Sometimes it is necessary to process data that is so large that it is no longer practical to load it all into memory. This is often the case in mobile apps, where computational resources are very limited, so it’s better to stream the data and avoid an OutOfMemoryException. Recently I was working in an app written in Kotlin that processes big files, up to 300 MB, so I decided to write down some useful tips for working with byte streams in Kotlin.
Extension functions
The Kotlin standard library has lots of extension functions that improve existing Java APIs. Most of them are really easy to implement, but nevertheless it’s great to have them at your disposal, plus, they often make code more readable. Here are some of my favorite extensions for byte streams:
File.inputStream()Convenient function for opening anInputStreamto a file.File.outputStream()Convenient function for opening anOutputStreamto a file.InputStream.reader()Creates aReaderfrom anInputStream. Useful for parsing text files.Filealso has a similar function, in case you don’t need theInputStream.Closeable.use(). This makes sure that the stream is closed after you are done using it. This is great for avoiding leaking resources.InputStream.copyTo(). This copies the contents of anInputStreamto aOutputStream. This is super useful for copying or downloading files.
As an example of the usage of some of these functions here’s how you could implement a function for copying files:
fun copyFile(sourceFile: File, destinationFile: File) {
sourceFile.inputStream().use { input ->
destinationFile.outputStream().use { output ->
input.copyTo(output)
}
}
}This looks nice, but there’s no need to add this to your codebase because there is already and extension function for copying files, File.copyTo(), and it does exactly this but contains additional validations and configurable options.
The standard library is great, but it can only contain so many functions. As it is included as a regular dependency in Android projects, new problems could arise if it becomes too bloated. There are some things that you will have to implement yourself or look elsewhere. The next section is an example of this.
Tracking the number of processed bytes
Neither Java nor Kotlin have a built-in solution for tracking the number of processed bytes in a stream so that the UI can display the progress of the task. This is important because if you are processing large amounts of data, it will take a proportionally long amount of time. The user should be able to see the progress of the task and estimate how much time is left to complete it.
The solution is to create wrapper classes that monitor the invocations of the read() and write() methods, tracking the number of processed bytes and calling a lambda function whenever this number increases. Here’s how I implemented it:
class ObservableInputStream(private val wrapped: InputStream,
private val onBytesRead: (Long) -> Unit): InputStream() {
private var bytesRead: Long = 0
@Throws(IOException::class)
override fun read(): Int {
val res = wrapped.read()
if (res > -1) {
bytesRead++
}
onBytesRead(bytesRead)
return res
}
@Throws(IOException::class)
override fun read(b: ByteArray): Int {
val res = wrapped.read(b)
if (res > -1) {
bytesRead += res
onBytesRead(bytesRead)
}
return res
}
@Throws(IOException::class)
override fun read(b: ByteArray, off: Int, len: Int): Int {
val res = wrapped.read(b, off, len)
if (res > -1) {
bytesRead += res
onBytesRead(bytesRead)
}
return res
}
@Throws(IOException::class)
override fun skip(n: Long): Long {
val res = wrapped.skip(n)
if (res > -1) {
bytesRead += res
onBytesRead(bytesRead)
}
return res
}
@Throws(IOException::class)
override fun available(): Int {
return wrapped.available()
}
override fun markSupported(): Boolean {
return wrapped.markSupported()
}
override fun mark(readlimit: Int) {
wrapped.mark(readlimit)
}
@Throws(IOException::class)
override fun reset() {
wrapped.reset()
}
@Throws(IOException::class)
override fun close() {
wrapped.close()
}
}
class ObservableOutputStream(private val wrapped: OutputStream,
private val onBytesWritten: (Long) -> Unit): OutputStream() {
private var bytesWritten: Long = 0
@Throws(IOException::class)
override fun write(b: Int) {
wrapped.write(b)
bytesWritten++
onBytesWritten(bytesWritten)
}
@Throws(IOException::class)
override fun write(b: ByteArray) {
wrapped.write(b)
bytesWritten += b.size.toLong()
onBytesWritten(bytesWritten)
}
@Throws(IOException::class)
override fun write(b: ByteArray, off: Int, len: Int) {
wrapped.write(b, off, len)
bytesWritten += len.toLong()
onBytesWritten(bytesWritten)
}
@Throws(IOException::class)
override fun flush() {
wrapped.flush()
}
@Throws(IOException::class)
override fun close() {
wrapped.close()
}
}EDIT 2021-01-14: A minor bug in ObservableInputStream has been fixed.
