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In addition to Stream, which is a stream of object references, there are primitive specializations for IntStream, LongStream, and DoubleStream, all of which are referred to as "streams" and conform to the characteristics and restrictions described here. To perform a computation, stream operations are composed into a stream pipeline. A stream pipeline consists of a source (which might be an array, a collection, a generator function, an I/O channel, etc), zero or more intermediate operations (which transform a stream into another stream, such as filter(Predicate)), and a terminal operation (which produces a result or side-effect, such as count() or forEach(Consumer)). Streams are lazy; computation on the source data is only performed when the terminal operation is initiated, and source elements are consumed only as needed. Collections and streams, while bearing some superficial similarities, have different goals. Collections are primarily concerned with the efficient management of, and access to, their elements. By contrast, streams do not provide a means to directly access or manipulate their elements, and are instead concerned with declaratively describing their source and the computational operations which will be performed in aggregate on that source. However, if the provided stream operations do not offer the desired functionality, the BaseStream.iterator() and BaseStream.spliterator() operations can be used to perform a controlled traversal. A stream pipeline, like the "widgets" example above, can be viewed as a query on the stream source. Unless the source was explicitly designed for concurrent modification (such as a ConcurrentHashMap), unpredictable or erroneous behavior may result from modifying the stream source while it is being queried. Most stream operations accept parameters that describe user-specified behavior, such as the lambda expression w -> w.getWeight() passed to mapToInt in the example above. To preserve correct behavior, these behavioral parameters: must be non-interfering (they do not modify the stream source); and in most cases must be stateless (their result should not depend on any state that might change during execution of the stream pipeline). Such parameters are always instances of a functional interface such as Function, and are often lambda expressions or method references. Unless otherwise specified these parameters must be non-null. A stream should be operated on (invoking an intermediate or terminal stream operation) only once. This rules out, for example, "forked" streams, where the same source feeds two or more pipelines, or multiple traversals of the same stream. A stream implementation may throw IllegalStateException if it detects that the stream is being reused. However, since some stream operations may return their receiver rather than a new stream object, it may not be possible to detect reuse in all cases. Streams have a BaseStream.close() method and implement AutoCloseable, but nearly all stream instances do not actually need to be closed after use. Generally, only streams whose source is an IO channel (such as those returned by Files.lines(Path, Charset)) will require closing. Most streams are backed by collections, arrays, or generating functions, which require no special resource management. (If a stream does require closing, it can be declared as a resource in a try-with-resources statement.) Stream pipelines may execute either sequentially or in parallel. This execution mode is a property of the stream. Streams are created with an initial choice of sequential or parallel execution. (For example, Collection.stream() creates a sequential stream, and Collection.parallelStream() creates a parallel one.) This choice of execution mode may be modified by the BaseStream.sequential() or BaseStream.parallel() methods, and may be queried with the BaseStream.isParallel() method.Since:1.8See Also:IntStream, LongStream, DoubleStream, java.util.streamNested Class SummaryNested Classes Modifier and TypeInterface and Descriptionstatic interface Stream.BuilderA mutable builder for a Stream.Method SummaryAll Methods Static Methods Instance Methods Abstract Methods Default Methods Modifier and TypeMethod and DescriptionbooleanallMatch(Predicate

This document contains two primary sections and a third section for notes. Thefirst section explains how to use existing streams within an application. Thesecond section explains how to create new types of streams.

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The stream/promises API provides an alternative set of asynchronous utilityfunctions for streams that return Promise objects rather than usingcallbacks. The API is accessible via require('node:stream/promises')or require('node:stream').promises.

All streams created by Node.js APIs operate exclusively on strings and Buffer(or Uint8Array) objects. It is possible, however, for stream implementationsto work with other types of JavaScript values (with the exception of null,which serves a special purpose within streams). Such streams are considered tooperate in "object mode".

The amount of data potentially buffered depends on the highWaterMark optionpassed into the stream's constructor. For normal streams, the highWaterMarkoption specifies a total number of bytes. For streams operatingin object mode, the highWaterMark specifies a total number of objects.

Data is buffered in Readable streams when the implementation callsstream.push(chunk). If the consumer of the Stream does notcall stream.read(), the data will sit in the internalqueue until it is consumed.

