Given this example code from Play documentation:
def upload = Action(parse.temporaryFile) { request =>
request.body.moveTo(new File("/tmp/picture/uploaded"))
Ok("File uploaded")
}
How 100 simultaneous slow upload requests will be handled (number of threads)?
Will be uploaded file buffered in memory or streamed directly to disk?
How 100 simultaneous slow upload requests will be handled (number of threads)?
It depends. The number of actual threads being used isn't really relevant. By default, Play uses a number of threads equal to the number of CPU cores available. But this doesn't mean that if you have 4 cores, you're limited to 4 concurrent processes at once. HTTP requests in Play are processed asynchronously in a special internal ExecutionContext provisioned by Akka. Processes running in an ExecutionContext can share threads, so long as they are non-blocking--which is abstracted away by Akka. All of this can be configured in different ways. See Understanding Play Thread Pools.
The Iteratee that consumes the client data must do some blocking in order to write the file chunks to disk, but done in small (and fast) enough chunks, this shouldn't cause other file uploads to become blocked.
What I would be more worried about is the amount of disk I/O your server can handle. 100 slow uploads might be okay, but you can't really say without benchmarking. At some point you will run into trouble when the client input exceeds the rate that your server can write to disk. This will also not work in a distributed environment. I almost always choose to bypass the Play server entirely and direct uploads to Amazon S3.
Will be uploaded file buffered in memory or streamed directly to disk?
All temporary files are streamed to disk. Under the hood, all data sent from the client to the server is read using the iteratee library asynchronously. For multipart uploads, it is no different. The client data is consumed by an Iteratee, which streams the file chunks to a temporary file on disk. So when using the parse.temporaryFile BodyParser, request.body is just a handle to a temporary file on disk, and not the file stored in memory.
It is worth noting that while Play can handle those requests in a non-blocking manner, moving the file once complete will block. That is, request.body.moveTo(...) will block the controller function until the move is complete. This means that if several of the 100 uploads complete at about the same time, Play's internal ExecutionContext for handling requests can quickly become overloaded. The underlying API of moveTo is also deprecated in Play 2.3, as it uses FileInputStream and FileOutputStream to copy the TemporaryFile to a permanent location. The docs advise you to use the Java 7 File API, instead, as it is much more efficient.
This may be a little crude, but something more like this should do it:
import java.io.File
import java.nio.file.Files
def upload = Action(parse.temporaryFile) { request =>
Files.copy(request.body.file.toPath, new File("/tmp/picture/uploaded").toPath)
Ok("File uploaded")
}
Related
Can i get help with this? I can't seem to understand the question
"In this problem you are to compare reading a file using a single-threaded file server with a multi- threaded file server. It takes 16 msec to get a request for work, dispatch it, and do the rest of the necessary processing, assuming the data are in the block cache. If a disk operation is needed (assume a spinning disk drive with 1 head), as is the case one-fourth of the time, an additional 32 msec is required."
Can i get help with this?
I don't think so (I don't think there's enough information in the question for anyone to be able to understand it).
Example 1
The file server is single-threaded and handles asynchronous requests, and the "16 msec" is primarily "request delivery latency" (time between a process sending a request and the file server receiving the request). A process sends a single request asking to read from 1000 files, the file server receives this request, "immediately" sends back 750 replies (for file data that was cached) and sends a single request asking something (file system code, disk driver?) to fetch the remaining 250 things; then file server "immediately" waits for more requests while waiting for the reply from something (file system code, disk driver?) to complete the early 250 things. In this case you can say that throughput for single-threaded file server is virtually infinite (e.g. infinite throughput for "file data cache hit", which is the only thing that matters because you can make more requests while waiting for slow disk IO).
Example 2
The file server has 8 threads and handles synchronous requests. A single-threaded process sends 1 request (to read from 1 file) and then has to wait for the reply, the request is given to one of the file server's threads (doesn't matter which) and that thread takes an average of "16 + 32*0.25 = 24 msec" for the request to be handled before the process can make it's next request; and the process does this in a loop because it wants to read 1000 files. In this case throughput is "1/0.024 = 41.66 requests per second", which is extremely bad (primarily because the single-threaded process can't send requests fast enough to keep all threads of the multi-threaded server busy).
Example 3
The file server has 8 threads and handles synchronous requests. A process with 1000 threads send 1 request (to read from 1 file) from each of its threads. In this case we need to know how many CPUs there are (and how scheduler works) to determine anything about throughput. E.g. if there's only 2 CPUs then you're not going to get 8 file server threads running in parallel at the same time.
I know very similar questions have been asked before. But I don't think the solutions I found on google/stackoverflow are suitable for me.
I started to write some web services with Scala/Spray, and it seems the best way to send large files without consuming large amouns of memory is using the stream marshalling. This way Spray will send http chunks. Two questions:
Is it possible to send the file without using HTTP chunks and without reading the entire file into memory?
AFAIK akka.io only process one write at a time, meaning it can buffer one write until it has been passed on to the O/S kernel in full. Would it be possible to tell Spray, for each HTTP response, the length of the content? Thereafter Spray would ask for new data (through akka messages) untill the entire content length is completed. Eg, I indicate my content length is 100 bytes. Spray sends a message asking for data to my actor, I provide 50 bytes. Once this data is passed on to the O/S, spray sends another message asking for new data. I provide the remaining 50 bytes... the response is completed then.