EDIT 2021-01-25: It has been brought to my attention that not all InputStream methods were originally implemented in this snippet and this could cause problems. Methods available(), mark(), markSupported(), skip(), and reset() have now been added. Also, Java 9 introduces new InputStream methods readNBytes() and readAllBytes(). If you target Java 9 or later then you should override these methods as well to update the read bytes counter appropriately, otherwise you may end up with mysterious bugs.
To use this, wrap the original stream with one of these classes, attaching a lambda function to execute every time the number of processed bytes increases. For example here’s how you could report progress while downloading a file via HTTP:
fun downloadFileViaHTTP(url: String, destinationFile: file) {
val urlObj = URL(url)
val connection = urlObj.openConnection()
// To calculate progress, we need to now the total size
// beforehand. We can get that info from HTTP headers
val contentLength = connection.getHeaderField("Content-Length")
val totalDownloadSize = contentLength.toLong()
// Wrap the connection input stream with ObservableInputStream
// in order to monitor it.
val urlInputStream = ObservableInputStream(connection.getInputStream()) {
val progress = it * 100 / totalDownloadSize
// updateProgress() should post a message to the UI thread
// to update a progress bar or a similar widget
updateProgress(progress.toInt())
}
urlInputStream.use { input ->
destinationFile.outputStream().use { output ->
input.copyTo(output)
}
}
}Once again I use the convenient InputStream.copyTo() function to move bytes from the ObservableInputStream into the OutputStream that writes the file to internal storage. But, what if you wanted to do the opposite? There is no OutputStream.copyTo(), so how could you transfer data from OutputStream to InputStream?
Transferring data from OutputStream to InputStream
This might sound a little odd, but there are legitimate cases for doing this. Just google “Java OutputStream to InputStream” and you’ll get thousands of results. Besides, it’s a good use case for one of Kotlin’s most recent features. Ideally you should always read from InputStream and then write into OutputStream, but imagine a scenario in which you know that some external library is about to write data to an OutputStream but you’d like to read that output, apply some transformation and then write it to a new stream.
At first glance this seems impossible because OutputStream doesn’t have a read() method, only write(). Java’s byte streams aren’t as easy to compose as UNIX streams in which you can just use the | operator to redirect the output of one program to the input of another. What you would have to do here is write to a “mocked” OutputStream that doesn’t really write data anywhere, just holds on to it until you request it. There is a Java class for this, PipedOutputStream, but there’s still one little problem.
PipedOutputStream lets you create a PipedInputStream so that you can read() the data that is being written, but none of this changes the fact that write() blocks the calling thread. This means that if you want to read the data as it is being written, you need to do it concurrently. The good news is that Kotlin has coroutines now, so you can easily offload the writing to new coroutine, and read on the current one.
Finally, here is a little example to illustrate how to use PipedOutputStream. Suppose you want to download a file via FTP, but you want to decompress it before it gets written to the internal storage. The solution is to download in one coroutine and decompress in the other one.
private fun downloadStream(ftpClient: FTPClient,
fileName: String,
output: PipedOutputStream) {
// ftpClient immediately writes the downloaded bytes
// to the output stream
ftpClient.retrieveFile(fileName, output)
}
private fun decompressAndWriteStream(
sourceStream: PipedOutputStream
destinationStream: OutputStream) {
val srcInputStream = PipedInputStream(sourceStream)
val decompressedInputStream =
GZIPInputStream(srcInputStream)
decompressedInputStream.copyTo(destinationStream)
}
suspend fun downloadFile(coroutineScope: CoroutineScope,
destFile: File,
fileName: String) {
val ftpClient = FTPClient()
prepareForDownload(ftpClient)
val fileOutputStream = destFile.outputStream()
try {
val pipedOutputStream = PipedOutputStream()
// launch a new coroutine to download the data
val job = coroutineScope.launch {
downloadStream(ftpClient, fileName, pipedOutputStream)
}
// decompress in the current coroutine while
// the other one downloads the data
decompressAndWriteStream(
sourceStream = pipedOutputStream,
destinationStream = fileOutputStream)
// Once the file is completely written, the coroutine should
// have finished, but just to be safe make sure it exits.
job.join()
} finally {
fileOutputStream.close()
ftpClient.disconnect()
}
}Coroutines make dealing with concurrency much easier. In fact, I was very surprised to see this code work correctly on my first try. Since I process multiple large files in parallel I make extensive use of Coroutines. I think this is my favorite feature and the biggest reason to switch to Kotlin for Android development.