You can easily configure rate limiting with Spring Cloud Gateway. For a basic introduction to this feature, you may refer to my article Rate Limiting in Spring Cloud Gateway with Redis. Similarly, today we will also use Redis as a backend for a rate limiter. Moreover, we will configure an HTTP basic authentication. Of course, you can provide some more advanced authentication mechanisms like an X509 certificate or OAuth2 login. If you think about it, read my article Spring Cloud Gateway OAuth2 with Keycloak.

Source Code

If you would like to try it by yourself, you may always take a look at my source code. In order to do that you need to clone my repository sample-spring-cloud-gateway. Then you should go to the src/test/java directory, and just follow my instructions in the next sections.

1. Dependencies

Let’s start with dependencies. Since we will create an integration test, we need some additional libraries. Firstly, we will use the Testcontainers library. It allows us to run Docker containers during the JUnit test. We will use it for running Redis and a mock server, which is responsible for mocking a downstream service. Of course, we need to include a starter with Spring Cloud Gateway and Spring Data Redis. To implement an HTTP basic authentication we also need to include Spring Security. Here’s a full list of required dependencies in Maven pom.xml.

<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-gateway</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-data-redis-reactive</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-security</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-test</artifactId>
   <scope>test</scope>
</dependency>
<dependency>
   <groupId>org.testcontainers</groupId>
   <artifactId>mockserver</artifactId>
   <scope>test</scope>
</dependency>
<dependency>
   <groupId>org.mock-server</groupId>
   <artifactId>mockserver-client-java</artifactId>
   <scope>test</scope>
</dependency>

2. Configure an HTTP Basic Authentication

In order to configure an HTTP basic authentication, we need to create the @Configuration bean annotated with @EnableWebFluxSecurity. That’s because Spring Cloud Gateway is built on top of Spring WebFlux and Netty. Also, we will create a set of test users with MapReactiveUserDetailsService.

@Configuration
@EnableWebFluxSecurity
public class SecurityConfig {

   @Bean
   public SecurityWebFilterChain filterChain(ServerHttpSecurity http) {
      http.authorizeExchange(exchanges -> 
         exchanges.anyExchange().authenticated())
            .httpBasic();
      http.csrf().disable();
      return http.build();
   }

   @Bean
   public MapReactiveUserDetailsService users() {
      UserDetails user1 = User.builder()
            .username("user1")
            .password("{noop}1234")
            .roles("USER")
            .build();
      UserDetails user2 = User.builder()
            .username("user2")
            .password("{noop}1234")
            .roles("USER")
            .build();
      UserDetails user3 = User.builder()
            .username("user3")
            .password("{noop}1234")
            .roles("USER")
            .build();
      return new MapReactiveUserDetailsService(user1, user2, user3);
   }
}

3. Configure Spring Cloud Gateway Rate Limiter key

A request rate limiter feature needs to be enabled using the component called GatewayFilter. This filter takes an optional keyResolver parameter. The KeyResolver interface allows you to create pluggable strategies derive the key for limiting requests. In our case, it will be a user login. Once a user has been successfully authenticated, its login is stored in the Spring SecurityContext. In order to retrieve the context for a reactive application, we should use ReactiveSecurityContextHolder.

@Bean
KeyResolver authUserKeyResolver() {
   return exchange -> ReactiveSecurityContextHolder.getContext()
           .map(ctx -> ctx.getAuthentication()
              .getPrincipal().toString());
}

4. Test Scenario

In the test scenario, we are going to simulate incoming traffic. Every single request needs to have a Authorization header with the user credentials. A single user may send 4 requests per minute. After exceeding that limit Spring Cloud Gateway will return the HTTP code HTTP 429 - Too Many Requests. The traffic is addressed to the downstream service. Therefore, we are running a mock server using Testcontainers.

5. Testing Spring Cloud Gateway secure rate limiter

Finally, we may proceed to the test implementation. I will use JUnit4 since I used it before for the other examples in the sample repository. We have three parameters used for rate limiter configuration: replenishRate, burstCapacity and requestedTokens. Since we also allow less than 1 request per second we need to set the right values for burstCapacity and requestedTokens. In short, the requestedTokens property sets how many tokens a request costs. On the other hand, the burstCapacity property is the maximum number of requests (or cost) that is allowed for a user.

During the test we randomly set the username between user1, user2 and user3. The test is repeated 20 times.

@SpringBootTest(webEnvironment = 
   SpringBootTest.WebEnvironment.DEFINED_PORT,
                properties = {"rateLimiter.secure=true"})
@RunWith(SpringRunner.class)
public class GatewaySecureRateLimiterTest {

   private static final Logger LOGGER = 
      LoggerFactory.getLogger(GatewaySecureRateLimiterTest.class);
   private Random random = new Random();

   @Rule
   public TestRule benchmarkRun = new BenchmarkRule();

   @ClassRule
   public static MockServerContainer mockServer = 
      new MockServerContainer();
   @ClassRule
   public static GenericContainer redis = 
      new GenericContainer("redis:5.0.6").withExposedPorts(6379);

   @Autowired
   TestRestTemplate template;

   @BeforeClass
   public static void init() {
      System.setProperty("spring.cloud.gateway.routes[0].id", "account-service");
      System.setProperty("spring.cloud.gateway.routes[0].uri", "http://" + mockServer.getHost() + ":" + mockServer.getServerPort());
      System.setProperty("spring.cloud.gateway.routes[0].predicates[0]", "Path=/account/**");
      System.setProperty("spring.cloud.gateway.routes[0].filters[0]", "RewritePath=/account/(?<path>.*), /$\\{path}");
      System.setProperty("spring.cloud.gateway.routes[0].filters[1].name", "RequestRateLimiter");
      System.setProperty("spring.cloud.gateway.routes[0].filters[1].args.redis-rate-limiter.replenishRate", "1");
      System.setProperty("spring.cloud.gateway.routes[0].filters[1].args.redis-rate-limiter.burstCapacity", "60");
      System.setProperty("spring.cloud.gateway.routes[0].filters[1].args.redis-rate-limiter.requestedTokens", "15");
      System.setProperty("spring.redis.host", redis.getHost());
      System.setProperty("spring.redis.port", "" + redis.getMappedPort(6379));
      new MockServerClient(mockServer.getContainerIpAddress(), mockServer.getServerPort())
            .when(HttpRequest.request()
                    .withPath("/1"))
            .respond(response()
                    .withBody("{\"id\":1,\"number\":\"1234567890\"}")
                    .withHeader("Content-Type", "application/json"));
   }