Is it possible to send the file without using HTTP chunks [on the wire]
Yes, you need to enable chunkless streaming. See http://spray.io/documentation/1.2.4/spray-routing/advanced-topics/response-streaming/
Chunkless streaming works regardless whether you use the Stream marshaller or provide the response as MessageChunks yourself. See the below example.
without reading the entire file into memory
Yes, that should work if you supply data as a Stream[Array[Byte]] or Stream[ByteString].
[...] Thereafter Spray would ask for new data [...]
That's actually almost like it already works: If you manually provide the chunks you can request a custom Ack message that will be delivered back to you when the spray-can layer is able to process the next part. See this example for how to stream from a spray route.
I indicate my content length is 100 bytes
A note upfront: In HTTP you don't strictly need to specify a content-length for responses because a response body can be delimited by closing the connection which is what spray does if chunkless streaming is enable. However, if you don't want to close the connection (because you would lose this persistent connection) you can now specify an explicit Content-Length header in your ChunkedResponseStart message (see #802) which will prevent the closing of the connection.
I am using Spray 1.3, with incoming-auto-chunking-threshold-size set, to allow streaming of incoming requests.
When a very large request comes in from my client, I want to stream it through the app and out to a backing store in chunks, to limit the memory used by the Spray app.
I am finding that Spray will slurp in the response as fast as it can, creating MessageChunks of the configured size and passing them to my app.
If the backend store is slow, then this results in Spray caching most of the request in local memory, defeating the streaming design.
Is there any way I can get Spray to block or throttle the request stream so that the input data rate matches the output data rate, to cap my app's memory usage?
Relevant spray code:
The HttpMessagePartParser.parseBodyWithAutoChunking method is the one which breaks up the request byte stream into MessageChunk objects. It does so greedily, consuming as many chunks as are immediately available, then returning a NeedMoreData object.
The request pipeline accepts NeedMoreData in the handleParsingResult method of the RawPipelineStage, with the following code:
case Result.NeedMoreData(next) ⇒ parser = next // wait for the next packet
... so it looks to me like there is no "pull" control of the chunking stream in Spray, and the framework will always read in the request as fast as it can manage, and push it out to the app's Actors as MessageChunks. Once a MessageChunk message is in the queue for my Actor, its memory can't be cached to disk.
So there is no way to limit the memory used by Spray for a request?
There is a workaround discussed here: https://github.com/spray/spray/issues/281#issuecomment-40455433
This may be addressed in a future spray release.
EDIT: Spray is now Akka HTTP, which has "Reactive Streams" which gives back-pressure to the TCP stream while still being async: https://groups.google.com/forum/#!msg/akka-dev/PPleJEfI5sM/FbeptEYlicoJ
I'm pretty new in play + scala + non-blocking I/O world and I read everywhere that every request should be non-blocking I/O. But not sure whether I should go that way with retrieving cached data from different server? I am planning to host web application and Redis cache on a separate amazon servers and use cached data heavily.
Your opinions/expertise would be much appreciated.
Non-blocking IO is all about optimal resource usage i.e untill the network/disk/other IO system is performing the IO operation we can do something else rather then waiting for it to complete.
In your case it does make sense to perform the IO on cache as non-blocking.
I have written a very simple webserver in Scala (based on Actors). The purpose
of it so to log events from our frontend server (such as if a user clicks a
button or a page is loaded). The file will need to be rotated every 64-100mb or so and
it will be send to s3 for later analysis with Hadoop. the amount of traffic will
be about 50-100 calls/s
Some questions that pops into my mind:
How do I make sure that all actors can write to one file in a thread safe way?
What is the best way to rotate the file after X amount of mb. Should I do this
in my code or from the filesystem (if I do it from the filesystem, how do I then verify
that the file isn't in the middle of a write or that the buffer is flushed)
One simple method would be to have a single file writer actor that serialized all writes to the disk. You could then have multiple request handler actors that fed it updates as they processed logging events from the frontend server. You'd get concurrency in request handling while still serializing writes to your log file. Having more than a single actor would open the possibility of concurrent writes, which would at best corrupt your log file. Basically, if you want something to be thread-safe in the actor model, it should be executed on a single actor. Unfortunately, your task is inherently serial at the point you write to disk. You could do something more involved like merge log files coming from multiple actors at rotation time but that seems like overkill. Unless you're generating that 64-100MB in a second or two, I'd be surprised if the extra threads doing I/O bought you anything.
Assuming a single writing actor, it's pretty trivial to calculate the amount that has been written since the last rotation and I don't think tracking in the actor's internal state versus polling the filesystem would make a difference one way or the other.
U can use Only One Actor to write every requests from different threads, since all of the requests go through this actor, there will be no concurrency problems.
As per file write rolling, if your write requests can be logged in line by line, then you can resort to log4j or logback's FileRollingAppender things. Otherwise, you can write your own which will be easy as long as remembering to lock the file before performing any delete or update operations.
The rolling usually means you rename the older files and current file to other names and then create a new file with current file name, at last, u can always write to the file with current file name.