Module 5 - Redis Caching with Spring Boot

Lecture: Key Concepts of Caching with Redis

1. Introduction to Caching

Caching is a technique that stores frequently accessed data in a high-speed data storage layer to reduce the time needed to access that data from the original, slower storage location. By temporarily storing copies of data in a cache, applications can significantly improve performance and reduce latency.

Benefits of Caching:

  • Improved Performance: Faster data retrieval
  • Reduced Database Load: Fewer queries to your primary database
  • Lower Latency: Quicker response times for end users
  • Increased Throughput: Higher capacity to handle concurrent requests
  • Cost Efficiency: Reduced resource consumption

Cache Hit vs. Cache Miss:

  • Cache Hit: When requested data is found in the cache
  • Cache Miss: When requested data is not found and must be retrieved from the primary data source

2. Introduction to Redis

Redis (Remote Dictionary Server) is an open-source, in-memory data structure store that can be used as a database, cache, message broker, and stream processing engine.

Key Features of Redis:

  • In-Memory Storage: Extremely fast operations
  • Data Structures: Supports strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes
  • Persistence Options: Can persist data to disk
  • Atomic Operations: Supports transactions and Lua scripting
  • Pub/Sub Capabilities: Can be used for messaging
  • High Availability: Supports replication and Redis Sentinel
  • Clustering: Supports data sharding across multiple Redis nodes

Redis vs. Traditional Databases:

  • Redis is primarily in-memory, making it much faster
  • Redis offers simpler data structures and operations
  • Redis has limited querying capabilities compared to relational databases
  • Redis typically stores smaller data sets due to memory constraints

3. Spring Boot Cache Abstraction

Spring provides a cache abstraction that allows applications to use different caching providers through a unified API.

Key Components:

  • @EnableCaching: Annotation to enable caching support
  • CacheManager: Interface that manages caches and acts as a factory for them
  • Cache: Interface that defines the basic cache operations
  • @Cacheable: Indicates that the result of a method can be cached
  • @CachePut: Updates the cache without affecting the method execution
  • @CacheEvict: Removes entries from the cache
  • @Caching: Allows multiple cache operations to be applied
  • @CacheConfig: Allows sharing common cache configuration at the class level

4. Integrating Redis with Spring Boot

Spring Boot provides auto-configuration for Redis, making it easy to integrate with your application.

Setup Steps:

  1. Add Spring Data Redis dependency
  2. Configure Redis connection in application properties
  3. Define cache configuration class
  4. Add caching annotations to service methods

Key Spring Boot Redis Properties:

  • spring.redis.host: Redis server host
  • spring.redis.port: Redis server port
  • spring.redis.password: Redis server password
  • spring.redis.database: Redis database index
  • spring.redis.timeout: Connection timeout
  • spring.cache.type: Set to “redis” to use Redis as the cache provider
  • spring.cache.redis.time-to-live: Default cache entry expiration

5. Caching Strategies and Best Practices

Common Caching Strategies:

  • Cache-Aside (Lazy Loading): Application checks cache first, loads from DB if missing
  • Write-Through: Data is written to cache and DB simultaneously
  • Write-Behind (Write-Back): Data is written to cache and asynchronously to DB
  • Refresh-Ahead: Cache proactively refreshes entries before they expire

Cache Eviction Policies:

  • Time-Based (TTL): Entries expire after a set time
  • LRU (Least Recently Used): Evicts least recently accessed entries
  • LFU (Least Frequently Used): Evicts least frequently accessed entries
  • FIFO (First In First Out): Evicts oldest entries first

Caching Best Practices:

  • Cache data that’s expensive to compute or retrieve
  • Cache data that changes infrequently
  • Set appropriate TTL values based on data volatility
  • Use cache keys that are meaningful and consistent
  • Implement cache invalidation strategies
  • Monitor cache hit rates and adjust accordingly
  • Be cautious with caching mutable objects

6. Cache Consistency Challenges

Maintaining consistency between the cache and the database can be challenging:

