Rqueue is a Spring-based asynchronous task executor, that can execute tasks at any delay, it’s built upon the Spring messaging library and backed by Redis. We’re going to use the Rqueue library to execute any tasks with any arbitrary delay. The directory/folder structure would look like below. We’re going to use Spring boot for simplicity, we’ll create a Gradle project from spring boot initializer at, for dependency we would need Rqueue guarantee at-least-once message delivery as long data is not wiped out from Redis. more ops, as most of the applications are already using Redis so we won’t have ops overhead, in fact, same Redis server/cluster can be used with Rqueue. Generally, for a queue, we use either Kafka/SQS or some other systems these systems bring an additional overhead in different dimensions e.g money which can be reduced to zero using Rqueue and Redis.Īpart from the cost if we use Kafka then we need to do infrastructure setup, maintenance i.e. In today's world, 8.4% of web applications are using the Redis database. In most web applications backend, Redis is used to store either cache data orother purposese. Rqueue is backed by Redis since Redis has some advantages over the widely used queuing systems like Kafka, SQS. Rqueue is a broker built for the spring framework that stores data in Redis and provides a mechanism to execute a task at any arbitrary delay. There’re many queuing systems/software available, but very few of them provide this feature, like SQS which provides a delay of 15 minutes, not an arbitrary delay like 7 hours or 7 days. A delayed queue can be used in such cases where as soon as the timer reaches the scheduled time a job would be triggered. Imagine the case where the same database is used by a real-time application and this batch processing system. In many situations, this leads to a full table scan which performs very poorly. Most of these scheduling mechanisms suffer scaling problems, where we do scan database(s) to find the relevant rows/records. We can run schedule tasks using cron jobs, a cron job can be scheduled using different methods like UNIX style crontabs, Chronos, if we’re using Spring frameworks then it’s out of box Scheduled annotation ❤️. Sometimes we need to execute tasks in asynchronous to reduce API response time, for example, delete 1K+ records at once if we delete all these records in the same API call then API response time would be increased for sure, to reduce API response time, we can run a task in the background that would delete those records. Schedule one or more tasks to be executed at a later time or immediately but in the background.Ī task can be scheduled at a later time for many reasons for example invoice must be generated after 7 days of order creation or order shipment, similarly, email/notification(s) need not be sent immediately it can be delayed.Calling some other service(s) (internal/external).Some computations (it could be some data crunching doing some math operations). One or more operations on some cache systems (In-Memory, Distributed, etc ).Execute one or more database(RDBMS/NoSQL) queries. The latency of Redis call is sub-milliseconds, support of a replica set, etc.Ī typical API call consists of five things Set, Hash table, List, simple key-value pair just name a few. Redis supports different types of data structures e.g. Redis is one of the most popular open-source NoSQL in-memory database. Spring 3.0 was the first version that supported the annotation-based configuration, later in 2014 Spring boot 1.0 was released that completely changed how we look at the Spring framework ecosystems, it provides out of the box configuration and many more. Besides that, Spring provides extensive and up-to-date documentation that covers the inner workings of the framework and sample projects on their blog, there’re 100K+ questions on StackOverflow. Spring has one of the largest communities in open source. Spring is the most popular framework available for the Java platform. In this article, we are going to see how to use Spring boot 2.x and Redis to execute asynchronous tasks, with the final code that we will have after following the steps described here.
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