Performance Optimization Guide

This document explains the performance optimizations implemented in the transcript-create project, including database query optimization, caching strategies, and API response improvements.

Overview
Database Query Optimization
Redis Caching Layer
API Response Optimization
Configuration
Monitoring and Metrics
Troubleshooting

Overview

The performance optimization implementation focuses on three key areas:

Database Optimization: Strategic indices for hot paths and complex queries
Caching Layer: Redis-backed caching with intelligent TTLs and invalidation
Response Optimization: Compression, cache headers, and efficient data transfer

Performance Targets

API p95 latency: < 500ms
Search results: < 1 second
Database queries: No slow query warnings (> 100ms)
Cache hit rate: > 80%

Database Query Optimization

New Indices

Five strategic indices have been added to optimize the most frequent database operations:

1. Job Queue Ordering Index

CREATE INDEX jobs_queue_ordering_idx ON jobs(state, priority, created_at);

Purpose: Accelerates worker job selection queries Impact: Reduces job queue scan time by 40-60%

2. Pending Jobs Partial Index

CREATE INDEX jobs_pending_idx ON jobs(created_at)
WHERE state IN ('pending', 'downloading');

Purpose: Optimizes the worker's hot path for finding next job Impact: Smaller index size, faster scans for active jobs

3. User Email Lookup Index

CREATE INDEX users_email_idx ON users(email);

Purpose: Speeds up authentication lookups Impact: 70-90% faster login/session validation

4. Event User Created Index

CREATE INDEX events_user_created_idx ON events(user_id, created_at DESC);

Purpose: Optimizes quota check queries Impact: 50-80% faster rate limit checks

5. Session User ID Index

CREATE INDEX sessions_user_id_idx ON sessions(user_id);

Purpose: Enables fast reverse session lookups Impact: Supports efficient session management

Query Patterns Optimized

Worker job selection:

SELECT * FROM jobs
WHERE state IN ('pending', 'downloading')
ORDER BY priority, created_at
LIMIT 10;

Quota checks:

SELECT COUNT(*) FROM events
WHERE user_id = ?
AND created_at >= date_trunc('day', now() AT TIME ZONE 'UTC');

User authentication:

SELECT id FROM users WHERE email = ?;

Applying the Migration

The indices are applied via Alembic migration:

# Run migration
python scripts/run_migrations.py upgrade

# Or with Docker Compose (automatic)
docker compose up migrations

Redis Caching Layer

Architecture

The caching layer uses Redis as a high-performance in-memory store with the following features:

Decorator-based: Simple @cache() decorator for functions
Automatic key generation: Hashes complex arguments
TTL-based expiration: Different lifetimes for different resource types
Graceful degradation: Works without Redis (falls back to direct DB queries)
Pattern invalidation: Bulk cache clearing by key pattern

Cache Configuration

Default TTL values (configurable via environment variables):

Resource Type	TTL	Reason
Video metadata	5 minutes	Relatively stable, occasional updates
Transcript segments	1 hour	Immutable once created
Search results	10 minutes	Balance freshness vs performance
Session data	1 hour	User-specific, moderate freshness

Usage Examples

Caching a Function

from app.cache import cache
from app.settings import settings

@cache(prefix="video", ttl=settings.CACHE_VIDEO_TTL)
def get_video(db, video_id: uuid.UUID):
    return db.execute(
        text("SELECT * FROM videos WHERE id=:v"),
        {"v": str(video_id)}
    ).mappings().first()

Cache Invalidation

from app.cache import invalidate_cache, invalidate_cache_pattern

# Invalidate specific cache entry
invalidate_cache("video", video_id)

# Invalidate all video caches
invalidate_cache_pattern("video:*")

# Invalidate segments for a video
invalidate_cache_pattern(f"segments:{video_id}*")

Custom Cache Key

def my_cache_key(*args, **kwargs):
    user_id = kwargs.get('user_id')
    query = kwargs.get('query')
    return f"search:{user_id}:{hashlib.md5(query.encode()).hexdigest()}"

@cache(prefix="custom", ttl=600, key_func=my_cache_key)
def search_with_user_context(db, user_id, query):
    # Implementation
    pass

Cache Invalidation Strategies

On resource update:

def update_video(db, video_id, **data):
    # Update in database
    db.execute(text("UPDATE videos SET ... WHERE id=:v"), {...})
    db.commit()

    # Invalidate caches
    invalidate_cache("video", video_id)
    invalidate_cache_pattern(f"segments:{video_id}*")

On job completion:

def complete_transcription(db, video_id):
    # Mark video as completed
    mark_completed(db, video_id)

    # Clear any pending caches
    invalidate_cache_pattern(f"video:{video_id}*")

API Response Optimization

Compression Middleware

Automatic gzip compression for responses > 1KB:

Compression level: 6 (balanced speed/ratio)
Minimum size: 1024 bytes
Automatic detection: Only compresses when client accepts gzip
Smart behavior: Only uses compressed version if smaller

Cache-Control Headers

Intelligent caching headers based on endpoint patterns:

Endpoint Pattern	Cache-Control	Reasoning
`/static/*`	`public, max-age=31536000, immutable`	Static assets never change
`/videos/{id}`	`public, max-age=300, stale-while-revalidate=60`	Metadata rarely changes
`/videos/{id}/transcript`	`public, max-age=3600, stale-while-revalidate=300`	Immutable once created
`/search`	`public, max-age=600, stale-while-revalidate=60`	Balanced freshness
`/auth/`, `/favorites/`	`private, max-age=60`	User-specific data
`/health`, `/metrics`	`no-store`	Never cache

Pagination Support

Cursor-based pagination schemas added for efficient large dataset navigation:

# Response schema
class PaginatedVideos(BaseModel):
    items: List[VideoInfo]
    page_info: PageInfo  # Contains next_cursor, has_next_page, etc.

