object MultipartParser
A low-level multipart-parsing pipe. Most end users will prefer EntityDecoder[Multipart].
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- MultipartParser.scala
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- final case class Out[+A](a: A, tail: Option[ByteVector] = None) extends Product with Serializable
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def
generateHeaders(byteVector: ByteVector)(acc: Headers): Headers
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- def generatePart(byteVector: ByteVector): (Headers, ByteVector)
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- def parseStreamed[F[_]](boundary: Boundary, limit: Int = 1024)(implicit arg0: Sync[F]): Pipe[F, Byte, Multipart[F]]
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def
parseStreamedFile[F[_]](boundary: Boundary, limit: Int = 1024, maxSizeBeforeWrite: Int = 52428800, maxParts: Int = 20, failOnLimit: Boolean = false)(implicit arg0: Sync[F]): Pipe[F, Byte, Multipart[F]]
Same as the other streamed parsing, except after a particular size, it buffers on a File.
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def
parseToParts[F[_]](byteVector: ByteVector)(boundary: Boundary)(implicit F: Sync[F]): F[ByteVector]
parseToParts - Removes Prelude and Trailer
parseToParts - Removes Prelude and Trailer
splitParts - Splits Into Parts splitPart - Takes a Single Part of the Front generatePart - Generates a tuple of Headers and a ByteVector of the Body, effectively a Part
generateHeaders - Generate Headers from ByteVector splitHeader - Splits a Header into the Name and Value
- def parseToPartsStream[F[_]](boundary: Boundary, limit: Int = 1024)(implicit arg0: Sync[F]): Pipe[F, Byte, Part[F]]
- def parseToPartsStreamedFile[F[_]](boundary: Boundary, limit: Int = 1024, maxSizeBeforeWrite: Int = 52428800, maxParts: Int = 20, failOnLimit: Boolean = false)(implicit arg0: Sync[F]): Pipe[F, Byte, Part[F]]
- def splitHeader(byteVector: ByteVector): Option[(ByteVector, ByteVector)]
- def splitPart(byteVector: ByteVector)(boundary: Boundary): Option[(ByteVector, ByteVector)]
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def
splitParts(byteVector: ByteVector)(boundary: Boundary)(acc: List[Either[Headers, ByteVector]]): List[Either[Headers, ByteVector]]
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def
parse[F[_]](boundary: Boundary)(implicit arg0: Sync[F]): Pipe[F, Byte, Either[Headers, ByteVector]]
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(Since version 0.18.12) This parser buffers the entire contents to memory. Consider using the streaming alternatives