fs2 Streaming utilities for interacting with AWS
fs2-aws provides an fs2 interface to AWS services
The design goals are the same as fs2:
compositionality, expressiveness, resource safety, and speed
Find the latest release version and add the following dependency:
libraryDependencies += "io.laserdisc" %% "fs2-aws" % "VERSION"The module fs2-aws-s3 provides a purely functional API to operate with the AWS-S3 API. It defines four functions:
trait S3[F[_]] {
def delete(bucket: BucketName, key: FileKey): F[Unit]
def uploadFile(bucket: BucketName, key: FileKey): Pipe[F, Byte, ETag]
def uploadFileMultipart(bucket: BucketName, key: FileKey, partSize: PartSizeMB): Pipe[F, Byte, ETag]
def readFile(bucket: BucketName, key: FileKey): Stream[F, Byte]
def readFileMultipart(bucket: BucketName, key: FileKey, partSize: PartSizeMB): Stream[F, Byte]
}You can find out more in the scaladocs for each function, but as a rule of thumb for:
- Small files: use
readFileanduploadFile. - Big files: use
readFileMultipartanduploadFileMultipart.
You can also combine them as you see fit. For example, use uploadFileMultipart and then read it in one shot using readFile.
In order to create an instance of S3 we need to first create an S3Client. Here's an example of the former:
def s3StreamResource: Resource[IO, S3AsyncClientOp[IO]] =
for {
credentials = AwsBasicCredentials.create("accesskey", "secretkey")
port = 4566
s3 <- S3Interpreter[IO](blocker).S3AsyncClientOpResource(
S3AsyncClient
.builder()
.credentialsProvider(StaticCredentialsProvider.create(credentials))
.endpointOverride(URI.create(s"http://localhost:$port"))
.region(Region.US_EAST_1)
)
} yield s3Now we can create our S3[IO] instance:
s3StreamResource.map(S3.create[IO]).use { s3 =>
// do stuff with s3 here (or just share it with other functions)
}Create it once and share it as an argument, as any other resource.
For more details on how to work with S3 streams follow link
The simple way:
s3.readFile(BucketName("test"), FileKey("foo"))
.through(fs2.text.utf8Decode)
.through(fs2.text.lines)
.evalMap(line => IO(println(line)))The streaming way in a multipart fashion (part size is indicated in MBs and must be 5 or higher):
s3.readFileMultipart(BucketName("test"), FileKey("foo"), partSize = 5)
.through(fs2.text.utf8Decode)
.through(fs2.text.lines)
.evalMap(line => IO(println(line)))The simple way:
Stream.emits("test data".getBytes("UTF-8"))
.through(s3.uploadFile(BucketName("foo"), FileKey("bar"))
.evalMap(t => IO(println(s"eTag: $t")))The streaming way in a multipart fashion. Again, part size is indicated in MBs and must be 5 or higher.
Stream.emits("test data".getBytes("UTF-8"))
.through(s3.uploadFileMultipart(BucketName("foo"), FileKey("bar"), partSize = 5))
.evalMap(t => IO(println(s"eTag: $t")))There is a simple function to delete a file.
s3.delete(BucketName("foo"), FileKey("bar"))Example using IO for effects (any monad F <: ConcurrentEffect can be used):
val stream: Stream[IO, CommittableRecord] = readFromKinesisStream[IO]("appName", "streamName")There are a number of other stream constructors available where you can provide more specific configuration for the KCL worker.
TODO: Implement better test consumer
For now, you can stub CommitableRecord and create a fs2.Stream to emit these records:
val record = new Record()
.withApproximateArrivalTimestamp(new Date())
.withEncryptionType("encryption")
.withPartitionKey("partitionKey")
.withSequenceNumber("sequenceNum")
.withData(ByteBuffer.wrap("test".getBytes))
val testRecord = CommittableRecord(
"shardId0",
mock[ExtendedSequenceNumber],
0L,
record,
mock[RecordProcessor],
mock[IRecordProcessorCheckpointer])Records must be checkpointed in Kinesis to keep track of which messages each consumer has received. Checkpointing a record in the KCL will automatically checkpoint all records upto that record. To checkpoint records, a Pipe and Sink are available. To help distinguish whether a record has been checkpointed or not, a CommittableRecord class exists to denote a record that hasn't been checkpointed, while the base Record class denotes a committed record.
readFromKinesisStream[IO]("appName", "streamName")
.through(someProcessingPipeline)
.to(checkpointRecords_[IO]())A Pipe and Sink allow for writing a stream of tuple2 (partitionKey, ByteBuffer) to a Kinesis stream.
Example:
Stream("testData")
.map { d => ("partitionKey", ByteBuffer.wrap(d.getBytes))}
.to(writeToKinesis_[IO]("streamName"))AWS credential chain and region can be configured by overriding the respective fields in the KinesisProducerClient parameter to writeToKinesis. Defaults to using the default AWS credentials chain and us-east-1 for region.
In some situations (e.g. local dev and automated tests), it may be desirable to be able to consume from and publish to Kinesis running inside LocalStack.
Ensure that the endpoint setting is set correctly (e.g. http://localhost:4566) and that retrievalMode is set to Polling (LocalStack doesn't support FanOut).
TODO: Stream get data, Stream send data
Example
implicit val messageDecoder: Message => Either[Throwable, Quote] = { sqs_msg =>
io.circe.parser.decode[Quote](sqs_msg.asInstanceOf[TextMessage].getText)
}
fs2.aws
.sqsStream[IO, Quote](
sqsConfig,
(config, callback) => SQSConsumerBuilder(config, callback))
.through(...)
.compile
.drain
.as(ExitCode.Success)Testing
//create stream for testing
def stream(deferredListener: Deferred[IO, MessageListener]) =
aws.testkit
.sqsStream[IO, Quote](deferredListener)
.through(...)
.take(2)
.compile
.toList
//create the program for testing the stream
import io.circe.fs2.aws.examples.syntax._
import io.circe.generic.auto._
val quote = Quote(...)
val program : IO[List[(Quote, MessageListener)]] = for {
d <- Deferred[IO, MessageListener]
r <- IO.racePair(stream(d), d.get).flatMap {
case Right((streamFiber, listener)) =>
//simulate SQS stream fan-in here
listener.onMessage(new SQSTextMessage(Printer.noSpaces.pretty(quote.asJson)))
streamFiber.join
case _ => IO(Nil)
}
} yield r
//Assert results
val result = program
.unsafeRunSync()
result should be(...)TODO: Stream send SQS messages
This project is supported by YourKit with monitoring and profiling Tools. YourKit supports open source with innovative and intelligent tools for monitoring and profiling Java and .NET applications. YourKit is the creator of YourKit Java Profiler, YourKit .NET Profiler, and YourKit YouMonitor.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent