When deploying Memcached in a Linux environment for high-performance caching, many users believe it's incredibly fast and resource-efficient. However, as business scale expands or concurrent access increases, Memcached performance may degrade or even become a bottleneck. Optimizing Memcached requires a thorough understanding of its operating principles, potential bottlenecks, and specific optimization methods.

Memcached is essentially an in-memory key-value cache system. All data is stored in memory, using hashing algorithms to quickly locate data, resulting in low read and write latency. In a Linux environment, its operating efficiency depends not only on Memcached itself but also on operating system parameters, network conditions, hardware resources, and other factors, which can be the root cause of many performance issues.

If Memcached is deployed on a low-spec VPS or cloud server, insufficient memory allocation will frequently trigger data eviction, increasing CPU overhead and reducing cache hit rates. The solution to this problem is to allocate sufficient memory to Memcached. For example, you can specify 2GB of memory at startup using the command:

memcached -m 2048 -u memcache -p 11211 -c 1024 -P /var/run/memcached.pid

to ensure a high cache hit rate even under high concurrency. Furthermore, in a multi-core CPU environment, Memcached uses only a single thread by default, which can waste CPU resources. To address this, you can add the startup parameter:

-t 4

to enable it to run multiple threads simultaneously, thereby improving concurrent processing capabilities.

The network is also a key factor affecting performance. In high-concurrency scenarios, the number of TCP connections between Memcached and application servers may reach thousands or even tens of thousands. The default file descriptor and connection queue sizes in Linux may limit performance. In this case, you need to increase system parameters, such as adding:

net.core.somaxconn = 1024
net.ipv4.tcp_max_syn_backlog = 2048 in /etc/sysctl.conf

Then run sysctl -p to enable these settings. This will reduce connection rejections. For applications deployed on a local area network or on the same machine, consider using UDP mode to access Memcached to reduce TCP handshake overhead, but be aware of UDP packet size limitations.

In addition, data distribution and hashing strategies also directly impact performance. Using the default simple hashing algorithm can easily cause a large number of cache invalidations when scaling nodes, resulting in short-term performance fluctuations. A better approach is to use a consistent hashing algorithm, which ensures that only a small number of keys are redistributed when nodes change. This can be achieved through configuration in the client library (such as libmemcached). This is particularly important for clusters of multiple Memcached servers, as it can significantly reduce cache thrashing.

During the tuning process, pay attention to the data storage structure. Memcached is suitable for storing small, frequently accessed data. Using it to cache large amounts of data will not only consume a large amount of memory but also increase network transmission latency. Therefore, it is recommended to keep the size of a single key-value pair below 1MB and serialize complex data structures before storing them to avoid the performance loss caused by frequent large object transfers.

Monitoring and debugging are fundamental to performance optimization. Memcached provides the stats command, which allows you to view real-time information such as the number of connections, hit rate, and memory usage. If the hit rate is too low, you need to analyze whether the business logic is frequently requesting uncached data or whether insufficient memory is causing data to be prematurely evicted. If the number of connections is consistently high, consider increasing the number of servers or optimizing the application-layer connection pool implementation. In Linux environments, you can also use tools such as top, htop, and iftop to monitor real-time CPU, memory, and network usage to help identify bottlenecks.

In summary, as business traffic, data scale, and server environments change, the original configuration may no longer be suitable. Continuous monitoring, analysis, and adjustment are necessary. These measures, such as rational memory allocation, thread optimization, system parameter adjustments, and improved data distribution strategies, can significantly improve Memcached performance in Linux environments. For scenarios seeking extreme performance, consider deploying Memcached on low-latency, in-memory cloud servers or integrating it with an SSD caching layer to further reduce latency.