The task is to identify and analyze orders placed within a specific date range and calculate various statistics like total order count, average order

Solution Using Stream API why Stream API

The Stream API in Java is a powerful tool for handling sequences of data in a declarative manner. Here’s a brief overview suitable for a Java architect:

### Key Features:

1. Functional Operations: Supports operations like map, filter, reduce, collect, forEach.

2. Laziness: Intermediate operations are lazy; they’re not executed until a terminal operation is invoked.

3. Parallel Processing: Streams can be processed in parallel using parallelStream.

4. Pipelines: Streams allow creating pipelines of operations which are efficient and readable.

### Best Practices:

1. Immutability: Use immutable collections to avoid side effects.

2. Parallel Streams: Use cautiously, ensure thread safety and performance benefits.

3. Short-Circuiting: Utilize short-circuiting operations (`findFirst`, anyMatch, noneMatch) for performance.

The Stream API enhances readability, maintainability, and performance of data processing in Java applications.

Here's a detailed example demonstrating various features of the Stream API in Java:

Example Scenario:

You have a list of employees, and you want to find the names of employees who are older than 30, sorted by their names, and collect them into a list.

### Employee Class:

```java

public class Employee {

private String name;

private int age;

// Constructor, getters, and setters

public Employee(String name, int age) {

this.name = name;

this.age = age;

}

public String getName() {

return name;

}

public int getAge() {

return age;

}

@Override

public String toString() {

return "Employee{name='" + name + "', age=" + age + '}';

}

}

Main Class:

`

import java.util.Arrays;

import java.util.List;

import java.util.stream.Collectors;

public class StreamExample {

public static void main(String[] args) {

List<Employee> employees = Arrays.asList(

new Employee("Alice", 25),

new Employee("Bob", 35),

new Employee("Charlie", 30),

new Employee("David", 40)

);

List<String> employeeNames = employees.stream()

.filter(employee -> employee.getAge() > 30) // Filter employees older than 30

.sorted((e1, e2) -> e1.getName().compareTo(e2.getName())) // Sort by name

.map(Employee::getName) // Extract names

.collect(Collectors.toList()); // Collect into a list

employeeNames.forEach(System.out::println);

}

}

```

### Explanation:

1. Stream Creation: employees.stream() creates a stream from the list of employees.

2. Filtering: .filter(employee -> employee.getAge() > 30) filters employees older than 30.

3. Sorting: .sorted((e1, e2) -> e1.getName().compareTo(e2.getName())) sorts the filtered employees by their names.

4. Mapping: .map(Employee::getName) transforms the Employee objects into a stream of names.

5. Collecting: .collect(Collectors.toList()) collects the names into a list.

6. Printing: employeeNames.forEach(System.out::println) prints each name in the resulting list.

Solution

// Load orders from a data source

List<Order> orders = ...;

// Define the date range

LocalDate startDate = LocalDate.of(2024, 06, 01);

LocalDate endDate = LocalDate.now();

// Stream of orders within the date range

Stream<Order> filteredOrders = orders.stream() .filter(order -> order.getOrderDate().isAfter(startDate.minusDays(1)) // inclusive of start date

&& order.getOrderDate().isBefore(endDate.plusDays(1))); // exclusive of end date

// Total order count

long totalOrders = filteredOrders.count();

// Average order value (assuming Order has a getTotalPrice() method)

double averageOrderValue = filteredOrders.mapToDouble(Order::getTotalPrice).average().orElse(0.0);

// Map to product IDs and count occurrences (using Collectors.groupingBy)

Map<Long, Long> productCountMap = filteredOrders.flatMap(order -> order.getProducts().stream()).collect(Collectors.groupingBy(Product::getId, Collectors.counting()));

// Find most popular product (based on count)

Optional<Long> mostPopularProductId = productCountMap.entrySet().stream() .max(Comparator.comparingLong(Map.Entry::getValue)) .map(Map.Entry::getKey);

// Process further based on the collected statistics