What is the advantage of using Source Streaming vs the regular way of handling requests? My understanding that in both cases
The TCP connection will be reused
Back-pressure will be applied between the client and the server
The only advantage of Source Streaming I can see is if there is a very large response and the client prefers to consume it in smaller chunks.
My use case is that I have a very long list of users (millions), and I need to call a service that performs some filtering on the users, and returns a subset.
Currently, on the server side I expose a batch API, and on the client, I just split the users into chunks of 1000, and make X batch calls in parallel using Akka HTTP Host API.
I am considering switching to HTTP streaming, but cannot quite figure out what would be the value
You are missing one other huge benefit: memory efficiency. By having a streamed pipeline, client/server/client, all parties safely process data without running the risk of blowing up the memory allocation. This is particularly useful on the server side, where you always have to assume the clients may do something malicious...
Client Request Creation
Suppose the ultimate source of your millions of users is a file. You can create a stream source from this file:
val userFilePath : java.nio.file.Path = ???
val userFileSource = akka.stream.scaladsl.FileIO(userFilePath)
This source can you be use to create your http request which will stream the users to the service:
import akka.http.scaladsl.model.HttpEntity.{Chunked, ChunkStreamPart}
import akka.http.scaladsl.model.{RequestEntity, ContentTypes, HttpRequest}
val httpRequest : HttpRequest =
HttpRequest(uri = "http://filterService.io",
entity = Chunked.fromData(ContentTypes.`text/plain(UTF-8)`, userFileSource))
This request will now stream the users to the service without consuming the entire file into memory. Only chunks of data will be buffered at a time, therefore, you can send a request with potentially an infinite number of users and your client will be fine.
Server Request Processing
Similarly, your server can be designed to accept a request with an entity that can potentially be of infinite length.
Your questions says the service will filter the users, assuming we have a filtering function:
val isValidUser : (String) => Boolean = ???
This can be used to filter the incoming request entity and create a response entity which will feed the response:
import akka.http.scaladsl.server.Directives._
import akka.http.scaladsl.model.HttpResponse
import akka.http.scaladsl.model.HttpEntity.Chunked
val route = extractDataBytes { userSource =>
val responseSource : Source[ByteString, _] =
userSource
.map(_.utf8String)
.filter(isValidUser)
.map(ByteString.apply)
complete(HttpResponse(entity=Chunked.fromData(ContentTypes.`text/plain(UTF-8)`,
responseSource)))
}
Client Response Processing
The client can similarly process the filtered users without reading them all into memory. We can, for example, dispatch the request and send all of the valid users to the console:
import akka.http.scaladsl.Http
Http()
.singleRequest(httpRequest)
.map { response =>
response
.entity
.dataBytes
.map(_.utf8String)
.foreach(System.out.println)
}
Related
I am doing some test to request some data to a remote database from a client. For that, I have a client gRPC that call a method in the gRPC, this gRPC server use EF to get the data and send the result to the client.
Well, in my case, I get about 3MB of data, that is higher than the default maximum size allowed for the channel.
I know that I can resolve the problem when I create the channel in the client, in this way, for example, to 60 mb:
var channel = GrpcChannel.ForAddress("http://localhost:5223",
new GrpcChannelOptions
{
MaxReceiveMessageSize = 62914560,
MaxSendMessageSize = 62914560,
});
But although I can increase this when I create the channel, I can't ensure that some query returns more data than the maximum allowed.
So I would like to know how I can handle this.
In this case, the method is unaray, it is not a stream.
Thanks.
Currently I am able to see the streaming values exposed by the code below, but only one http client will receive the continuous stream of values, the others will not be able to.
The code, a modified version of the quarkus quickstart for kafka reactive streaming is:
#Path("/migrations")
public class StreamingResource {
private volatile Map<String, String> counterBySystemDate = new ConcurrentHashMap<>();
#Inject
#Channel("migrations")
Flowable<String> counters;
#GET
#Path("/stream")
#Produces(MediaType.SERVER_SENT_EVENTS) // denotes that server side events (SSE) will be produced
#SseElementType("text/plain") // denotes that the contained data, within this SSE, is just regular text/plain data
public Publisher<String> stream() {
Flowable<String> mainStream = counters.doOnNext(dateSystemToCount -> {
String key = dateSystemToCount.substring(0, dateSystemToCount.lastIndexOf("_"));
counterBySystemDate.put(key, dateSystemToCount);
});
return fromIterable(counterBySystemDate.values().stream().sorted().collect(Collectors.toList()))
.concatWith(mainStream)
.onBackpressureLatest();
}
}
Is it possible to make any modification that would allow multiple clients to consume the same data, in a broadcast fashion?
I guess this implies letting go of backpressure, because that would imply a state per consumer?
I saw that Observable is not accepted as a return type in the resteasy-rxjava2 for the Server Side Events media-tpe.
Please let me know any ideas,
Thank you
Please find the full code in Why in multiple connections to PricesResource Publisher, only one gets the stream?
