I'm using some sample Scala code to make a server that receives a file over websocket, stores the file temporarily, runs a bash script on it, and then returns stdout by TextMessage.
Sample code was taken from this github project.
I edited the code slightly within echoService so that it runs another function that processes the temporary file.
object WebServer {
def main(args: Array[String]) {
implicit val actorSystem = ActorSystem("akka-system")
implicit val flowMaterializer = ActorMaterializer()
val interface = "localhost"
val port = 3000
import Directives._
val route = get {
pathEndOrSingleSlash {
complete("Welcome to websocket server")
}
} ~
path("upload") {
handleWebSocketMessages(echoService)
}
val binding = Http().bindAndHandle(route, interface, port)
println(s"Server is now online at http://$interface:$port\nPress RETURN to stop...")
StdIn.readLine()
binding.flatMap(_.unbind()).onComplete(_ => actorSystem.shutdown())
println("Server is down...")
}
implicit val actorSystem = ActorSystem("akka-system")
implicit val flowMaterializer = ActorMaterializer()
val echoService: Flow[Message, Message, _] = Flow[Message].mapConcat {
case BinaryMessage.Strict(msg) => {
val decoded: Array[Byte] = msg.toArray
val imgOutFile = new File("/tmp/" + "filename")
val fileOuputStream = new FileOutputStream(imgOutFile)
fileOuputStream.write(decoded)
fileOuputStream.close()
TextMessage(analyze(imgOutFile))
}
case BinaryMessage.Streamed(stream) => {
stream
.limit(Int.MaxValue) // Max frames we are willing to wait for
.completionTimeout(50 seconds) // Max time until last frame
.runFold(ByteString(""))(_ ++ _) // Merges the frames
.flatMap { (msg: ByteString) =>
val decoded: Array[Byte] = msg.toArray
val imgOutFile = new File("/tmp/" + "filename")
val fileOuputStream = new FileOutputStream(imgOutFile)
fileOuputStream.write(decoded)
fileOuputStream.close()
Future(Source.single(""))
}
TextMessage(analyze(imgOutFile))
}
private def analyze(imgfile: File): String = {
val p = Runtime.getRuntime.exec(Array("./run-vision.sh", imgfile.toString))
val br = new BufferedReader(new InputStreamReader(p.getInputStream, StandardCharsets.UTF_8))
try {
val result = Stream
.continually(br.readLine())
.takeWhile(_ ne null)
.mkString
result
} finally {
br.close()
}
}
}
}
During testing using Dark WebSocket Terminal, case BinaryMessage.Strict works fine.
Problem: However, case BinaryMessage.Streaming doesn't finish writing the file before running the analyze function, resulting in a blank response from the server.
I'm trying to wrap my head around how Futures are being used here with the Flows in Akka-HTTP, but I'm not having much luck outside trying to get through all the official documentation.
Currently, .mapAsync seems promising, or basically finding a way to chain futures.
I'd really appreciate some insight.
Yes, mapAsync will help you in this occasion. It is a combinator to execute Futures (potentially in parallel) in your stream, and present their results on the output side.
In your case to make things homogenous and make the type checker happy, you'll need to wrap the result of the Strict case into a Future.successful.
A quick fix for your code could be:
val echoService: Flow[Message, Message, _] = Flow[Message].mapAsync(parallelism = 5) {
case BinaryMessage.Strict(msg) => {
val decoded: Array[Byte] = msg.toArray
val imgOutFile = new File("/tmp/" + "filename")
val fileOuputStream = new FileOutputStream(imgOutFile)
fileOuputStream.write(decoded)
fileOuputStream.close()
Future.successful(TextMessage(analyze(imgOutFile)))
}
case BinaryMessage.Streamed(stream) =>
stream
.limit(Int.MaxValue) // Max frames we are willing to wait for
.completionTimeout(50 seconds) // Max time until last frame
.runFold(ByteString(""))(_ ++ _) // Merges the frames
.flatMap { (msg: ByteString) =>
val decoded: Array[Byte] = msg.toArray
val imgOutFile = new File("/tmp/" + "filename")
val fileOuputStream = new FileOutputStream(imgOutFile)
fileOuputStream.write(decoded)
fileOuputStream.close()
Future.successful(TextMessage(analyze(imgOutFile)))
}
}
Related
I'm new to Akka Stream. I used following code for CSV parsing.
class CsvParser(config: Config)(implicit system: ActorSystem) extends LazyLogging with NumberValidation {
import system.dispatcher
private val importDirectory = Paths.get(config.getString("importer.import-directory")).toFile
private val linesToSkip = config.getInt("importer.lines-to-skip")
private val concurrentFiles = config.getInt("importer.concurrent-files")
private val concurrentWrites = config.getInt("importer.concurrent-writes")
private val nonIOParallelism = config.getInt("importer.non-io-parallelism")
def save(r: ValidReading): Future[Unit] = {
Future()
}
def parseLine(filePath: String)(line: String): Future[Reading] = Future {
val fields = line.split(";")
val id = fields(0).toInt
try {
val value = fields(1).toDouble
ValidReading(id, value)
} catch {
case t: Throwable =>
logger.error(s"Unable to parse line in $filePath:\n$line: ${t.getMessage}")
InvalidReading(id)
}
}
val lineDelimiter: Flow[ByteString, ByteString, NotUsed] =
Framing.delimiter(ByteString("\n"), 128, allowTruncation = true)
val parseFile: Flow[File, Reading, NotUsed] =
Flow[File].flatMapConcat { file =>
val src = FileSource.fromFile(file).getLines()
val source : Source[String, NotUsed] = Source.fromIterator(() => src)
// val gzipInputStream = new GZIPInputStream(new FileInputStream(file))
source
.mapAsync(parallelism = nonIOParallelism)(parseLine(file.getPath))
}
val computeAverage: Flow[Reading, ValidReading, NotUsed] =
Flow[Reading].grouped(2).mapAsyncUnordered(parallelism = nonIOParallelism) { readings =>
Future {
val validReadings = readings.collect { case r: ValidReading => r }
val average = if (validReadings.nonEmpty) validReadings.map(_.value).sum / validReadings.size else -1
ValidReading(readings.head.id, average)
}
}
val storeReadings: Sink[ValidReading, Future[Done]] =
Flow[ValidReading]
.mapAsyncUnordered(concurrentWrites)(save)
.toMat(Sink.ignore)(Keep.right)
val processSingleFile: Flow[File, ValidReading, NotUsed] =
Flow[File]
.via(parseFile)
.via(computeAverage)
def importFromFiles = {
implicit val materializer = ActorMaterializer()
val files = importDirectory.listFiles.toList
logger.info(s"Starting import of ${files.size} files from ${importDirectory.getPath}")
val startTime = System.currentTimeMillis()
val balancer = GraphDSL.create() { implicit builder =>
import GraphDSL.Implicits._
val balance = builder.add(Balance[File](concurrentFiles))
val merge = builder.add(Merge[ValidReading](concurrentFiles))
(1 to concurrentFiles).foreach { _ =>
balance ~> processSingleFile ~> merge
}
FlowShape(balance.in, merge.out)
}
Source(files)
.via(balancer)
.withAttributes(ActorAttributes.supervisionStrategy { e =>
logger.error("Exception thrown during stream processing", e)
Supervision.Resume
})
.runWith(storeReadings)
.andThen {
case Success(_) =>
val elapsedTime = (System.currentTimeMillis() - startTime) / 1000.0
logger.info(s"Import finished in ${elapsedTime}s")
case Failure(e) => logger.error("Import failed", e)
}
}
}
I wanted to to use Akka HTTP which would give all ValidReading entities parsed from CSV but I couldn't understand on how would I do that.
The above code fetches file from server and parse each lines to generate ValidReading.
How can I pass/upload CSV via akka-http, parse the file and stream the resulted response back to the endpoint?
The "essence" of the solution is something like this:
import akka.http.scaladsl.server.Directives._
val route = fileUpload("csv") {
case (metadata, byteSource) =>
val source = byteSource.map(x => x)
complete(HttpResponse(entity = HttpEntity(ContentTypes.`text/csv(UTF-8)`, source)))
}
You detect that the uploaded thing is a multipart-form-data with a chunk named "csv". You get the byteSource from that. Do the calculation (insert your logic to the .map(x=>x) part). Convert your data back to ByteString. Complete the request with the new source. This will make your endoint like a proxy.
