Interview At Sapient

Question 1: Did you also deploy applications in the cloud?

There are several cloud platforms and services that you can use to deploy Spring Boot applications, such as:

1. Cloud Platforms:

• AWS (Amazon Web Services)
• Google Cloud Platform (GCP)
• Microsoft Azure
• DigitalOcean
• Heroku

1. Container Orchestration Platforms:

• Docker
• Kubernetes

The general process for deploying a Spring Boot application to the cloud involves:

1. Packaging the Application: Use the mvn clean package command to create a JAR or WAR file of your Spring Boot application.
2. Containerizing the Application: Create a Docker image for your application using a Dockerfile. This step is often automated using tools like Docker Compose or cloud-native buildpacks.
3. Pushing the Image to a Registry: Push the Docker image to a container registry like Docker Hub, AWS Elastic Container Registry (ECR), Google Container Registry (GCR), or Azure Container Registry.
4. Deploying to the Cloud: Deploy the containerized application to your chosen cloud platform or container orchestration service. This can involve creating compute resources (e.g., EC2 instances, GCP Virtual Machines), configuring load balancers, setting up databases and other services, and managing the deployment process.
5. Scaling and Monitoring: Once deployed, you can scale your application horizontally or vertically based on demand and monitor its performance using the tools and services provided by your cloud platform.

The specific steps and configurations may vary depending on the cloud platform or service you choose. Many cloud providers offer managed services like AWS Elastic Beanstalk, Google Cloud Run, or Azure Spring Cloud that can simplify the deployment process for Spring Boot applications.

It’s worth noting that while I can provide an overview of the deployment process, the actual implementation and configuration details may require hands-on experience and familiarity with the specific cloud platform and tools you plan to use.

Question 2: Which services in the AWS cloud did you use?

Question 3: In SQS, how do we add fault tolerance?

Question 4: Like if error, if the message fails while processing, how do we handle those errors?

Question 5: what was the usage for lambda?

Question 6: what other best practices for microservices have you followed?

Question 6: what are the key features of orchestration? So, since it is a single point of failure, what is an alternate approach?

Question 7: What is the use case for circuit breakers?

Question 8: Given a log file, return IP address(es) which accesses the site most often. Can you make any Java optimization there using lambda expression?

import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.Map;
import java.util.stream.Collectors;

public class InvalidLogEntry {
    public static void main(String [] args){
        String[] lines = {
                "10.0.0.1 - log entry 1 11",
                "10.0.0.1 - log entry 2 213",
                "10.0.0.3 - log entry 133132",
                "Invalid log entry"
        };
        System.out.println(findTopIpAddress_v2(lines));
    }
    public static String findTopIpAddress(String [] lines){
        return Arrays.stream(lines)
                .filter(line -> line.contains("-"))
                .map(line -> line.split("-")[0].trim())
                .collect(Collectors.groupingBy(id -> id, Collectors.counting()))
                .entrySet()
                .stream()
                .max(Map.Entry.comparingByValue())
                .map(Map.Entry::getKey)
                .orElse(null);
    }
    public static String findTopIpAddress_v2(String [] lines){
        if(lines == null){
            return null;
        }
        Map<String, Long> ipAddressFrequencyMap =
                Arrays.stream(lines)
                .filter(line -> line.contains("-"))
                        .map(InvalidLogEntry::extractIpAddress)
                        .collect(Collectors.groupingBy(ip -> ip, Collectors.counting()));
        long maxFrequency = Collections.max(ipAddressFrequencyMap.values());
        return ipAddressFrequencyMap
                .entrySet()
                .stream()
                .filter(entry -> entry.getValue() == maxFrequency)
                .min(Map.Entry.comparingByKey())
                .map(Map.Entry::getKey)
                .orElse(null);

    }
    private static String extractIpAddress(String logEntry){
        return logEntry.split("-")[0].trim();
    }
}

Can you think of any edge cases in which the board might not work and what extra handling we need to add for it?

private static String extractIpAddress(String logEntry){
    if (logEntry == null || logEntry.isEmpty()) {
        return null;
    }
    String[] parts = logEntry.split("-");
    if (parts.length < 2) {
        return null;
    }
    return parts[0].trim();
}

Now, suppose during filtering, no element in the stream matches the filter. So, after the filter operation, the stream becomes empty. So, what will happen to the next operator in this case? Will it throw an error?

If no elements in the stream match the filter, the stream becomes empty. However, this will not cause an error. The stream simply becomes a stream with no elements, and the subsequent operations are performed on an empty stream.

For example, if you perform a map operation on an empty stream, it will still return an empty stream. Similarly, if you perform a collect operation on an empty stream, it will return an empty collection.