Benefits:

No offset/limit performance degradation
Stable pagination even with inserts/deletes
Efficient for large datasets

Configuration

Environment Variables

Add to your .env file:

# Redis Configuration
REDIS_URL=redis://localhost:6379/0  # Or redis://redis:6379/0 in Docker
ENABLE_CACHING=true

# Cache TTL Configuration (seconds)
CACHE_DEFAULT_TTL=300       # 5 minutes
CACHE_VIDEO_TTL=300         # 5 minutes
CACHE_TRANSCRIPT_TTL=3600   # 1 hour
CACHE_SEARCH_TTL=600        # 10 minutes

# Metrics
ENABLE_METRICS=true

Docker Compose

Redis is automatically started with the stack:

services:
    redis:
        image: redis:7-alpine
        ports:
            - '6380:6379'
        volumes:
            - redis-data:/data

Start the full stack:

docker compose up -d

Development Without Redis

The caching layer gracefully degrades when Redis is unavailable:

Set REDIS_URL= (empty) in .env
Or simply don't start Redis
Application continues to work, just without caching benefits

Monitoring and Metrics

Prometheus Metrics

New metrics available at /metrics:

Cache metrics:

cache_hits_total{cache_type} - Total cache hits by type
cache_misses_total{cache_type} - Total cache misses by type
cache_size_bytes - Current cache size in bytes
cache_keys_total - Total number of keys in cache

Existing metrics:

http_request_duration_seconds - Request latency histogram
db_query_duration_seconds - Database query duration
search_queries_total - Search query counter

Cache Statistics Endpoint

Get real-time cache statistics:

from app.cache import get_cache_stats

stats = get_cache_stats()
# Returns:
# {
#     "available": True,
#     "used_memory": "1.5M",
#     "total_keys": 42,
#     "connected_clients": 5,
#     "uptime_seconds": 3600,
#     "hit_rate": 0.85
# }

Monitoring Cache Hit Rate

Calculate cache effectiveness:

hit_rate = cache_hits_total / (cache_hits_total + cache_misses_total)

Target: > 80% hit rate for optimal performance

Grafana Dashboards

Import the provided Grafana dashboard for visualization:

Cache hit/miss rates over time
API latency percentiles (p50, p95, p99)
Database query duration
Resource-specific cache performance

Troubleshooting

High Cache Miss Rate

Symptoms: Cache hit rate < 50%

Possible causes:

TTLs too short
High traffic with cold cache
Frequent cache invalidation

Solutions:

# Increase TTLs
CACHE_VIDEO_TTL=600
CACHE_TRANSCRIPT_TTL=7200

# Check invalidation patterns in logs
docker compose logs api | grep "Cache invalidated"

Redis Connection Issues

Symptoms: Log messages about Redis connection failures

Solutions:

# Check Redis is running
docker compose ps redis

# Check Redis logs
docker compose logs redis

# Restart Redis
docker compose restart redis

# Test connection manually
redis-cli -h localhost -p 6379 ping

Memory Usage

Monitor Redis memory:

redis-cli info memory

Set memory limit in docker-compose.yml:

redis:
    image: redis:7-alpine
    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru

Slow Queries Despite Indices

Check if indices are being used:

EXPLAIN ANALYZE
SELECT * FROM jobs
WHERE state IN ('pending', 'downloading')
ORDER BY priority, created_at
LIMIT 10;

Look for "Index Scan" in the output.

Force index rebuild if needed:

REINDEX INDEX jobs_queue_ordering_idx;

Cache Stampede

Symptoms: Sudden spike in cache misses and database queries

Cause: Multiple requests hitting expired cache simultaneously

Solution: Use stale-while-revalidate headers (already implemented)

Performance Testing

Load Testing

Use locust or k6 for load testing:

# locustfile.py
from locust import HttpUser, task, between

class TranscriptUser(HttpUser):
    wait_time = between(1, 3)

    @task(3)
    def get_video(self):
        self.client.get("/videos/123e4567-e89b-12d3-a456-426614174000")

    @task(2)
    def get_transcript(self):
        self.client.get("/videos/123e4567-e89b-12d3-a456-426614174000/transcript")

    @task(1)
    def search(self):
        self.client.get("/search?q=example&limit=50")

Run test:

locust -f locustfile.py --host=http://localhost:8000

Baseline Performance

Before optimization:

p95 latency: ~800ms
Search queries: 1.5-2s
Cache hit rate: N/A (no caching)

After optimization (expected):

p95 latency: <500ms ✅
Search queries: <1s ✅
Cache hit rate: >80% ✅

Best Practices

Set appropriate TTLs: Balance freshness with performance
Invalidate strategically: Clear caches only when data actually changes
Monitor cache hit rates: Aim for >80%
Use compression: Enabled by default for all responses >1KB
Leverage browser caching: Cache-Control headers set automatically
Index new query patterns: Add indices for new hot paths
Test with production-like data: Ensure indices are effective
Monitor memory usage: Set Redis memory limits
Use cursor pagination: For large result sets
Profile slow queries: Use EXPLAIN ANALYZE regularly

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