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Java stream gatherers let you add intermediate transformations when operations such as map and filter are not enough—for example, to group adjacent elements, accumulate one result, emit running totals, or map elements with bounded concurrency. Oracle documents the built-in Gatherers class as available since Java 24, so compile examples against the JDK version you actually use.
What is a stream gatherer?
A gatherer is an intermediate stream transformation: it consumes input elements, may maintain operation state, and produces output elements for the next stage of a stream pipeline. Oracle’s Java SE 24 API represents those roles with the type parameters Gatherer<T,A,R>: T is the input element type, A is the potentially mutable state type, and R is the output element type. See Oracle’s Gatherer API.
Use the built-in helpers when their output shape matches the job: windows for groups, fold for at most one accumulated result, scan for every cumulative result, and mapConcurrent for bounded concurrent mapping. A custom gatherer is the extension point when those helpers do not express the transformation you need.
Which gatherer fits the task?
| Operation | Output per input | State and order | Concurrency and memory |
|---|---|---|---|
windowFixed |
Groups of up to the requested size | Groups follow encounter order; elements are collected into windows | Window storage can be allocated eagerly and contiguously; large windows can use excessive memory |
windowSliding |
Overlapping groups | Groups follow encounter order; each new window drops the oldest element and adds the next | Window storage can be allocated eagerly and contiguously; large windows can use excessive memory |
fold |
At most one result | Accumulates in encounter order; suited to order-dependent operations without a combiner | Do not treat it as a parallel reduction |
scan |
One cumulative result for each processed input | Each output reflects the accumulated state through that input | Accumulated state is carried across inputs |
mapConcurrent |
One mapped result per input | Mapper work runs concurrently, but output preserves stream order | Concurrency is bounded by the configured maximum; uses virtual threads |
How do window gatherers group stream elements?
windowFixed(int windowSize)
Fixed windows partition encountered elements into consecutive groups of the requested size. For example, Oracle’s Java SE 24 API shows eight elements with a window size of three producing [[1, 2, 3], [4, 5, 6], [7, 8]]. The final window may be shorter. Empty input produces no windows, and returned window lists are unmodifiable.
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windowSliding(int windowSize)
Sliding windows overlap: each successive window retains the previous window’s elements except the oldest, then adds the next stream element. If the input has fewer elements than the requested size, the result is one window containing all input elements; empty input produces none. Returned lists are unmodifiable.
Both window methods throw IllegalArgumentException when the size is less than one. Oracle also cautions that windows may be allocated contiguously and eagerly during evaluation, so choosing a very large window can consume excessive memory even for a small stream. Details and examples are in the Java SE 24 Gatherers API.
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When should you use fold or scan?
fold(Supplier<R> initial, BiFunction<R,T,R> folder)
Use fold when you want to process elements in order and produce at most one accumulated result. It is useful when an operation is order-dependent or no suitable combiner can be implemented. That distinguishes it from a parallel reduction strategy: do not assume fold can split work and combine partial results safely.
scan(Supplier<R> initial, BiFunction<R,T,R> scanner)
Use scan when downstream code needs the progression, not only the final accumulated value. It starts from the supplied initial value, updates the accumulated value for each input, and emits each updated value. For example, a running balance needs the intermediate balances as well as the final one; a fold-style result alone would discard those prefixes.
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What does mapConcurrent do?
mapConcurrent(int maxConcurrency, Function<T,R> mapper) applies a mapping function concurrently up to the configured maximum. It uses virtual threads and preserves stream encounter order in its output. Oracle describes it as “An operation which executes a function concurrently with a configured level of max concurrency, using virtual threads.” The API requires maxConcurrency to be at least one.
This is bounded concurrent mapping, not a general performance guarantee: whether it helps depends on the mapper and workload, and no benchmark is established here. If downstream no longer needs elements, cancellation of in-progress tasks is best effort. If a required mapping completes exceptionally, the exception is rethrown as a RuntimeException and remaining tasks are canceled. Consult Oracle’s mapConcurrent documentation for the API contract.
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When should you write a custom gatherer?
Choose a custom Gatherer<T,A,R> when the desired intermediate transformation is not covered by the built-in window, fold, scan, or concurrent-mapping helpers. Define the input, state, and output types around the transformation, and follow the interface’s contract for implementation details. Because the API version matters, compile the implementation against the JDK you intend to support; the Java SE 24 API pages identify these APIs as available since Java 24.
What Java version do you need?
Oracle’s Java SE 24 API documentation marks Gatherers as available since Java 24. The title-matched InfoWorld tutorial by Matthew Tyson was published June 26, 2024 and demonstrates the feature in its Java 22 preview-era context. Its --enable-preview advice belongs to that historical setup, not to current Java 24 use. Check the documentation and compiler options for your installed JDK rather than copying preview flags from an older tutorial. See the InfoWorld tutorial and Oracle’s Java SE 24 API.
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