When your backend systems are not fast as you want them to be, a cache gives you a holding pen for quick retrieval and a faster response time. Response time refers to the amount of time in milliseconds that an application takes to respond to a user interaction. When Caching is part of your architecture, you can often reduce response times from hundreds of milliseconds to less than a millisecond. Redis is an in-memory data store, which means everything you put in sits in memory, so it has very quick, high-performance functionality for your workloads. A cache is a component (hardware or software) that stores data so that you can access future requests for that data faster. Redis cache can be structured from strings, hashes, and lists.

Redis cache is an open-source tool that runs as a service in the background and allows you to store data in memory for high-performance data retrieval and storage purposes. For example, if you have an application inside your server and your web application needs to retrieve data from your MySQL or PostgreSQL database. The query to get your data from your database can take a long time, depending on the query's complexity. Instead of directly querying a database, you can store the data inside a Redis Cache instance and make the retrieval that data directly from the server running the Redis service. Your web server can now check with Redis if it has the data it wants; if not, the Redis cache can be populated with the database.

There are different ways you can deploy a Redis cache instance; the first and easiest way to do that is to use a Docker container. You can deploy Redis as a stand-alone Docker container in your application if you’re just getting started. You can also deploy your Redis instance in a managed service in your cloud providers like Azure, AWS, and GCP.

Prerequisites

  • Docker installed on your local machine.
  • Simple knowledge about Redis

Deploy and run Redis in Docker.

  • Check if Docker is running on your system by running the `docker` command in your terminal.

  • Before we get started, we have to pull the Redis image using docker pull redis and it’ll download the latest version of the Redis image.

  • List all your Docker images to check if Redis is installed using the docker images command.

  • Next, run the Redis container using the docker run –name rediscontainer redis You can choose to run your container in daemon mode using the d flag; this will run your container in the background completely detached from your current shell.  docker run —name rediscontainer -d redis
  • Confirm if your container is running using docker ps In your Redis container, you can see the Container ID, Image, Command, when it was created, status, and the Redi Port number.

  • Next, let’s interact with the Redis container. To interact with it, we’ll use the docker exec -it <container_id> sh command.  This will open a shell for your Docker container.
  • Now that we’ve our shell terminal, we can work with Redis commands. In your CLI, run redis-cli
  • Get and list your existing keys.
  • Set an example name using the set command set name redis-workflow
  • Redis is a key:value store, so you can also set other key:value pairs like set parity “method”
  • If you want to get a value, all you have to do is type GET and the key name.

Now we can work with Redis using the Docker container. Redis is an advanced key-value store that can function as a NoSQL database or as a memory-cache store to improve performance when serving data stored in system memory.

Conclusion

Are you planning out your next application architecture and don’t know if you want to try something new? As Platform engineers, we often have to decide what to use in our infrastructure and how to scale our resources to meet customer needs. Using CTO.ai workflows for Redis, you can remove that friction from setting up your Redis-managed instance and deploying it in your application environment.