Troubleshooting Slow Redis Performance With Lettuce Connection Delays

Aug 4, 2025 by ADMIN 70 views

Cost Too Much Time Between Write() Done and Closing Redis Connection

Experiencing slow Redis performance with Lettuce? This article dives into a reported issue where significant delays occur between the write() operation and the closing of Redis connections, potentially impacting your application's responsiveness. We'll break down the problem, analyze the debug logs, explore potential causes, and discuss solutions to optimize your Redis performance. Let's get started!

Understanding the Problem

The user reports slow performance when performing GET and SET operations in Redis, particularly under high load (5000 queries per second). Debug logs reveal a substantial time gap, approximately 35 milliseconds in one instance and 112 milliseconds in another, between the completion of the write() operation and the closing of the Redis connection. This delay contributes to the overall latency and impacts the application's ability to handle requests efficiently.

Analyzing the Debug Logs

To better understand the situation, let's examine the provided debug log snippets:

2025-08-01T17:15:14.465+08:00 DEBUG 20417 --- [tr069] [pool-6-thread-49] i.l.c.c.PooledClusterConnectionProvider  : getConnection(READ, 12904)
2025-08-01T17:15:14.465+08:00 DEBUG 20417 --- [tr069] [pool-6-thread-49] i.lettuce.core.protocol.DefaultEndpoint  : [channel=0x86685ab9, /178.28.201.148:56892 -> 178.28.201.227/178.28.201.227:6384, epid=0x3d] write() writeAndFlush command ClusterCommand [command=AsyncCommand [type=GET, output=ValueOutput [output=null, error='null'], commandType=io.lettuce.core.protocol.Command], redirections=0, maxRedirections=5]
**2025-08-01T17:15:14.465+08:00** DEBUG 20417 --- [tr069] [pool-6-thread-49] i.lettuce.core.protocol.DefaultEndpoint  : [channel=0x86685ab9, /178.28.201.148:56892 -> 178.28.201.227/178.28.201.227:6384, epid=0x3d] write() done
**2025-08-01T17:15:14.500+08:00** DEBUG 20417 --- [tr069] [pool-6-thread-49] o.s.d.redis.core.RedisConnectionUtils    : Closing Redis Connection

This snippet highlights a 35-millisecond gap between the write() done message and the Closing Redis Connection message. This seemingly small delay, when multiplied across thousands of operations per second, can significantly impact overall performance. We observe a similar pattern in the second log snippet:

2025-08-01T17:15:14.376+08:00 DEBUG 20417 --- [tr069] [https-jsse-nio-9090-exec-468] i.l.c.c.PooledClusterConnectionProvider  : getConnection(READ, 4650)
2025-08-01T17:15:14.376+08:00 DEBUG 20417 --- [tr069] [https-jsse-nio-9090-exec-468] i.lettuce.core.protocol.DefaultEndpoint  : [channel=0x145b4a06, /178.28.201.148:54994 -> 178.28.201.148/178.28.201.148:6385, epid=0x3e] write() writeAndFlush command ClusterCommand [command=AsyncCommand [type=GET, output=ValueOutput [output=null, error='null'], commandType=io.lettuce.core.protocol.Command], redirections=0, maxRedirections=5]
**2025-08-01T17:15:14.376+08:00** DEBUG 20417 --- [tr069] [https-jsse-nio-9090-exec-468] i.lettuce.core.protocol.DefaultEndpoint  : [channel=0x145b4a06, /178.28.201.148:54994 -> 178.28.201.148/178.28.201.148:6385, epid=0x3e] write() done
**2025-08-01T17:15:14.488+08:00** DEBUG 20417 --- [tr069] [https-jsse-nio-9090-exec-468] o.s.d.redis.core.RedisConnectionUtils    : Closing Redis Connection

Here, the delay is even more pronounced, at 112 milliseconds. These delays indicate a potential bottleneck in the connection handling process.

Examining the Code and Configuration

The user provides the following code snippet used for retrieving data from Redis:

String json = stringRedisTemplate.opsForValue().get(key);

This code uses StringRedisTemplate to perform a GET operation. While the code itself appears straightforward, the configuration plays a crucial role in performance. The provided application.yml configuration reveals the following settings:

data:
 redis:
 host: 10.57.82.137
 port: 6379
 password: ENC(JobybBGtklBUGJL28XI086GMArRfZy0KRBR2q++0qe45Kqx+NHhjOAzzl792SX3u)
 timeout: 5000ms
 lettuce:
 cluster:
 refresh:
 adaptive: true
 period: 30000
 pool:
 enabled: true
 max-active: 500
 max-idle: 500
 min-idle: 50
 max-wait: 5000ms

Key observations from the configuration include:

Lettuce Connection Pooling: Connection pooling is enabled, which is generally a good practice for performance. The pool is configured with a maximum of 500 active connections, 500 idle connections, and a minimum of 50 idle connections.
Cluster Configuration: The application is configured to connect to a Redis Cluster, indicated by the cluster settings. Adaptive refresh is enabled with a period of 30 seconds.
Timeout: A timeout of 5000 milliseconds is set.

