Hello! We are a writer team from Definer Inc. Redis is an open-source, in-memory data structure store that can be used as a cache server. It is known for its speed, simplicity, and versatility in handling various data types. Redis is designed to provide high-performance caching capabilities, enabling applications to store and retrieve frequently accessed data quickly. In this issue, you are wondering how to use Redis as a cache server. Let's take a look at the actual screens and resources to explain in detail.

The primary purpose of using Redis as a cache server is to improve the performance and scalability of applications by reducing the load on backend systems or databases. Here are some key benefits and purposes of using Redis as a cache server: (1) Faster Data Access (2) Reduced Database Load (3) Scalability (4) Flexible Data Structures (5) Cache Invalidation and Expiration (6) Pub/Sub and Distributed Locking

First, let's review the basic concepts.   ・Cache Server This function provides data that is frequently exchanged between the server and client, such as customer information and purchase history, as an alternative to servers and databases. This allows for reduced load on servers and databases as well as reduced traffic.   ・What is Redis? Redis is a cache engine that runs on a single thread and is also an in-memory database that runs in memory. More specifically, it is a NoSQL database that operates as a KVS (Key Value Store).   Let's take a look at Redis.

We will immediately try to use Redis as a cache. The OS used this time is ubuntu20.04.   (1) Installing Redis: To install Redis, you need to follow these steps: a. Download Redis: Visit the Redis official website or GitHub repository and download the latest stable release of Redis. b. Extract the Redis package: Once the download is complete, extract the Redis package to a directory of your choice. c. Compile Redis: Open a terminal or command prompt, navigate to the extracted Redis directory, and execute the make command. This will compile Redis. d. Install Redis: After the compilation process completes, execute the make install command to install Redis on your system. This will install both the Redis server (redis-server) and the command-line client (redis-cli). Once the installation is complete, you can start the Redis server by executing the redis-server command. By default, it listens on port 6379.   (2) Installation of redis-py: To use Redis in Python, you need to install the redis-py package, which is the Python interface for Redis. You can install it using a package manager like pip by executing the following command:

## Installing Redis
sudo add-apt-repository ppa:redislabs/redis
sudo apt-get update
sudo apt-get install redis

## Check the version of Redis CLI（Installation Confirmation）
redis-cli -v

## Check Redis version（Installation Confirmation）
redis-server -v


## Installing Redis-py
pip install redis                

With these steps, you have successfully installed Redis and the redis-py package, making Redis available for use in your Python applications.

Please note that the exact steps and commands may vary depending on your operating system and the package manager you are using. Ensure you have the necessary permissions and dependencies installed for a successful installation.

 

(3) Finally, we check the operation of Redis.

In this code example, we import the redis module and create a Redis client by specifying the host, port, and database. Adjust the host and port values if your Redis server is running on a different host or port.

The set command is used to set a value in Redis. In this case, we set the value of the key 'key' to 'test'.

The get command retrieves the value associated with the specified key from Redis. In this example, we retrieve the value of 'key'.

You can confirm that the value can be retrieved!

import redis
cache = redis.Redis(
    host='localhost',
    port=6379,
    db=0
)
 
# Set value in Redis
cache.set('key', 'test')
value = cache.get('key')
 
# Retrieve values from Redis
response = cache.get('key')
print(response)
 
cache.close()

## test is displayed                  

(1) Use Connection Pooling To optimize Redis performance, it is recommended to use connection pooling. This allows you to reuse connections instead of creating new connections for each operation, reducing the overhead of establishing a new connection. Here's an example using the redis-py library in Python:

import redis
from redis.connection import ConnectionPool

# Create a connection pool
pool = ConnectionPool(host='localhost', port=6379, db=0)

# Create a Redis client using the connection pool
r = redis.Redis(connection_pool=pool)

# Perform Redis operations using the client
r.set('my_key', 'my_value')
                

(2) Set Expiry Time for Cache Items

If you're using Redis as a cache server, it's good practice to set an expiry time for cache items. This helps to automatically remove stale data from the cache, preventing it from taking up unnecessary memory.

(3) Use Pipelining for Batch Operations

Redis supports pipelining, which allows you to send multiple commands to the server in a single network roundtrip. This can significantly improve performance when performing batch operations. Here's an example of using pipelining:

# Create a pipeline object
pipeline = r.pipeline()

# Queue multiple commands in the pipeline
pipeline.set('key1', 'value1')
pipeline.set('key2', 'value2')
pipeline.set('key3', 'value3')

# Execute the commands in a single roundtrip
pipeline.execute()
                  

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This article provides useful introductory information free of charge. For consultation and inquiries, please contact "Definer Inc".

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