I am trying to build a class that starts a system process which waits for stdin. The class should have another method which takes a string, inputs that into the system process, and return the process' output.
The reason is that starting the process involves loading a lot of data and hence takes a while.
I am trying to dummy-test this with bc, so that bc is started and waits for input. I would envision an interface like this:
case class BcWrapper(executable: File) {
var bc: Option[???] = None
def startBc(): Unit = bc = Some(???)
def calc(input: String): String = bc.get.???
def stopBc(): Unit = bc.get.???
}
I would like to be able to use it like this:
val wrapper = BcWrapper(new File("/usr/bin/bc"))
wrapper.startBc()
val result1 = wrapper.calc("1 + 1") // should be "2"
val result2 = wrapper.calc(???)
[...]
wrapper.stopBc()
This topic has been touched in multiple questions, but never fully answered for a use case like this one. This question or this one seems to come close. However, I am not sure how to implement the ProcessLogger, nor whether to use one in the first place.
Unfortunately, the Scala documentation is not very elaborate either.
Note that I do not want to read from stdin, but want to call a function.
The background is that I want to read a large file, read it line by line, preprocess the lines, pass them to the external process, and post-process the output.
You can get something similar, but simpler, like so.
import sys.process._
import util.Try
class StdInReader(val reader :String) {
def send(input :String) :Try[String] =
Try(s"/bin/echo $input".#|(reader).!!.trim)
}
usage:
val bc = new StdInReader("/usr/bin/bc")
bc.send("2 * 8") //res0: scala.util.Try[String] = Success(16)
bc.send("12 + 8") //res1: scala.util.Try[String] = Success(20)
bc.send("22 - 8") //res2: scala.util.Try[String] = Success(14)
Programs that send a non-zero exit-code (bc doesn't) will result with a Failure().
If you need more fine-grained control you might start with something like this and expand on it.
import sys.process._
class ProcHandler(val cmnd :String) {
private val resbuf = collection.mutable.Buffer.empty[String]
def run(data :Seq[String]) :Unit = {
cmnd.run(new ProcessIO(
in => {
val writer = new java.io.PrintWriter(in)
data.foreach(writer.println)
writer.close()
},
out => {
val src = io.Source.fromInputStream(out)
src.getLines().foreach(resbuf += _)
src.close()
},
_.close() //maybe create separate buffer for stderr?
)).exitValue()
}
def results() :Seq[String] = {
val rs = collection.mutable.Buffer.empty[String]
resbuf.copyToBuffer(rs)
resbuf.clear()
rs
}
}
usage:
val bc = new ProcHandler("/usr/bin/bc")
bc.run(List("4+5","6-2","2*5"))
bc.run(List("99/3","11*77"))
bc.results() //res0: Seq[String] = ArrayBuffer(9, 4, 10, 33, 847)
OK, I did some more research and found this. It appears to get at what you want but there are limitations. In particular, the process stays open for input until you want to get output. At that point IO streams are closed to insure all buffers are flushed.
import sys.process._
import util.Try
class ProcHandler(val cmnd :String) {
private val procInput = new java.io.PipedOutputStream()
private val procOutput = new java.io.PipedInputStream()
private val proc = cmnd.run( new ProcessIO(
{ in => // attach to the process's internal input stream
val istream = new java.io.PipedInputStream(procInput)
val buf = Array.fill(100)(0.toByte)
Iterator.iterate(istream.read(buf)){ br =>
in.write(buf, 0, br)
istream.read(buf)
}.takeWhile(_>=0).toList
in.close()
},
{ out => // attach to the process's internal output stream
val ostream = new java.io.PipedOutputStream(procOutput)
val buf = Array.fill(100)(0.toByte)
Iterator.iterate(out.read(buf)){ br =>
ostream.write(buf, 0, br)
out.read(buf)
}.takeWhile(_>=0).toList
out.close()
},
_ => () // ignore stderr
))
private val procO = new java.io.BufferedReader(new java.io.InputStreamReader(procOutput))
private val procI = new java.io.PrintWriter(procInput, true)
def feed(str :String) :Unit = procI.println(str)
def feed(ss :Seq[String]) :Unit = ss.foreach(procI.println)
def read() :List[String] = {
procI.close() //close input before reading output
val lines = Stream.iterate(Try(procO.readLine)){_ =>
Try(procO.readLine)
}.takeWhile(_.isSuccess).map(_.get).toList
procO.close()
lines
}
}
usage:
val bc = new ProcHandler("/usr/bin/bc")
bc.feed(List("9*3","4+11")) //res0: Unit = ()
bc.feed("4*13") //res1: Unit = ()
bc.read() //res2: List[String] = List(27, 15, 52)
bc.read() //res3: List[String] = List()
OK, this is my final word on the subject. I think this ticks every item on your wish list: start the process only once, it stays alive until actively closed, allows alternating the writing and reading.