   @Test
   @BenchmarkOptions(warmupRounds = 0, concurrency = 1, benchmarkRounds = 20)
   public void testAccountService() {
      String username = "user" + (random.nextInt(3) + 1);
      HttpHeaders headers = createHttpHeaders(username,"1234");
      HttpEntity<String> entity = new HttpEntity<String>(headers);
      ResponseEntity<Account> r = template
         .exchange("/account/{id}", HttpMethod.GET, entity, Account.class, 1);
      LOGGER.info("Received({}): status->{}, payload->{}, remaining->{}",
            username, r.getStatusCodeValue(), r.getBody(), r.getHeaders().get("X-RateLimit-Remaining"));
    }

   private HttpHeaders createHttpHeaders(String user, String password) {
      String notEncoded = user + ":" + password;
      String encodedAuth = Base64.getEncoder().encodeToString(notEncoded.getBytes());
      HttpHeaders headers = new HttpHeaders();
      headers.setContentType(MediaType.APPLICATION_JSON);
      headers.add("Authorization", "Basic " + encodedAuth);
      return headers;
   }

}

Let’s run the test. Thanks to the junit-benchmarks library we may configure the number of rounds for the test. Each time I’m logging the response from the gateway that includes username, HTTP status, payload, and a header X-RateLimit-Remaining that shows a number of remaining tokens. Here’s the result.

The post Secure Rate Limiting with Spring Cloud Gateway appeared first on Piotr's TechBlog.

Spring Cloud video course

You may also find a video guide, where I’m showing all the features described here with more details. It is available on my YouTube channel and consists of four parts:

Part 1 – Introduction to Spring Boot (~35 minutes)
Part 2 – Distributed Configuration and Service Discovery (~36 minutes)
Part 3 – Inter-service communication (~34 minutes)
Part 4 – API Gateway (~22 minutes)

Architecture

The diagram visible below illustrates the architecture of our sample system. Here we have the characteristic elements for microservices like API gateway, discovery server, and configuration server. In the next sections of this article, I’ll show how to use Spring Cloud components that provide an implementation of those patterns. Currently, the main component for adding the API gateway to your system is Spring Cloud Gateway.
Spring Cloud provides integrations to several solutions that may be used as a discovery server: Netflix Eureka, HashiCorp Consul, Alibaba Nacos, or Apache ZooKeeper. The most common choice is between the first two of them. While Spring Cloud Netflix Eureka is dedicated just for discovery, Spring Cloud Consul may realize both discovery feature basing on Consul Services, and distributed configuration feature basing on Consul Key/Value engine.

In turn, Spring Cloud Config is responsible just for providing a mechanism for configuration management. However, it may also be integrated with third-party tools like Vault from HashiCorp.
We will figure out how to integrate our applications with discovery and configuration servers on the example of two simple Spring Boot applications callme-service and caller-service. The application caller-service is also calling endpoints exposed by the callme-service. We will enable such mechanisms on the caller-service like client-side load balancer with new Spring Cloud Load Balancer, and circuit breaker with new Spring Cloud Circuit Breaker built on top of Resilience4J.

Service discovery

Switching between discovery servers on the client-side is very easy with Spring Cloud due to the DiscoveryClient abstraction. This switch comes down to the replacement of a single dependency in Maven pom.xml. So if you are using Eureka you should add the following starter in your microservice.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId>
</dependency>

On the other hand, if you are using Consul you should add the following starter in your microservice.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-consul-discovery</artifactId>
</dependency>

The situation is a little bit more complicated if you are defining some non-default configuration settings for a discovery client. In that case, you need to use properties specific just for Eureka, or just for Consul. For example, if you are running more than one instance of a single application on the same host with dynamic HTTP server port feature enabled (option server.port=0), you have to set a unique id of every instance. Here’s the property used for Eureka’s client.

eureka:
  instance:
    instanceId: ${spring.cloud.client.hostname}:${spring.application.name}:${random.value}

For the Consul client the same configuration looks as shown below.

spring:
  cloud:
    consul:
      discovery:
        instanceId: ${spring.cloud.client.hostname}:${spring.application.name}:${random.value}

You can easily configure and run Eureka discovery in your microservices architecture using Spring Cloud Netflix Eureka Server module. You just need to create the Spring Boot application that includes that module.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-netflix-eureka-server</artifactId>
</dependency>

We also need to enable Eureka for the application by annotating the main class with @EnableEurekaServer.

@SpringBootApplication
@EnableEurekaServer
class DiscoveryServerApplication

fun main(args: Array<String>) {
    runApplication<DiscoveryServerApplication>(*args)
}

The most convenient way to run Consul on the local machine is by using its Docker image. We can Consul on Docker container in development mode by executing the following command.

$ docker run -d --name=consul -e CONSUL_BIND_INTERFACE=eth0 -p 8500:8500 consul:1.7.2

Distributed configuration with Spring cloud

The next important element in our architecture is a configuration server. The most popular solution in Spring Cloud that provides mechanisms for distributed configuration is Spring Cloud Config. Spring Cloud Config provides server-side and client-side support for externalized configuration in a distributed system.
With the config server, you have a central place to manage external properties for applications across all environments. Some other solutions may be used in microservices-based architecture as a configuration server: Consul, ZooKeeper, or Alibaba Nacos. However, all these solutions are not strictly dedicated to distributed configuration, they can act as a discovery server as well.
Spring Cloud Config may integrate with different tools for storing data. The default implementation of the server storage backend uses git, but we can use some other tools like HashiCorp Vault for managing secrets and protecting sensitive data or just a simple file system. Such different backends may be together in the single config server application. We just need to activate the appropriate profiles in application properties with spring.profiles.active. We may override some default, for example, change the address of a Vault server or set an authentication token.

spring:
  application:
    name: config-server
  profiles:
    active: native,vault
  cloud:
    config:
      server:
        native:
          searchLocations: classpath:/config-repo
        vault:
          host: 192.168.99.100
          authentication: TOKEN
          token: spring-microservices-course

The same as for Consul we should use Docker to run an instance of Vault in development mode. We can set a static root token for authentication using the environment variable VAULT_DEV_ROOT_TOKEN_ID.

