Redis python get value by key

Python + Redis: Powering Performance and Scalability

Python Redis is a powerful and fast in-memory data store that we can use as a cache, message broker, and database. Developers use it to build fast and scalable web applications. In this article, we will learn Python Redis and show you a way to use it to build a simple cache for your Python application.

What is Redis?

Redis is an in-memory data store that can be used as a cache, message broker, and database. It is open-source, fast, and highly scalable. Redis stores data in memory, which makes it extremely fast. It supports various data structures, such as strings, hashes, lists, and sorted sets. Redis is also highly available, supporting master-slave replication and automatic failover.

What is Python Redis?

Python Redis is a Python library that provides a simple and easy-to-use interface for accessing Redis from Python. Python developers can easily store and retrieve data from Redis using a simple API.

Getting Started with Python Redis

To begin, you will need to install Redis on your machine. You can download Redis from the Redis website.

Once Redis is installed, you can install the Python Redis library using pip:

You can now connect to Redis from your Python application with Python Redis installed.

Connecting to Redis

To connect to Redis, you can create a Redis client using the redis module:

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

This makes a Redis client that connects to Redis running on localhost on port 6379 .

Storing and Retrieving Data

You can store data in Redis using the set method:

This stores the string «Alice» with the key «name» in Redis.

To retrieve the value of a key, use the get method:

It will retrieve the «name» key’s value from Redis and prints it to the console.

Expire Time

You can set an expiration time for a key in Redis using the expire method:

r.set('name', 'Alice') r.expire('name', 60)

This sets the «name» key to expire after 60 seconds. After 60 seconds, the key will be automatically deleted from Redis.

Using Redis as a Cache

One of the most common use cases for Redis is as a cache. Here, we will show how to use Redis as a cache for your application.

To use Redis as a cache, you can store the results of expensive computations in Redis and retrieve them from Redis instead of recomputing them. It significantly speeds up the application while reducing the server load at the same time.

Here is an example of using Redis as a cache:

import redis r = redis.Redis(host='localhost', port=6379, db=0) def fibonacci(n): # Check if the result is already in the cache result = r.get(str(n)) if result: return int(result) # Compute the result if n < 2: result = n else: result = fibonacci(n-1) + fibonacci(n-2) # Store the result in the cache r.set(str(n), str(result)) return result print(fibonacci(10))

In this example, we define a function fibonacci that computes the nth Fibonacci number. Firstly check whether the result is present in the cache using get method. If it is, we return the cached result. If it is not, we compute the result and store it in Redis using the set method. This allows us to retrieve the result from Redis in future calls to the function.

Python redis async

In recent versions of redis-py, asynchronous support has been added using Python’s asyncio library. This allows you to use Redis asynchronously in a non-blocking way in Python.

Here’s an example of how to use redis-py in an asynchronous way:

import asyncio import aioredis async def main(): redis = await aioredis.create_redis_pool('redis://localhost') await redis.set('mykey', 'myvalue') result = await redis.get('mykey') print(result) redis.close() await redis.wait_closed() asyncio.run(main())

In this example, we first import the necessary libraries, including asyncio and aioredis . We then define an asynchronous function main() that creates a Redis connection pool using the create_redis_pool() function from aioredis , and connects to the Redis instance running on localhost .

We then use the set() method to set a key-value pair in Redis and the get() method to retrieve the value of the key. Finally, we print the result, which should be the value of the key mykey that we just set.

After we’re done, we close the connection pool using the close() method, and wait for all connections to be closed using the wait_closed() method.

Python redis add to list

To add an element to a Redis list using redis-py , you can use the rpush() method. The rpush() method appends one or more values to the end of a list in Redis. Here’s an example:

import redis r = redis.Redis(host='localhost', port=6379, db=0) r.rpush('mylist', 'value1', 'value2', 'value3')

In this example, we first create a Redis connection using the redis.Redis() method, and connect to the Redis instance running on localhost on port 6379 , using database 0 .

We then use the rpush() method to append the values 'value1' , 'value2' , and 'value3' to the end of a Redis list called 'mylist' .

If the list doesn’t exist, Redis will create it before adding the values. If you want to insert the values at the beginning of the list, you can use the lpush() method instead.

You can also add a single value to the end of a list using the rpushx() method, or the lpushx() method to add a single value to the beginning of a list.

Here’s an example of using rpushx() to add a single value to a list.

Python redis bytes to string

When you retrieve data from Redis using redis-py , you may get binary data (i.e., bytes) instead of strings. To convert bytes to a string in Python, you can use the decode() method, which converts a byte object to a string using a specified character encoding.

