Redis Toolkit

# Advanced Features

Welcome to the advanced features section of Redis Toolkit! Here we'll introduce more powerful and professional features to help you build high-performance applications.

# 🚀 Advanced Features Overview

Redis Toolkit provides not only basic data access functionality but also many advanced features:

🎨 Media Processing

Built-in image, audio, and video converters for easy multimedia data handling

  • Image encoding/decoding & resizing
  • Audio format conversion
  • Video frame extraction
Learn More →

⚡ Batch Operations

Efficiently process large amounts of data with significant performance improvements

  • Batch read/write
  • Pipeline operations
  • Transaction support
Learn More →

🔗 Connection Pool Management

Smart connection pooling for optimized resource usage

  • Automatic connection management
  • Connection pool sharing
  • Health checks
Learn More →

🛡️ Error Handling

Comprehensive error handling mechanisms for system stability

  • Automatic retry
  • Graceful degradation
  • Detailed error tracking
Learn More →

📊 Performance Optimization

Multiple optimization techniques to make your application fly

  • Performance profiling
  • Optimization recommendations
  • Benchmarking
Learn More →

# 📚 Learning Suggestions

# Choose Based on Your Needs

Different advanced features suit different application scenarios:

If you need to... We recommend learning
Process images, audio, or video Media Processing
Improve performance for bulk data operations Batch Operations
Optimize connection resource usage Connection Pool Management
Build stable and reliable systems Error Handling
Overall performance tuning Performance Optimization

# Learning Path

graph TD
    A[Basic Features] --> B[Batch Operations]
    A --> C[Error Handling]
    B --> D[Connection Pool Management]
    C --> D
    D --> E[Performance Optimization]
    A --> F[Media Processing]
    F --> E
1
2
3
4
5
6
7
8

# 🎯 Real-World Examples Preview

# Image Caching System

from redis_toolkit import RedisToolkit
from redis_toolkit.converters import encode_image, decode_image

class ImageCache:
    def __init__(self):
        self.toolkit = RedisToolkit()
    
    def cache_image(self, image_id, image_array, ttl=3600):
        # Encode and cache image
        encoded = encode_image(image_array, format='jpg', quality=85)
        self.toolkit.setter(f"img:{image_id}", encoded, ex=ttl)
    
    def get_cached_image(self, image_id):
        # Get and decode image from cache
        encoded = self.toolkit.getter(f"img:{image_id}")
        if encoded:
            return decode_image(encoded)
        return None
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

# High-Performance Batch Processing

# Process 10,000 data items using batch operations
batch_data = {
    f"data:{i}": {"value": i, "timestamp": time.time()}
    for i in range(10000)
}

# Batch write (10x faster than individual writes)
toolkit.batch_set(batch_data)

# Batch read
keys = [f"data:{i}" for i in range(10000)]
results = toolkit.batch_get(keys)
1
2
3
4
5
6
7
8
9
10
11
12

# 💡 Advanced Tips Summary

# 1. Combine Multiple Features

# Combine batch operations + error handling + connection pooling
from redis_toolkit import RedisToolkit, RedisOptions
from redis_toolkit.utils import with_retry

options = RedisOptions(
    use_connection_pool=True,
    max_connections=100
)

toolkit = RedisToolkit(options=options)

@with_retry(max_attempts=3)
def reliable_batch_operation(data):
    return toolkit.batch_set(data)
1
2
3
4
5
6
7
8
9
10
11
12
13
14

# 2. Monitoring and Debugging

# Enable detailed logging for debugging
debug_options = RedisOptions(
    is_logger_info=True,
    log_level="DEBUG",
    max_log_size=1024
)

# Performance monitoring
import time
start = time.time()
# ... your operations ...
elapsed = time.time() - start
print(f"Operation took: {elapsed:.3f} seconds")
1
2
3
4
5
6
7
8
9
10
11
12
13

  1. Real-time Image Processing Service

    • Use media processing features for user-uploaded images
    • Batch operations improve thumbnail generation efficiency
    • Connection pool management handles high concurrent requests

  2. Big Data Analytics Platform

    • Batch operations for fast data loading
    • Error handling ensures data integrity
    • Performance optimization reduces processing time

  3. IoT Data Collection System

    • Connection pool manages numerous device connections
    • Batch operations process sensor data
    • Error handling deals with network instability

# 🚀 Ready to Dive Deeper?

Choose your most interesting topic to start learning:

🎨 Media Processing Handle images, audio, video ⚡ Batch Operations Efficiently process bulk data 🔗 Connection Pool Optimize connection resources

Learning Tips

  • Choose learning priorities based on actual needs
  • Practice with example code hands-on
  • Apply learned knowledge in real projects
  • Refer to the API Documentation for more details

Media Processing →