import sys.process._
class ProcHandler(val cmnd :Seq[String]) {
private var os: java.io.OutputStream = null
private var is: java.io.InputStream = null
private val pio = new ProcessIO(os = _, is = _, _.close())
private val proc = cmnd.run(pio)
def feed(ss :String*) :Unit = {
ss.foreach(_.foreach(os.write(_)))
os.flush()
}
def ready :Boolean = is.available() > 0
def read() :String = {
Seq.fill[Char](is.available())(is.read().toChar).mkString
}
def close() :Unit = {
proc.exitValue()
os.close()
is.close()
}
}
There are still issues and much room for improvement. IO is handled at a basic level (streams) and I'm not sure what I'm doing here is completely safe and correct. The input, feed(), is required to supply the necessary NewLine terminations, and the output, read(), is just a raw String and not separated into a nice collection of string results.
Note that this will bleed system resources if the client code fails to close() all processes.
Note also that reading doesn't wait for content (i.e. no blocking). After writing the response might not be immediately available.
usage:
val bc = new ProcHandler(Seq("/usr/bin/bc","-q"))
bc.feed("44-21\n", "21*4\n")
bc.feed("67+11\n")
if (bc.ready) bc.read() else "not ready" // "23\n84\n78\n"
bc.feed("67-11\n")
if (bc.ready) bc.read() else "not ready" // "56\n"
bc.feed("67*11\n", "1+2\n")
if (bc.ready) bc.read() else "not ready" // "737\n3\n"
if (bc.ready) bc.read() else "not ready" // "not ready"
bc.close()
I am trying to send multiple data to kafka producer using akka stream , meanwhile I wrote the producer itself , but struggling of how to use akka-streamIO in order to get multiple files which will be the data I want to send to my kafka Producer this is my code:
object App {
def main(args: Array[String]): Unit = {
val file = Paths.get("233339.8.1231731728115136.1722327129833578.log")
// val file = Paths.get("example.csv")
//
// val foreach: Future[IOResult] = FileIO.fromPath(file)
// .to(Sink.ignore)
// .run()
println("Hello from producer")
implicit val system:ActorSystem = ActorSystem("producer-example")
implicit val materializer:Materializer = ActorMaterializer()
val producerSettings = ProducerSettings(system,new StringSerializer,new StringSerializer)
val done: Future[Done] =
Source(1 to 955)
.map(value => new ProducerRecord[String, String]("test-topic", s"$file : $value"))
.runWith(Producer.plainSink(producerSettings))
implicit val ec: ExecutionContextExecutor = system.dispatcher
done onComplete {
case Success(_) => println("Done"); system.terminate()
case Failure(err) => println(err.toString); system.terminate()
}
}
}
Given multiple file names:
val fileNames : Iterable[String] = ???
It is possible to create a Source that emits the contents of the files concatenated together using flatMapConcat:
val chunkSize = 8192
val chunkSource : Source[ByteString, _] =
Source.apply(fileNames)
.map(fileName => Paths get fileName)
.flatMapConcat(path => FileIO.fromPath(path, chunkSize))
This will emit fixed size ByteString values that are all chunkSize length, except for possibly the last value which may be smaller.
