Using the Java Stream API can improve code simplicity and parallelism, but the scenarios need to be selected reasonably. 1. Advantages: Declarative programming separates logic and implementation, such as filter, map and collect operations, making filtering, conversion and collection more intuitive; 2. Notes: Avoid modifying external variables in map or filter to prevent concurrency problems; 3. Operation classification: Intermediate operations (filter, map) are lazy to execute, and terminal operations (collect, forEach) are required to trigger execution, and terminal operations cannot be called multiple times; 4. Parallel flow: suitable for large data volumes and complex operations, but performance needs to be tested to avoid shared state operations.

The Java Streams API is a powerful tool for processing collection data. If used well, you can write concise and efficient code. The key is to understand its design logic and when to use what operations.

Why use Stream instead of traditional loops?

The advantage of Stream is that it makes the code more declarative, separating "what to do" and "how to do it". For example, if you want to filter out all even numbers from a list and write them as list.stream().filter(n -> n % 2 == 0).collect(Collectors.toList()) is not only clearer, but also easier to parallelize than writing for loops and if judgments.

But don't abuse it. In some cases, ordinary loops are more intuitive, such as when you need to frequently access the index or make complex conditional judgments.

Common operations: How to use filter, map, collect so as not to get stuck?

These are the most commonly used intermediate and terminal operations:

• filter() : Keep elements according to conditions, note that the lambda passed in should have no side effects
• map() : Convert element type or structure, such as converting a string to length, or converting it to another object
• collect() : Collect it into List, Set or Map. The error-prone place here is the choice of Collector

For example, if you want to convert a list of strings to their length and collect them as Set:

 Set<Integer> lengths = list.stream()
    .map(String::length)
    .collect(Collectors.toSet());

This is fine to write, but if external variables are modified in map or filter, it may cause concurrency problems, especially in parallel streams.

Intermediate operations vs terminal operations: The order is very important!

The operations of Stream are divided into intermediate operations (such as filter, map) and terminal operations (such as collect, forEach). Intermediate operations are lazy and will only be truly executed when encountering terminal operations.

This is easily overlooked. For example, you wrote:

 stream.filter(s -> s != null).map(String::toUpperCase);

This line of code does nothing because it has no terminal operations.

So remember:

• Intermediate operations can be called chained, but will not be executed immediately
• Terminal operation triggers the execution of the entire pipeline
• Do not call multiple terminal operations on a Stream, as an error will be reported

How to use parallel streams? Is it really faster?

ParallelStream utilizes multi-core CPUs, which can theoretically speed up the processing of large data volumes. But not all scenarios are suitable for use, such as:

• When the data volume is small, turning on parallel increases thread switching overhead
• The operation itself is very lightweight (such as just traversal) and is not suitable for parallelism.
• If your operations are stateful or shared variables, errors are prone to

Recommended usage:

• Test performance first, then decide whether to enable it
• Avoid using side effects in parallel streams
• You can use Collection.parallelStream() or .stream().parallel() to enable it

Overall, the Stream API is a good tool, but don't force it to show off. Choose the right method and the code is both elegant and efficient.

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