Here’s an example of how to convert bytes to a string when retrieving data from Redis:

import redis r = redis.Redis(host='localhost', port=6379, db=0) # Set a key-value pair r.set('mykey', 'myvalue') # Get the value of the key as bytes value_bytes = r.get('mykey') # Convert the bytes to a string value_str = value_bytes.decode('utf-8') print(value_str)

We first set a key-value pair in Redis using the set() method in this example. We then retrieve the value of the key using the get() method, which returns a bytes object. We use the decode() method to convert the bytes object to a string using the UTF-8 character encoding.

Python redis delete

To delete a key from Redis using redis-py , you can use the delete() method. The delete() method removes the specified key and its associated value(s) from Redis.

import redis r = redis.Redis(host='localhost', port=6379, db=0) # Set a key-value pair r.set('mykey', 'myvalue') # Delete the key r.delete('mykey')

Here, we first set a key-value pair in Redis using the set() method. We then use the delete() method to delete the key 'mykey' and its associated value from Redis.

If you try to delete a key that doesn’t exist, Redis will ignore the command and return 0. If you want to delete multiple keys, you can pass multiple key names to the delete() method as separate arguments.

Python redis get all keys

To get a list of all keys in Redis using redis-py , you can use the keys() method. The keys() method returns a list of all keys matching a specified pattern.

import redis r = redis.Redis(host='localhost', port=6379, db=0) # Set some key-value pairs r.set('mykey1', 'myvalue1') r.set('mykey2', 'myvalue2') r.set('myotherkey', 'myothervalue') # Get a list of all keys keys = r.keys('*') print(keys)

We first set some key-value pairs in Redis using the set() method in this example. We then use the keys() method to get a list of all keys in Redis matching the pattern '*' , which matches all keys.

The keys() method returns a list of bytes objects, so you may need to convert the keys to strings using the decode() method to work with them as strings.

Python redis json

You can store JSON data in Redis using redis-py by serializing the JSON data into a string and then storing the string as the value of a Redis key. You can use the json module in Python to serialize JSON data into a string and then deserialize it back into a Python object when retrieving it from Redis.

Here’s an example of how to store and retrieve JSON data in Redis using redis-py :

import redis import json r = redis.Redis(host='localhost', port=6379, db=0) # Store some JSON data in Redis data = json_str = json.dumps(data) r.set('mykey', json_str) # Retrieve the JSON data from Redis json_str = r.get('mykey') data = json.loads(json_str) print(data['name']) # Output: John print(data['age']) # Output: 30

In this example, we first create a dictionary containing some JSON data. We then use the json.dumps() method to serialize the data into a JSON string, which we store in Redis using the set() method. When we retrieve the data from Redis using the get() method, we get a string containing the JSON data, which we deserialize using the json.loads() method.

Python redis vs. strictredis

Feature	redis-py	StrictRedis
Redis commands	Supports all Redis commands as instance methods	Supports all Redis commands as instance methods
Connection pool	Supports multiple connection pools with different configurations	Supports a single global connection pool with a fixed configuration
Connection parameters	Accepts connection parameters as arguments or as a dictionary	Accepts connection parameters only as keyword arguments
Response handling	Returns responses as raw Redis protocol data	Parses responses into Python objects when possible
Error handling	Raises redis.exceptions.RedisError for all errors	Raises more specific exceptions for different types of errors
Pipeline execution	Supports pipeline execution using the pipeline() method	Supports pipeline execution using the pipeline() method
Transactions	Supports transactions using the transaction() method	Supports transactions using the transaction() method
Lua scripting	Supports Lua scripting using the register_script() method	Supports Lua scripting using the register_script() method
Pub/sub	Supports pub/sub using the pubsub() method	Supports pub/sub using the pubsub() method
Cluster support	Supports Redis clusters using the ClusterConnectionPool class	Supports Redis clusters using the ClusterStrictRedis class

Redis vs. StrictRedis

In general, StrictRedis is a more opinionated and user-friendly interface to Redis, whereas redis-py provides a more flexible and low-level interface. StrictRedis handles errors more gracefully and parses responses into Python objects when possible, which can make it easier to use in most cases. However, redis-py provides more advanced features, such as multiple connection pools, support for custom response parsing, and more fine-grained control over Redis commands.

FAQs

Redis is used as a cache because of its scalability and fast speed. It is an in-memory data store, meaning it can provide low latency and high throughput for cache operations.

The amount of data that can be stored in the Redis cache depends on several factors, such as the available memory, the configuration of Redis, and the size of the data being stored. By default, Redis is configured to use a maximum of 4GB of memory, which can be increased by changing the configuration settings.

Conclusion

In this article, we have shown you how to begin and how to use it to build a simple cache for your Python application. Hopefully, this article is helpful and motivates you to dig deep inside the Redis.

References

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