$ docker run -d --name vault --cap-add=IPC_LOCK -e 'VAULT_DEV_ROOT_TOKEN_ID=spring-microservices-course' -p 8200:8200 vault:1.4.0

When using Spring Cloud Config together with the discovery we may choose between two available approaches called Config First Bootstrap and Discovery First Bootstrap. In Discovery First Bootstrap a config server is registering itself in discovery service. Thanks to that each microservice can localize a config server basing on its registration id.
Since a configuration is injected in the bootstrap phase we need to use bootstrap.yml for setting properties on the client-side. To enable “discovering” for the config server on the client side we should set the property spring.cloud.config.discovery.enabled to true. We should also override registered service id of config server if it is different from auto-configured configserver (in our case it is config-server). Of course, we can also use Consul as a configuration properties source.

spring:
  application:
    name: callme-service
  cloud:
    config:
      discovery:
        enabled: true
        serviceId: config-server
    consul:
      host: 192.168.99.100
      config:
        format: YAML

Inter-service communication

Currently, there are three Spring components for inter-service communication over HTTP that have integration with service discovery: synchronous RestTemplate, reactive WebClient and declarative REST client OpenFeign. The RestTemplate component is available within the Spring Web module, WebClient within Spring WebFlux. To include Spring Cloud OpenFeign we need to include a dedicated starter.

<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-openfeign</artifactId>
</dependency>

To use RestTemplate or WebClient for communication with discovery support, we need to register the beans and annotate them with @LoadBalanced. It is also worth setting the proper timeouts for such communication, especially if you are not using a circuit breaker.

@SpringBootApplication
@EnableFeignClients
class InterCallerServiceApplication {

    @Bean
    @LoadBalanced
    fun template(): RestTemplate = RestTemplateBuilder()
        .setReadTimeout(Duration.ofMillis(100))
        .setConnectTimeout(Duration.ofMillis(100))
        .build()

    @Bean
    @LoadBalanced
    fun clientBuilder(): WebClient.Builder {
        val tcpClient: TcpClient = TcpClient.create()
            .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, 100)
            .doOnConnected { conn ->
                conn.addHandlerLast(ReadTimeoutHandler(100, TimeUnit.MILLISECONDS))
            }
        val connector = ReactorClientHttpConnector(HttpClient.from(tcpClient))
        return WebClient.builder().clientConnector(connector)
    }
    
}

Although Ribbon has been moved to maintenance mode almost 1.5 years ago it is still used as a default client-side load balancer in the newest stable version of Spring Cloud. Since Spring Cloud Load Balancer is included in common dependencies it is available in your application. Therefore, the only thing you need to do is to disable Ribbon in the configuration using spring.cloud.loadbalancer.ribbon.enabled property.
Currently, we don’t have many options for load balancer customization. One of them is the ability to configure client cache settings. By default, each client is caching the list of target services and refreshing them every 30 seconds. Such an interval may be too long in your situation.
We can easily change it in the configuration as shown below and set it to for example 1 second. If your load balancer is integrated with Eureka discovery you also need to decrease an interval of the fetching registry, which is by default 30 seconds. After those, both changes your client can refresh the list of currently running services almost immediately.

spring:
  cloud:
    loadbalancer:
      cache:
        ttl: 1s
      ribbon:
        enabled: false
eureka:
  client:
    registryFetchIntervalSeconds: 1

Circuit breaker

The circuit breaker is a popular design pattern used in a microservices architecture. It is designed to detect failures and encapsulates the logic of preventing a failure from constantly recurring. Spring Cloud provides an abstraction for using different circuit breaker implementations. Currently, we may use Netflix Hystrix, Sentinel, Spring Retry, and Resilience4J. To enable Spring Cloud Circuit Breaker based on Resilience4J we need to include the following dependency.

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-circuitbreaker-resilience4j</artifactId>
</dependency>

Here’s the code responsible for registering Customizer bean, that configures a circuit breaker behavior.

@Bean
fun defaultCustomizer(): Customizer<Resilience4JCircuitBreakerFactory> {
  return Customizer { factory: Resilience4JCircuitBreakerFactory ->
    factory.configureDefault { id: String? ->
      Resilience4JConfigBuilder(id)
        .timeLimiterConfig(TimeLimiterConfig.custom()
          .timeoutDuration(Duration.ofMillis(500))
          .build())
        .circuitBreakerConfig(CircuitBreakerConfig.custom()
          .slidingWindowSize(10)
          .failureRateThreshold(33.3F)
          .slowCallRateThreshold(33.3F)
        .build())
      .build()
    }
  }
}

The settings of the circuit breaker have been visualized in the picture below. The sliding window size sets the number of requests which are used for calculating the error rate. If we have more than 3 errors in the window of size 10 a circuit is open.

In the last step, we need to create a circuit breaker instance using Resilience4JCircuitBreakerFactory bean and enable it for the HTTP client as shown below.

