Streaming decoding of large payloads with Protobuf in Swift is a powerful technique for handling vast amounts of data efficiently. By breaking the data into manageable chunks, you can decode only what you need, reducing memory usage and improving performance. In this example, we will illustrate how to implement streaming decoding using the Swift Protobuf library.
// Import the necessary Protobuf libraries
import Foundation
import SwiftProtobuf
// Example of decoding a large Protobuf file in chunks
func streamDecodeProtobufData(data: Data) {
var messageStream = MyProtobufMessage()
let input = InputStream(data: data)
input.open()
defer { input.close() }
do {
while input.hasBytesAvailable {
var buffer = [UInt8](repeating: 0, count: 4096)
let bytesRead = input.read(&buffer, maxLength: buffer.count)
if bytesRead > 0 {
let chunkData = Data(bytes: buffer, count: bytesRead)
// Decode the chunk
let decoded = try messageStream.mergeFrom(data: chunkData)
print("Decoded Message: \(decoded)")
}
}
} catch {
print("Error decoding Protobuf data: \(error)")
}
}
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?