Application of Redis in Python

Redis is an open source, high-performance key-value storage system, commonly used in cache, message queue, counter and other scenarios. As a concise and efficient scripting language, Python is also widely used in Web background processing, data analysis and mining, machine learning, artificial intelligence and other fields. This article will discuss the application of Redis in Python, including the installation of Redis, the use of Python Redis client module and specific application cases.

1. Redis installation

1. Download the installation file
   Redis official website provides source code and precompiled version. If you choose the precompiled version, you can download the latest version directly.
2. Decompression and Compilation
   After the download is completed, decompress it to the specified directory. Use the make command to compile Redis into executable files, startup scripts, etc. In Linux systems, you can use the following command:

$ tar xzf redis-5.0.3.tar.gz
$ cd redis-5.0.3
$ make

3. Start the Redis server
   After compilation is completed, you can use the redis-server command to start the Redis server. The default listening port is 6379:

$ src/redis-server

4. Test connection
   You can use the redis-cli command to connect to the redis server for operation. For example, use the SET command to set a key-value pair:

$ src/redis-cli
127.0.0.1:6379> SET mykey "Hello Redis"
OK
127.0. 0.1:6379> GET mykey
"Hello Redis"

2. Use of Python Redis client module

In order to facilitate the use of Redis in Python, you can use the redis-py module as Redis client library. You can use the pip command to install:

$ pip install redis

1. Connect to the Redis server
   First you need to create a Redis object to connect to the Redis server. You can use the following code:

import redis
rds = redis.Redis(host='localhost', port=6379, db=0)

where host and port are respectively is the address and port number of the Redis server, db represents the number of the Redis database, and the default is 0.

2. Data operation
   Using Redis objects can perform operations such as setting, obtaining, and deleting key-value pairs. For example:

rds.set('name', 'Alice')
name = rds.get('name')
print(name) # Output: b'Alice'

Among them, the set method is used to set the key-value pair, and the get method is used to obtain the key-value pair. It should be noted that the data type returned by the get method is bytes and needs to be converted to a string using the decode method.

3. Batch operation
   In order to improve efficiency, Redis supports batch operations. Using pipeline, multiple operations can be packaged and sent to the Redis server, reducing network overhead and latency. For example:

pipe = rds.pipeline()
pipe.set('key1', 'value1')
pipe.set('key2', 'value2')
pipe.set('key3', 'value3')
pipe.execute()

4. Pub/Sub mode
   Redis provides a publish/subscribe mode for transmission information. The Python Redis client library also provides corresponding APIs. For example:

import time
pubsub = rds.pubsub()
pubsub.subscribe('channel')
rds.publish('channel', 'Hello')
time.sleep(1) # Wait for 1 second
msg = pubsub.get_message()
print(msg) # Output: {'type': 'message', 'channel': b'channel' , 'data': b'Hello'}

Among them, the subscribe method means subscribing to a channel, and the publish method means publishing a message. Use the get_message method to get the message.

3. Specific application cases

1. Caching
   The Python Redis client library can be used to cache commonly used data and speed up the response speed of web applications. For example:

import time
import redis
class Cache:

def __init__(self):
    self.rds = redis.Redis(host='localhost', port=6379, db=0)

def get(self, key):
    val = self.rds.get(key)
    if val:
        return val.decode()
    return None

def set(self, key, val, ttl=60):
    self.rds.set(key, val, ex=ttl)

cache = Cache()
val = cache.get('key')
if not val:

# 从数据库中查询数据
val = 'value'
cache.set('key', val, ttl=60)

print(val)

The Cache class encapsulates the implementation of Redis cache, and you can use the get and set methods to obtain or set cache data. Query the cache before getting the data. If it does not exist in the cache, read the data from the database and cache it.

2. Distributed lock
   Distributed lock is a synchronization mechanism used to avoid resource competition when multiple processes/threads/nodes collaborate. The Python Redis client library can be used to implement distributed locks. For example:

import time
import redis
class Lock:

def __init__(self):
    self.rds = redis.Redis(host='localhost', port=6379, db=0)
    self.locked = False

def acquire(self, lockname, ttl=60):
    identifier = str(time.time())
    self.locked = self.rds.setnx(lockname, identifier)
    if self.locked:
        self.rds.expire(lockname, ttl)
    return self.locked

def release(self, lockname):
    if self.locked:
        self.rds.delete(lockname)

lock = Lock()
if lock.acquire('mylock'):

# 处理业务逻辑...
lock.release('mylock')

The Lock class encapsulates the implementation of distributed locks, and the acquire and release methods can be used to acquire or release locks. When acquiring a lock, return False if the lock is already occupied; if the lock is not occupied, acquire the lock and set the expiration time.

In summary, Redis is widely used in Python and can be used in cache, distributed locks, message queues, counters and other scenarios. The Python Redis client library also provides a simple and easy-to-use API for convenient data operations.

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