[Java DSL] I'm trying to post a stream of bytes to a server (as the body) using the client api in real-time but won't know the length prior to the start of the request.
I can't figure out how to do this from the akka-http documentation, has anyone attempted this?
Given that, you have created a materializer from the Akka context and have a Source that generates ByteString objects called mysource:
Http httpContext =
Http.get(context().system());
Source<ByteString, NotUsed> chunked =
mysource.map(str -> ByteString(str.concat("\n")))
.concat(Source.single(ByteString.empty()));
HttpRequest post = HttpRequest.POST("http://some-server/address")
.withEntity(HttpEntities.createChunked(ContentTypes.APPLICATION_OCTET_STREAM, chunked))
.withProtocol(HttpProtoclas.HTTP_1_1);
CompletionStage<HttpResponse> result =
httpContext.singleRequest(post, materializer);
Note that we concatenate an empty ByteString Source object to the original Source in order to signal the end of the chunked stream.
If you are issuing this from within an actor it's best to use a pipe() to submit the final request.
I was given an API url, and a method getUserPost() which returns the data needed for my data processing function. I am able to get the data by using Client from suds.client as follow:
from suds.client import Client
from suds.xsd.doctor import ImportDoctor, Import
url = 'url'
imp = Import('http://schemas.xmlsoap.org/soap/encoding/')
imp.filter.add('filter')
d = ImportDoctor(imp)
client = Client(url, doctor=d)
tempResult = client.service.getUserPosts(user_ids = '',date_from='2016-07-01 03:19:57', date_to='2016-08-01 03:19:57', limit=100, offset=0)
Now, each tempResult will contain 100 records. I want to stream the data from given API url to RDD for parallelized processing. However, after reading the pySpark.Streaming documentation I can't find a streaming method for customized data source. Could anyone give me an ideal how to do so?
Thank you.
After a while digging, I found out how to solve the problem. I employed the use of Kafka Streaming. Basically you need to create a producer from given API, specify topic and Port for communication. Then a consumer to listen to that specific topic and Port to start streaming the data.
Note that the Producer and Consumer must be working as different threads in order to archive real-time streaming.
I am fairly new to Scala and I'm working on an application (library) which is a client to a 3rd-party service (I'm not able to modify the server side and it uses custom binary protocol). I use Netty for networking.
I want to design an API which should allow users to:
Send requests to the server
Send requests to the server and get the response asynchronously
Subscribe to events triggered by the server (having multiple asynchronous event handlers which should be able to send requests as well)
I am not sure how should I design it. Exploring Scala, I stumble upon a bunch of information about Actor model, but I am not sure if it can be applied there and if it can, how.
I'd like to get some recommendations on the way I should take.
In general, the Scala-ish way to expose asynchronous functionality to user code is to return a scala.concurrent.Future[T].
If you're going the actor route, you might consider encapsulating the binary communication within the context of a single actor class. You can scale the instances of this proxy actor using Akka's router support, and you could produce response futures easily using the ask pattern. There are a few nice libraries (Spray, Play Framework) that make wrapping e.g. a RESTful or even WebSocket layer over Akka almost trivial.
A nice model for the pub-sub functionality might be to define a Publisher trait that you can mix in to some actor subclasses. This could define some state to keep track of subscribers, handle Subscribe and Unsubscribe messages, and provide some sort of convenient method for broadcasting messages:
/**
* Sends a copy of the supplied event object to every subscriber of
* the event object class and superclasses.
*/
protected[this] def publish[T](event: T) {
for (subscriber <- subscribersFor(event)) subscriber ! event
}
These are just some ideas based on doing something similar in some recent projects. Feel free to elaborate on your use case if you need more specific direction. Also, the Akka user list is a great resource for general questions like this, if indeed you're interested in exploring actors in Scala.
Observables
This looks like a good example for the Obesrvable pattern. This pattern comes from the Reactive Extensions of .NET, but is also available for Java and Scala. The library is provided by Netflix and has a really good quality.
This pattern has a good theoretical foundation --- it is the dual to the iterator in the category theoretical sense. But more important, it has a lot of practical ideas in it. Especially it handles time very good, e.g. you can limit the event rate you want to get.
With an observable you can process events on avery high level. In .NET it looks a lot like an SQL query. You can register for certain events ("FROM"), filter them ("WHERE") and finally process them ("SELECT"). In Scala you can use standard monadic API (map, filter, flatMap) and of course "for expressions".
An example can look like
stackoverflowQuestions.filter(_.tag == "Scala").map(_.subject).throttleLast(1 second).subscribe(println _)
Obeservables take away a lot of problems you will have with event based systems
Handling subsrcriptions
Handling errors
Filtering and pre-processing events
Buffering events
Structuring the API
Your API should provide an obesrvable for each event source you have. For procedure calls you provide a function that will map the function call to an obesrvable. This function will call the remote procedure and provide the result through the obeservable.
