Database and Cache Consistency Issues and Solutions

Database and Cache Consistency Issues and Solutions

Problem Description
In database systems, to improve read performance, a caching layer (such as Redis) is often introduced to store frequently accessed data. However, when data exists in both the cache and the database, ensuring that users always read the latest data (i.e., data consistency between the cache and the database) becomes a critical issue. For example, after updating the database, if the cache is not updated synchronously, subsequent requests might read stale data.

Core Challenges

  1. Concurrent Operations: Multiple threads or services simultaneously reading from and writing to the database and cache.
  2. Operation Atomicity: Database updates and cache updates are not atomic operations and might fail midway.
  3. Ordering Issue: Should the cache be updated first or the database? Different orders can lead to consistency problems.

Evolution of Solutions

1. Solution One: Update Database First, Then Delete Cache (Cache-Aside Pattern)

  • Steps:
    1. The application updates the data in the database.
    2. Delete the corresponding key in the cache.
    3. Subsequent read requests find the cache missing, load the new data from the database, and refill the cache.
  • Advantages: Simple and easy to implement, avoids the complexity of simultaneous cache updates.
  • Risks:
    • If cache deletion fails, old data may persist in the cache for a long time.
    • In concurrent scenarios, brief inconsistencies may occur (e.g., a read request reads an old value after cache deletion but before refilling).

2. Solution Two: Delete Cache First, Then Update Database

  • Steps:
    1. Delete the cache.
    2. Update the database.
  • Problem: In high-concurrency scenarios, if Thread A deletes the cache and Thread B reads an old value and refills the cache before Thread A updates the database, the cache may permanently hold stale data.

3. Solution Three: Delayed Double Deletion (Addressing Solution Two's Problem)

  • Steps:
    1. Delete the cache.
    2. Update the database.
    3. Sleep for a short time (e.g., 500ms), then delete the cache again.
  • Purpose: To clear any stale data that might have been refilled during the concurrent process.
  • Drawback: The sleep time is difficult to set precisely and reduces throughput.

4. Solution Four: Asynchronous Updates Based on Database Transaction Logs (e.g., Canal)

  • Principle:
    1. After a database update, the transaction log (e.g., MySQL's binlog) records the change.
    2. A log subscription tool (e.g., Canal) parses the log and asynchronously updates the cache.
  • Advantages: Decouples application logic from cache synchronization, ensuring eventual consistency.
  • Disadvantages: Increases system complexity and introduces latency.

5. Solution Five: Using Distributed Locks

  • Steps:
    1. Acquire a distributed lock before performing a write operation.
    2. Update the database and delete the cache.
    3. For read operations, if the cache is missing, acquire the lock and load the data from the database.
  • Advantages: Guarantees strong consistency.
  • Disadvantages: Lock contention impacts performance, requiring a trade-off between consistency and efficiency.

Summary and Selection Recommendations

  • Prioritizing Performance: Prefer Solution One (Cache-Aside), combined with retry mechanisms or message queues to compensate for cache deletion failures.
  • Strong Consistency Scenarios: Use Solution Five (Distributed Locks), but evaluate the performance impact.
  • High-Concurrency Write Scenarios: Solution Four (Asynchronous Synchronization) can reduce application pressure, accepting brief delays.

Key Principle: Choose a balance based on business requirements for consistency (strong consistency/eventual consistency) and performance needs, and mitigate the impact of failures through monitoring and retry mechanisms.