If you want to breakup the lines by some delimiter then you can use Framing:
val delimiter : ByteString = ???
val maxFrameLength : Int = ???
val framingSource : Source[ByteString, _] =
chunkSource.via(Framing.delimiter(delimiter, maxFrameLength))
I am parsing 50000 records which contain their titles and URLs on the web page. While parsing, I am writing them to the database, which is PostgreSQL. I deployed my application using docker-compose. However, it keeps stopping on some page without any reason. I tried to write some logs to figure out what's happening, but there is no connection error or anything like that.
Here is my code for parsing and writing to the database:
object App {
val db = Database.forURL("jdbc:postgresql://db:5432/toloka?user=user&password=password")
val browser = JsoupBrowser()
val catRepo = new CategoryRepo(db)
val torrentRepo = new TorrentRepo(db)
val torrentForParseRepo = new TorrentForParseRepo(db)
val parallelismFactor = 10
val groupFactor = 10
implicit val system = ActorSystem("TolokaParser")
implicit val materializer = ActorMaterializer()
implicit val executionContext = system.dispatcher
def parseAndWriteTorrentsForParseToDb(doc: App.browser.DocumentType) = {
Source(getRecordsLists(doc))
.grouped(groupFactor)
.mapAsync(parallelismFactor) { torrentForParse: Seq[TorrentForParse] =>
torrentForParseRepo.createInBatch(torrentForParse)
}
.runWith(Sink.ignore)
}
def getRecordsLists(doc: App.browser.DocumentType) = {
val pages = generatePagesFromHomePage(doc)
println("torrent links generated")
println(pages.size)
val result = for {
page <- pages
} yield {
println(s"Parsing torrent list...$page")
val tmp = getTitlesAndLinksTuple(getTitlesList(browser.get(page)), getLinksList(browser.get(page)))
println(tmp.size)
tmp
}
println("torrent links and names tupled")
result flatten
}
}
What may be the cause of such problems?
Put a supervision strategy to avoid stream finalization in case of error. Such as:
val decider: Supervision.Decider = {
case _ => Supervision.Resume
}
def parseAndWriteTorrentsForParseToDb = {
Source.fromIterator(() => List(1,2,3).toIterator)
.grouped(1)
.mapAsync(1) { torrentForParse: Seq[Int] =>
Future { 0 }
}
.withAttributes(ActorAttributes.supervisionStrategy(decider))
.runWith(Sink.ignore)
}
The stream should not stop with this async stage config
Over the past few days I have been trying to figure out the best way to download a HTTP resource to a file using Akka Streams and HTTP.
Initially I started with the Future-Based Variant and that looked something like this:
def downloadViaFutures(uri: Uri, file: File): Future[Long] = {
val request = Get(uri)
val responseFuture = Http().singleRequest(request)
responseFuture.flatMap { response =>
val source = response.entity.dataBytes
source.runWith(FileIO.toFile(file))
}
}
That was kind of okay but once I learnt more about pure Akka Streams I wanted to try and use the Flow-Based Variant to create a stream starting from a Source[HttpRequest]. At first this completely stumped me until I stumbled upon the flatMapConcat flow transformation. This ended up a little more verbose:
def responseOrFail[T](in: (Try[HttpResponse], T)): (HttpResponse, T) = in match {
case (responseTry, context) => (responseTry.get, context)
}
def responseToByteSource[T](in: (HttpResponse, T)): Source[ByteString, Any] = in match {
case (response, _) => response.entity.dataBytes
}
def downloadViaFlow(uri: Uri, file: File): Future[Long] = {
val request = Get(uri)
val source = Source.single((request, ()))
val requestResponseFlow = Http().superPool[Unit]()
source.
via(requestResponseFlow).
map(responseOrFail).
flatMapConcat(responseToByteSource).
runWith(FileIO.toFile(file))
}
Then I wanted to get a little tricky and use the Content-Disposition header.
