What are the application scenarios of lambda expressions in parallel programming?

Parallel programming uses lambda expressions in the following scenarios: 1. Parallel mapping: perform operations on each element in the collection; 2. Parallel filtering: filter elements from the collection; 3. Parallel reduction: perform cumulative operations on elements ;4. Parallel sorting: Sort elements according to customized comparators. These scenarios can be applied to parallel processing of large data sets to improve processing efficiency.

Application scenarios of Lambda expressions in parallel programming

In parallel programming, lambda expressions play a vital role role. They allow us to express parallel operations in simpler, more readable code. The following are some common application scenarios:

1. Parallel mapping

Lambda expressions are useful when applying an operation to each element in a collection. For example, the following code uses a lambda expression to increase each element in the collection by 1:

List<Integer> numbers = List.of(1, 2, 3, 4, 5);

// 使用 lambda 表达式对集合进行并行映射
List<Integer> incrementedNumbers = numbers.parallelStream()
        .map(n -> n + 1)
        .toList();

System.out.println(incrementedNumbers); // 输出：[2, 3, 4, 5, 6]

2. Parallel filtering

Using lambda expressions you can easily filter from a collection Filter elements in. For example, the following code uses a lambda expression to filter out elements in the collection that are greater than 3:

List<Integer> numbers = List.of(1, 2, 3, 4, 5);

// 使用 lambda 表达式对集合进行并行过滤
List<Integer> filteredNumbers = numbers.parallelStream()
        .filter(n -> n > 3)
        .toList();

System.out.println(filteredNumbers); // 输出：[4, 5]

3. Parallel reduction

lambda expressions also allow us to run parallel streams Perform reduction operations on elements. For example, the following code uses a lambda expression to calculate the sum of the elements in a collection:

List<Integer> numbers = List.of(1, 2, 3, 4, 5);

// 使用 lambda 表达式对集合进行并行归约
int sum = numbers.parallelStream()
        .reduce(0, (a, b) -> a + b);

System.out.println(sum); // 输出：15

4. Parallel sorting

lambda expressions can be used to sort parallel streams. For example, the following code uses a lambda expression to sort a collection of strings based on the length of the elements:

List<String> strings = List.of("Apple", "Banana", "Cherry", "Dog", "Elephant");

// 使用 lambda 表达式对集合进行并行排序
List<String> sortedStrings = strings.parallelStream()
        .sorted((a, b) -> a.length() - b.length())
        .toList();

System.out.println(sortedStrings); // 输出：[Dog, Apple, Banana, Cherry, Elephant]

Practical Case: Parallel Processing of Large Data Sets

Suppose we have a A large data set containing a million records, we need to do some processing on each record. Using parallel streams and lambda expressions, we can effectively parallelize this processing:

// 伪代码，模拟大数据集
List<MyData> data = new ArrayList<>(1_000_000);

// 使用并行流和 lambda 表达式并行处理数据
data.parallelStream()
        .forEach(d -> process(d));

By using parallel streams and lambda expressions, this processing can be executed in parallel, greatly improving overall performance.

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