Cyclic api calls in microservices

Assume there are two services,

Assume there are two services,

  1. Product Service
  2. Coupon service

Coupon is tagged against the product. And when the coupon is changed for any product there is an API call from product service to the coupon service which calculates the coupon discount and sends back to the product service. And product service maintains that cache. However, To calculate the discount, coupon service need the product information such as product category, SKU type etc. and discount varies based on that so there is again an API call to the product service making it cyclic call. There are a couple of solutions,

  1. maintain product cache at the coupon service. - This is an overhead as we need to maintain the cache on every product field update and this is not the solution we are looking for.
  2. When product service makes an API call to the coupon service, send all the fields it needs - There is tight coupling in this approach.

What is the correct way to solve this problem?

What is REST API? | Restful Web Service

What is REST API? | Restful Web Service

In this post "Restful Web Service", you'll learn: What is Web services, what is API, What is REST API, How REST works and Implementation of REST API

What is REST API? | Restful Web Service

A REST API defines a set of functions to process requests and responses via HTTP protocol.

REST is used in mobile application as well as in web applications.

Web Service Tutorial: Streaming Data with Spring Boot RESTful

Web Service Tutorial: Streaming Data with Spring Boot RESTful

In this article, we are going to look at an example to download files using StreamingResponseBody. In this approach, data is processed and written in chunks to the OutputStream.

In this article, we are going to look at an example to download files using StreamingResponseBody. In this approach, data is processed and written in chunks to the OutputStream.

Streaming data is a radical new approach to sending data to web browsers which provides for dramatically faster page load times. Quite often, we need to allow users to download files in web applications. When the data is too large, it becomes quite a challenge to provide a good user experience.

Spring offers support for asynchronous request processing via StreamingResponseBody. In this approach, an application can write data directly to the response OutputStream without holding up the Servlet container thread. There are a few other methods in Spring to handle asynchronous request processing.

Setting Up Spring Boot Project

Create a sample Spring Boot application. Here is my sample project structure. I have created the project manually, but you could also create using Spring Intializer.

Project structure

Let us add some basic dependencies to Maven POM.

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns=""
        <relativePath />

We will now create a controller and add an API endpoint for download. Here is my complete controller.

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.http.HttpStatus;
import org.springframework.http.MediaType;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.web.servlet.mvc.method.annotation.StreamingResponseBody;
import javax.servlet.http.HttpServletResponse;
@RequestMapping ("/api")
public class DownloadController {
    private final Logger logger = LoggerFactory.getLogger(DownloadController.class);
    @GetMapping (value = "/download", produces = MediaType.APPLICATION_JSON_VALUE)
    public ResponseEntity<StreamingResponseBody> download(final HttpServletResponse response) {
        StreamingResponseBody stream = out -> {
            final String home = System.getProperty("user.home");
            final File directory = new File(home + File.separator + "Documents" + File.separator + "sample");
            final ZipOutputStream zipOut = new ZipOutputStream(response.getOutputStream());
            if(directory.exists() && directory.isDirectory()) {
                try {
                    for (final File file : directory.listFiles()) {
                        final InputStream inputStream=new FileInputStream(file);
                        final ZipEntry zipEntry=new ZipEntry(file.getName());
                        byte[] bytes=new byte[1024];
                        int length;
                        while (( >= 0) {
                            zipOut.write(bytes, 0, length);
                } catch (final IOException e) {
                    logger.error("Exception while reading and streaming data {} ", e);
        };"steaming response {} ", stream);
        return new ResponseEntity(stream, HttpStatus.OK);

In this API endpoint, we are reading multiple files from a directory and creating a zip file. We are executing this process within StreamingResponseBody*. It writes data directly to an OutputStream before passing that written information back to the client using aResponseEntity. *This means that the download process will start immediately on the client, while the server is processing and writing data in chunks.

Start the server and test this endpoint using http://localhost:8080/api/download.