Consistency Patterns:

  • Read-Through: Cache transparently loads missing values from database
  • Write-Through: Updates go to both cache and database
  • Write-Behind: Updates go to cache and are asynchronously persisted to database

Handling Cache Invalidation:

  • Time-Based Expiration: Set appropriate TTL values
  • Explicit Invalidation: Manually invalidate cache entries when data changes
  • Event-Based Invalidation: Listen for data change events to update cache

Cache Synchronization in Distributed Systems:

  • Distributed Caching: Multiple cache instances across nodes
  • Cache Replication: Changes to one cache instance propagate to others
  • Consistent Hashing: Distributes cache keys across multiple nodes

7. Performance Monitoring and Optimization

Key Metrics to Monitor:

  • Cache Hit Ratio: Percentage of cache hits vs. total requests
  • Memory Usage: Amount of memory consumed by the cache
  • Cache Throughput: Number of cache operations per second
  • Cache Latency: Time taken for cache operations
  • Eviction Rate: Number of entries being evicted from the cache

Troubleshooting Common Issues:

  • High cache miss rates
  • Excessive memory consumption
  • Cache thrashing
  • Inconsistent data
  • Cache stampede (thundering herd problem)

Quiz: Redis Caching with Spring Boot

  1. What is the primary purpose of implementing caching in an application? a) To provide data persistence b) To reduce latency and improve performance c) To ensure data consistency d) To enable distributed computing

  2. Which annotation in Spring Boot enables caching support for an application? a) @Cache b) @EnableCache c) @EnableCaching d) @CacheSupport

  3. What does a “cache hit” refer to? a) When data is successfully written to the cache b) When the cache becomes full c) When requested data is found in the cache d) When a cache error occurs

  4. Which Redis feature makes it particularly suitable for caching? a) Disk-based storage b) In-memory storage c) ACID compliance d) Column-based architecture

  5. Which Spring annotation is used to indicate that a method’s result should be cached? a) @CachePut b) @CacheEvict c) @Cacheable d) @CacheStore

  6. What is a common issue that occurs when multiple requests try to rebuild a cache entry simultaneously? a) Cache collision b) Cache stampede c) Cache overflow d) Cache fragmentation

  7. What does TTL stand for in the context of Redis caching? a) Time To Live b) Total Time Limit c) Transaction Timeline d) Time To Load

  8. Which caching strategy checks the cache first and only loads from the database if the data is not found in the cache? a) Write-Through b) Read-Through c) Cache-Aside d) Write-Behind

  9. What property would you set in application.properties to specify the Redis server host? a) redis.host b) spring.redis.server c) spring.redis.host d) cache.redis.host

  10. Which of the following is NOT a valid cache eviction policy? a) LRU (Least Recently Used) b) FIFO (First In First Out) c) MFU (Most Frequently Used) d) LFU (Least Frequently Used)

Quiz Answers

  1. What is the primary purpose of implementing caching in an application? Answer: b) To reduce latency and improve performance

  2. Which annotation in Spring Boot enables caching support for an application? Answer: c) @EnableCaching

  3. What does a “cache hit” refer to? Answer: c) When requested data is found in the cache

  4. Which Redis feature makes it particularly suitable for caching? Answer: b) In-memory storage

  5. Which Spring annotation is used to indicate that a method’s result should be cached? Answer: c) @Cacheable

  6. What is a common issue that occurs when multiple requests try to rebuild a cache entry simultaneously? Answer: b) Cache stampede

  7. What does TTL stand for in the context of Redis caching? Answer: a) Time To Live

  8. Which caching strategy checks the cache first and only loads from the database if the data is not found in the cache? Answer: c) Cache-Aside

  9. What property would you set in application.properties to specify the Redis server host? Answer: c) spring.redis.host

  10. Which of the following is NOT a valid cache eviction policy? Answer: c) MFU (Most Frequently Used)

By Wahid Hamdi