Blinking Fast Api's

What is Redis?

Remote Dictionary Server, aka Redis, an in-memory data store, is one of the many options for implementing caching in Spring Boot applications due to its speed, versatility, and simplicity of use. It is a versatile key-value store that supports several data structures, such as Strings, Sorted Sets, Hashes, Lists, Streams, Bitmaps, Sets, etc., because it is a NoSQL database and doesn’t need a predetermined schema.

Redis can be used in various ways, including:

1)** In-Memory Database: ** In today’s data-driven world, handling vast amounts of real-time data is a common challenge for businesses. A real-time database is a type of data repository designed to acquire, analyze, and/or augment an incoming stream of data points in real time, often immediately after the data is produced. Redis may be used to build data infrastructure for real-time applications that need high throughput and low latency.

2) *Cache: * Many applications struggle with the need to store and retrieve data quickly, especially in systems with high latency. Due to its speed, Redis is the ideal choice for caching API calls, session states, complex computations, and database queries.

3) *Message Broker (MQ): * It has always been difficult to stream data around the organization and make it accessible for various system components. Redis supports messaging, event sources, alerts, and high-speed data intake using its stream data type.

How Does Redis Caching Work?

Redis Cache effectively stores the results of database retrieval operations, allowing subsequent requests to retrieve the data directly from the cache. This significantly enhances application performance by reducing unnecessary database calls.

When a request is made, the service initially looks in the Redis cache for the desired data. When a cache hit occurs, the data is swiftly retrieved from the cache and promptly provided back to the service, avoiding the need to interact with the database.

However, if the requested data is not found in the cache (cache miss), the service intelligently falls back to the database to retrieve the required information. Subsequently, the fetched data is stored in the Redis cache, enabling future requests for the same data to be served directly from the cache, thereby eliminating further database queries and speeding up overall response times.

Redis can also be used for deleting and updating tasks, guaranteeing consistent and latest data in the cache and further boosting overall efficiency.

Let's Set up Our Spring Boot Application!

Step 1: Add Redis dependency

<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-data-redis</artifactId>
   <version>3.1.2</version>
</dependency>

Redis-related libraries are part of the Spring Data Package, which provides easy access to databases, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. The spring-boot-starter-data-redis will have all the necessary dependencies prepared.

Step 2: Configure Redis using application.yml or you can use application.properties file as well.

spring:
  cache:
    type: redis
    host: localhost
    port: 6379
    redis:
      time-to-live: 60000
  datasource:
    driver-class-name: org.postgresql.Driver
    url: jdbc:postgresql://localhost:5432/inventory
    username: root
    password: ****
  jpa:
    hibernate:
      ddl-auto: update
    properties:
      hibernate:
        dialect: org.hibernate.dialect.PostgreSQLDialect

The PostgreSQL database is used, and the necessary database connection properties are introduced. In addition, we define Redis as our cache provider, along with its hostname and port.

Time To Live(TTL): A good practice for caching is to ensure that excess and redundant data is not accumulated indefinitely, as this can result in stale or outdated data being served to users. To serve this, we can take advantage of the time-to-live property, which is an optional setting that allows us to set the expiration time for cached data. After the specified time has elapsed, the cached entry is automatically removed from the cache. This makes space for new data to be fetched and stored in the cache the next time it’s requested. If no value is assigned to the property, it becomes -1 by default, which means the data will stay in the cache indefinitely.

We have set our time to live property to be 60000 ms in the example, which means that the data will be cleared from the cache after every minute.

Step 3: Creating entity and repository**

Set up the entity and repository to perform CRUD operations like any other Spring Boot project. Since Redis is an in-memory database, we need to transform our object into a stream of bytes for storing as well as the other way around for retrieving data. Therefore, we need to serialize/deserialize our data by ensuring that the entity class implements the Serializable class.

@Entity
public class Product implements Serializable {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private long id;
    private String name;
    private String code;
    private int quantity;
    private double price;
}

We have an entity responsible for storing and fetching data, so we can create our repository by extending the JpaRepository interface. It will provide basic CRUD operations to work with, like save, find, and delete.

@Repository
public interface ProductRepository extends JpaRepository<Product, Long> {
}

Step 4: Enable caching in spring boot

Add the annotation @EnableCaching to the starter class. It will trigger a post-processor that inspects every Spring bean for the presence of caching annotations on public methods.

@SpringBootApplication
@EnableCaching
public class RedisDemoApplication {

  public static void main(String[] args) {
  SpringApplication.run(RedisDemoApplication.class, args);
 }
}

Step 5: Annotation-based caching on the controller layer

Use caching annotations in the controller layer to enable caching for specific methods:

@Cacheable is employed to fetch data from the database, storing it in the cache. Upon future invocations, the method retrieves the cached value directly, eliminating the need to execute the method again.

