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GitHub - parapet-io/parapet: A purely functional library to build distributed and event-driven systems
Parapet - purely functional library to develop distributed and event-driven systems
Motivation
It's not a secret that writing distributed systems is a challenging task that can be logically broken into two main aspects: implementing distributed algorithms and running them. Parapet plays the role of execution framework for distributed algorithms - it can be viewed as an intermediate layer between a low-level effect library and high-level operations exposed in the form of DSL. Distributed engineers who mainly focused on designing and implementing distributed algorithms don't need to be worried about low-level abstractions such as IO
or have a piece of deep knowledge in certain computer science subjects, for instance, Concurrency. All they need to know is what properties the library satisfies and what guarantees it provides. On the other hand, engineers who are specializing in writing low-level libraries can concentrate on implementing core abstractions such as IO
or Task
, working on performance optimizations and implementing new features.
Parapet is the modular library where almost any component can be replaced with a custom implementation.
Distributed engineers unite!
Sure, here are some high-level comparisons between Parapet and Akka:
1. Architecture: Parapet is designed to be a minimalistic, purely functional library that provides only the core abstractions needed to build concurrent and distributed applications, whereas Akka is a much more comprehensive toolkit that includes many more features and abstractions, such as actors, streams, and distributed data.
2. Language: Parapet is primarily designed for Scala, although it also supports other JVM languages such as Java and Kotlin. Akka, on the other hand, supports both Scala and Java, making it more accessible to developers who are more comfortable with Java.
3. Performance: Parapet is built on top of the Cats Effect library, which provides high-performance abstractions for asynchronous and concurrent programming in Scala. Akka is also designed for high performance, but its performance characteristics may depend on which features are being used and how they are configured.
4. Complexity: Because Parapet is a smaller and more focused library, it can be easier to reason about and use than Akka, which has a much larger API surface area and many more features.
5. Community: Akka has a larger and more established community, with more resources and examples available online. Parapet is a newer library with a smaller community, although it has been gaining popularity in recent years.
Ultimately, the choice between Parapet and Akka depends on your specific needs and preferences. If you are looking for a more lightweight and functional library for building concurrent and distributed applications in Scala, Parapet may be a good choice. If you need a more comprehensive toolkit with a larger community and more resources, Akka may be a better fit.
NOTE - Github documentation may be out of date. It's preferable to use the official documentation.
Contents
- Key Features
- DSL
- Process
- Channel
- Error Handling and DeadLetterProcess
- EventLog
- Configuration
- Correctness Properties
- Distributed Algorithms in Parapet
- Performance Analysis
- Contribution
Key Features
- Purely functional library written in scala using Tagless-Final Style and Free Monads; thoughtfully designed for people who prefer functional style over imperative
- Modular - almost any component can be replaced with a custom implementation
- DSL provides a set of operations sufficient to write distributed algorithms
- Lightweight and Performant. The library utilizes resources (CPU and Memory) smartly, the code is optimized to reduce CPU consumption when your application in idle state
- Built-in support for the following effect libraries: Cats Effect, Monix, and Scalaz ZIO. The library can be extended to support other effect libraries
Getting started
The first thing you need to do is to add two dependencies into your project: parapet-core
and interop-{effect_library}
for a specific effect library. You can find the latest version in maven central.
libraryDependencies += "io.parapet" %% "core" % "0.0.1-RC1"
For Cats Effect add libraryDependencies += "io.parapet" %% "interop-cats" % "0.0.1-RC1"
For Monix add libraryDependencies += "io.parapet" %% "interop-monix" % "0.0.1-RC1"
For Scalaz ZIO add libraryDependencies += "io.parapet" %% "interop-scalaz-zio" % "0.0.1-RC1"
Once you added the library, you can start writing your first program. However, it's worth taking a few minutes and getting familiar with two main approaches to write processes: generic and effect specific. I'll describe both in a minute. For those who aren't familiar with effect systems like Cats Effect, I'd strongly recommend you to read some articles about IO monad. Fortunately, you don't need to be an expert in Cats Effect to use Parapet.
The first approach we'll consider is Generic. It's recommended to stick to this style when writing processes. Let's develop a simple printer process that will print users requests to the system output.
import io.parapet.core.{Event, Process}
class Printer[F[_]] extends Process[F] {
import Printer._ // import Printer API
import dsl._ // import DSL operations
override def handle: Receive = {
case Print(data) => eval(println(data))
}
}
object Printer {
case class Print(data: Any) extends Event
}
Let's walk through this code. You start writing your processes by extending Process
trait and parameterizing it with an effect type. In this example, we left so-called hole F[_]
in our Printer
type which can be any type constructor with a single argument, e.g. F[_]
is a generic type constructor, cats effect IO
is a specific type constructor and IO[Unit]
is a concrete type. Starting from this moment, it should become clear what it means for a process to be generic. Simply speaking, it means that a process doesn't depend on any specific effect type e.g. IO
. Thus we can claim that our Printer
process is surely generic. The next step is to define a process API or contract that defines a set of events that it can send and receive. Process contract is an important part of any process specification that should be taken seriously. API defines a protocol that other processes will use to communicate with your process. Please remember that it's a very important aspect of any process definition and take it seriously. The next step would be importing DSL
, Parapet DSL is a small set of operations that we will consider in detail in the next chapters. In this example, we need only eval
operator that suspends a side effect in F
, in our Printer process we suspend println
effectful computation. Finally, every process should override handle
function defined in Process
trait. handle
function is a partial function that matches input events and produces an executable flows
. If you ever tried Akka framework you may find this approach familiar (for the curious, Receive
is simply a type alias for PartialFunction[Event, DslF[F, Unit]]
). In our Printer process, we match on Print
event using a well known pattern-matching feature in Scala language. If you are new in functional programming, I'd strongly recommend to read about pattern-matching - it's a very powerful instrument.
That's it. We have considered every important aspect of our Printer
process. Let's move forward and write a simple client process that will talk to our Printer
.
import io.parapet.core.Event.Start
import io.parapet.core.{Process, ProcessRef}
import io.parapet.examples.Printer._ // import Printer API
class PrinterClient[F[_]](printer: ProcessRef) extends Process[F] {
override def handle: Receive = {
// Start is a lifecycle event that gets delivered when a process started
case Start => Print("hello world") ~> printer
}
}
As you already might have noticed, we are repeating the same steps we made when were writing our Printer
process:
- Create a new Process with a hole
F[_]
in its type definition - Extend
io.parapet.core.Process
trait and parametrizing it with generic effect typeF
- Implement
handle
partial function
Let's consider some new types and operators we have used to write our client: ProcessRef
, Start
lifecycle event and ~>
(send) infix operator. Let's start from ProcessRef
. ProcessRef
is a unique process identifier (UUID by default). It represents a process address in Parapet system and must be unique - it's recommended to use ProcessRef
instead of a Process
object directly unless you are sure you want otherwise. It's not prohibited to use Process
object directly, however using a process reference may be useful in some scenarios. Let's consider one such case. Imagine we want to dynamically change the current Printer
process in our client so that it will store data in a file on disk instead of printing it to the console. We can add a new event ChangePrinter
:
case class ChangePrinter(printer: ProcessRef) extends Event
Then our client will look like this:
class PrinterClient[F[_]](private var printer: ProcessRef) extends Process[F] {
import PrinterClient._
import dsl._
override def handle: Receive = {
case Start => Print("hello world") ~> printer
case ChangePrinter(newPrinter) => eval(printer = newPrinter)
}
}
object PrinterClient {
case class ChangePrinter(printer: ProcessRef) extends Event
}
This design cannot be achieved when using direct processes b/c it's not possible to send Process
objects, processes are not serializable in general. One more thing, you can override a Process#ref
field, only make sure it's unique otherwise Parapet system will return an error during the startup.
Ok, we are almost done! There are a few more things left we need to cover: Start
lifecycle event and ~>
operator and there is nothing special about these two. Parapet has two lifecycle events:
io.parapet.core.Event.Start
is sent to a process once it's created in Parapet systemio.parapet.core.Event.Stop
is sent to a process when an application is interrupted withCtrl-C
or when some other process sentStop
orKill
event to that process. The main difference betweenStop
andKill
is that in the former case a process can finish processing all pending events before it will receiveStop
event, whereasKill
will interrupt a process and then deliverStop
event, all pending events will be discarded. If you familiar with JavaExecutorService
then you can think ofStop
asshutdown
andKill
asshutdownNow
.
