search
HomeJavajavaTutorialHow to Perform Nested GroupBy Operations in Java 8 for Complex Data Aggregation?

How to Perform Nested GroupBy Operations in Java 8 for Complex Data Aggregation?

Nested (Multi-Level) GroupBy in Java 8

Multi-level grouping in Java 8 allows for complex aggregation of data objects based on multiple fields. In your case, you have a set of classes Pojo, Item, and SubItem, and you want to group items based on key1 and then subitems within that group by key2.

To achieve this, we can't simply use nested Collectors.groupingBy calls, as this would not allow for grouping by multiple keys from different objects. Instead, we resort to a combination of flatMap and grouping collectors:

<code class="java">Map<t map list>>> result = pojo.getItems().stream()
    .flatMap(item -> item.getSubItems().stream()
        .map(sub -> new AbstractMap.SimpleImmutableEntry(item.getKey1(), sub)))
    .collect(Collectors.groupingBy(AbstractMap.SimpleImmutableEntry::getKey,
                Collectors.groupingBy(Map.Entry::getValue, Collectors.toList())));</t></code>

In this approach, we first use flatMap to create a stream of pairs containing the key1 from each Item and the corresponding SubItem. Then, we apply Collectors.groupingBy twice: once to group the pairs by key1 and again to group the SubItems by key2.

An alternative solution would be to define a custom collector that provides a flatMapping operation similar to Java 9's Collectors.flatMapping:

<code class="java">static <t> Collector<t> flatMapping(
    Function super T,? extends Stream extends U>> mapper,
    Collector super U,A,R> downstream) {

    BiConsumer<a super u> acc = downstream.accumulator();
    return Collector.of(downstream.supplier(),
        (a, t) -> { try(Stream extends U> s=mapper.apply(t)) {
            if(s!=null) s.forEachOrdered(u -> acc.accept(a, u));
        }},
        downstream.combiner(), downstream.finisher(),
        downstream.characteristics().toArray(new Collector.Characteristics[0]));
}</a></t></t></code>

With this custom collector, the grouping operation can be simplified:

<code class="java">Map<t map list>>> result = pojo.getItems().stream()
    .collect(Collectors.groupingBy(Item::getKey1,
                Collectors.flatMapping(item -> item.getSubItems().stream(),
                    Collectors.groupingBy(SubItem::getKey2))));</t></code>

The above is the detailed content of How to Perform Nested GroupBy Operations in Java 8 for Complex Data Aggregation?. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Top 4 JavaScript Frameworks in 2025: React, Angular, Vue, SvelteTop 4 JavaScript Frameworks in 2025: React, Angular, Vue, SvelteMar 07, 2025 pm 06:09 PM

This article analyzes the top four JavaScript frameworks (React, Angular, Vue, Svelte) in 2025, comparing their performance, scalability, and future prospects. While all remain dominant due to strong communities and ecosystems, their relative popul

Spring Boot SnakeYAML 2.0 CVE-2022-1471 Issue FixedSpring Boot SnakeYAML 2.0 CVE-2022-1471 Issue FixedMar 07, 2025 pm 05:52 PM

This article addresses the CVE-2022-1471 vulnerability in SnakeYAML, a critical flaw allowing remote code execution. It details how upgrading Spring Boot applications to SnakeYAML 1.33 or later mitigates this risk, emphasizing that dependency updat

Node.js 20: Key Performance Boosts and New FeaturesNode.js 20: Key Performance Boosts and New FeaturesMar 07, 2025 pm 06:12 PM

Node.js 20 significantly enhances performance via V8 engine improvements, notably faster garbage collection and I/O. New features include better WebAssembly support and refined debugging tools, boosting developer productivity and application speed.

How do I implement multi-level caching in Java applications using libraries like Caffeine or Guava Cache?How do I implement multi-level caching in Java applications using libraries like Caffeine or Guava Cache?Mar 17, 2025 pm 05:44 PM

The article discusses implementing multi-level caching in Java using Caffeine and Guava Cache to enhance application performance. It covers setup, integration, and performance benefits, along with configuration and eviction policy management best pra

How does Java's classloading mechanism work, including different classloaders and their delegation models?How does Java's classloading mechanism work, including different classloaders and their delegation models?Mar 17, 2025 pm 05:35 PM

Java's classloading involves loading, linking, and initializing classes using a hierarchical system with Bootstrap, Extension, and Application classloaders. The parent delegation model ensures core classes are loaded first, affecting custom class loa

How to Share Data Between Steps in CucumberHow to Share Data Between Steps in CucumberMar 07, 2025 pm 05:55 PM

This article explores methods for sharing data between Cucumber steps, comparing scenario context, global variables, argument passing, and data structures. It emphasizes best practices for maintainability, including concise context use, descriptive

Iceberg: The Future of Data Lake TablesIceberg: The Future of Data Lake TablesMar 07, 2025 pm 06:31 PM

Iceberg, an open table format for large analytical datasets, improves data lake performance and scalability. It addresses limitations of Parquet/ORC through internal metadata management, enabling efficient schema evolution, time travel, concurrent w

How can I implement functional programming techniques in Java?How can I implement functional programming techniques in Java?Mar 11, 2025 pm 05:51 PM

This article explores integrating functional programming into Java using lambda expressions, Streams API, method references, and Optional. It highlights benefits like improved code readability and maintainability through conciseness and immutability

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.