@RestController
@RequestMapping("/caller")
class CallerController(private val template: RestTemplate, private val factory: Resilience4JCircuitBreakerFactory) {

  private var id: Int = 0

  @PostMapping("/random-send/{message}")
  fun randomSend(@PathVariable message: String): CallmeResponse? {
    val request = CallmeRequest(++id, message)
    val circuit = factory.create("random-circuit")
    return circuit.run { template.postForObject("http://inter-callme-service/callme/random-call",
      request, CallmeResponse::class.java) }
  }
    
}

Spring Cloud API gateway

The last missing element in our microservices architecture is an API Gateway. Spring Cloud Gateway is the project that helps us in implementing such a component. Currently, it is the second most popular Spring Cloud project just after Spring Cloud Netflix. It has around 2k stars on GitHub. It is built on top of the Spring WebFlux and Reactor project. It works reactively and requires Netty as a runtime framework.
The main goal of API gateway is to hide the complexity of the microservices system from an external client by providing an effective way of routing to APIs, but it can also solve some problems around security or resiliency. The main component used for configuring Spring Cloud Gateway is a route.
It is defined by an ID, a destination URI, a collection of predicates, and a collection of filters. A route is matched if the aggregate predicate is true. With filters, you can modify requests and responses before or after sending the downstream request.
With a predefined set of gateway filters, we may enable such mechanisms like path rewriting, rate limiting, discovery client, circuit breaking, fallback, or routing metrics. To enable all these features on the gateway we first need to include the following dependencies.

<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-gateway</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
   <groupId>org.jetbrains.kotlin</groupId>
   <artifactId>kotlin-reflect</artifactId>
</dependency>
<dependency>
   <groupId>org.jetbrains.kotlin</groupId>
   <artifactId>kotlin-stdlib</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-data-redis-reactive</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-circuitbreaker-reactor-resilience4j</artifactId>
</dependency>

To enable all the previously listed features we don’t have to implement much code. Almost everything is configurable in application properties.

spring:
  application:
    name: api-gateway
  cloud:
    gateway:
      discovery:
        locator:
          enabled: true 
          lowerCaseServiceId: true
      routes:
        - id: inter-callme-service
          uri: lb://inter-callme-service
          predicates:
            - Path=/api/callme/**
          filters:
            - RewritePath=/api(?/?.*), $\{path}
            - name: RequestRateLimiter
              args:
                redis-rate-limiter.replenishRate: 20
                redis-rate-limiter.burstCapacity: 40
            - name: CircuitBreaker
              args:
                name: sampleSlowCircuitBreaker
                fallbackUri: forward:/fallback/test
        - id: inter-caller-service
          uri: lb://inter-caller-service
          predicates:
            - Path=/api/caller/**
          filters:
            - StripPrefix=1
            - name: RequestRateLimiter
              args:
                redis-rate-limiter.replenishRate: 20
                redis-rate-limiter.burstCapacity: 40
    loadbalancer:
      ribbon:
        enabled: false
  redis:
    host: 192.168.99.100

management:
  endpoints.web.exposure.include: '*'
  endpoint:
    health:
      show-details: always

Some settings still need to be configured in the code. It is a configuration of the circuit breaker, that is based on Resilience4J project, where we need to register the bean Customizer. We also have to define a key for rate-limiting responsible setting a strategy of choosing requests for counting the limits.

@SpringBootApplication
class ApiGatewayApplication {

  @Bean
  fun keyResolver(): KeyResolver = KeyResolver { _ -> Mono.just("1") }

  @Bean
  fun defaultCustomizer(): Customizer<ReactiveResilience4JCircuitBreakerFactory> {
    return Customizer { factory: ReactiveResilience4JCircuitBreakerFactory ->
      factory.configureDefault { id: String? ->
        Resilience4JConfigBuilder(id)
          .timeLimiterConfig(TimeLimiterConfig.custom()
            .timeoutDuration(Duration.ofMillis(500))
            .build())
          .circuitBreakerConfig(CircuitBreakerConfig.custom()
            .slidingWindowSize(10)
            .failureRateThreshold(33.3F)
            .slowCallRateThreshold(33.3F)
            .build())
          .build()
      }
    }
  }

}

fun main(args: Array<String>) {
   runApplication<ApiGatewayApplication>(*args)
}

Conclusion

In this article, you may take a quick introduction to using the latest Spring Cloud components for building microservices architecture. For more details, you may refer to my video course published on YouTube.

The post A New Era Of Spring Cloud appeared first on Piotr's TechBlog.

1. Dependencies

We will test our sample application against Spring Cloud Gateway rate limiting under higher traffic. First, we need to include some dependencies. Of course, Spring Cloud Gateway starter is required. For handling rate limiter with Redis we also need to add dependency to spring-boot-starter-data-redis-reactive starter. Other dependencies are used for test purposes. Module mockserver provided within Testcontainers. It is responsible for mocking a target service. In turn, the library mockserver-client-java is used for integration with mockserver container during the test. And the last library junit-benchmarks is used for a benchmarking test method and running the test concurrently.

<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-gateway</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-data-redis-reactive</artifactId>
</dependency>
<dependency>
   <groupId>org.projectlombok</groupId>
   <artifactId>lombok</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-test</artifactId>
   <scope>test</scope>
</dependency>
<dependency>
   <groupId>org.testcontainers</groupId>
   <artifactId>mockserver</artifactId>
   <version>1.12.3</version>
   <scope>test</scope>
</dependency>
<dependency>
   <groupId>org.mock-server</groupId>
   <artifactId>mockserver-client-java</artifactId>
   <version>3.10.8</version>
   <scope>test</scope>
</dependency>
<dependency>
   <groupId>com.carrotsearch</groupId>
   <artifactId>junit-benchmarks</artifactId>
   <version>0.7.2</version>
   <scope>test</scope>
</dependency>

The sample application is built on top of Spring Boot 2.2.1.RELEASE and uses Spring Cloud Hoxton.RC2 Release Train.