Finally ~>
is the most frequently used operator that is defined for any type that extends io.parapet.core.Event
trait. ~>
is just a symbolic name for send(event, processRef)
operator.
By this moment we have two processes: Printer
and PrinterClient
, nice! But wait, we need to run them somehow, right?
Fortunately, it's extremely easy to do so, all we need is to create PrinterApp
object which represents our application and extend it from CatsApp
abstract class. CatsApp
extends ParApp by specifying concrete effect type IO
:
abstract class CatsApp extends ParApp[IO]
CatsApp
is provided by the library.
import cats.effect.IO
import io.parapet.CatsApp
import io.parapet.core.Process
object PrinterApp extends CatsApp {
override def processes: IO[Seq[Process[IO]]] = IO {
val printer = new Printer[IO]
val printerClient = new PrinterClient[IO](printer.ref)
Seq(printer, printerClient)
}
}
This is Cats Effect specific application, meaning it uses Cats IO type under the hood. If you run your program you should see hello world
printed to the console. Also notice that we are using concrete effect type IO to fill the hole in our Printer
type, e.g.: new Printer[IO]
in practice it can be any other effect type like Task
, although it requires some extra work in the library.
In our example, we created PrinterClient
which does nothing but sending Print
event at the startup. In my opinion, it doesn't deserve to be a standalone process, would be better if we create a process in place:
object PrinterApp extends CatsApp {
override def processes: IO[Seq[Process[IO]]] = IO {
val printer = new Printer[IO]
val start = Process[IO](_ => {
case Start => Printer.Print("hello world") ~> printer.ref
})
Seq(start, printer)
}
}
Although it's a matter of taste, there is no hard rule.
DSL
This chapter describes each DSL operator in details. Let's get started.
Contents
unit
unit
- semantically this operator is equivalent with Monad.unit
and obeys the same laws. Having said that the following expressions are equivalent:
event ~> process <-> unit ++ event ~> process
event ~> process <-> event ~> process ++ unit
This operator can be used in fold
operator to combine multiple flows. Example:
processes.map(event ~> _).fold(unit)(_ ++ _)
It also can be used to represent an empty flow:
{
case Start => unit // do nothing
case Stop => unit // do nothing
}
flow
flow
- suspends the thunk that produces flow. Semantically this operator is equivalent with suspend
for effects however it's strongly not recommended to perform any side effects within flow
.
Not recommended:
def print(str: String) = flow {
println(str)
unit
}
Recommended:
def print(str: String) = flow {
eval(println(str))
}
flow
may be useful to implement recursive flows. Example:
def times[F[_]](n: Int) = {
def step(remaining: Int): DslF[F, Unit] = flow {
if (remaining == 0) unit
else eval(print(remaining)) ++ step(remaining - 1)
}
step(n)
}
If you try to remove flow
you will get StackOverflowError
Another useful application is using lazy values inside flow
. Example:
lazy val lazyValue: String = {
println("evaluated")
"hello"
}
val useLazyValue = flow {
val tmp = lazyValue + " world"
eval(println(tmp))
}
send
send
- sends an event to one or more receivers. Event will be delivered to all receivers in the specified order.
Parapet provides a symbolic name for this operator ~>
although in the current implementation it doesn't allow to send an event to multiple receivers. It will be added in the future releases.
Examples:
send(Ping, processA, processB, processC)
Ping
event will be sent to the processA
then processB
and finaly processC
. It's not guaranteed that processA
will receive Ping
event before processC
as it depends on it's processing speed and current workload.
Ping ~> processA
Not supported:
Ping ~> Seq(processA, processB, processC)
Possible workaround:
Seq(processA, processB, processC).map(Ping ~> _).fold(unit)(_ ++ _)
Send multiple events to a process:
Seq(e1, e2, e3) ~> process
forward
forward
- sends an event to the receiver using original sender reference. This may be useful for implementing a proxy process.
Example:
val server = Process[IO](_ => {
case Request(body) => withSender(sender => eval(println(s"$sender-$body")))
})
val proxy = Process[IO](_ => {
case Request(body) => forward(Request(s"proxy-$body"), server.ref)
})
val client = Process.builder[IO](_ => {
case Start => Request("ping") ~> proxy
}).ref(ProcessRef("client")).build
The code above will print: client-proxy-ping
par
par
- executes operations from the given flow in parallel. Example:
par(eval(print(1)) ++ eval(print(2)))
Possible outputs: 12 or 21
delay
delay
- delays every operation in the given flow for the given duration.
For sequential flows the flowing expressions are semantically equivalent:
delay(duration, x~>p ++ y~>p) <-> delay(duration, x~>p) ++ delay(duration, y~>p)
delay(duration, x~>p ++ y~>p) <-> delay(duration) ++ x~>p ++ delay(duration) ++ y~>p
For parallel flows:
delay(duration, par(x~>p ++ y~>p)) <-> delay(duration) ++ par(x~>p ++ y~>p)
Note: since the following flow will be executed in parallel the second operation won't be delayed:
par(delay(duration) ++ eval(print(1)))
instead, use:
par(delay(duration, eval(print(1))))
withSender
withSender
- accepts a callback function that takes a sender reference and produces a new flow. Example:
val server = Process[IO](_ => {
case Request(data) => withSender(sender => eval(print(s"$sender says $data")))
})
val client = Process.builder[IO](_ => {
case Start => Request("hello") ~> server
}).ref(ProcessRef("client")).build
The code above will print: client says hello
fork
fork
- does what exactly the name says, executes the given flow concurrently. Example:
val process = Process[IO](_ => {
case Start => fork(eval(print(1))) ++ fork(eval(print(2)))
})
Possible outputs: 12 or 21
register
register
- registers a child process in the Parapet context. It's guaranteed that a child process will receive Stop
event before its parent. Example:
val server = Process[IO](ref => {
case Start => register(ref, Process[IO](_ => {
case Stop => eval(println("stop worker"))
}))
case Stop => eval(println("stop server"))
})
The code above will print:
stop worker
stop server
race
race
- runs two flows concurrently. The loser of the race is canceled.
Example:
val forever = eval(while (true) {})
val process: Process[IO] = Process[IO](_ => {
case Start => race(forever, eval(println("winner")))
})
Output: winner
suspend
suspend
- adds an effect which produces F
to the current flow. Example:
suspend(IO(print("hello world")))
Output: hello world
Not recommended:
suspend {
println("hello world")
IO.unit
}
suspendWith
suspendWith
- suspends an effect which produces F
and then feeds that into a function that takes a normal value and returns a new flow. All operations from produced flow added to the current flow. Example:
suspend(IO.pure(1))) { i => eval(print(i)) }
Output: 1
eval
eval
- suspends a side effect in F
and then adds that to the current flow. Example:
eval(println("hello world"))
Output: hello world
evalWith
evalWith
- Suspends a side effect in F
and then feeds that into a function that takes a normal value and returns a new flow. All operations from a produced flow will be added to the current flow. Example:
evalWith("hello world")(a => eval(println(a)))
Output: hello world
Process
Process
is a key abstraction in Parapet, any application must have a least one process. If you try to run an application w/o processes you will get an error saying that at least one process required. This section covers some useful features that we haven't seen yet, below you will find a shortlist of features:
- Predefined processes and reserved references
- Switching process behavior
- Direct process call
- Process combinators:
and
andor
- Testing your processes
- Basic patterns and tips: implementing timeouts, designing API
Predefined processes and reserved references
Parapet has some reserved process references, e.g.: KernelRef(parapet-kernel)
, SystemRef(parapet-system)
, DeadLetterRef(parapet-deadletter)
, UndefinedRef(parapet-undefined)
. The general rule is that any reference that starts with parapet-
prefix can be used by the platform code for any purpose.
Parapet has a SystemProcess
that cannot be overridden by users. SystemProcess
is a starting point, i.e. it's created before any other process. Lifecycle event Start
is sent by SystemProcess
. Any event sent to the SystemProcess
will be ignored and dropped. Don't try to send any events to SystemProcess
b/c it can lead to unpredictable errors.
DeadLetterProcess
is another process that is created by default, although it can be overridden, for more details check DeadLetterProcess
section under Event Handling
Switching process behavior
Sometimes it might be useful to dynamically switch a process behavior, e.g.: from uninitialized
to ready
state. Thankfully Process
provides switch
method that does exactly that.