Potential Causes for the Delay

Several factors could contribute to the observed delay between write() completion and connection closing. Let's explore some of the most likely culprits:

Network Latency: Although unlikely to be the sole cause, network latency between the application server and the Redis cluster nodes can contribute to the delay. 35-112 milliseconds is a high latency for local network communication.
Redis Server Load: If the Redis server is under heavy load, it may take longer to process commands and respond, leading to delays in connection closing. Check CPU, memory, and network utilization on the Redis server.
Lettuce Client-Side Processing: The Lettuce client itself might be experiencing processing overhead, such as handling responses, managing connections, or executing internal tasks. While Lettuce is generally efficient, certain configurations or scenarios could lead to performance bottlenecks. Verify Lettuce's internal metrics if available.
Connection Pool Exhaustion: Although the connection pool is configured with a relatively high maximum size (500), it's possible that the application is exhausting the pool under peak load. If all connections are in use, the application will have to wait for a connection to become available, leading to delays.
TCP Nagle's Algorithm: Nagle's algorithm, which delays sending small packets over the network, could be contributing to the delay. While generally beneficial, it can introduce latency in certain scenarios. You might want to disable Nagle's Algorithm if confirmed as the cause.
Cluster Redirection Overhead: In a Redis Cluster, requests might be redirected to different nodes if the initial node doesn't own the requested key. This redirection process adds overhead and can contribute to latency. The debug logs show redirections=0, so this may not be the major factor, but it can be an accumulated factor if redirections do happen frequently.
Spring Transaction Management: If the Redis operations are part of a Spring-managed transaction, the connection might be held open until the transaction commits or rolls back. This could explain the delay if the transaction processing takes a significant amount of time.

Troubleshooting and Solutions

Now that we've identified potential causes, let's explore troubleshooting steps and solutions to address the issue:

1. Network Latency Investigation:

Start by assessing the network latency between the application server and the Redis cluster nodes. Utilize tools like ping or traceroute to measure round-trip times. If high latency is observed, investigate potential network issues, such as congestion or faulty hardware. While network latency is likely a contributing factor, the observed delays (35-112 milliseconds) suggest other factors are also at play.

2. Redis Server Performance Monitoring:

Monitor the Redis server's performance metrics, including CPU utilization, memory usage, and network I/O. Tools like redis-cli info or monitoring solutions like Prometheus and Grafana can provide valuable insights. High resource utilization on the Redis server can indicate a bottleneck. Slow queries, large data volumes, or inefficient data structures can contribute to performance issues. Optimize queries, consider data sharding, or scale up the Redis server if necessary.

3. Lettuce Client-Side Analysis:

Examine Lettuce client-side metrics if available. Lettuce provides some internal metrics that can help identify bottlenecks within the client library itself. Look for metrics related to connection pool usage, command execution time, and internal processing overhead. Consider upgrading to the latest version of Lettuce, as newer versions often include performance improvements and bug fixes.

4. Connection Pool Optimization:

Evaluate the connection pool configuration. While the current configuration seems reasonable, it's crucial to ensure that the pool size is appropriate for the application's workload. If connection pool exhaustion is suspected, increase the max-active setting. However, increasing the pool size excessively can lead to resource contention and performance degradation. Monitor connection pool usage to find the optimal balance. Ensure that connections are released back to the pool promptly after use. Check for any code that might be holding onto connections unnecessarily.

5. TCP Nagle's Algorithm Evaluation:

Consider disabling Nagle's algorithm on the client side if it's suspected to be contributing to the delay. This can be achieved by setting the TCP_NODELAY option on the socket. However, disabling Nagle's algorithm can increase network traffic, so it's essential to test the impact in your specific environment. Disabling Nagle's Algorithm might help in scenarios where small packets are frequently sent.

6. Redis Cluster Redirection Analysis:

Analyze the frequency of cluster redirections. Frequent redirections can indicate a suboptimal key distribution or cluster configuration. Lettuce provides metrics related to redirections, which can help identify this issue. Ensure that keys are distributed evenly across the cluster nodes to minimize redirections. Use hash tags in keys to influence key distribution if necessary. Optimize your key distribution strategy to minimize cross-slot operations and redirections.

7. Spring Transaction Management Review:

If Redis operations are part of a Spring-managed transaction, carefully review the transaction boundaries and processing time. Long-running transactions can hold connections open for extended periods, leading to delays. Minimize the scope of transactions and ensure that they complete quickly. Consider using techniques like read-only transactions or asynchronous processing to improve performance.

8. Lettuce Asynchronous API Exploration:

Consider leveraging Lettuce's asynchronous API for non-blocking operations. Asynchronous operations can improve throughput and reduce latency by allowing the application to perform other tasks while waiting for Redis responses. Evaluate if the asynchronous API aligns with your application's architecture and requirements.

9. Connection Release Verification:

Double-check that connections are being released correctly in your code, especially when exceptions occur. Use try-finally blocks or resource management techniques to ensure that connections are always returned to the pool, even in error scenarios. Failure to release connections can lead to connection pool exhaustion and performance degradation.

10. Redis Pipelining Consideration:

If your application performs multiple Redis operations in sequence, consider using pipelining to reduce round-trip times. Pipelining allows you to send multiple commands to the Redis server without waiting for the responses in between. The server processes the commands in a batch and sends the responses back in a single reply. Pipelining can significantly improve performance for certain workloads.

Input Code and Configuration

To recap, the user's code snippet for fetching data from Redis is:

String json = stringRedisTemplate.opsForValue().get(key);

The application.yml configuration includes:

data:
 redis:
 host: 10.57.82.137
 port: 6379
 password: ENC(JobybBGtklBUGJL28XI086GMArRfZy0KRBR2q++0qe45Kqx+NHhjOAzzl792SX3u)
 timeout: 5000ms
 lettuce:
 cluster:
 refresh:
 adaptive: true
 period: 30000
 pool:
 enabled: true
 max-active: 500
 max-idle: 500
 min-idle: 50
 max-wait: 5000ms

Conclusion

The delay between write() completion and connection closing in Lettuce can significantly impact Redis performance, especially under high load. By systematically investigating potential causes, monitoring key metrics, and applying appropriate solutions, you can optimize your Redis interactions and improve your application's responsiveness. Remember to analyze your specific environment and workload to identify the most effective strategies. Guys, remember to implement monitoring so that you can catch the problems early. Good luck optimizing your Redis performance!