<parent>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-parent</artifactId>
   <version>2.2.1.RELEASE</version>
</parent>

<properties>
   <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
   <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
   <java.version>11</java.version>
   <spring-cloud.version>Hoxton.RC2</spring-cloud.version>
</properties>

<dependencyManagement>
   <dependencies>
      <dependency>
         <groupId>org.springframework.cloud</groupId>
         <artifactId>spring-cloud-dependencies</artifactId>
         <version>${spring-cloud.version}</version>
         <type>pom</type>
         <scope>import</scope>
      </dependency>
   </dependencies>
</dependencyManagement>

2. Implementation

Request rate limiting is realized using a Spring Cloud Gateway component called GatewayFilter. Each instance of this filter is constructed in a specific factory. Filter is of course responsible for modifying requests and responses before or after sending the downstream request. Currently, there are 30 available built-in gateway filter factories.
The GatewayFilter takes an optional keyResolver parameter and parameters specific to the rate limiter implementation (in that case an implementation using Redis). Parameter keyResolver is a bean that implements the KeyResolver interface. It allows you to apply different strategies to derive the key for limiting requests. Parameter keyResolver is a bean that implements the KeyResolver interface. It allows you to apply different strategies to derive the key for limiting requests. Following Spring Cloud Gateway documentation:

The default implementation of KeyResolver is the PrincipalNameKeyResolver which retrieves the Principal from the ServerWebExchange and calls Principal.getName(). By default, if the KeyResolver does not find a key, requests will be denied. This behavior can be adjusted with the spring.cloud.gateway.filter.request-rate-limiter.deny-empty-key (true or false) and spring.cloud.gateway.filter.request-rate-limiter.empty-key-status-code properties.

Since, we have discussed some theoretical aspects of Spring Cloud Gateway rate limiting we may proceed to the implementation. First, let’s define the main class and very simple KeyResolver bean, that is always equal to one.

@SpringBootApplication
public class GatewayApplication {

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

   @Bean
   KeyResolver userKeyResolver() {
      return exchange -> Mono.just("1");
   }
}

Assuming we have the following configuration and a target application running on port 8091 we may perform some test calls. You may set two properties for customizing the process. The redis-rate-limiter.replenishRate decides how many requests per second a user is allowed to send without any dropped requests. This is the rate that the token bucket is filled. The second property redis-rate-limiter.burstCapacity is the maximum number of requests a user is allowed to do in a single second. This is the number of tokens the token bucket can hold. Setting this value to zero will block all requests.

server:
  port: ${PORT:8085}

spring:
  application:
    name: gateway-service
  redis:
    host: 192.168.99.100
    port: 6379
  cloud:
    gateway:
      routes:
      - id: account-service
        uri: http://localhost:8091
        predicates:
        - Path=/account/**
        filters:
        - RewritePath=/account/(?.*), /$\{path}
      - name: RequestRateLimiter
          args:
            redis-rate-limiter.replenishRate: 10
            redis-rate-limiter.burstCapacity: 20

Now, if you call the endpoint exposed by the gateway you get the following response. It includes some specific headers, which are prefixed by x-ratelimit. Header x-ratelimit-burst-capacity indicates to burstCapacity value, x-ratelimit-replenish-rate indicates to replenishRate value, and the most important x-ratelimit-remaining, which shows you the number of requests you may send in the next second.

If you exceed the number of allowed requests Spring Cloud Gateway return response with code HTTP 429 - Too Many Requests, and will not process the incoming request.

3. Testing Spring Cloud Gateway rate limiting

We have the Spring Boot test that uses two Docker containers provided by Testcontainers: MockServer and Redis. Because the exposed port is generated dynamically we need to set gateway properties in @BeforeClass method before running the test. Inside the init method we also use MockServerClient to define mock service on the mock server container. Our test method is running concurrently in six threads and is repeated 600 times.

@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT)
@RunWith(SpringRunner.class)
public class GatewayRateLimiterTest {

    private static final Logger LOGGER = LoggerFactory.getLogger(GatewayRateLimiterTest.class);

    @Rule
    public TestRule benchmarkRun = new BenchmarkRule();

    @ClassRule
    public static MockServerContainer mockServer = new MockServerContainer();
    @ClassRule
    public static GenericContainer redis = new GenericContainer("redis:5.0.6").withExposedPorts(6379);

    @Autowired
    TestRestTemplate template;

    @BeforeClass
    public static void init() {
        System.setProperty("spring.cloud.gateway.routes[0].id", "account-service");
        System.setProperty("spring.cloud.gateway.routes[0].uri", "http://192.168.99.100:" + mockServer.getServerPort());
        System.setProperty("spring.cloud.gateway.routes[0].predicates[0]", "Path=/account/**");
        System.setProperty("spring.cloud.gateway.routes[0].filters[0]", "RewritePath=/account/(?<path>.*), /$\\{path}");
        System.setProperty("spring.cloud.gateway.routes[0].filters[1].name", "RequestRateLimiter");
        System.setProperty("spring.cloud.gateway.routes[0].filters[1].args.redis-rate-limiter.replenishRate", "10");
        System.setProperty("spring.cloud.gateway.routes[0].filters[1].args.redis-rate-limiter.burstCapacity", "20");
        System.setProperty("spring.redis.host", "192.168.99.100");
        System.setProperty("spring.redis.port", "" + redis.getMappedPort(6379));
        new MockServerClient(mockServer.getContainerIpAddress(), mockServer.getServerPort())
                .when(HttpRequest.request()
                        .withPath("/1"))
                .respond(response()
                        .withBody("{\"id\":1,\"number\":\"1234567890\"}")
                        .withHeader("Content-Type", "application/json"));
    }

    @Test
    @BenchmarkOptions(warmupRounds = 0, concurrency = 6, benchmarkRounds = 600)
    public void testAccountService() {
        ResponseEntity<Account> r = template.exchange("/account/{id}", HttpMethod.GET, null, Account.class, 1);
        LOGGER.info("Received: status->{}, payload->{}, remaining->{}", r.getStatusCodeValue(), r.getBody(), r.getHeaders().get("X-RateLimit-Remaining"));
    }

}

Let’s take a look at the test result. After starting gateway allows a user to send max 20 requests in a single second. After exceeding this value it starts to return HTTP 429.

After dropping some incoming requests gateway starts to accept them in the next second. But this time it allows to process only 10 requests, which is equal to replenishRate parameter value.

The source code is available in GitHub repository: https://github.com/piomin/sample-spring-cloud-gateway.git.

The post Rate Limiting In Spring Cloud Gateway With Redis appeared first on Piotr's TechBlog.