Once the total size of the internal read buffer reaches the threshold specifiedby highWaterMark, the stream will temporarily stop reading data from theunderlying resource until the data currently buffered can be consumed (that is,the stream will stop calling the internal readable._read() method that isused to fill the read buffer).

Data is buffered in Writable streams when thewritable.write(chunk) method is called repeatedly. While thetotal size of the internal write buffer is below the threshold set byhighWaterMark, calls to writable.write() will return true. Oncethe size of the internal buffer reaches or exceeds the highWaterMark, falsewill be returned.

A key goal of the stream API, particularly the stream.pipe() method,is to limit the buffering of data to acceptable levels such that sources anddestinations of differing speeds will not overwhelm the available memory.

The highWaterMark option is a threshold, not a limit: it dictates the amountof data that a stream buffers before it stops asking for more data. It does notenforce a strict memory limitation in general. Specific stream implementationsmay choose to enforce stricter limits but doing so is optional.

Because Duplex and Transform streams are both Readable andWritable, each maintains two separate internal buffers used for reading andwriting, allowing each side to operate independently of the other whilemaintaining an appropriate and efficient flow of data. For example,net.Socket instances are Duplex streams whose Readable side allowsconsumption of data received from the socket and whose Writable side allowswriting data to the socket. Because data may be written to the socket at afaster or slower rate than data is received, each side shouldoperate (and buffer) independently of the other.

Applications that are either writing data to or consuming data from a streamare not required to implement the stream interfaces directly and will generallyhave no reason to call require('node:stream').

The 'close' event is emitted when the stream and any of its underlyingresources (a file descriptor, for example) have been closed. The event indicatesthat no more events will be emitted, and no further computation will occur.

The primary intent of writable.cork() is to accommodate a situation in whichseveral small chunks are written to the stream in rapid succession. Instead ofimmediately forwarding them to the underlying destination, writable.cork()buffers all the chunks until writable.uncork() is called, which will pass themall to writable._writev(), if present. This prevents a head-of-line blockingsituation where data is being buffered while waiting for the first small chunkto be processed. However, use of writable.cork() without implementingwritable._writev() may have an adverse effect on throughput.

Destroy the stream. Optionally emit an 'error' event, and emit a 'close'event (unless emitClose is set to false). After this call, the writablestream has ended and subsequent calls to write() or end() will result inan ERR_STREAM_DESTROYED error.This is a destructive and immediate way to destroy a stream. Previous calls towrite() may not have drained, and may trigger an ERR_STREAM_DESTROYED error.Use end() instead of destroy if data should flush before close, or wait forthe 'drain' event before destroying the stream.

Calling the writable.end() method signals that no more data will be writtento the Writable. The optional chunk and encoding arguments allow onefinal additional chunk of data to be written immediately before closing thestream.

When using writable.cork() and writable.uncork() to manage the bufferingof writes to a stream, defer calls to writable.uncork() usingprocess.nextTick(). Doing so allows batching of allwritable.write() calls that occur within a given Node.js event loop phase.

The writable.write() method writes some data to the stream, and calls thesupplied callback once the data has been fully handled. If an erroroccurs, the callback will be called with the error as itsfirst argument. The callback is called asynchronously and before 'error' isemitted.

The return value is true if the internal buffer is less than thehighWaterMark configured when the stream was created after admitting chunk.If false is returned, further attempts to write data to the stream shouldstop until the 'drain' event is emitted.

While a stream is not draining, calls to write() will buffer chunk, andreturn false. Once all currently buffered chunks are drained (accepted fordelivery by the operating system), the 'drain' event will be emitted.Once write() returns false, do not write more chunksuntil the 'drain' event is emitted. While calling write() on a stream thatis not draining is allowed, Node.js will buffer all written chunks untilmaximum memory usage occurs, at which point it will abort unconditionally.Even before it aborts, high memory usage will cause poor garbage collectorperformance and high RSS (which is not typically released back to the system,even after the memory is no longer required). Since TCP sockets may neverdrain if the remote peer does not read the data, writing a socket that isnot draining may lead to a remotely exploitable vulnerability. 17dc91bb1f