Python server programming: Learn to use memcached to optimize performance

Python server programming: Learn to use memcached to optimize performance

In Python server programming, performance optimization is a very important issue. In web applications, database queries are very time-consuming operations. Therefore, in order to improve the performance of web applications, one of the methods is to use a caching system. In Python, memcached is a very popular caching system that is very fast and can greatly reduce the time required for database operations.

This article will introduce the basic concepts and usage of memcached, and show how to use memcached in Python to improve application performance.

What is memcached?

Memcached is a high-performance distributed memory object caching system. It can store frequently accessed data in memory, thereby avoiding repeated reads of the database and improving the performance of web applications.

Memcached stores data in the form of key-value pairs. When storing data, you need to provide a key and a value. To retrieve a value stored in memcached, just provide the corresponding key.

In order to improve reliability, memcached distributes data to multiple servers for storage. When a server goes down, memcached will automatically migrate its data to other storage servers. This approach can reduce single points of failure and improve program availability.

Installing and running memcached

Before using memcached, you need to install it first. On most Linux distributions, memcached can be installed through the package manager. For example, in Ubuntu, you can use the following command to install:

$ sudo apt-get install memcached

After the installation is complete, you can use the following command to start memcached:

$ memcached -m 64 -p 11211 -u nobody -l 127.0.0.1

This command will start a memcached instance that takes up 64MB of memory. , and listen on port 11211 of the local host. The user-specified -nobody option means that memcached runs as the nobody user, which is an unprivileged user and usually does not pose a risk to system security.

Connecting memcached

PyLibmc in Python is a Python client library for memcached. To use PyLibmc, you need to install it first. You can install it using the following command:

$ pip install pylibmc

After the installation is complete, you can use the following code to connect to memcached:

import memcache

mc = memcache.Client(['127.0.0.1:11211'], debug=0)

This will create a memcached client object mc, which connects to the 11211 port of the local host superior.

Storing and retrieving data

The method of using PyLibmc to store data is very simple. Here is an example:

mc.set("foo", "bar")

This will store the string "bar" on the key "foo" in memcached.

To get the stored data, you can use the following code:

value = mc.get("foo")
print(value)  # 输出：bar

In most cases, memcached can respond quickly to get and set requests. However, if the requested key-value pair is not in the cache, you need to query the data in the database. In this case, memcached can't provide much help. Therefore, when using memcached, you need to consider which data is suitable for caching, and you need to set the memcached strategy according to the needs of the application.

Set expiration time

memcached allows setting an expiration time for each key-value pair. This time is calculated from the time when the key-value pair is stored, and after the time is reached, memcached will automatically delete the key-value pair from the cache.

Here is an example:

mc.set("foo", "bar", time=60)

This code will delete the key-value pair from the cache after 60 seconds.

Batch operation

Using Python's memcached client library, multiple key-value pairs can be operated in batches, thereby improving the performance of the operation.

The following is an example:

mc.set_multi({"foo": "bar", "hello": "world"})

This will store the two key-value pairs "foo" and "hello" into memcached at the same time.

Using memcached to optimize performance

Using memcached to optimize the performance of web applications is not an easy task. The following are several tips for using memcached to optimize performance:

1. Cache frequently read data: In web applications, certain data often need to be read, such as user configuration information, article information, etc. Number of likes and so on. This data needs to be obtained from the database every time it is read. You can use memcached to store this data in the cache, thereby avoiding multiple reads from the database and improving application performance.
2. Use expiration time: In some cases, the data in the cache may become outdated. For example, a user's configuration information may change after 5 minutes. If the data in the cache is out of date, then each read will need to get the latest data from the database. In order to avoid this situation, you can set the expiration time of memcached. When the expiration time arrives, memcached will automatically delete the key-value pair from the cache.
3. Use distributed cache: If the data that needs to be cached is very large, there may be a situation where a single memcached instance cannot store all the cached data. You can consider using multiple memcached instances to store cached data in multiple instances.
4. Use local cache: In some cases, you can consider using local cache. Local caching is faster than distributed caching because data is stored in local memory, and in multi-threaded or multi-process situations, local caching can avoid locking issues. However, the disadvantage of local cache is that it does not have the advantages of distributed cache and cannot handle load balancing of multiple servers.

Summarize

This article introduces the basic concepts and usage of memcached, and shows how to use memcached in Python to improve the performance of web applications. Using memcached can avoid repeated reads of the database, thereby improving program performance. However, you need to pay attention to some issues when using memcached, such as which data the cached data is suitable for and setting the expiration time, etc. By using memcached correctly, you can effectively improve the performance of web applications and improve user experience.

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