Redis as Configuration Server

If you have already built microservices with Spring Cloud, you probably have a touch with Spring Cloud Config. It is responsible for providing distributed configuration patterns for microservices. Unfortunately Spring Cloud Config does not support Redis as a property sources backend repository. That’s why I decided to fork the Spring Cloud Config project and implement this feature. I hope my implementation will soon be included into the official Spring Cloud release, but for now you may use my forked repo to run it. It is available on my GitHub account piomin/spring-cloud-config. How to use it? Very simple. Let’s see.
The current SNAPSHOT version of Spring Boot is 2.2.0.BUILD-SNAPSHOT, the same as for Spring Cloud Config. While building Spring Cloud Config Server we need to include only those two dependencies as shown below.

<parent>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-parent</artifactId>
   <version>2.2.0.BUILD-SNAPSHOT</version>
</parent>
<artifactId>config-service</artifactId>
<groupId>pl.piomin.services</groupId>
<version>1.0-SNAPSHOT</version>

<dependencies>
   <dependency>
      <groupId>org.springframework.cloud</groupId>
      <artifactId>spring-cloud-config-server</artifactId>
      <version>2.2.0.BUILD-SNAPSHOT</version>
   </dependency>
</dependencies>

By default, Spring Cloud Config Server uses the Git repository backend. We need to activate the redis profile to force it using Redis as a backend. If your Redis instance listens on another address than localhost:6379 you need to overwrite auto-configured connection settings with spring.redis.* properties. Here’s our bootstrap.yml file.

spring:
  application:
    name: config-service
  profiles:
    active: redis
  redis:
    host: 192.168.99.100

The application main class should be annotated with @EnableConfigServer.

@SpringBootApplication
@EnableConfigServer
public class ConfigApplication {

   public static void main(String[] args) {
      new SpringApplicationBuilder(ConfigApplication.class).run(args);
   }

}

Before running the application we need to start the Redis instance. Here’s the command that runs it as a Docker container and exposes on port 6379.

$ docker run -d --name redis -p 6379:6379 redis

The configuration for every application has to be available under the key ${spring.application.name} or ${spring.application.name}-${spring.profiles.active[n]}.
We have to create hash with the keys corresponding to the names of configuration properties. Our sample application driver-management uses three configuration properties: server.port for setting HTTP listening port, spring.redis.host for changing default Redis address used as a message broker and database, and sample.topic.name for setting name of topic used for asynchronous communication between our microservices. Here’s the structure of Redis hash created for driver-management visualized with RDBTools.

That visualization is an equivalent of running Redis CLI command HGETALL that returns all the fields and values in a hash.

>> HGETALL driver-management
{
  "server.port": "8100",
  "sample.topic.name": "trips",
  "spring.redis.host": "192.168.99.100"
}

After setting keys and values in Redis and running Spring Cloud Config Server with active redis profile, we need to enable distributed configuration feature on the client side. To do that we just need to include spring-cloud-starter-config dependency to pom.xml of every microservice.

<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-config</artifactId>
</dependency>

We use the newest stable version of Spring Cloud.

<dependencyManagement>
   <dependencies>
      <dependency>
         <groupId>org.springframework.cloud</groupId>
         <artifactId>spring-cloud-dependencies</artifactId>
         <version>Greenwich.SR1</version>
         <type>pom</type>
         <scope>import</scope>
      </dependency>
   </dependencies>
</dependencyManagement>

The name of application is taken from property spring.application.name on startup, so we need to provide the following bootstrap.yml file.

spring:
  application:
    name: driver-management

Redis as Message Broker

Now we can proceed to the second use case of Redis in microservices-based architecture – message broker. We will implement a typical asynchronous system, which is visible on the picture below. Microservice trip-management send notification to Redis Pub/Sub after creating new trip and after finishing current trip. The notification is received by both driver-management and passenger-management, which are subscribed to the particular channel.

Our application is very simple. We just need to add the following dependencies in order to provide REST API and integrate with Redis Pub/Sub.

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

We should register bean with the channel name and publisher. TripPublisher is responsible for sending messages to the target topic.

@Configuration
public class TripConfiguration {

   @Autowired
   RedisTemplate<?, ?> redisTemplate;

   @Bean
   TripPublisher redisPublisher() {
      return new TripPublisher(redisTemplate, topic());
   }

   @Bean
   ChannelTopic topic() {
      return new ChannelTopic("trips");
   }

}

TripPublisher uses RedisTemplate for sending messages to the topic. Before sending it converts every message from object to JSON string using Jackson2JsonRedisSerializer.

public class TripPublisher {

   private static final Logger LOGGER = LoggerFactory.getLogger(TripPublisher.class);

   RedisTemplate<?, ?> redisTemplate;
   ChannelTopic topic;

   public TripPublisher(RedisTemplate<?, ?> redisTemplate, ChannelTopic topic) {
      this.redisTemplate = redisTemplate;
      this.redisTemplate.setValueSerializer(new Jackson2JsonRedisSerializer(Trip.class));
      this.topic = topic;
   }

   public void publish(Trip trip) throws JsonProcessingException {
      LOGGER.info("Sending: {}", trip);
      redisTemplate.convertAndSend(topic.getTopic(), trip);
   }

}

We have already implemented the logic on the publisher side. Now, we can proceed to the implementation on subscriber sides. We have two microservices driver-management and passenger-management that listens for the notifications sent by trip-management microservice. We need to define RedisMessageListenerContainer bean and set a message listener implementation class.

@Configuration
public class DriverConfiguration {

   @Autowired
   RedisConnectionFactory redisConnectionFactory;

   @Bean
   RedisMessageListenerContainer container() {
      RedisMessageListenerContainer container = new RedisMessageListenerContainer();
      container.addMessageListener(messageListener(), topic());
      container.setConnectionFactory(redisConnectionFactory);
      return container;
   }

   @Bean
   MessageListenerAdapter messageListener() {
      return new MessageListenerAdapter(new DriverSubscriber());
   }

   @Bean
   ChannelTopic topic() {
      return new ChannelTopic("trips");
   }

}

The class responsible for handling incoming notification needs to implement MessageListener interface. After receiving message DriverSubscriber deserializes it from JSON to object and change driver status.

