My primary use case for Redis is to enhance the performance of our web applications by using it as a caching layer. By caching frequently accessed data, we reduce the load on our primary databases, resulting in faster data retrieval and a more responsive user experience. We also use Redis for session storage, managing user sessions in a stateless manner, which ensures quick access to session data, supporting high-traffic scenarios without compromising performance. Additionally, Redis handles real-time analytics and leaderboard features, providing fast and efficient data processing capabilities. For real-time notifications and updates, we utilize Redis' Pub/Sub messaging feature. This facilitates real-time communication and synchronization between our services. Our Redis setup includes replication for high availability, persistence for data durability, and clustering for scalability. This integration of Redis in our environment has significantly boosted the performance, scalability, and reliability of our applications, making it an essential component of our infrastructure.
Software Engineer at a tech vendor with 5,001-10,000 employees
Real User
Top 10
2024-09-05T14:03:00Z
Sep 5, 2024
I've used Redis mainly to improve application performance through caching. I've also used it as a small project broker and an external lock mechanism. I used it in previous companies.
I use Redis for data deduplication or identifying duplicate records I receive. I have a very basic usage with Redis, where I insert a key value record.
I'm the only user utilizing Redis in my company, as my platform is relatively new and has around fifty users. However, in my previous role at Profit Clicks, we had approximately ten thousand users. We utilized a combination of databases, including Redis, MongoDB, and MariaDB. We needed to handle push notifications for our users, sending them three times a day, and managing a large volume of notifications efficiently was crucial. Redis played a role in managing this workload.
We use it primarily for real-time applications. In our web application, we added a feature where hundreds of people could play a quiz in real time. Instead of using traditional databases like SQL, we implemented Redis to make everything happen in real time – all those quick calculations, data hashing for easy retrieval, and so on. It was a live quiz feature, so Redis helped a lot. I also use Redis for caching and similar general use cases.
In our company, Redis is used for read operations. When we receive data from a user via the Read API, we cache the response for two or three minutes to minimize API calls to the database and preserve resources within the DB. This practice also helps prevent bot-triggered dummy requests to the DB.
It was a classic caching use case. In my previous company, we were running MongoDB on the cloud, and we were running Redis for caching. We were just starting, so the data size was pretty small there.
NoSQL databases represent a major departure from traditional relational databases. They are designed to handle a wide variety of data models beyond just relational data, including columnar, document, key-value and graph formats.
My primary use case for Redis is to enhance the performance of our web applications by using it as a caching layer. By caching frequently accessed data, we reduce the load on our primary databases, resulting in faster data retrieval and a more responsive user experience. We also use Redis for session storage, managing user sessions in a stateless manner, which ensures quick access to session data, supporting high-traffic scenarios without compromising performance. Additionally, Redis handles real-time analytics and leaderboard features, providing fast and efficient data processing capabilities. For real-time notifications and updates, we utilize Redis' Pub/Sub messaging feature. This facilitates real-time communication and synchronization between our services. Our Redis setup includes replication for high availability, persistence for data durability, and clustering for scalability. This integration of Redis in our environment has significantly boosted the performance, scalability, and reliability of our applications, making it an essential component of our infrastructure.
I used Redis for a one-time implementation while working on a college project. I implemented it locally on a machine and used it for a vector search.
In the past, I used Redis as a cache, and currently, I'm using it as a messaging system.
I've used Redis mainly to improve application performance through caching. I've also used it as a small project broker and an external lock mechanism. I used it in previous companies.
I use Redis for data deduplication or identifying duplicate records I receive. I have a very basic usage with Redis, where I insert a key value record.
I'm the only user utilizing Redis in my company, as my platform is relatively new and has around fifty users. However, in my previous role at Profit Clicks, we had approximately ten thousand users. We utilized a combination of databases, including Redis, MongoDB, and MariaDB. We needed to handle push notifications for our users, sending them three times a day, and managing a large volume of notifications efficiently was crucial. Redis played a role in managing this workload.
Our organization's main use case for Redis is to store and load static content for our multi-client products.
We use it primarily for real-time applications. In our web application, we added a feature where hundreds of people could play a quiz in real time. Instead of using traditional databases like SQL, we implemented Redis to make everything happen in real time – all those quick calculations, data hashing for easy retrieval, and so on. It was a live quiz feature, so Redis helped a lot. I also use Redis for caching and similar general use cases.
Sometimes, Redis is used as a cache and sometimes as the main database.
I use the solution for saving user sessions and user data for different applications.
In our company, Redis is used for read operations. When we receive data from a user via the Read API, we cache the response for two or three minutes to minimize API calls to the database and preserve resources within the DB. This practice also helps prevent bot-triggered dummy requests to the DB.
It was a classic caching use case. In my previous company, we were running MongoDB on the cloud, and we were running Redis for caching. We were just starting, so the data size was pretty small there.