Redis's double-write problem solved

In distributed systems, the double-write problem usually refers to the inconsistency of data when updated in multiple storage systems such as databases and caches. This problem is especially common when using Redis as the cache layer. Specifically, when data exists in the database and Redis cache, any update operation to the data needs to be performed in two places, namely "double write". This can lead to the following problems:

Cache data and database data are inconsistent：

The database update is successful, and the cache update fails.
The cache update is successful, and the database update fails.
The update order of the database and cache is out of sync.

Cache breakdown, penetration, avalanche：

Cache breakdown: Hotspot data is invalid, and a large number of requests are requested to access the database at the same time.
Cache penetration: Query non-existent data and penetrate directly into the database.
Cache avalanche: A large amount of cached data fails at the same time, resulting in a large number of requests to access the database directly.

Solution to the double-write problem:

1. Cache Aside Pattern (bypass cache mode)

This is the most commonly used caching strategy. The process is as follows:

Read operation：

Read data from the cache first.
If there is no data in the cache, read the data from the database and write the data to the cache.

Write operation：

Update the database.
Invalidate the cached data or update the cache.

Sample code：

public class CacheAsidePattern {
    private RedisCache redisCache;
    private Database database;

    public Data getData(String key) {
        // Read data from cache        Data data = (key);
        if (data == null) {
            // If there is no data in the cache, read data from the database            data = (key);
            // Write data to cache            (key, data);
        }
        return data;
    }

    public void updateData(String key, Data newData) {
        // Update the database        (key, newData);
        // Invalidate the cached data or update the cache        (key);
    }
}

advantage：

Simple implementation, common usage mode.
High reading efficiency, avoiding frequent access to the database.

shortcoming：

In high concurrency scenarios, short-term inconsistencies may occur.
Data may be inconsistent during the window period when cache expires and database updates.

Solution：

Add data version number or timestamp to ensure data consistency.
Use appropriate cache failure strategies to reduce inconsistent windows.

2. Write Through Cache

principle：

Read operation: Similar to Cache Aside Pattern, data is read from the cache.
Write operation: Directly update the cache, the cache is responsible for synchronous update of the database.

Sample code：

public class WriteThroughCache {
    private RedisCache redisCache;

    public void updateData(String key, Data newData) {
        // Update the cache and let the cache be responsible for synchronous update of the database        (key, newData);
    }
}

advantage：

Ensure cache and database consistency.
After the write operation is successful, the data between the database and cache is guaranteed to be consistent.

shortcoming：

The write operation has a high latency because each write operation requires synchronous update of the database.
It is highly complex and needs to ensure that cached update operations can be synchronized correctly to the database.

Solution：

Reduce the delay of a single write operation through batch updates and asynchronous operations.

3. Write Behind Cache (write back cache)

principle：

Read operation: Similar to the first two modes, data is read from the cache.
Write operation: Update the cache, and the database is updated asynchronously by the cache.

Sample code：

public class WriteBehindCache {
    private RedisCache redisCache;

    public void updateData(String key, Data newData) {
        // Update the cache and update the database asynchronously        (key, newData);
    }
}

advantage：

The latency of write operations is low because write operations are mainly concentrated in the cache.
Improves the throughput of write operations.

shortcoming：

There may be a risk of data loss (such as not updating the database in time when the cache is down).
The final consistency of data requires additional processing.

Solution：

Use a reliable message queue system to ensure the delivery and processing of data update messages.
Regularly synchronize cached and database data to ensure final consistency.

4. Use message queue for asynchronous updates

principle：

Read operation: Similar to other modes, data is read from cache.
Write operation: Update the cache and update the database asynchronously through the message queue.

Sample code：

public class CacheWithMessageQueue {
    private RedisCache redisCache;
    private MessageQueue messageQueue;

    public void updateData(String key, Data newData) {
        // Update cache        (key, newData);
        // Send asynchronous messages to update the database        (key, newData);
    }
}

Message Queue Processor:

public class DatabaseUpdater {
    private Database database;

    public void onMessage(UpdateMessage message) {
        String key = ();
        Data newData = ();
        // Update the database        (key, newData);
    }
}

advantage：

Improves the scalability and performance of the system.
Asynchronous update to reduce the delay in write operations.

shortcoming：

The reliability and data consistency of message queues need to be dealt with.
This increases the complexity of the system and requires handling of the idempotence and repeated consumption of messages.

Solution：

Ensure message queues are highly reliable and available.
Use idempotence design to ensure that messages are consumed repeatedly without causing data inconsistency.

Choose the right strategy

Choosing the right strategy depends on the specific needs and scenarios of the system:

Consistency is preferred:chooseCache Aside PatternorWrite Through Cache. Suitable for scenarios with high requirements for data consistency.
Performance priority:chooseWrite Behind CacheOr use message queue for asynchronous updates. Suitable for scenarios with high requirements for write operation performance.
Mixed strategy: In practical applications, different strategies can be used in combination. For example, some critical data use synchronous updates, while non-critical data use asynchronous updates.

Practical application examples

Suppose we have an e-commerce system that needs to handle updates and inquiries of product inventory. We can adopt the following hybrid strategy:

Query inventory：

Read from the cache first. If there is no data in the cache, read from the database and write to the cache.

Update inventory：

After updating the database, update the cache immediately (synchronous update).
At the same time, asynchronous messages are sent, and the cache is updated asynchronously through the message queue to cope with the delay problem under high concurrency.

Sample code：

public class InventoryService {
    private RedisCache redisCache;
    private Database database;
    private MessageQueue messageQueue;

    public int getInventory(String productId) {
        // Read data from cache        Integer inventory = (productId);
        if (inventory == null) {
            // If there is no data in the cache, read data from the database            inventory = (productId);
            // Write data to cache            (productId, inventory);
        }
        return inventory;
    }

    public void updateInventory(String productId, int newInventory) {
        // Update the database        (productId, newInventory);
        // Update cache        (productId, newInventory);
        // Send asynchronous message to update the cache        (productId, newInventory);
    }
}

Message Queue Processor:

public class InventoryUpdateProcessor {
    private RedisCache redisCache;

    public void onMessage(UpdateMessage message) {
        String productId = ();
        int newInventory = ();
        // Update cache        (productId, newInventory);
    }
}

Through this hybrid strategy, the performance and scalability of the system can be improved as much as possible while ensuring data consistency. Selecting appropriate cache and database update strategies based on specific business needs and scenarios is an important part of building a high-performance and highly available distributed system.

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