In the context of your log file analysis code, if no log entries contain a dash (“-”), the filter operation will result in an empty stream. The subsequent map operation will also operate on an empty stream, so the collect operation will return an empty map. Finally, the max operation will return an empty Optional, and the orElse method will return null.

As for Java 8 optimizations, above code is already making good use of Java 8 features such as streams, lambda expressions, and method references. One possible optimization could be to combine the filter and map operations into a single operation using the flatMap operation, but this would likely have a negligible impact on performance. The most important thing is that your code is clear and easy to understand, which it currently is.

public static String findTopIpAddress(String [] lines){
    return Arrays.stream(lines)
            .flatMap(line -> {
                if (line.contains("-")) {
                    return Stream.of(extractIpAddress(line));
                } else {
                    return Stream.empty();
                }
            })
            .collect(Collectors.groupingBy(id -> id, Collectors.counting()))
            .entrySet()
            .stream()
            .max(Map.Entry.comparingByValue())
            .map(Map.Entry::getKey)
            .orElse(null);
}

Question 9: What is the functional interface that is used by filter operation?

The filter operation in Java 8 streams uses the Predicate functional interface. The Predicate interface is a functional interface that takes a single argument and returns a boolean value. Here’s the definition of the Predicate interface:

@FunctionalInterface
public interface Predicate<T> {
    boolean test(T t);
}

The filter operation on a stream takes a Predicate as an argument and returns a new stream containing only the elements that match the given predicate. For example:

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
List<Integer> evenNumbers = numbers.stream()
                                   .filter(n -> n % 2 == 0)
                                   .collect(Collectors.toList());
// evenNumbers = [2, 4, 6, 8, 10]

In the above example, the lambda expression n -> n % 2 == 0 is an instance of the Predicate<Integer> functional interface. It takes an Integer value n and returns true if n is even, and false otherwise.

The filter operation applies this predicate to each element in the stream and creates a new stream containing only the elements for which the predicate returns true.

Question 10: What is the method signature for the function Interface?

Question 11: What is the return type for the functional interface?

In Java, a functional interface is an interface that has exactly one abstract method (apart from the methods defined in the java.lang.Object class).

The return type of a functional interface refers to the return type of its single abstract method.

For example, consider the following functional interface:

@FunctionalInterface
public interface MyFunctionalInterface {
    int performOperation(int a, int b); // This is the single abstract method
}

In this case, the return type of the functional interface MyFunctionalInterface is int, because its single abstract method performOperation returns an int value.

Another example:

@FunctionalInterface
public interface StringFunction {
    String apply(String s); // This is the single abstract method
}

Here, the return type of the functional interface StringFunction is String, as its single abstract method apply returns a String value.

It’s important to note that a functional interface can have multiple default and static methods, but it must have exactly one abstract method. The return type of a functional interface is determined by the return type of this single abstract method.

Functional interfaces are commonly used in Java for lambda expressions and method references, which are key features of functional programming in Java.

Question 12: What are the database products that you have worked on?

Question 13: So, what are the relational database guarantees that database provides?

Question 14: Are you familiar with the asset properties?

Question 15: Can you name the four asset properties?

Question 16: Have you used any tool for documenting your APIs?

There are several tools available for documenting APIs in Java, with the most popular ones being:

1. Javadoc: The official documentation generator tool for Java developed by Oracle. It parses source code and generates HTML documentation from structured comments (doc comments). Javadoc is widely used and supported by most IDEs like IntelliJ IDEA, Eclipse, and NetBeans.
2. Doxygen: Primarily designed for documenting C++ code but also supports Java. It can generate documentation in various formats like HTML, PDF, and LaTeX, with features like cross-referencing and diagrams.
3. Dokka: A documentation engine for Kotlin that also supports generating documentation for Java code. Dokka can output documentation in multiple formats, including HTML, Markdown, and Javadoc.
4. Swagger: A popular tool for documenting RESTful APIs. While not Java-specific, it integrates well with Java frameworks like Spring Boot, allowing you to generate interactive documentation for your APIs.
5. Spring REST Docs: A tool specific to the Spring framework that helps generate documentation for RESTful APIs by combining hand-written documentation with auto-generated snippets from test cases.
6. Asciidoctor: A documentation generation tool that supports multiple formats, including HTML, PDF, and e-books. It can be used to document Java APIs by writing documentation in the AsciiDoc format.
7. Sandcastle: A documentation compiler developed by Microsoft, primarily for .NET but also supports Java.

Most of these tools can be integrated with your build process, allowing you to generate documentation as part of your build pipeline. Additionally, many IDEs like IntelliJ IDEA and Eclipse have built-in support for generating and viewing Javadoc documentation.

The choice of tool often depends on your specific requirements, such as the desired output format, integration with your existing toolchain, and the need for advanced features like diagrams or interactive documentation.