate limit timeout exceeded")
Best Practices
- Set appropriate limits: Match your Wiki.js instance capabilities
- Monitor rate limit hits: Track timeout errors
- Use burst capacity: Allow short bursts of traffic
- Implement retry logic: Handle timeout errors gracefully
- Test limits: Validate under load
Recommended Limits
- Development: 10-20 requests/second
- Production: 5-10 requests/second
- High-volume: Configure based on Wiki.js capacity
- Batch operations: Lower rate (1-2 requests/second)
Example: Batch Processing
from wikijs import WikiJSClient
import time
client = WikiJSClient(
"https://wiki.example.com",
auth="your-api-key",
rate_limit=2.0 # Conservative rate for batch
)
page_ids = range(1, 101)
results = []
for page_id in page_ids:
try:
page = client.pages.get(page_id)
results.append(page)
except TimeoutError:
print(f"Rate limit timeout for page {page_id}")
except Exception as e:
print(f"Error processing page {page_id}: {e}")
print(f"Processed {len(results)} pages")
Monitoring
Combine with metrics to track rate limiting impact:
metrics = client.get_metrics()
print(f"Requests: {metrics['total_requests']}")
print(f"Avg latency: {metrics['latency']['avg']}")
# Increased latency may indicate rate limiting