When using StreamingResponseBody, it is highly recommended to configure TaskExecutor used in Spring MVC for executing asynchronous requests. TaskExecutor is an interface that abstracts the execution of a Runnable.

Let us configure the TaskExecutor. Here is the AsyncConfiguration class which configures timeout using WebMvcCofigurer and also registers an interceptor that is called when there's a timeout in case you need some special handling.

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.aop.interceptor.AsyncUncaughtExceptionHandler;
import org.springframework.aop.interceptor.SimpleAsyncUncaughtExceptionHandler;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.task.AsyncTaskExecutor;
import org.springframework.scheduling.annotation.AsyncConfigurer;
import org.springframework.scheduling.annotation.EnableAsync;
import org.springframework.scheduling.annotation.EnableScheduling;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;
import org.springframework.web.context.request.NativeWebRequest;
import org.springframework.web.context.request.async.CallableProcessingInterceptor;
import org.springframework.web.context.request.async.TimeoutCallableProcessingInterceptor;
import org.springframework.web.servlet.config.annotation.AsyncSupportConfigurer;
import org.springframework.web.servlet.config.annotation.WebMvcConfigurer;
import java.util.concurrent.Callable;
public class AsyncConfiguration implements AsyncConfigurer {
    private final Logger log = LoggerFactory.getLogger(AsyncConfiguration.class);
    @Bean (name = "taskExecutor")
    public AsyncTaskExecutor getAsyncExecutor() {
        log.debug("Creating Async Task Executor");
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        return executor;
    public AsyncUncaughtExceptionHandler getAsyncUncaughtExceptionHandler() {
        return new SimpleAsyncUncaughtExceptionHandler();
    /** Configure async support for Spring MVC. */
    public WebMvcConfigurer webMvcConfigurerConfigurer(AsyncTaskExecutor taskExecutor, CallableProcessingInterceptor callableProcessingInterceptor) {
        return new WebMvcConfigurer() {
            public void configureAsyncSupport(AsyncSupportConfigurer configurer) {
    public CallableProcessingInterceptor callableProcessingInterceptor() {
        return new TimeoutCallableProcessingInterceptor() {
            public <T> Object handleTimeout(NativeWebRequest request, Callable<T> task) throws Exception {
                return super.handleTimeout(request, task);


Using StreamingResponseBody, we can now stream data easily for highly-concurrent applications. I hope you enjoyed this article. Let me know if you have any comments or suggestion in the comments section below.

The example for this article can be found on GitHub repository.

Discovering RESTful Web Microservices: A Traveler's Guide

Discovering RESTful Web Microservices: A Traveler's Guide

Using a mix of story-telling and code examples, this talk identifies key elements of each of these three things -- REST, the Web, and Microservices -- and shows how you can apply these elements to your own projects

Navigating the landscape of scalable, resilient Microservices can be tricky. While each service fulfills a different purpose, there are a handful of shared properties of microservices

Knowing what these properties are and how to implement them is vital to creating robust components that are available and reliable on demand. The scalability and resilience of the WWW of documents and WebApps gives us some clues on how powerful and reliable Internet-level Microservices can be implemented. And, almost 20 years ago, Roy Fielding documented an approach to meeting the challenge of network-based software; an approach often called RESTful.

Using a mix of story-telling and code examples, this talk identifies key elements of each of these three things -- REST, the Web, and Microservices -- and shows how you can apply these elements to your own projects to gain the flexibility, resiliency, and scalability you need in order to build the three types of Microservices (Stateless, Persistence, and Aggregator) with the interoperability of the WWW and the adaptability of RESTful network systems.

What You'll Learn Topics/Concepts covered in this talk include:

  •   The three basic types of microservices (Stateless, Persistence, Aggregator)
  •   How to apply Michael Nygard's Stability Patterns to microservices
  •   How REST architecture can improve microservices
  •   What we can learn from the WWW when implementing Microservices
  •   How use runtime service discovery to improve microservices

Thanks for reading

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An Introduction to Microservices

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