Example:
Lazy server:
// for some effect `F[_]`
val server = new Process[F] {
val init = eval(println("acquire resources: create socket and etc."))
def ready: Receive = {
case Request(data) => withSender(Success(data) ~> _)
case Stop => eval(println("release resources: close socket and etc."))
}
def uninitialized: Receive = {
case Start => unit // ignore Start event, wait for Init
case Stop => unit // process is not initialized, do nothing
case Init => init ++ switch(ready)
case _ => withSender(Failure("process is not initialized", ErrorCodes.ProcessUninitialized) ~> _)
}
override def handle: Receive = uninitialized
}
// API
object Init extends Event
case class Request(data: Any) extends Event
sealed trait Response extends Event
case class Success(data: Any) extends Event
case class Failure(data: Any, errorCode: Int) extends Event
object ErrorCodes {
val ProcessUninitialized = 0
}
A client which sends Request
event w/o sending Init
:
val impatientClient = Process[F](_ => {
case Start => Request("PING") ~> server
case Success(_) => eval(println("that is not going to happen"))
case f:Failure => eval(println(f))
})
The code above will print: Failure(process is not initialized,0)
A client which sends Init
first and then Request
:
val humbleClient = Process[F](_ => {
case Start => Seq(Init, Request("PING")) ~> server
case Success(data) => eval(println(s"client receive response from server: $data"))
case _:Failure => eval(println("that is not going to happen"))
})
The code above will print:
acquire resources: create socket and etc.
client receive response from server: PING
release resources: close socket and etc.
switch
is NOT an atomic operation, avoid using switch
in concurrent flows because it may result in an error or lead to unpredictable behavior.
Bad:
val process = new Process[F] {
def ready: Receive = _
override def handle: Receive = {
case Init => fork(switch(ready)) // bad, may lead to unpredictable behaviour
}
}
If you need to switch behavior from a concurrent flow just send an event e.g. Swith(State.Ready)
to itself. Process will eventually switch its behavior:
val process = new Process[F] {
def ready: Receive = _
override def handle: Receive = {
case Init => fork {
eval(println("do some work in parallel"))
Switch(Ready) ~> ref // notify the process that it's time to switch it's behaviour
}
case Switch(Ready) => switch(ready)
}
}
sealed trait State
object Ready extends State
case class Switch(next: State) extends Event
Direct process call
Sometimes it may be useful to call a process directly. Especially it's a common case for short living processes. For instance, you may want to create a process, call it and then abandon, garbage collector will do its job. However, if you try to send an event to a process that doesn't exist in the system you will receive Failure
event with UnknownProcessException
. This is where direct call
comes to rescue.
Example:
// API
case class Sum(a: Int, b: Int) extends Event
case class Result(value: Int) extends Event
class Calculator[F[_]] extends Process[F] {
override def handle: Receive = {
case Sum(a, b) => withSender(Result(a + b - 1) ~> _) // yes, very poor calculator
}
}
val student = Process[F](ref => {
case Start => new Calculator().apply(ref, Sum(2, 2))
case Result(value) => eval(println(s"2 + 2 = $value"))
})
Output: 2 + 2 = 3
Note that apply
method doesn't return a normal value rather it returns a program which will be executed as normal flow.
In other words the following expressions are equivalent:
Sum(2, 2) ~> calculator <-> new Calculator().apply(ref, Sum(2, 2)) // where ref belongs to the same process in both cases
Process combinators
Processes can be combined using two logical operators: or
and and
.
and
- combines two processes by producing a new process with ref
of the first process; combines flows iff 'handle' function is defined for the given event in both processes. Sends an error to the sender if either of two processes isn't defined for the given event.
Example:
import cats.effect.IO
import io.parapet.CatsApp
import io.parapet.core.Event.Start
import io.parapet.core.{Event, Process}
object Example extends CatsApp {
import dsl._
case class Print(data: Any) extends Event
override def processes: IO[Seq[Process[IO]]] =
for {
printerA <- IO.pure(Process[IO](_ => {
case Print(data) => eval(println(s"printerA: $data"))
}))
printerB <- IO.pure(Process[IO](_ => {
case Print(data) => eval(println(s"printerB: $data"))
}))
client <- IO.pure(Process[IO](ref => {
case Start => printerA.and(printerB).apply(ref, Print("test"))
}))
} yield Seq(printerA, printerB, client)
}
If you want to register a combined process then you don't need to register printerA
.
Example:
import cats.effect.IO
import io.parapet.CatsApp
import io.parapet.core.Event.Start
import io.parapet.core.{Event, Process}
object Example extends CatsApp {
import dsl._
case class Print(data: Any) extends Event
override def processes: IO[Seq[Process[IO]]] =
for {
printerA <- IO.pure(Process[IO](_ => {
case Print(data) => eval(println(s"printerA: $data"))
}))
printerB <- IO.pure(Process[IO](_ => {
case Print(data) => eval(println(s"printerB: $data"))
}))
combined <- IO.pure(printerA.and(printerB))
client <- IO.pure(Process[IO](_ => {
case Start => Print("test") ~> combined
}))
} yield Seq(combined, printerB, client)
}
or
- creates a new process with ref
of the first process. A combined process refers to the first process if its handle
is defined for the given event, otherwise, to the second process. Sends an error to the sender if neither process is defined for the given event.
Example:
import cats.effect.IO
import io.parapet.CatsApp
import io.parapet.core.Event.Start
import io.parapet.core.{Event, Process}
object Example extends CatsApp {
import dsl._
case class Print(data: Any) extends Event
override def processes: IO[Seq[Process[IO]]] =
for {
printerA <- IO.pure(Process[IO](_ => {
case Print(data: Int) => eval(println(s"printerA: $data"))
}))
printerB <- IO.pure(Process[IO](_ => {
case Print(data: String) => eval(println(s"printerB: $data"))
}))
combined <- IO.pure(printerA.or(printerB))
client <- IO.pure(Process[IO](_ => {
case Start => Print("test") ~> combined ++ Print(1) ~> combined
}))
} yield Seq(combined, printerB, client)
}
Testing your processes
Integration tests in parapet written in a generic style that we discussed before so that the same tests can be run against any effect system. Let's try to write a simple test for a proxy process. The first thing you need to do is to add test-utils
library into your project:
libraryDependencies += "io.parapet" %% "test-utils" % version
A simple proxy process that receives requests and forwards them to a service
class Proxy(service: ProcessRef) extends Process[F] {
override def handle: Receive = {
case Request(data) => Request(s"proxy-$data") ~> service
}
}
Test for our Proxy
:
import io.parapet.core.{Event, Process, ProcessRef}
import io.parapet.tests.intg.ProxySpec._
import io.parapet.testutils.{EventStore, IntegrationSpec}
import org.scalatest.FunSuite
import org.scalatest.Matchers._
import org.scalatest.OptionValues._
abstract class ProxySpec[F[_]] extends FunSuite with IntegrationSpec[F] {
import dsl._
test("proxy") {
val eventStore = new EventStore[F, Event]
val testService = Process(ref => {
case req: Request => eval(eventStore.add(ref, req))
})
val proxy = new Proxy[F](testService.ref)
val init = onStart(Request("req") ~> proxy)
unsafeRun(eventStore.await(1, createApp(ct.pure(Seq(init, testService, proxy))).run))
eventStore.get(testService.ref).headOption.value shouldBe Request("proxy-req")
}
}
In order to run this test against Cats Effect IO you need to extend BasicCatsIOSpec
:
import cats.effect.IO
import io.parapet.testutils.BasicCatsIOSpec
class ProxySpec extends io.parapet.tests.intg.ProxySpec[IO] with BasicCatsIOSpec
Basic patterns and tips
TODO
Channel
Channel is a process that implements strictly synchronous request-reply dialog. The channel sends an event to a receiver and then waits for a response in one step, i.e. it blocks asynchronously until it receives a response. Doing any other sequence, e.g., sending two request or reply events in a row will return a failure to the sender.
Example for some F[_]
:
val server = new Process[F] {
override def handle: Receive = {
case Request(data) => withSender(sender => Response(s"echo: $data") ~> sender)
}
}
val client = new Process[F] {
lazy val ch = Channel[F]
override def handle: Receive = {
case Start => register(ref, ch) ++
ch.send(Request("PING"), server.ref, {
case scala.util.Success(Response(data)) => eval(println(data))
case scala.util.Failure(err) => eval(println(s"server failed to process request. err: ${err.getMessage}"))
})
}
}
case class Request(data: Any) extends Event
case class Response(data: Any) extends Event
Error Handling and DeadLetterProcess
There are some scenarios when a process may receive a Failure
event:
- When a target process failed to handle an event sent by another process.
