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Table of Contents
Understand the difference between intermediate operations and terminal operations
Reasonably select the terminal operation type
Parallel flow is not a universal accelerator
Try to avoid unnecessary object creation and boxing
Home Java javaTutorial Efficiently Using Java Streams for Data Processing

Efficiently Using Java Streams for Data Processing

Jul 05, 2025 am 01:16 AM

Five points to pay attention to when using Java Streams: 1. Intermediate operations (such as filters, maps) must be executed through terminal operations (such as collect, forEach), otherwise it will not take effect; 2. Avoid modifying external variables in intermediate operations to prevent concurrency problems; 3. Select the terminal operation type according to the scene, such as anyMatch, which is more efficient to judge existence; 4. Parallel streams are suitable for complex operations in large data volumes, while small data increases overhead and is sensitive to sequence; 5. Reduce object creation and packing, and give priority to basic type streams such as IntStream to improve performance.

Efficiently Using Java Streams for Data Processing

Java Streams is a powerful feature introduced in Java 8 to simplify the processing of collection data. Many people use it to write more concisely, but it still takes some skills and understanding to really use Streams efficiently.

Efficiently Using Java Streams for Data Processing

Understand the difference between intermediate operations and terminal operations

Stream operations are divided into intermediate operations (such as filter , map ) and terminal operations (such as collect , forEach ). Intermediate operations will not be executed immediately, they are "lazy loading" and will only actually start processing data when encountering terminal operations.

Efficiently Using Java Streams for Data Processing

This is crucial: if you only write a few intermediate operations but don't have terminal operations, then Stream actually does nothing. for example:

 list.stream().filter(item -> item.isValid()); // No terminal operation will not be executed

suggestion:

Efficiently Using Java Streams for Data Processing
  • Intermediate operations can be called in chains, but in the end there must be a terminal operation.
  • Avoid side effects (such as modifying external variables) in intermediate operations, which can easily cause concurrency problems.

Reasonably select the terminal operation type

Different terminal operations have different performance and applicable scenarios. For example:

  • collect(Collectors.toList()) is suitable for collecting results;
  • forEach() is suitable for traversal execution actions;
  • reduce() is used for aggregation calculation;
  • findFirst() and findAny() have different behaviors in parallel streams.

A common misunderstanding is that when you only need to determine whether there are elements that meet the conditions, you mistakenly use filter().collect() and then check whether it is empty. The correct way is to use anyMatch() directly:

 // Not recommended boolean found = list.stream().filter(item -> item.isValid()).collect(Collectors.toList()).size() > 0;

// Recommend boolean found = list.stream().anyMatch(item -> item.isValid());

This will terminate the processing process in advance and improve efficiency.

Parallel flow is not a universal accelerator

Java provides parallelStream() to take advantage of multi-core advantages, but this does not mean that it is always faster. In fact, when using parallel streams is used in small data volumes or simple operations, it may actually cause additional overhead due to thread scheduling.

suggestion:

  • When the data volume is large (such as more than tens of thousands) and the operation is complicated, parallelism will be considered.
  • Sequence-sensitive operations (such as findFirst() ) may not achieve the expected results in parallel streams.
  • Pay attention to the synchronization of shared resources when using parallel streams.

For example, the following example:

 int sum = numbers.parallelStream().mapToInt(Integer::intValue).sum();

If numbers are large, they can indeed increase the speed; but if there are only a few dozen elements, it is more appropriate to use a normal stream.

Try to avoid unnecessary object creation and boxing

The syntax of the Stream API is elegant, but you also need to pay attention to performance details. For example, frequent use of map() to create new objects will increase GC pressure. Also, it is best to use special Streams for processing basic types, such as IntStream and DoubleStream , rather than generic Streams.

For example:

 // May cause packing overhead int sum = list.stream().mapToInt(Item::getValue).sum();

// Better int sum = IntStream.range(0, 100).sum();

Also, if you just traversal without returning values, you should give priority to normal loops, especially in hotspot code.

Basically that's it. Stream is very convenient, but to write clear and efficient code, you still have to make trade-offs based on the specific scenario.

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