{"id":5076,"date":"2024-08-21T12:07:14","date_gmt":"2024-08-21T12:07:14","guid":{"rendered":"https:\/\/www.aegissofttech.com\/insights\/?p=5076"},"modified":"2026-03-18T11:22:54","modified_gmt":"2026-03-18T11:22:54","slug":"balancing-consistency-spring-boot-caching-partitioning-schedulers","status":"publish","type":"post","link":"https:\/\/www.aegissofttech.com\/insights\/balancing-consistency-spring-boot-caching-partitioning-schedulers\/","title":{"rendered":"Balancing Consistency & Performance in Spring Boot with Caching, Partitioning, & Schedulers"},"content":{"rendered":"\n

Introduction<\/strong>:<\/h2>\n\n\n\n

In this blog, you will learn about how to balance consistency and Performance with strategic Use of caching, database partitioning, and Scheduler in spring Boot microservices:<\/p>\n\n\n\n

In the trend of microservices, to optimize performance at the same time to maintain data consistency is really a major challenge. Spring Boot microservices also has same challenges. One effective way to achieve this balance between performance n data consistency during high traffic is through strategic caching.<\/p>\n\n\n\n

Caching can reduce latency and decrease the load on our microservices as it stores frequently accessed data of the client. In this blog, I have explained the principles of caching in Spring Boot microservices with a complex real-time Java application development services<\/a> example implementation.<\/p>\n\n\n\n

Concepts of Caching in Microservices<\/strong><\/h2>\n\n\n\n

Caching is a way used to store copies of frequently accessed data in a Cache (i.e. a temporary storage area, allowing for faster data retrieval. It reduces the need to frequently fetch the same data from the main source, such as a primary database or an enterprise data warehousing<\/a> platform, where repeated high-volume queries can be resource-intensive.<\/p>\n\n\n\n

Major Benefits of Caching:<\/strong><\/h2>\n\n\n\n
    \n
  1. Performance gets Improved<\/strong>: It reduces API response time by fetching data from the cache instead of making every time database or API calls.<\/li>\n\n\n\n
  2. Load gets reduced<\/strong>: It reduces or decreases the load on the backend services as well as on databases side so that resources get less occupied and they can be used for other operations.<\/li>\n\n\n\n
  3. Scalability gets enhanced<\/strong>: It increases the scalability of microservices as it can serve more requests with lower latency.<\/li>\n<\/ol>\n\n\n\n

    Diff. Types of Caching:<\/strong><\/h2>\n\n\n\n
      \n
    1. In-Memory Caching<\/strong>: With this type of caching, it stores data in the RAM so that access time is fast. For examples; Ehcache and Caffeine.<\/li>\n\n\n\n
    2. Distributed Caching<\/strong>: With this type of caching, data is stored across multiple nodes so that scalability is increased as well as fault tolerance is achieved. For Example :Redis and Hazelcast.<\/li>\n<\/ol>\n\n\n\n

      Achieving balance btw Consistency and Performance<\/strong><\/h2>\n\n\n\n

      See as you know caching increases performance nd as a result stale data is also increased if we are not managing these correctly. So at some point we have to maintain a balance between data consistency and performance and it should be must. Below are some strategies I have explained:<\/p>\n\n\n\n

        \n
      1. Invalidate Cache<\/strong>:  We have to always make sure that whatever cached data that should get invalidated or updated whenever the underlying data gets changed.<\/li>\n\n\n\n
      2. Time-to-Live (TTL)<\/strong>: One more thing is to set an cache expiration time for the cached data so that after that many intervals again fresh cache is populated.<\/li>\n\n\n\n
      3. Read-Through Caching and Write-Through<\/strong>: With this configuration,  cache gets synchronized with the data source during every read and write operations.<\/li>\n\n\n\n
      4. Event-Driven Caching<\/strong>: Best practice is to leverage events to update or invalidate the cache data\/entries whenever there is a change happens in the data source.<\/li>\n<\/ol>\n\n\n\n

        Real-Time Example Implementation for Strategic Caching with Spring Boot Microservice<\/strong><\/h2>\n\n\n\n

        I have explained here a real-time example of an e-commerce application where we need to optimize product catalog service performance using strategic caching.<\/p>\n\n\n\n

        1. Setting Up the Project<\/strong><\/h3>\n\n\n\n

        First basic step is to create a Spring Boot project with the required dependencies. Add below dependencies to your pom.xml:<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        2. Configuring Redis Cache<\/strong><\/h3>\n\n\n\n

        Configure Redis as the caching solution in application.properties:<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        3. Enabling Caching in Spring Boot<\/strong><\/h3>\n\n\n\n

        Enable caching by annotating the main application class:<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        4. Implementing Cacheable Methods<\/strong><\/h3>\n\n\n\n

        Use below annotations with @Cacheable, @CachePut, and @CacheEvict to manage caching behavior.<\/p>\n\n\n\n

        Service Class Example:<\/strong><\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        Explanation:<\/strong><\/p>\n\n\n\n