Example:
// for some effect F[_]
val faultyServer = Process.builder[F](_ => {
case Request(_) => eval(throw new RuntimeException("server is down"))
}).ref(ProcessRef("server")).build
val client = Process.builder[F](_ => {
case Start => Request("PING") ~> faultyServer
case Failure(Envelope(me, event, receiver), EventHandlingException(errMsg, cause)) => eval {
println(s"self: $me")
println(s"event: $event")
println(s"receiver: $receiver")
println(s"errMsg: $errMsg")
println(s"cause: ${cause.getMessage}")
}
}).ref(ProcessRef("client")).build
The code above will output:
self: client
event: Request(PING)
receiver: server
errMsg: process [name=undefined, ref=server] has failed to handle event: Request(PING)
cause: server is down
EventHandlingException
indicates that a receiver process failed to handle an event.
- When a process event queue is full. It's possible when a process experiencing performance degradation due to heavy load.
Example:
For this example we need to tweak SchedulerConfig:
queueSize = 10000
processQueueSize = 100
// for some effect F[_]
val slowServer = Process.builder[F](_ => {
case Request(_) => eval(while (true) {}) // very slow process...
}).ref(ProcessRef("server")).build
val client = Process.builder[F](_ => {
case Start =>
generateRequests(1000) ~> slowServer
case Failure(Envelope(me, event, receiver), EventDeliveryException(errMsg, cause)) => eval {
println(s"self: $me")
println(s"event: $event")
println(s"receiver: $receiver")
println(s"errMsg: $errMsg")
println(s"cause: ${cause.getMessage}")
println("=====================================================")
}
}).ref(ProcessRef("client")).build
def generateRequests(n: Int): Seq[Event] = {
(0 until n).map(Request)
}
The code above will print a dozens of lines, four lines per Failure
event:
client sent events
self: client
event: Request(101)
receiver: server
errMsg: System failed to deliver an event to process [name=undefined, ref=server]
cause: process [name=undefined, ref=server] event queue is full
=====================================================
self: client
event: Request(102)
receiver: server
errMsg: System failed to deliver an event to process [name=undefined, ref=server]
cause: process [name=undefined, ref=server] event queue is full
=====================================================
self: client
event: Request(103)
receiver: server
errMsg: System failed to deliver an event to process [name=undefined, ref=server]
cause: process [name=undefined, ref=server] event queue is full
=====================================================
EventDeliveryException
indicates that the system failed to deliver an event. Handling such types of errors may be useful for runtime analysis, e.g. a sender process might consider lowering event send rate or even stop sending events to let a target process to finish processing pending events. It's worth noting that you should avoid any long-running computations when processing Failure
events because it could lead to cascading failures.
- A process event handler isn't defined for some events.
Example:
// for some effect F[_]
val uselessService = Process.builder[F](_ => {
case Start => unit
case Stop => unit
}).ref(ProcessRef("server")).build
val client = Process.builder[F](_ => {
case Start =>
Request("PING") ~> uselessService
case Failure(Envelope(me, event, receiver), EventMatchException(errMsg)) => eval {
println(s"self: $me")
println(s"event: $event")
println(s"receiver: $receiver")
println(s"errMsg: $errMsg")
}
}).ref(ProcessRef("client")).build
The code above will print:
self: client
event: Request(PING)
receiver: server
errMsg: process [name=undefined, ref=server] handler is not defined for event: Request(PING)
- A process doesn't exist in Parapet system.
Example:
// for some effect F[_]
val unknownService = Process.builder[F](_ => {
case Start => unit
case Stop => unit
}).ref(ProcessRef("server")).build
val client = Process.builder[F](_ => {
case Start =>
Request("PING") ~> unknownService
case Failure(Envelope(me, event, receiver), UnknownProcessException(errMsg)) => eval {
println(s"self: $me")
println(s"event: $event")
println(s"receiver: $receiver")
println(s"errMsg: $errMsg")
}
}).ref(ProcessRef("client")).build
The code above will print:
self: client
event: Request(PING)
receiver: server
errMsg: there is no such process with id=server registered in the system
Final notes regarding error handling:
- All
Failure
events sent byparapet-system
process (if you are curious you can check it by yourself usingwithSender
). - If a process has no error handling then
Failure
event will be sent toDeadLetterProcess
. More aboutDeadLetterProcess
you will find below
DeadLetterProcess
The library by default provides an implementation of DeadLetterProcess
which just logs failures. Although it might be not very practical, for instance, you may prefer to store failures into a database for further analyses. The library allows providing a custom implementation of DeadLetterProcess
.
Example using CatsApp
:
import cats.effect.IO
import io.parapet.CatsApp
import io.parapet.core.Event.{DeadLetter, Start}
import io.parapet.core.processes.DeadLetterProcess
import io.parapet.core.{Event, Process, ProcessRef}
object CustomDeadLetterProcessDemo extends CatsApp {
import dsl._
override def deadLetter: IO[DeadLetterProcess[IO]] = IO.pure {
new DeadLetterProcess[IO] {
override def handle: Receive = {
// can be stored in database
case DeadLetter(envelope, error) => eval {
println(s"sender: ${envelope.sender}")
println(s"receiver: ${envelope.receiver}")
println(s"event: ${envelope.event}")
println(s"errorType: ${error.getClass.getSimpleName}")
println(s"errorMsg: ${error.getMessage}")
}
}
}
}
val faultyServer = Process.builder[IO](_ => {
case Request(_) => eval(throw new RuntimeException("server is down"))
}).ref(ProcessRef("server")).build
val client = Process.builder[IO](_ => {
case Start => Request("PING") ~> faultyServer
// no error handling
}).ref(ProcessRef("client")).build
override def processes: IO[Seq[Process[IO]]] = IO {
Seq(client, faultyServer)
}
case class Request(data: Any) extends Event
}
The code above will print:
sender: client
receiver: server
event: Request(PING)
errorType: EventHandlingException
errorMsg: process [name=undefined, ref=server] has failed to handle event: Request(PING)
Configuration
Parapet system can be configured by providing an instance of ParConfig
.
Example:
import cats.effect.IO
import io.parapet.core.Parapet.ParConfig
import io.parapet.{CatsApp, core}
object ConfigExample extends CatsApp{
override def processes: IO[Seq[core.Process[IO]]] = _
override val config: ParConfig = ParConfig(...)
}
ParConfig
has the following properties:
- schedulerConfig:
- queueSize - size of event queue shared by workers
- numberOfWorkers - number of workers; default = availableProcessors
- processQueueSize - size of event queue per individual process,
-1
- unbounded
You should set queueSize
to a value that would match the expected workload. For example, if you are going to send 1M events within the same flow it's recommended to set queueSize
to 1M. However, it depends on how fast your consumer processes and amount of available memory, if that's possible to keep some amount of events in memory - go for it, if not - you will probably need to reconsider your design decisions.
In a case the event queue is full all events will be redirected to EventLog
(see the corresponding section).
EventLog
EventLog
can be used to store events on disk. Latter, events can be retrieved and resubmitted.
In a case, the event queue is full unsubmitted events will be redirected to EventLog
. The default implementation just logs such events. In future releases, more practical implementation will be provided.
Correctness Properties
Safty properties:
- It's guaranteed that events will be delivered to a process in a strictly synchronous request-reply dialog, i.e. a process will receive a new event iff it completed processing the current one.
- All events delivered in send order
Liveness properties:
- Sent events eventually delivered
- A sender eventually receives a response
Distributed Algorithms in Parapet
Please refer to components/algorithms subproject
Performance Analysis
Performance mainly dependents on the underlying effect system. In general, there is always some performance and memory overhead associated with the use of Monads and immutable data structures.
Performance test spec:
- 1M requests + 1M responses = 2M events
- CatsApp based
- Number of workers - 12
- Number of processes - 2 (one publisher, one consumer)
- CPU: Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz, 2208 Mhz, 6 Core(s), 12 Logical Processor(s)
- RAM: 32GB
- OS: Windows 10 x64
Total time: 25206 ms
Contribution
The project in its early stage and many things are subject to change. Now is a good time to join! If you want to become a contributor please send me email or text in gitter channel.
