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System Design: Cache System

Problem Statement

Design a caching system that stores frequently accessed data in memory to reduce latency and improve performance. The system should handle high read/write throughput, provide cache eviction strategies, and support horizontal scaling. The cache can be used as a layer between applications and databases to speed up data retrieval.

Requirements

Functional Requirements:

  1. Cache Read/Write: Store key-value pairs in memory for fast retrieval.
  2. TTL (Time-to-Live): Support expiry times for cached items (e.g., keys should expire after a certain time).
  3. Cache Eviction: Implement cache eviction policies such as LRU (Least Recently Used), LFU (Least Frequently Used), FIFO (First-In, First-Out) to manage memory usage.
  4. Cache Invalidation: Provide mechanisms to invalidate (remove) stale or old cache entries when the underlying data changes.
  5. Persistence (Optional): Optionally, allow the cache to persist data to disk to survive restarts.
  6. Sharding: Support sharding of data across multiple cache nodes to handle large data sets and high traffic.

Non-Functional Requirements:

  1. Low Latency: Data retrieval from the cache should be extremely fast (sub-millisecond access times).
  2. Scalability: The system should scale horizontally to handle high throughput and large data volumes.
  3. Fault Tolerance: Ensure that data is replicated across nodes to prevent data loss in case of cache server failure.
  4. Consistency: Ensure cache consistency when multiple clients are accessing/updating the cache.

Components of the System

  1. Cache Client: The application or service that interacts with the cache to perform read/write operations.
  2. Cache Server: Stores key-value pairs in memory and supports eviction, TTL, and read/write operations.
  3. Eviction Manager: Manages cache eviction policies such as LRU, LFU, or FIFO to decide which keys should be removed when the cache is full.
  4. Replication Manager (Optional): Manages replication of cached data across multiple cache nodes to ensure fault tolerance.
  5. Sharding Manager: Distributes the cache across multiple nodes to handle large datasets and traffic.

High-Level Design

1. Cache Server:

  • In-Memory Storage: Stores key-value pairs in memory (e.g., using a hash table for O(1) access time).
  • TTL Support: Items can be configured with a time-to-live (TTL), after which they expire and are evicted from the cache.
  • Eviction Policies: When the cache reaches capacity, items are evicted based on the configured eviction policy (LRU, LFU, FIFO).
  • Persistence (Optional): Some cache systems (like Redis) support persisting the cache to disk to survive restarts.

2. Cache Client:

  • Applications interact with the cache through a client library, which abstracts away the details of how data is stored and retrieved.
  • The client can read from the cache or write to the cache when data is updated.

3. Eviction Manager:

  • Implements cache eviction policies to ensure that the cache does not exceed its memory limit.
  • LRU (Least Recently Used): Evicts the least recently accessed items when the cache is full.
  • LFU (Least Frequently Used): Evicts items that are accessed the least number of times.
  • FIFO (First-In, First-Out): Evicts items in the order they were added to the cache.

4. Replication Manager (Optional):

  • Replicates cached data across multiple cache servers to ensure fault tolerance.
  • If one cache server fails, another replica can serve the cached data.

5. Sharding Manager:

  • Distributes the cache across multiple servers based on key hashing or consistent hashing.
  • Each cache server holds a subset of the overall data.

Data Flow

1. Cache Write:

  • A write request is sent to the cache from the client.
  • The cache stores the key-value pair in memory.
  • If the key has a TTL, it is set at the time of writing.
  • The eviction manager ensures that the cache doesn’t exceed its memory limit by evicting items when necessary.

2. Cache Read:

  • A read request is sent to the cache from the client.
  • If the key exists in the cache, the value is returned.
  • If the key does not exist (cache miss), the client fetches the data from the original data source (e.g., a database), writes it to the cache, and returns it.

3. Cache Eviction:

  • When the cache reaches its memory limit, the eviction manager removes entries based on the chosen eviction policy (LRU, LFU, FIFO).
  • Expired items are also automatically removed by the cache based on their TTL.

4. Cache Invalidation:

  • When the underlying data changes (e.g., a database update), the corresponding cache entry must be invalidated (removed) to ensure consistency.
  • This can be done through cache invalidation messages or automatic detection by the cache system.

Key Challenges

1. Consistency:

  • Problem: Cache consistency can be an issue when multiple clients are reading and writing to the cache simultaneously.
  • Solution: Use cache invalidation or read-through/write-through caching techniques to keep the cache in sync with the underlying data.

2. Cache Eviction:

  • Problem: When the cache reaches its memory limit, it must evict items. Choosing the right eviction policy can affect cache performance.
  • Solution: Use policies like LRU (for general-purpose caching) or LFU (for high read-to-write ratio workloads).

3. Scalability:

  • Problem: As traffic and data volumes increase, the cache must scale horizontally to handle the load.
  • Solution: Use sharding to distribute the cache across multiple nodes and replicate data for fault tolerance.

4. Fault Tolerance:

  • Problem: If a cache server fails, the data stored on that server is lost.
  • Solution: Use replication to store copies of cached data across multiple servers to ensure that data is still available in the event of a failure.

Scaling the System

1. Horizontal Scaling:

  • Add more cache nodes to scale the system horizontally. Sharding ensures that each node stores a portion of the total data.
  • Load balancing is used to distribute requests evenly across all cache nodes.

2. Replication:

  • Use replication to ensure that cache data is available even if one or more cache nodes fail. Each key can be replicated across multiple nodes.

3. Consistent Hashing:

  • Use consistent hashing to distribute keys across cache nodes. Consistent hashing ensures minimal rehashing when new cache nodes are added or removed.

4. Partitioning:

  • Partition the cache into smaller segments and assign each segment to a specific node. This reduces the load on any single node and allows the system to handle more data.

Advanced Features

1. Write-Through and Read-Through Caching:

  • Write-Through Caching: Data is written to both the cache and the underlying data store simultaneously. This ensures consistency between the cache and the data store.
  • Read-Through Caching: If a key is not found in the cache, the system fetches it from the underlying data store, stores it in the cache, and returns the result to the client.

2. Cache Warming:

  • Pre-populate the cache with data that is expected to be frequently accessed, reducing cache misses when the system first starts.

3. Cache Monitoring:

  • Monitor cache performance and usage through metrics like hit rate, miss rate, eviction rate, and memory usage.
  • Use monitoring tools like Prometheus to collect and visualize cache metrics.

4. Multilevel Cache:

  • Implement a multilevel cache, where Level 1 (L1) cache is in-memory (e.g., Redis), and Level 2 (L2) cache is on disk. This helps store larger data sets that can’t fit in memory.

Conclusion

A well-designed caching system is essential for improving performance and reducing latency in data-intensive applications. By using appropriate eviction policies, sharding, and replication, the system can scale to handle large datasets and high traffic loads while ensuring low-latency access to frequently used data.

Further Reading