Going back to the Future-Based Variant:
def destinationFile(downloadDir: File, response: HttpResponse): File = {
val fileName = response.header[ContentDisposition].get.value
val file = new File(downloadDir, fileName)
file.createNewFile()
file
}
def downloadViaFutures2(uri: Uri, downloadDir: File): Future[Long] = {
val request = Get(uri)
val responseFuture = Http().singleRequest(request)
responseFuture.flatMap { response =>
val file = destinationFile(downloadDir, response)
val source = response.entity.dataBytes
source.runWith(FileIO.toFile(file))
}
}
But now I have no idea how to do this with the Future-Based Variant. This is as far as I got:
def responseToByteSourceWithDest[T](in: (HttpResponse, T), downloadDir: File): Source[(ByteString, File), Any] = in match {
case (response, _) =>
val source = responseToByteSource(in)
val file = destinationFile(downloadDir, response)
source.map((_, file))
}
def downloadViaFlow2(uri: Uri, downloadDir: File): Future[Long] = {
val request = Get(uri)
val source = Source.single((request, ()))
val requestResponseFlow = Http().superPool[Unit]()
val sourceWithDest: Source[(ByteString, File), Unit] = source.
via(requestResponseFlow).
map(responseOrFail).
flatMapConcat(responseToByteSourceWithDest(_, downloadDir))
sourceWithDest.runWith(???)
}
So now I have a Source that will emit one or more (ByteString, File) elements for each File (I say each File since there is no reason the original Source has to be a single HttpRequest).
Is there anyway to take these and route them to a dynamic Sink?
I'm thinking something like flatMapConcat, such as:
def runWithMap[T, Mat2](f: T => Graph[SinkShape[Out], Mat2])(implicit materializer: Materializer): Mat2 = ???
So that I could complete downloadViaFlow2 with:
def destToSink(destination: File): Sink[(ByteString, File), Future[Long]] = {
val sink = FileIO.toFile(destination, true)
Flow[(ByteString, File)].map(_._1).toMat(sink)(Keep.right)
}
sourceWithDest.runWithMap {
case (_, file) => destToSink(file)
}
The solution does not require a flatMapConcat. If you don't need any return values from the file writing then you can use Sink.foreach:
def writeFile(downloadDir : File)(httpResponse : HttpResponse) : Future[Long] = {
val file = destinationFile(downloadDir, httpResponse)
httpResponse.entity.dataBytes.runWith(FileIO.toFile(file))
}
def downloadViaFlow2(uri: Uri, downloadDir: File) : Future[Unit] = {
val request = HttpRequest(uri=uri)
val source = Source.single((request, ()))
val requestResponseFlow = Http().superPool[Unit]()
source.via(requestResponseFlow)
.map(responseOrFail)
.map(_._1)
.runWith(Sink.foreach(writeFile(downloadDir)))
}
Note that the Sink.foreach creates Futures from the writeFile function. Therefore there's not much back-pressure involved. The writeFile could be slowed down by the hard drive but the stream would keep generating Futures. To control this you can use Flow.mapAsyncUnordered (or Flow.mapAsync) :
val parallelism = 10
source.via(requestResponseFlow)
.map(responseOrFail)
.map(_._1)
.mapAsyncUnordered(parallelism)(writeFile(downloadDir))
.runWith(Sink.ignore)
If you want to accumulate the Long values for a total count you need to combine with a Sink.fold:
source.via(requestResponseFlow)
.map(responseOrFail)
.map(_._1)
.mapAsyncUnordered(parallelism)(writeFile(downloadDir))
.runWith(Sink.fold(0L)(_ + _))
The fold will keep a running sum and emit the final value when the source of requests has dried up.
Using the play Web Services client injected in ws, remmebering to import scala.concurrent.duration._:
def downloadFromUrl(url: String)(ws: WSClient): Future[Try[File]] = {
val file = File.createTempFile("my-prefix", new File("/tmp"))
file.deleteOnExit()
val futureResponse: Future[WSResponse] =
ws.url(url).withMethod("GET").withRequestTimeout(5 minutes).stream()
futureResponse.flatMap { res =>
res.status match {
case 200 =>
val outputStream = java.nio.file.Files.newOutputStream(file.toPath)
val sink = Sink.foreach[ByteString] { bytes => outputStream.write(bytes.toArray) }
res.bodyAsSource.runWith(sink).andThen {
case result =>
outputStream.close()
result.get
} map (_ => Success(file))
case other => Future(Failure[File](new Exception("HTTP Failure, response code " + other + " : " + res.statusText)))
}
}
}