If you'd like to donate in order to help with ongoing development and maintenance:
License
Copyright [2019] The Parapet Project Developers
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
∏åRÂπ∑†
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https://github.com/travisbrown/iteratee
Iteratees for Cats. Contribute to travisbrown/iteratee development by creating an account on GitHub.
- 91Fast, secure JSON library with tight ZIO integration.
https://github.com/zio/zio-json
Fast, secure JSON library with tight ZIO integration. - zio/zio-json
- 92Schema registry for CSV, TSV, JSON, AVRO and Parquet schema. Supports schema inference and GraphQL API.
https://github.com/indix/schemer
Schema registry for CSV, TSV, JSON, AVRO and Parquet schema. Supports schema inference and GraphQL API. - indix/schemer
- 93Play2.x Authentication and Authorization module
https://github.com/t2v/play2-auth
Play2.x Authentication and Authorization module. Contribute to t2v/play2-auth development by creating an account on GitHub.
- 94Jawn is for parsing jay-sawn (JSON)
https://github.com/typelevel/jawn
Jawn is for parsing jay-sawn (JSON). Contribute to typelevel/jawn development by creating an account on GitHub.
- 95An ONNX (Open Neural Network eXchange) API and backend for typeful, functional deep learning and classical machine learning in Scala 3
https://github.com/EmergentOrder/onnx-scala
An ONNX (Open Neural Network eXchange) API and backend for typeful, functional deep learning and classical machine learning in Scala 3 - EmergentOrder/onnx-scala
- 96Labeled records for Scala based on structural refinement types and macros.
https://github.com/scala-records/scala-records
Labeled records for Scala based on structural refinement types and macros. - scala-records/scala-records
- 97Breeze is a numerical processing library for Scala.
https://github.com/scalanlp/breeze
Breeze is a numerical processing library for Scala. - scalanlp/breeze
- 98Scala Library for Reading Flat File Data (CSV/TSV/XLS/XLSX)
https://github.com/frugalmechanic/fm-flatfile
Scala Library for Reading Flat File Data (CSV/TSV/XLS/XLSX) - frugalmechanic/fm-flatfile
- 99Platform to build distributed, scalable, enterprise-wide business applications
https://github.com/annetteplatform/annette
Platform to build distributed, scalable, enterprise-wide business applications - annetteplatform/annette
- 100Cassovary is a simple big graph processing library for the JVM
https://github.com/twitter/cassovary
Cassovary is a simple big graph processing library for the JVM - twitter/cassovary
- 101A concurrent reactive programming framework.
https://github.com/storm-enroute/reactors
A concurrent reactive programming framework. Contribute to storm-enroute/reactors development by creating an account on GitHub.
- 102Library to register and lookup actors by names in an Akka cluster
https://github.com/xitrum-framework/glokka
Library to register and lookup actors by names in an Akka cluster - xitrum-framework/glokka
- 103:cake: doddle-model: machine learning in Scala.
https://github.com/picnicml/doddle-model
:cake: doddle-model: machine learning in Scala. Contribute to picnicml/doddle-model development by creating an account on GitHub.
- 104A Scala API for Apache Beam and Google Cloud Dataflow.
https://github.com/spotify/scio
A Scala API for Apache Beam and Google Cloud Dataflow. - spotify/scio
- 105A mini Scala utility library
https://github.com/scala-hamsters/hamsters
A mini Scala utility library. Contribute to scala-hamsters/hamsters development by creating an account on GitHub.
- 106Powerful new number types and numeric abstractions for Scala.
https://github.com/typelevel/spire
Powerful new number types and numeric abstractions for Scala. - typelevel/spire
- 107A Persistence Framework for Scala and NoSQL
https://github.com/longevityframework/longevity
A Persistence Framework for Scala and NoSQL. Contribute to longevityframework/longevity development by creating an account on GitHub.
- 108Async lightweight Scala web framework
https://github.com/dvarelap/peregrine
Async lightweight Scala web framework. Contribute to dvarelap/peregrine development by creating an account on GitHub.
- 109CSV Reader/Writer for Scala
https://github.com/tototoshi/scala-csv
CSV Reader/Writer for Scala. Contribute to tototoshi/scala-csv development by creating an account on GitHub.
- 110A Thrift parser/generator
https://github.com/twitter/scrooge
A Thrift parser/generator. Contribute to twitter/scrooge development by creating an account on GitHub.
- 111N-dimensional / multi-dimensional arrays (tensors) in Scala 3. Think NumPy ndarray / PyTorch Tensor but type-safe over shapes, array/axis labels & numeric data types
https://github.com/SciScala/NDScala
N-dimensional / multi-dimensional arrays (tensors) in Scala 3. Think NumPy ndarray / PyTorch Tensor but type-safe over shapes, array/axis labels & numeric data types - SciScala/NDScala
- 112A JSR-310 port of nscala_time
https://github.com/chronoscala/chronoscala
A JSR-310 port of nscala_time. Contribute to chronoscala/chronoscala development by creating an account on GitHub.
- 113Lightweight Scala Dependency Injection Library
https://github.com/scaldi/scaldi
Lightweight Scala Dependency Injection Library. Contribute to scaldi/scaldi development by creating an account on GitHub.
- 114A fault tolerant, protocol-agnostic RPC system
https://github.com/twitter/finagle
A fault tolerant, protocol-agnostic RPC system. Contribute to twitter/finagle development by creating an account on GitHub.
- 115Scala library for accessing various file, batch systems, job schedulers and grid middlewares.
https://github.com/openmole/gridscale
Scala library for accessing various file, batch systems, job schedulers and grid middlewares. - openmole/gridscale
- 116JVM - Java, Kotlin, Scala image processing library
https://github.com/sksamuel/scrimage
JVM - Java, Kotlin, Scala image processing library - sksamuel/scrimage
- 117A group of neural-network libraries for functional and mainstream languages
https://github.com/mrdimosthenis/Synapses
A group of neural-network libraries for functional and mainstream languages - mrdimosthenis/Synapses
- 118Alpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka.
https://github.com/akka/alpakka-kafka
Alpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka. - akka/alpakka-kafka
- 119Productivity-oriented collection of lightweight fancy stuff for Scala toolchain
https://github.com/7mind/izumi
Productivity-oriented collection of lightweight fancy stuff for Scala toolchain - 7mind/izumi
- 120Real Time Analytics and Data Pipelines based on Spark Streaming
https://github.com/Stratio/sparta
Real Time Analytics and Data Pipelines based on Spark Streaming - Stratio/sparta
- 121C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
https://github.com/Clustering4Ever/Clustering4Ever
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering. - Clustering4Ever/Clustering4Ever
- 122Scala extensions for Google Guice
https://github.com/codingwell/scala-guice
Scala extensions for Google Guice. Contribute to codingwell/scala-guice development by creating an account on GitHub.
- 123Purely functional genetic algorithms for multi-objective optimisation
https://github.com/openmole/mgo
Purely functional genetic algorithms for multi-objective optimisation - openmole/mgo
- 124A composable command-line parser for Scala.
https://github.com/bkirwi/decline
A composable command-line parser for Scala. Contribute to bkirwi/decline development by creating an account on GitHub.
- 125ABANDONED Pure Scala serialization library with annotations
https://github.com/fomkin/pushka
ABANDONED Pure Scala serialization library with annotations - fomkin/pushka
- 126Modify deeply nested case class fields
https://github.com/softwaremill/quicklens
Modify deeply nested case class fields. Contribute to softwaremill/quicklens development by creating an account on GitHub.
- 127Non-blocking, ultra-fast Scala Redis client built on top of Akka IO, used in production at Livestream
https://github.com/Livestream/scredis
Non-blocking, ultra-fast Scala Redis client built on top of Akka IO, used in production at Livestream - Livestream/scredis
- 128RxScala – Reactive Extensions for Scala – a library for composing asynchronous and event-based programs using observable sequences
https://github.com/ReactiveX/RxScala
RxScala – Reactive Extensions for Scala – a library for composing asynchronous and event-based programs using observable sequences - ReactiveX/RxScala
- 129Asynchronous, Reactive Programming for Scala and Scala.js.
https://github.com/monix/monix
Asynchronous, Reactive Programming for Scala and Scala.js. - monix/monix
- 130Easy way to create Free Monad using Scala macros with first-class Intellij support.
https://github.com/Thangiee/Freasy-Monad
Easy way to create Free Monad using Scala macros with first-class Intellij support. - Thangiee/Freasy-Monad
- 131Eff monad for cats - https://atnos-org.github.io/eff
https://github.com/atnos-org/eff
Eff monad for cats - https://atnos-org.github.io/eff - atnos-org/eff
- 132The missing MatPlotLib for Scala + Spark
https://github.com/vegas-viz/Vegas
The missing MatPlotLib for Scala + Spark. Contribute to vegas-viz/Vegas development by creating an account on GitHub.