@Service
public class DriverSubscriber implements MessageListener {

   private final Logger LOGGER = LoggerFactory.getLogger(DriverSubscriber.class);

   @Autowired
   DriverRepository repository;
   ObjectMapper mapper = new ObjectMapper();

   @Override
   public void onMessage(Message message, byte[] bytes) {
      try {
         Trip trip = mapper.readValue(message.getBody(), Trip.class);
         LOGGER.info("Message received: {}", trip.toString());
         Optional<Driver> optDriver = repository.findById(trip.getDriverId());
         if (optDriver.isPresent()) {
            Driver driver = optDriver.get();
            if (trip.getStatus() == TripStatus.DONE)
               driver.setStatus(DriverStatus.WAITING);
            else
               driver.setStatus(DriverStatus.BUSY);
            repository.save(driver);
         }
      } catch (IOException e) {
         LOGGER.error("Error reading message", e);
      }
   }

}

Redis as Primary Database

Although the main purpose of using Redis is in-memory caching or key/value store it may also act as a primary database for your application. In that case it is worth it to run Redis in persistent mode.

$ docker run -d --name redis -p 6379:6379 redis redis-server --appendonly yes

Entities are stored inside Redis using hash operations and mmap structure. Each entity needs to have a hash key and id.

@RedisHash("driver")
public class Driver {

   @Id
   private Long id;
   private String name;
   @GeoIndexed
   private Point location;
   private DriverStatus status;

   // setters and getters ...
}

Fortunately, Spring Data Redis provides a well-known repositories pattern for Redis integration. To enable it we should annotate configuration or main class with @EnableRedisRepositories. When using the Spring repositories pattern we don’t have to build any queries to Redis by ourselves.

@Configuration
@EnableRedisRepositories
public class DriverConfiguration {
   // logic ...
}

With Spring Data repositories we don’t have to build any Redis queries, but just name methods following Spring Data convention. For more details, you may refer to my previous article Introduction to Spring Data Redis. For our sample purposes we can use default methods implemented inside Spring Data. Here’s declaration of repository interface in driver-management.

public interface DriverRepository extends CrudRepository<Driver, Long> {}

Don’t forget to enable Spring Data repositories by annotating the main application class or configuration class with @EnableRedisRepositories.

@Configuration
@EnableRedisRepositories
public class DriverConfiguration {
   ...
}

Conclusion

As I have mentioned in the preface there are various use cases for Redis in microservices architecture. I have just presented how you can easily use it together with Spring Cloud and Spring Data to provide configuration server, message broker and database. Redis is commonly considered as a cache, but I hope that after reading this article you will change your mind about it. The sample applications source code is as usual available on GitHub: https://github.com/piomin/sample-redis-microservices.git.

The post Redis in Microservices Architecture appeared first on Piotr's TechBlog.

Redis is usually used for caching data stored in a relational database. In the current example, it will be treated as a primary database – just for simplification. Spring Data repositories do not require any deeper knowledge about Redis from a developer. You just need to annotate your domain class properly. As usual, we will examine the main features of Spring Data Redis based on the sample application. Supposing we have the system, which consists of three domain objects: Customer, Account and Transaction, here’s the picture that illustrates relationships between elements of that system. Transaction is always related with two accounts: sender (fromAccountId) and receiver (toAccountId). Each customer may have many accounts.

Although the picture visible above shows three independent domain models, customer and account are stored in the same, single structure. All customer’s accounts are stored as a list inside a customer object. Before proceeding to the sample application implementation details let’s begin by starting the Redis database.

1. Running Redis on Docker

We will run Redis standalone instance locally using its Docker container. You can start it in in-memory mode or with a persistence store. Here’s the command that runs a single, in-memory instance of Redis on a Docker container. It is exposed outside on default 6379 port.

$ docker run -d --name redis -p 6379:6379 redis

2. Enabling Spring Data Redis Repositories and Configuring Connection

I’m using Docker Toolbox, so each container is available for me under address 192.168.99.100. Here’s the only one property that I need to override inside configuration settings (application.yml).

spring:
  application:
    name: sample-spring-redis
  redis:
    host: 192.168.99.100

To enable Redis repositories for Spring Boot application we just need to include the single starter <code>spring-boot-starter-data-redis</code>.

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

We may choose between two supported connectors: Lettuce and Jedis. For Jedis I had to include one additional client’s library to dependencies, so I decided to use a simpler option – Lettuce, that does not require any additional libraries to work properly. To enable Spring Data Redis repositories we also need to annotate the main or the configuration class with @EnableRedisRepositories and declare RedisTemplate bean. Although we do not use RedisTemplate directly, we still need to declare it, while it is used by CRUD repositories for integration with Redis.

@Configuration
@EnableRedisRepositories
public class SampleSpringRedisConfiguration {

    @Bean
    public LettuceConnectionFactory redisConnectionFactory() {
        return new LettuceConnectionFactory();
    }

    @Bean
    public RedisTemplate<?, ?> redisTemplate() {
        RedisTemplate<byte[], byte[]> template = new RedisTemplate<>();
        template.setConnectionFactory(redisConnectionFactory());
        return template;
    }

}

3. Implementing domain entities

Each domain entity has at least to be annotated with @RedisHash, and contains property annotated with @Id. Those two items are responsible for creating the actual key used to persist the hash. Besides identifier properties annotated with @Id you may also use secondary indices. The good news about it is that it can be not only with dependent single objects, but also on lists and maps. Here’s the definition of Customer entity. It is available on Redis under customer key. It contains a list of Account entities.

