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Table of Contents
Understand the basic structure and life cycle of flows
Make good use of collect and groupingBy for aggregation statistics
Pay attention to the balance between performance and readability
Home Java javaTutorial Mastering Java Streams API for Data Processing

Mastering Java Streams API for Data Processing

Jul 08, 2025 am 02:44 AM
java data processing

To master the Java Streams API, you need to understand the stream structure, avoid reusing streams, make good use of collect and groupingBy, and balance performance and readability. First, stream operations are divided into three parts: creation, intermediate operations, and terminal operations; second, streams can only be used once, and reused use will cause errors; second, collect and groupingBy can simplify aggregation statistics; finally, use Stream moderately to take into account performance and code clarity.

Mastering Java Streams API for Data Processing

The Java Streams API is a powerful tool for processing collection data. If used well, you can write concise and efficient code. However, many people are easily confused by various methods when they first come into contact. In fact, as long as they master a few core points, they can easily control it.

Mastering Java Streams API for Data Processing

Understand the basic structure and life cycle of flows

The operation of Stream is usually divided into three parts: creating streams, intermediate operations, and terminal operations . Creation can start with a collection, array, or static methods such as Stream.of() ; intermediate operations such as filter and map are "lazy loading" and will not be executed immediately; only when terminal operations (such as collect or forEach ) are encountered will they actually start processing data.

Mastering Java Streams API for Data Processing

A common misunderstanding is trying to reuse the same Stream, but once the terminal operation is executed, the stream is "closed". For example:

 Stream<String> stream = list.stream().filter(s -> s.length() > 3);
stream.forEach(System.out::println); // No problem for the first time stream.forEach(System.out::println); // Error: stream has already been operated upon or closed

So either regenerate the stream or consider collecting the results into intermediate variables.

Mastering Java Streams API for Data Processing

Make good use of collect and groupingBy for aggregation statistics

collect method combined with the Collectors tool class is almost one of the most commonly used operations on Stream. Especially when doing grouping statistics, groupingBy can simplify a lot of logic.

For example, if you have a bunch of orders, you want to group them by users, and then calculate the total amount of each user:

 Map<User, Integer> totalPerUser = orders.stream()
    .collect(Collectors.groupingBy(Order::getUser,
             Collectors.summmingInt(Order::getAmount)));

This writing method is clear and efficient, much cleaner than manually traversing the Map to determine whether the key exists. In addition, partitioningBy can be used for binary classification, such as dividing orders into two groups greater than 100 and less than or equal to 100.


Pay attention to the balance between performance and readability

Although Stream is elegant to write, not all situations are suitable for use. For example, when the number of loops is very small, traditional for loops is more efficient. And excessive nesting will make the code difficult to understand, especially when multiple maps, filters, and flatMaps are mixed together.

A few suggestions:

  • If the logic is simple, do not break the filter collect that can be solved in one line.
  • Complex logic can be split appropriately, and comments are added to illustrate the purpose of each step.
  • ParallelStreams are not always faster, thread switching is overhead, and it is more suitable when there is a large amount of data and stateless operation.

Basically that's it. The Stream API has many functions, but after mastering these key points, it is enough for daily development. What is not complicated but easy to ignore is when you should not use Stream, and keeping it moderate is the key.

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