- 133Scala extensions for the Kryo serialization library
https://github.com/twitter/chill
Scala extensions for the Kryo serialization library - twitter/chill
- 134Minimal, type-safe RPC Scala library.
https://github.com/yakivy/poppet
Minimal, type-safe RPC Scala library. Contribute to yakivy/poppet development by creating an account on GitHub.
- 135Reactive Microservices for the JVM
https://github.com/lagom/lagom
Reactive Microservices for the JVM. Contribute to lagom/lagom development by creating an account on GitHub.
- 136A scala extension for Project Reactor's Flux and Mono
https://github.com/spring-attic/reactor-scala-extensions
A scala extension for Project Reactor's Flux and Mono - spring-attic/reactor-scala-extensions
- 137Scala testing library with actionable errors and extensible APIs
https://github.com/scalameta/munit
Scala testing library with actionable errors and extensible APIs - scalameta/munit
- 138Scala wrapper for SnakeYAML
https://github.com/jcazevedo/moultingyaml
Scala wrapper for SnakeYAML. Contribute to jcazevedo/moultingyaml development by creating an account on GitHub.
- 139SynapseGrid is a framework for constructing dynamic low latency data flow systems.
https://github.com/Primetalk/SynapseGrid
SynapseGrid is a framework for constructing dynamic low latency data flow systems. - Primetalk/SynapseGrid
- 140Distributed NoSQL Database
https://github.com/stephenmcd/curiodb
Distributed NoSQL Database. Contribute to stephenmcd/curiodb development by creating an account on GitHub.
- 141Abstract Algebra for Scala
https://github.com/twitter/algebird
Abstract Algebra for Scala. Contribute to twitter/algebird development by creating an account on GitHub.
- 142Spark package to "plug" holes in data using SQL based rules ⚡️ 🔌
https://github.com/indix/sparkplug
Spark package to "plug" holes in data using SQL based rules ⚡️ 🔌 - GitHub - indix/sparkplug: Spark package to "plug" holes in data using SQL based rules ⚡️ 🔌
- 143A simple testing framework for Scala
https://github.com/com-lihaoyi/utest
A simple testing framework for Scala. Contribute to com-lihaoyi/utest development by creating an account on GitHub.
- 144Scalable Image Analysis and Shape Modelling
https://github.com/unibas-gravis/scalismo
Scalable Image Analysis and Shape Modelling. Contribute to unibas-gravis/scalismo development by creating an account on GitHub.
- 145Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
https://github.com/apache/zeppelin
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more. - apache/zeppelin
- 146Mutation testing for Scala
https://github.com/stryker-mutator/stryker4s
Mutation testing for Scala. Contribute to stryker-mutator/stryker4s development by creating an account on GitHub.
- 147Streaming MapReduce with Scalding and Storm
https://github.com/twitter/summingbird
Streaming MapReduce with Scalding and Storm. Contribute to twitter/summingbird development by creating an account on GitHub.
- 148A library that toggles Scala code at compile-time, like #if in C/C++
https://github.com/ThoughtWorksInc/enableIf.scala
A library that toggles Scala code at compile-time, like #if in C/C++ - ThoughtWorksInc/enableIf.scala
- 149Casbah is now officially end-of-life (EOL).
https://github.com/mongodb/casbah
Casbah is now officially end-of-life (EOL). Contribute to mongodb/casbah development by creating an account on GitHub.
- 150Build highly concurrent, distributed, and resilient message-driven applications on the JVM
https://github.com/akka/akka
Build highly concurrent, distributed, and resilient message-driven applications on the JVM - akka/akka
- 151A framework to create embedded Domain-Specific Languages in Scala
https://github.com/ThoughtWorksInc/Dsl.scala
A framework to create embedded Domain-Specific Languages in Scala - ThoughtWorksInc/Dsl.scala
- 152Scala library for boilerplate-free, type-safe data transformations
https://github.com/scalalandio/chimney
Scala library for boilerplate-free, type-safe data transformations - scalalandio/chimney
- 153Interactive and Reactive Data Science using Scala and Spark.
https://github.com/spark-notebook/spark-notebook
Interactive and Reactive Data Science using Scala and Spark. - spark-notebook/spark-notebook
- 154TensorFlow API for the Scala Programming Language
https://github.com/eaplatanios/tensorflow_scala
TensorFlow API for the Scala Programming Language. Contribute to eaplatanios/tensorflow_scala development by creating an account on GitHub.
- 155numsca is numpy for scala
https://github.com/botkop/numsca
numsca is numpy for scala. Contribute to botkop/numsca development by creating an account on GitHub.
- 156A cohesive & pragmatic framework of FP centric Scala libraries
https://github.com/frees-io/freestyle
A cohesive & pragmatic framework of FP centric Scala libraries - frees-io/freestyle
- 157Build software better, together
https://github.com/Sciss/ScalaCollider
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
- 158A lightweight, clean and simple JSON implementation in Scala
https://github.com/spray/spray-json
A lightweight, clean and simple JSON implementation in Scala - spray/spray-json
- 159A new Scala wrapper for Joda Time based on scala-time
https://github.com/nscala-time/nscala-time
A new Scala wrapper for Joda Time based on scala-time - nscala-time/nscala-time
- 160Convenient and performant logging library for Scala wrapping SLF4J.
https://github.com/lightbend-labs/scala-logging
Convenient and performant logging library for Scala wrapping SLF4J. - lightbend-labs/scala-logging
- 161Blindsight is a Scala logging API with DSL based structured logging, fluent logging, semantic logging, flow logging, and context aware logging.
https://github.com/tersesystems/blindsight
Blindsight is a Scala logging API with DSL based structured logging, fluent logging, semantic logging, flow logging, and context aware logging. - tersesystems/blindsight
- 162Command Line Interface Scala Toolkit
https://github.com/backuity/clist
Command Line Interface Scala Toolkit. Contribute to backuity/clist development by creating an account on GitHub.
- 163command line options parsing for Scala
https://github.com/scopt/scopt
command line options parsing for Scala. Contribute to scopt/scopt development by creating an account on GitHub.
- 164Schema safe, type-safe, reactive Scala driver for Cassandra/Datastax Enterprise
https://github.com/outworkers/phantom
Schema safe, type-safe, reactive Scala driver for Cassandra/Datastax Enterprise - outworkers/phantom
- 165A Scala API for Cascading
https://github.com/twitter/scalding
A Scala API for Cascading. Contribute to twitter/scalding development by creating an account on GitHub.
- 166The fastest logging library in the world. Built from scratch in Scala and programmatically configurable.
https://github.com/outr/scribe
The fastest logging library in the world. Built from scratch in Scala and programmatically configurable. - outr/scribe
- 167State of the Art Natural Language Processing
https://github.com/JohnSnowLabs/spark-nlp
State of the Art Natural Language Processing. Contribute to JohnSnowLabs/spark-nlp development by creating an account on GitHub.
- 168Essential Building Blocks for Scala
https://github.com/wvlet/airframe
Essential Building Blocks for Scala. Contribute to wvlet/airframe development by creating an account on GitHub.
- 169ZIO — A type-safe, composable library for async and concurrent programming in Scala
https://github.com/zio/zio
ZIO — A type-safe, composable library for async and concurrent programming in Scala - zio/zio
- 170Lightweight, modular, and extensible library for functional programming.
https://github.com/typelevel/cats
Lightweight, modular, and extensible library for functional programming. - typelevel/cats
- 171Scala validation library
https://github.com/jap-company/fields
Scala validation library. Contribute to jap-company/fields development by creating an account on GitHub.
- 172Cask: a Scala HTTP micro-framework
https://github.com/com-lihaoyi/cask
Cask: a Scala HTTP micro-framework. Contribute to com-lihaoyi/cask development by creating an account on GitHub.
- 173Slick extensions for PostgreSQL
https://github.com/tminglei/slick-pg
Slick extensions for PostgreSQL. Contribute to tminglei/slick-pg development by creating an account on GitHub.