@GetMapping("/product/{id}")  
@Cacheable(value = "product", key = "#id")
public Product getProductById(@PathVariable long id) {...}

The value attribute establishes a cache with a specific name, while the key attribute permits the use of Spring Expression Language to compute the key dynamically. Consequently, the method result is stored in the ‘product’ cache, where respective ‘product_id’ serves as the unique key. This approach optimizes caching by associating each result with a distinct key.

1. @CachePut is used to update data in the cache when there is any update in the source database.

@PutMapping("/product/{id}")
@CachePut(cacheNames = "product", key = "#id")
public Product editProduct(@PathVariable long id, @RequestBody Product product) {...}

The cacheNames attribute is an alias for value, and can be used in a similar manner.

1. @CacheEvict is used for removing stale or unused data from the cache.

@DeleteMapping("/product/{id}")
@CacheEvict(cacheNames = "product", key = "#id", beforeInvocation = true)
public String removeProductById(@PathVariable long id) {...}

We use cacheName and key to remove specific data from the cache. The beforeInvocation attribute allows us to control the eviction process, enabling us to choose whether the eviction should occur before or after the method execution.

@DeleteMapping("/product/{id}")
 @CacheEvict(cacheNames = "product", allEntries = true)
 public String removeProductById(@PathVariable long id) {...}

Alternatively, all the data can also be removed for a given cache by using the allEntries attribute as true. The annotation allows us to clear data for multiple caches as well by providing multiple values as cacheName.

1. @Caching is used for multiple nested caching on the same method.

@PutMapping("/{id}")
@Caching(
     evict = {@CacheEvict(value = "productList", allEntries = true)},
     put = {@CachePut(value = "product", key = "#id")}
)
public Product editProduct(@PathVariable long id, @RequestBody Product product) {...}

As Java doesn’t allow multiple annotations of the same type to be declared for a method, doing so will generate a compilation error. We can group different annotations into Caching, as shown above.

1. @CacheConfig is used for centralized configuration.

@CacheConfig(cacheNames = "product")
public class ProductController {...}

Used as a class-level annotation, Cache config allows centralizing some of the configurations to avoid specifying it at each step. For the example above, we need not write cacheName for all methods, instead, specify the cache name at the class level.

Using keyGenerator attribute:

This is responsible for generating every key for each data item in the cache, which would be used to look up the data item on retrieval. The default implementation here is the SimpleKeyGenerator — which uses the method parameters provided to generate a key, as shown earlier. However, we may want to provide a more customized approach to generate the cache key based on specific criteria. To achieve this, we can use the keyGenerator attribute.

To give a different custom key generator, we need to implement the org.springframework.cache.interceptor.KeyGenerator interface. The class needs to implement a single method as shown:

public class CustomKeyGenerator implements KeyGenerator {
  @Override
  public Object generate(Object target, Method method, Object... params) {
      return target.getClass().getSimpleName() + "_"
        + method.getName() + "_"
        + StringUtils.arrayToDelimitedString(params, "_");
   }
}

We can now use the customeKeyGenerator as a value to keyGenerator attribute and provide a customized key generator for the given method.

@GetMapping("/product/{id}")  
@Cacheable(value = "product", keyGenerator = "customKeyGenerator")
public Product getProductById(@PathVariable long id) {...}

Conditional caching:

Conditional caching allows us to cache specific data using the unless and conditional attributes. The unlessattribute allows us to filter data after the method has been called. By using the Spring Expression Language, we can choose not to cache the data if a certain condition evaluates to true. In the given example, we have allowed caching only for products priced less than 1000. This way, we cache selectively based on the defined condition.

@GetMapping("/product/{id}")  
@Cacheable(value = "product", key = "#id" , unless = "#result.price > 1000")
public Product getProductById(@PathVariable long id) {...}

The conditional attribute, however, works quite the opposite. If the condition happens to be true, then the resulting data is cached in our cache. For the given example, only products named fridge will be stored in the cache.

@PutMapping("/{id}")
@Caching( value = "product", condition = "#product.name == "Fridge""))
public Product editProduct(@PathVariable long id, @RequestBody Product product) {...}

Wrapping Up

In this post, we discussed the basics of using Spring caching in your application. Remember to fine-tune your caching configuration based on your application’s specific needs.

Integrating Redis cache into your Spring Boot applications can significantly enhance their performance and scalability. Redis’s in-memory storage and blazing-fast read/write operations make it an excellent choice for caching frequently accessed data. By intelligently using caching strategies and leveraging Redis’ distributed architecture, you can create responsive and efficient applications that deliver an exceptional user experience.