@RedisHash("customer")
public class Customer {

    @Id private Long id;
    @Indexed private String externalId;
    private String name;
    private List<Account> accounts = new ArrayList<>();

    public Customer(Long id, String externalId, String name) {
        this.id = id;
        this.externalId = externalId;
        this.name = name;
    }

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public String getExternalId() {
        return externalId;
    }

    public void setExternalId(String externalId) {
        this.externalId = externalId;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public List<Account> getAccounts() {
        return accounts;
    }

    public void setAccounts(List<Account> accounts) {
        this.accounts = accounts;
    }

    public void addAccount(Account account) {
        this.accounts.add(account);
    }

}

Account does not have its own hash. It is contained by Customer has a list of objects. The property id is indexed on Redis in order to speed-up the search based on the property.

public class Account {

    @Indexed private Long id;
    private String number;
    private int balance;

    public Account(Long id, String number, int balance) {
        this.id = id;
        this.number = number;
        this.balance = balance;
    }

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public String getNumber() {
        return number;
    }

    public void setNumber(String number) {
        this.number = number;
    }

    public int getBalance() {
        return balance;
    }

    public void setBalance(int balance) {
        this.balance = balance;
    }

}

Finally, let’s take a look on Transaction entity implementation. It uses only account ids, not the whole object.

@RedisHash("transaction")
public class Transaction {

    @Id
    private Long id;
    private int amount;
    private Date date;
    @Indexed
    private Long fromAccountId;
    @Indexed
    private Long toAccountId;

    public Transaction(Long id, int amount, Date date, Long fromAccountId, Long toAccountId) {
        this.id = id;
        this.amount = amount;
        this.date = date;
        this.fromAccountId = fromAccountId;
        this.toAccountId = toAccountId;
    }

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public int getAmount() {
        return amount;
    }

    public void setAmount(int amount) {
        this.amount = amount;
    }

    public Date getDate() {
        return date;
    }

    public void setDate(Date date) {
        this.date = date;
    }

    public Long getFromAccountId() {
        return fromAccountId;
    }

    public void setFromAccountId(Long fromAccountId) {
        this.fromAccountId = fromAccountId;
    }

    public Long getToAccountId() {
        return toAccountId;
    }

    public void setToAccountId(Long toAccountId) {
        this.toAccountId = toAccountId;
    }

}

4. Implementing Spring Data Redis repositories

The implementation of repositories is the most pleasant part of our exercise. As usual with Spring Data projects, the most common methods like save, delete or findById are already implemented. So we only have to create our custom find methods if needed. Since usage and implementation of findByExternalId method is rather obvious, the method findByAccountsId may be not. Let’s move back to a model definition to clarify usage of that method. Transaction contains only account ids, it does not have direct relationship with Customer. What if we need to know the details about customers being a side of a given transaction? We can find a customer by one of its accounts from the list.

public interface CustomerRepository extends CrudRepository<Customer, Long> {

    Customer findByExternalId(String externalId);
    List<Customer> findByAccountsId(Long id);

}

Here’s implementation of repository for Transaction entity.

public interface TransactionRepository extends CrudRepository<Transaction, Long> {

    List<Transaction> findByFromAccountId(Long fromAccountId);
    List<Transaction> findByToAccountId(Long toAccountId);

}

5. Building repository tests

We can easily test Redis repositories functionality using Spring Boot Test project with @DataRedisTest. This test assumes you have a running instance of Redis server on the already configured address 192.168.99.100.

@RunWith(SpringRunner.class)
@DataRedisTest
@FixMethodOrder(MethodSorters.NAME_ASCENDING)
public class RedisCustomerRepositoryTest {

    @Autowired
    CustomerRepository repository;

    @Test
    public void testAdd() {
        Customer customer = new Customer(1L, "80010121098", "John Smith");
        customer.addAccount(new Account(1L, "1234567890", 2000));
        customer.addAccount(new Account(2L, "1234567891", 4000));
        customer.addAccount(new Account(3L, "1234567892", 6000));
        customer = repository.save(customer);
        Assert.assertNotNull(customer);
    }

    @Test
    public void testFindByAccounts() {
        List<Customer> customers = repository.findByAccountsId(3L);
        Assert.assertEquals(1, customers.size());
        Customer customer = customers.get(0);
        Assert.assertNotNull(customer);
        Assert.assertEquals(1, customer.getId().longValue());
    }

    @Test
    public void testFindByExternal() {
        Customer customer = repository.findByExternalId("80010121098");
        Assert.assertNotNull(customer);
        Assert.assertEquals(1, customer.getId().longValue());
    }
}

6. More advanced testing with Testcontainers

You may provide some advanced integration tests using Redis as Docker container started during the test by Testcontainer library. I have already published some articles about the Testcontainers framework. If you would like read more details about it please refer to my previous articles: Microservices Integration Tests with Hoverfly and Testcontainers and Testing Spring Boot Integration with Vault and Postgres using Testcontainers Framework.

@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
@RunWith(SpringRunner.class)
public class CustomerIntegrationTests {

    @Autowired
    TestRestTemplate template;

    @ClassRule
    public static GenericContainer redis = new GenericContainer("redis:5.0.3").withExposedPorts(6379);

    @Before
    public void init() {
        int port = redis.getFirstMappedPort();
        System.setProperty("spring.redis.host", String.valueOf(port));
    }

    @Test
    public void testAddAndFind() {
        Customer customer = new Customer(1L, "123456", "John Smith");
        customer.addAccount(new Account(1L, "1234567890", 2000));
        customer.addAccount(new Account(2L, "1234567891", 4000));
        customer = template.postForObject("/customers", customer, Customer.class);
        Assert.assertNotNull(customer);
        customer = template.getForObject("/customers/{id}", Customer.class, 1L);
        Assert.assertNotNull(customer);
        Assert.assertEquals("123456", customer.getExternalId());
        Assert.assertEquals(2, customer.getAccounts().size());
    }

}

7. Viewing data in Redis

Now, let’s analyze the data stored in Redis after our JUnit tests. We may use one of the GUI tools for that. I decided to install RDBTools available on site https://rdbtools.com. You can easily browse data stored on Redis using this tool. Here’s the result for customer entity with id=1 after running JUnit test.

Here’s the similar result for transaction entity with id=1.

Source Code

The sample application source code is available on GitHub in the repository sample-spring-redis

The post Introduction to Spring Data Redis appeared first on Piotr's TechBlog.