- 174First class syntax support for type classes in Scala
https://github.com/typelevel/simulacrum
First class syntax support for type classes in Scala - typelevel/simulacrum
- 175Persist-Json, a Fast Json Parser Written in Scala
https://github.com/nestorpersist/json
Persist-Json, a Fast Json Parser Written in Scala. Contribute to nestorpersist/json development by creating an account on GitHub.
- 176Refinement types for Scala
https://github.com/fthomas/refined
Refinement types for Scala. Contribute to fthomas/refined development by creating an account on GitHub.
- 177Generic programming for Scala
https://github.com/milessabin/shapeless
Generic programming for Scala. Contribute to milessabin/shapeless development by creating an account on GitHub.
- 178Lift Framework
https://github.com/lift/framework
Lift Framework. Contribute to lift/framework development by creating an account on GitHub.
- 179Fast, testable, Scala services built on TwitterServer and Finagle
https://github.com/twitter/finatra
Fast, testable, Scala services built on TwitterServer and Finagle - twitter/finatra
- 180Build software better, together
https://github.com/wireapp/wire-signals
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
- 181The Opinionated RabbitMQ Library for Scala and Akka
https://github.com/SpinGo/op-rabbit
The Opinionated RabbitMQ Library for Scala and Akka - SpinGo/op-rabbit
- 182JSON typeclasses that know the difference between null and absent fields
https://github.com/nrktkt/ninny-json
JSON typeclasses that know the difference between null and absent fields - nrktkt/ninny-json
- 183Scala macros for compile-time generation of safe and ultra-fast JSON codecs + circe booster
https://github.com/plokhotnyuk/jsoniter-scala
Scala macros for compile-time generation of safe and ultra-fast JSON codecs + circe booster - plokhotnyuk/jsoniter-scala
- 184LoMRF is an open-source implementation of Markov Logic Networks
https://github.com/anskarl/LoMRF
LoMRF is an open-source implementation of Markov Logic Networks - anskarl/LoMRF
- 185property based testing library for Scala
https://github.com/scalaprops/scalaprops
property based testing library for Scala. Contribute to scalaprops/scalaprops development by creating an account on GitHub.
- 186Yet another JSON library for Scala
https://github.com/circe/circe
Yet another JSON library for Scala. Contribute to circe/circe development by creating an account on GitHub.
- 187Native Scala mocking framework
https://github.com/paulbutcher/ScalaMock
Native Scala mocking framework. Contribute to paulbutcher/ScalaMock development by creating an account on GitHub.
- 188OAuth 2.0 server-side implementation written in Scala
https://github.com/nulab/scala-oauth2-provider
OAuth 2.0 server-side implementation written in Scala - nulab/scala-oauth2-provider
- 189A small, convenient, dependency-free library for command-line argument parsing in Scala
https://github.com/com-lihaoyi/mainargs
A small, convenient, dependency-free library for command-line argument parsing in Scala - com-lihaoyi/mainargs
- 190Scala + Druid: Scruid. A library that allows you to compose queries in Scala, and parse the result back into typesafe classes.
https://github.com/ing-bank/scruid
Scala + Druid: Scruid. A library that allows you to compose queries in Scala, and parse the result back into typesafe classes. - ing-bank/scruid
- 191akka-persistence-gcp-datastore is a journal and snapshot store plugin for akka-persistence using google cloud firestore in datastore mode.
https://github.com/innFactory/akka-persistence-gcp-datastore
akka-persistence-gcp-datastore is a journal and snapshot store plugin for akka-persistence using google cloud firestore in datastore mode. - GitHub - innFactory/akka-persistence-gcp-datastore: akk...
- 192Mirror of Apache Kafka
https://github.com/apache/kafka
Mirror of Apache Kafka. Contribute to apache/kafka development by creating an account on GitHub.
- 193:leaves: Non-blocking, Reactive MongoDB Driver for Scala
https://github.com/ReactiveMongo/ReactiveMongo
:leaves: Non-blocking, Reactive MongoDB Driver for Scala - ReactiveMongo/ReactiveMongo
- 194Scala GraphQL implementation
https://github.com/sangria-graphql/sangria
Scala GraphQL implementation. Contribute to sangria-graphql/sangria development by creating an account on GitHub.
- 195Functional, stream-based CSV processor for Scala
https://github.com/fingo/spata
Functional, stream-based CSV processor for Scala. Contribute to fingo/spata development by creating an account on GitHub.
- 196ActiveRecord-like ORM library for Scala
https://github.com/aselab/scala-activerecord
ActiveRecord-like ORM library for Scala. Contribute to aselab/scala-activerecord development by creating an account on GitHub.
- 197A Future-free Fs2 native pure FP Redis client
https://github.com/laserdisc-io/laserdisc
A Future-free Fs2 native pure FP Redis client. Contribute to laserdisc-io/laserdisc development by creating an account on GitHub.
- 198Scala framework for building beautiful and maintainable web applications.
https://github.com/UdashFramework/udash-core
Scala framework for building beautiful and maintainable web applications. - UdashFramework/udash-core
- 199Main Portal page for the Jackson project
https://github.com/FasterXML/jackson
Main Portal page for the Jackson project. Contribute to FasterXML/jackson development by creating an account on GitHub.
- 200Library to read, analyze, transform and generate Scala programs
https://github.com/scalameta/scalameta
Library to read, analyze, transform and generate Scala programs - scalameta/scalameta
- 201Compositional, streaming I/O library for Scala
https://github.com/typelevel/fs2
Compositional, streaming I/O library for Scala. Contribute to typelevel/fs2 development by creating an account on GitHub.
- 202The Community Maintained High Velocity Web Framework For Java and Scala.
https://github.com/playframework/playframework
The Community Maintained High Velocity Web Framework For Java and Scala. - playframework/playframework
- 203Scala lightweight, type-safe, asynchronous driver for neo4j
https://github.com/neotypes/neotypes
Scala lightweight, type-safe, asynchronous driver for neo4j - GitHub - neotypes/neotypes: Scala lightweight, type-safe, asynchronous driver for neo4j
- 204Type-safe general-cryptography library - https://jmcardon.github.io/tsec/
https://github.com/jmcardon/tsec
Type-safe general-cryptography library - https://jmcardon.github.io/tsec/ - jmcardon/tsec
- 205JSON library
https://github.com/json4s/json4s
JSON library. Contribute to json4s/json4s development by creating an account on GitHub.
- 206Web & mobile client-side akka-http sessions, with optional JWT support
https://github.com/softwaremill/akka-http-session
Web & mobile client-side akka-http sessions, with optional JWT support - softwaremill/akka-http-session
- 207Lightweight and Nonintrusive Scala Dependency Injection Library
https://github.com/softwaremill/macwire
Lightweight and Nonintrusive Scala Dependency Injection Library - softwaremill/macwire
- 208Rings: efficient JVM library for polynomial rings
https://github.com/PoslavskySV/rings
Rings: efficient JVM library for polynomial rings. Contribute to PoslavskySV/rings development by creating an account on GitHub.
- 209Typesafe, purely functional Computational Intelligence
https://github.com/ciren/cilib
Typesafe, purely functional Computational Intelligence - ciren/cilib
- 210An experimental library for Functional Reactive Programming in Scala
https://github.com/lihaoyi/scala.rx
An experimental library for Functional Reactive Programming in Scala - lihaoyi/scala.rx
- 211New ReactiveCouchbase driver using reactive-streams
https://github.com/ReactiveCouchbase/reactivecouchbase-rs-core
New ReactiveCouchbase driver using reactive-streams - ReactiveCouchbase/reactivecouchbase-rs-core
- 212Simple, safe and intuitive Scala I/O
https://github.com/pathikrit/better-files
Simple, safe and intuitive Scala I/O. Contribute to pathikrit/better-files development by creating an account on GitHub.
- 213Reactive type-safe Scala driver for SQL databases
https://github.com/outworkers/morpheus
Reactive type-safe Scala driver for SQL databases. Contribute to outworkers/morpheus development by creating an account on GitHub.
- 214Lamma schedule generator for Scala is a professional schedule generation library for periodic schedules like fixed income coupon payment, equity deravitive fixing date generation etc.
https://github.com/maxcellent/lamma
Lamma schedule generator for Scala is a professional schedule generation library for periodic schedules like fixed income coupon payment, equity deravitive fixing date generation etc. - GitHub - m...
- 215Tiny High Performance HTTP Server for Scala
https://github.com/analogweb/analogweb-scala
Tiny High Performance HTTP Server for Scala . Contribute to analogweb/analogweb-scala development by creating an account on GitHub.