Implementation details
Add the following dependency to your build.sbt:
libraryDependencies += "com.netflix.rxjava" % "rxjava-scala" % "0.15.0"
You can then use the following pattern to convert a callback to an obeservable (given your remote API has some way to register and unregister a callback):
private val callbackFunc : (rx.lang.scala.Observer[String]) => rx.lang.scala.Subscription = { o =>
val listener = {
case Value(s) => o.onNext(s)
case Error(e) => o.onError(o)
}
remote.subscribe(listener)
// Return an interface to cancel the subscription
new Subscription {
val unsubscribed = new AtomicBoolean(false)
def isUnsubscribed: Boolean = unsubscribed.get()
val asJavaSubscription: rx.Subscription = new rx.Subscription {
def unsubscribe() {
remote.unsubscribe(listener)
unsubscribed.set(true)
}
}
}
If you have some specific questions, just ask and I can refine the answer
Additional ressources
There is a very nice course from Martin Odersky et al. at coursera, covering Observables and other reactive techniques.
Take a look at the spray-client library. This provides HTTP request functionality (I'm assuming the server you want to talk to is a web service?). It gives you a pretty nice DSL for building requests and is all about being asynchronous. It does use the akka Actor model behind the scenes, but you do not have to build your own Actors to use it. Instead the you can just use scala's Future model for handling things asynchronously. A good introduction to the Future model is here.
The basic building block of spray-client is a "pipeline" which maps an HttpRequest to a Future containing an HttpResponse:
// this is from the spray-client docs
val pipeline: HttpRequest => Future[HttpResponse] = sendReceive
val response: Future[HttpResponse] = pipeline(Get("http://spray.io/"))
You can take this basic building block and build it up into a client API in a couple of steps. First, make a class that sets up a pipeline and defines some intermediate helpers demonstrating ResponseTransformation techniques:
import scala.concurrent._
import spray.can.client.HttpClient
import spray.client.HttpConduit
import spray.client.HttpConduit._
import spray.http.{HttpRequest, HttpResponse, FormData}
import spray.httpx.unmarshalling.Unmarshaller
import spray.io.IOExtension
type Pipeline = (HttpRequest) => Future[HttpResponse]
// this is basically spray-client boilerplate
def createPipeline(system: ActorSystem, host: String, port: Int): Pipeline = {
val httpClient = system.actorOf(Props(new HttpClient(IOExtension(system).ioBridge())))
val conduit = system.actorOf(props = Props(new HttpConduit(httpClient, host, port)))
sendReceive(conduit)
}
private var pipeline: Pipeline = _
// unmarshalls to a specific type, e.g. a case class representing a datamodel
private def unmarshallingPipeline[T](implicit ec:ExecutionContext, um:Unmarshaller[T]) = (pipeline ~> unmarshal[T])
// for requests that don't return any content. If you get a successful Future it worked; if there's an error you'll get a failed future from the errorFilter below.
private def unitPipeline(implicit ec:ExecutionContext) = (pipeline ~> { _:HttpResponse => () })
// similar to unitPipeline, but where you care about the specific response code.
private def statusPipeline(implicit ec:ExecutionContext) = (pipeline -> {r:HttpResponse => r.status})
// if you want standard error handling create a filter like this
// RemoteServerError and RemoteClientError are custom exception classes
// not shown here.
val errorFilter = { response:HttpResponse =>
if(response.status.isSuccess) response
else if(response.status.value >= 500) throw RemoteServerError(response)
else throw RemoteClientError(response)
}
pipeline = (createPipeline(system, "yourHost", 8080) ~> errorFilter)
Then you can use wrap these up in methods tied to specific requests/responses that becomes the public API. For example, suppose the service has a "ping" GET endpoint that returns a string ("pong") and a "form" POST endpoint where you post form data and receive a DataModel in return:
def ping()(implicit ec:ExecutionContext, um:Unmarshaller[String]): Future[String] =
unmarshallingPipeline(Get("/ping"))
def form(formData: Map[String, String])(implicit ec:ExecutionContext, um:Unmarshaller[DataModel]): Future[DataModel] =
unmarshallingPipeline(Post("/form"), FormData(formData))
And then someone could use the API like this:
import scala.util.{Failure, Success}
API.ping() foreach(println) // will print out "pong" when response comes back
API.form(Map("a" -> "b") onComplete {
case Success(dataModel) => println("Form accepted. Server returned DataModel: " + dataModel)
case Failure(e) => println("Oh noes, the form didn't go through! " + e)
}
I'm not sure if you will find direct support in spray-client for your third bullet point about subscribing to events. Are these events being generated by the server and somehow sent to your client outside the scope of a specific HTTP request? If so, then spray-client will probably not be able to help directly (though your event handlers could still use it to send requests). Are the events occurring on the client side, e.g. the completion of deferred processing initially triggered by a response from the server? If so, you could actually probably get pretty far just by using the functionality in Future, but depending on your use cases, using Actors might make sense.