流式计算
Node.js Streams, a user-land copy of the stream library from Node.js
Check if something is a Node.js stream
Get a stream as a string, Buffer, ArrayBuffer or array
Toggle the CLI cursor
tar-stream is a streaming tar parser and generator and nothing else. It operates purely using streams which means you can easily extract/parse tarballs without ever hitting the file system.
destroy a stream if possible
Call a callback when a readable/writable/duplex stream has completed or failed.
A streaming way to send data to a Node.js Worker Thread
Test if a value is Node writable stream-like.
Get and validate the raw body of a readable stream.
Test if a value is Node stream-like.
A stream that emits multiple other streams one after another.
Returns the next buffer/object in a stream's readable queue
Streaming data for JavaScript
Merge multiple streams into a unified stream
A tiny, zero-dependency yet spec-compliant asynchronous iterator polyfill/ponyfill for ReadableStreams.
An iteration of the Node.js core streams with a series of improvements
Streaming HTML parser with scripting support.
A micro-library of stream components for building custom JSON and JSONC processing pipelines with a minimal memory footprint — parse, filter, and transform JSON far larger than available memory with a SAX-inspired token API, on Node.js or Web Streams.
minimal implementation of a PassThrough stream
writable stream that concatenates strings or binary data and calls a callback with the result
the stream module from node core for browsers
Transform stream.
Treat your dataset like a: * stream of lines when it's efficient to process by lines * stream of field arrays when it's efficient to deal directly with fields * stream of lightweight objects when it's efficient to deal with objects Wukong is friends with Hadoop the elephant, Pig the query language, and the cat on your command line.
Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE’05
Compute statistics on stream data
Treat your dataset like a: * stream of lines when it's efficient to process by lines * stream of field arrays when it's efficient to deal directly with fields * stream of lightweight objects when it's efficient to deal with objects Wukong is friends with Hadoop the elephant, Pig the query language, and the cat on your command line.
Treat your dataset like a: * stream of lines when it's efficient to process by lines * stream of field arrays when it's efficient to deal directly with fields * stream of lightweight objects when it's efficient to deal with objects Wukong is friends with Hadoop the elephant, Pig the query language, and the cat on your command line.
Implemention of streams in the classic sense where a stream is a head and a promise to compute the tail. Utilities to use streams from enumerables
Compute MD5, SHA-1, SHA-256, SHA-512, and CRC32 checksums for strings and files. HMAC-SHA256 and HMAC-SHA512 with timing-safe verification. File checksums use streaming reads for constant memory usage. Supports generic dispatch via digest/file_digest, multi-algorithm single-pass computation, multi-file hashing, file comparison, and verification.
VectorSse employs x86 Streaming SIMD Extensions (SSE), v3 or greater, to accelerate basic vector and matrix computations in Ruby.
Implementation of quantile estimators based on. Cormode et. al.: "Effective Computation of Biased Quantiles over Data Streams"
Build CSV files from record collections using a declarative DSL with column definitions, custom transforms, filtering, sorting, pagination via limit/offset, computed footer rows, row numbers, streaming output, custom delimiters and line separators, TSV/PSV shorthands, row validation, header transforms, total rows, append-to-file support, and custom empty-value placeholders.
tlsh is a fuzzy matching library, which hashes can be used for similarity comparison. Given a byte stream with a minimum length of 256 bytes, TLSH generates a hash value which can be used for similarity comparisons. Similar objects will have similar hash values which allow for the detection of similar objects by comparing their hash values. The computed hash is 35 bytes long (output as 70 hexadecimal characters). The first 3 bytes are used to capture the information about the file as a whole (length, ...), while the last 32 bytes are used to capture information about incremental parts of the file.