- 216A test framework that runs everything in parallel.
https://github.com/disneystreaming/weaver-test
A test framework that runs everything in parallel. - GitHub - disneystreaming/weaver-test: A test framework that runs everything in parallel.
- 217Clickhouse Scala Client with Reactive Streams support
https://github.com/crobox/clickhouse-scala-client
Clickhouse Scala Client with Reactive Streams support - crobox/clickhouse-scala-client
- 218Memcached client for Scala
https://github.com/monix/shade
Memcached client for Scala. Contribute to monix/shade development by creating an account on GitHub.
- 219A schema-aware Scala library for data transformation
https://github.com/galliaproject/gallia-core
A schema-aware Scala library for data transformation - galliaproject/gallia-core
- 220Efficient CBOR and JSON (de)serialization in Scala
https://github.com/sirthias/borer
Efficient CBOR and JSON (de)serialization in Scala - sirthias/borer
- 221A purely functional Scala client for CouchDB
https://github.com/beloglazov/couchdb-scala
A purely functional Scala client for CouchDB. Contribute to beloglazov/couchdb-scala development by creating an account on GitHub.
- 222The Play JSON library
https://github.com/playframework/play-json
The Play JSON library. Contribute to playframework/play-json development by creating an account on GitHub.
- 223Image comparison by hash codes
https://github.com/poslegm/scala-phash
Image comparison by hash codes. Contribute to poslegm/scala-phash development by creating an account on GitHub.
- 224Avro schema generation and serialization / deserialization for Scala
https://github.com/sksamuel/avro4s
Avro schema generation and serialization / deserialization for Scala - sksamuel/avro4s
- 225Scala combinator library for working with binary data
https://github.com/scodec/scodec
Scala combinator library for working with binary data - scodec/scodec
- 226Minimal, idiomatic, customizable validation Scala library.
https://github.com/yakivy/dupin
Minimal, idiomatic, customizable validation Scala library. - yakivy/dupin
- 227An implementation of an OAuth2 server designed for mocking/testing
https://github.com/zalando-stups/OAuth2-mock-play
An implementation of an OAuth2 server designed for mocking/testing - zalando-stups/OAuth2-mock-play
- 228A type-safe, reflection-free, powerful enumeration implementation for Scala with exhaustive pattern match warnings and helpful integrations.
https://github.com/lloydmeta/enumeratum
A type-safe, reflection-free, powerful enumeration implementation for Scala with exhaustive pattern match warnings and helpful integrations. - lloydmeta/enumeratum
- 229Optics library for Scala
https://github.com/optics-dev/Monocle
Optics library for Scala. Contribute to optics-dev/Monocle development by creating an account on GitHub.
- 230Scala etcd client implementing V3 APIs
https://github.com/mingchuno/etcd4s
Scala etcd client implementing V3 APIs. Contribute to mingchuno/etcd4s development by creating an account on GitHub.
- 231An asynchronous programming facility for Scala
https://github.com/scala/scala-async
An asynchronous programming facility for Scala. Contribute to scala/scala-async development by creating an account on GitHub.
- 232Accord: A sane validation library for Scala
https://github.com/wix/accord
Accord: A sane validation library for Scala. Contribute to wix-incubator/accord development by creating an account on GitHub.
- 233A data access library for Scala + Postgres.
https://github.com/tpolecat/skunk
A data access library for Scala + Postgres. Contribute to typelevel/skunk development by creating an account on GitHub.
- 234tinylog is a lightweight logging framework for Java, Kotlin, Scala, and Android
https://github.com/tinylog-org/tinylog
tinylog is a lightweight logging framework for Java, Kotlin, Scala, and Android - tinylog-org/tinylog
- 235A purely functional library to build distributed and event-driven systems
https://github.com/parapet-io/parapet
A purely functional library to build distributed and event-driven systems - parapet-io/parapet
- 236Non-blocking, Reactive Redis driver for Scala (with Sentinel support)
https://github.com/etaty/rediscala
Non-blocking, Reactive Redis driver for Scala (with Sentinel support) - etaty/rediscala
- 237Wonderful reusable code from Twitter
https://github.com/twitter/util
Wonderful reusable code from Twitter. Contribute to twitter/util development by creating an account on GitHub.
- 238The Scala API for Quantities, Units of Measure and Dimensional Analysis
https://github.com/typelevel/squants
The Scala API for Quantities, Units of Measure and Dimensional Analysis - typelevel/squants
- 239Squid – type-safe metaprogramming and compilation framework for Scala
https://github.com/epfldata/squid
Squid – type-safe metaprogramming and compilation framework for Scala - epfldata/squid
- 240Principled Functional Programming in Scala
https://github.com/scalaz/scalaz
Principled Functional Programming in Scala. Contribute to scalaz/scalaz development by creating an account on GitHub.
- 241sbt plugin that generates Scala case classes for easy, statically typed and implicit access of JSON data e.g. from API responses
https://github.com/battermann/sbt-json
sbt plugin that generates Scala case classes for easy, statically typed and implicit access of JSON data e.g. from API responses - battermann/sbt-json
- 242Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, Phi, MiniCPM, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, GraphRAG, DeepSpeed, vLLM, FastChat, Axolotl, etc.
https://github.com/intel-analytics/BigDL
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, Phi, MiniCPM, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc,...
- 243Mockito for Scala language
https://github.com/mockito/mockito-scala
Mockito for Scala language. Contribute to mockito/mockito-scala development by creating an account on GitHub.
- 244High-performance SLF4J wrapper for Scala.
https://github.com/Log4s/log4s
High-performance SLF4J wrapper for Scala. Contribute to Log4s/log4s development by creating an account on GitHub.
- 245🔍 Elasticsearch Scala Client - Reactive, Non Blocking, Type Safe, HTTP Client
https://github.com/sksamuel/elastic4s
🔍 Elasticsearch Scala Client - Reactive, Non Blocking, Type Safe, HTTP Client - Philippus/elastic4s
- 246aossie / Agora · GitLab
https://gitlab.com/aossie/Agora/
An Electronic Voting Library implemented in Scala
- 247aossie / Scavenger · GitLab
https://gitlab.com/aossie/Scavenger
A theorem prover based on the conflict resolution calculus
- 248SBT plugin for tweaking various IDE settings
https://github.com/JetBrains/sbt-ide-settings
SBT plugin for tweaking various IDE settings. Contribute to JetBrains/sbt-ide-settings development by creating an account on GitHub.
- 249An HTTP Server and Client library for Scala.
https://github.com/criteo/lolhttp
An HTTP Server and Client library for Scala. Contribute to criteo/lolhttp development by creating an account on GitHub.
- 250Scalafmt · Code formatter for Scala
https://scalameta.org/scalafmt/
Code formatter for Scala
- 251sbt plugin that can check Maven and Ivy repositories for dependency updates
https://github.com/rtimush/sbt-updates
sbt plugin that can check Maven and Ivy repositories for dependency updates - rtimush/sbt-updates
- 252Scala command-line wrapper around ffmpeg, ffprobe, ImageMagick, and other tools relating to media.
https://github.com/outr/media4s
Scala command-line wrapper around ffmpeg, ffprobe, ImageMagick, and other tools relating to media. - outr/media4s
- 253friendly little parsers
https://github.com/tpolecat/atto
friendly little parsers. Contribute to tpolecat/atto development by creating an account on GitHub.
- 254simple combinator-based parsing for Scala. formerly part of the Scala standard library, now a separate community-maintained module
https://github.com/scala/scala-parser-combinators
simple combinator-based parsing for Scala. formerly part of the Scala standard library, now a separate community-maintained module - scala/scala-parser-combinators
Related Articlesto learn about angular.
- 1Introduction to Scala: Beginner’s Guide
- 2Understanding Scala’s Type System: Types and Generics
- 3Functional Programming with Scala
- 4Advanced Functional Programming in Scala: Monads, Functors, and More
- 5Building RESTful APIs with Scala and Akka HTTP: A Beginner’s Guide
- 6Play Framework for Scala Web Development: A Step-by-Step Guide
- 7Concurrency in Scala: Mastering Futures and Promises
- 8Optimizing Scala Performance: Tips for High-Performance Scala Applications
- 9Developing a Scala-based Chat Application: From Concept to Deployment
- 10Creating a Scala-based Data Processing Pipeline: Handling Big Data Efficiently
FAQ'sto learn more about Angular JS.
mail [email protected] to add more queries here 🔍.
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