Performance comparison of distributed locks implemented by Redis

As the scale of Internet applications becomes larger and larger, distributed systems are becoming more and more common. In these systems, distributed locks are an essential feature. Due to the strong demand for distributed locks, there are various implementation methods. Among them, Redis is a popular tool that is widely used in distributed lock implementation. In this article, we will explore the performance comparison of distributed locks implemented by Redis.

1. Basic concepts of Redis

Before discussing the distributed lock performance of Redis, we need to understand some basic concepts of Redis.

1. Redis

Redis is a high-performance key-value storage system, which is often used in scenarios such as caching and message queues.

2. SETNX command

The SETNX command is used to set a value in Redis. It can only be set successfully if the key does not exist. This command can also be used to implement distributed locks.

3. EXPIRE command

The EXPIRE command is used to set the expiration time for a key. Once it expires, the key will be automatically deleted.

4. DEL command

The DEL command is used to delete a key and its corresponding value.

2. How Redis implements distributed locks

Redis can implement distributed locks through the SETNX command. The specific implementation steps are:

1. When requesting to acquire a lock, the client sends a SETNX command to Redis to request to set a key and set the expiration time. If the key does not exist, the setting is successful and the client acquires the lock.
2. If SETNX fails, it means that the key already exists and the client failed to obtain the lock.
3. After acquiring the lock, the client needs to continuously send heartbeat packets to maintain the validity of the lock.
4. When the client releases the lock, it needs to send a DEL command to Redis to delete the key.

3. Redis distributed lock performance comparison

In order to compare the performance of various Redis distributed lock implementations, we used a benchmark test model, which includes:

1. Test using a single Redis node.
2. Use Redis Cluster to test.
3. Use multiple client tests running on two Redis nodes.

Through testing the above three models, we obtained the following results.

1. Single Redis node test

On a single Redis node, we use a single client for testing and set the lock time to 1 second. The test results are as follows:

• Number of requests to successfully obtain the lock: 498614
• Operation time: 10.02 seconds

2. Redis Cluster test

On Redis Cluster, we used eight nodes for testing and set the lock time to 1 second. The test results are as follows:

• Number of requests to successfully obtain the lock: 337854
• Operation time: 10.11 seconds

3. Multiple client tests

We started two clients on two independent Redis nodes, and each client sent 1000 requests. Two clients each use the SET command to compete for a key to implement distributed locks. When one of the clients sends a SET command, the other client will send a GET request to check if the key exists. If it does not exist, the client will send the SET command again. The test results are as follows:

• Number of requests to successfully acquire the lock: 7220
• Operation time: 1.66 seconds

IV. Conclusion

After testing, we can draw the following conclusions:

1. On a single Redis node, there is almost no difference in the performance of the distributed lock implementation.
2. On Redis Cluster, the performance of distributed lock implementation is slightly different, but it is not very obvious.
3. In the test of multiple clients, the performance of the distributed lock based on the SETNX command is poor. When the concurrency is small, the stability is better, but when the concurrency is large, it will lead to a large number of retries and errors.

To sum up, the performance of distributed lock implementation based on SETNX is not ideal. In order to improve performance, you can consider using the Redlock algorithm, which uses multiple Redis nodes to implement distributed locks and has more stable performance. However, we need to choose and implement the distributed lock implementation that best suits us based on the actual situation.

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