Client-Side Composition in Microservices: Patterns and Best Practices

6.3.2 Client-Side Composition

In the realm of microservices architecture, client-side composition emerges as a powerful pattern that empowers clients to aggregate data from multiple microservices. This approach shifts the responsibility of data aggregation to the client, enabling more flexible and dynamic data retrieval strategies. In this section, we will delve into the intricacies of client-side composition, exploring its responsibilities, implementation strategies, and best practices.

Defining Client-Side Composition

Client-side composition is a design pattern where the client application is tasked with aggregating data from various microservices to form a complete response. Unlike server-side aggregation, where a backend service compiles data from different sources, client-side composition allows the client to directly interact with multiple services. This pattern is particularly useful in scenarios where the client needs to customize the data it retrieves based on user interactions or specific application logic.

Identifying Aggregation Responsibilities

In client-side composition, the client assumes several key responsibilities:

1. Managing Multiple API Calls: The client must efficiently manage multiple API requests to different microservices, ensuring that data is fetched in a timely manner.
2. Data Aggregation: The client is responsible for combining data from various sources into a cohesive response that meets the application’s needs.
3. Handling Dependencies: The client must handle dependencies between different data sources, ensuring that the aggregated data is consistent and accurate.

Implementing Parallel Requests

To minimize latency and improve response times, it’s crucial to implement parallel API requests. By fetching data from multiple services concurrently, the client can significantly reduce the time it takes to aggregate data. Here’s a Java example using the CompletableFuture class to demonstrate parallel requests:

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ClientSideAggregator {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        CompletableFuture<String> serviceA = CompletableFuture.supplyAsync(() -> fetchDataFromServiceA());
        CompletableFuture<String> serviceB = CompletableFuture.supplyAsync(() -> fetchDataFromServiceB());

        CompletableFuture<Void> allOf = CompletableFuture.allOf(serviceA, serviceB);

        allOf.thenRun(() -> {
            try {
                String dataA = serviceA.get();
                String dataB = serviceB.get();
                System.out.println("Aggregated Data: " + dataA + " " + dataB);
            } catch (InterruptedException | ExecutionException e) {
                e.printStackTrace();
            }
        }).join();
    }

    private static String fetchDataFromServiceA() {
        // Simulate API call to Service A
        return "Data from Service A";
    }

    private static String fetchDataFromServiceB() {
        // Simulate API call to Service B
        return "Data from Service B";
    }
}

Handling Data Merging

Once data is fetched from multiple services, the client must merge it seamlessly. This involves aligning data structures and resolving any conflicts. For instance, if two services provide overlapping data, the client must decide how to prioritize or merge these data points.

Consider using libraries like Jackson or Gson in Java to parse and merge JSON responses effectively. Here’s a simple example of merging JSON data:

import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;

import java.io.IOException;

public class DataMerger {

    public static void main(String[] args) throws IOException {
        String jsonA = "{\"name\": \"John\", \"age\": 30}";
        String jsonB = "{\"city\": \"New York\", \"age\": 31}";

        ObjectMapper mapper = new ObjectMapper();
        JsonNode nodeA = mapper.readTree(jsonA);
        JsonNode nodeB = mapper.readTree(jsonB);

        JsonNode merged = merge(nodeA, nodeB);
        System.out.println("Merged JSON: " + merged.toString());
    }

    private static JsonNode merge(JsonNode mainNode, JsonNode updateNode) {
        updateNode.fields().forEachRemaining(entry -> {
            String fieldName = entry.getKey();
            JsonNode jsonNode = entry.getValue();
            if (mainNode.has(fieldName)) {
                ((ObjectNode) mainNode).replace(fieldName, jsonNode);
            } else {
                ((ObjectNode) mainNode).set(fieldName, jsonNode);
            }
        });
        return mainNode;
    }
}

Managing Caching Strategies

Client-side caching is essential to reduce the number of API calls and enhance performance. By caching responses, the client can quickly serve repeated requests without fetching data from the server again. Consider using caching libraries like Caffeine or Guava in Java to implement efficient caching strategies.

import com.github.benmanes.caffeine.cache.Cache;
import com.github.benmanes.caffeine.cache.Caffeine;

import java.util.concurrent.TimeUnit;

public class ClientCache {

    private static final Cache<String, String> cache = Caffeine.newBuilder()
            .expireAfterWrite(10, TimeUnit.MINUTES)
            .maximumSize(100)
            .build();

    public static void main(String[] args) {
        String data = getData("serviceA");
        System.out.println("Cached Data: " + data);
    }

    private static String getData(String key) {
        return cache.get(key, k -> fetchDataFromService(k));
    }

    private static String fetchDataFromService(String service) {
        // Simulate fetching data from a service
        return "Data from " + service;
    }
}

Ensuring Consistency and Integrity

Maintaining data consistency and integrity is crucial when aggregating data from multiple sources. The client should implement mechanisms to handle discrepancies and ensure that the data presented to the user is accurate. This might involve validating data, handling partial failures, and implementing retry mechanisms for failed requests.

Simplifying Client Logic

To manage the complexity of client-side composition, it’s important to simplify client logic. This can be achieved through modular design, code reuse, and leveraging libraries or frameworks that facilitate data aggregation. Consider using frameworks like React or Angular for frontend applications, which provide powerful tools for managing state and data flows.

Testing Client-Side Aggregation

Testing is vital to ensure that the client-side aggregation process functions correctly. Tests should cover various scenarios, including successful data aggregation, handling of partial failures, and performance under load. Use testing frameworks like JUnit for Java to automate these tests and ensure reliability.

import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.assertEquals;

public class AggregationTest {

    @Test
    public void testDataAggregation() {
        String dataA = "Data from Service A";
        String dataB = "Data from Service B";

        String aggregatedData = dataA + " " + dataB;
        assertEquals("Data from Service A Data from Service B", aggregatedData);
    }
}

Conclusion

Client-side composition offers a flexible and dynamic approach to data aggregation in microservices architecture. By effectively managing API calls, handling data merging, implementing caching strategies, and ensuring data consistency, clients can deliver rich and responsive user experiences. Emphasizing simplicity in client logic and rigorous testing further enhances the robustness of this pattern.

For further exploration, consider reviewing official documentation for libraries and frameworks mentioned, such as Jackson, Caffeine, and JUnit. Additionally, books like “Building Microservices” by Sam Newman provide deeper insights into microservices architecture and design patterns.

Quiz Time!