Raw performance numbers - Spring Boot 2 Webflux vs Spring Boot 1

Summary

Spring Boot 2 with Spring Webflux based application outperforms a Spring Boot 1 based application by a huge margin for IO heavy workloads. The following is a summarized result of a load test - Response time for a IO heavy transaction with varying concurrent users:

When the number of concurrent users remains low (say less than 1000) both Spring Boot 1 and Spring Boot 2 handle the load well and the 95 percentile response time remains milliseconds above a expected value of 300 ms.

At higher concurrency levels, the Async Non-Blocking IO and reactive support in Spring Boot 2 starts showing its colors - the 95th percentile time even with a very heavy load of 5000 users remains at around 312ms! Spring Boot 1 records a lot of failures and high response times at these concurrency levels.

Details

My set-up for the performance test is the following:

The sample applications expose an endpoint(/passthrough/message) which in-turn calls a downstream service. The request message to the endpoint looks something like this:

{
  "id": "1",
  "payload": "sample payload",
  "delay": 3000
}

The downstream service would delay based on the "delay" attribute in the message (in milliseconds).

Spring Boot 1 Application

I have used Spring Boot 1.5.8.RELEASE for the Boot 1 version of the application. The endpoint is a simple Spring MVC controller which in turn uses Spring's RestTemplate to make the downstream call. Everything is synchronous and blocking and I have used the default embedded Tomcat container as the runtime. This is the raw code for the downstream call:

public MessageAck handlePassthrough(Message message) {
    ResponseEntity<MessageAck> responseEntity = this.restTemplate.postForEntity(targetHost 
                                                            + "/messages", message, MessageAck.class);
    return responseEntity.getBody();
}

Spring Boot 2 Application

Spring Boot 2 version of the application exposes a Spring Webflux based endpoint and uses WebClient, the new non-blocking, reactive alternate to RestTemplate to make the downstream call - I have also used Kotlin for the implementation, which has no bearing on the performance. The runtime server is Netty:

import org.springframework.http.HttpHeaders
import org.springframework.http.MediaType
import org.springframework.web.reactive.function.BodyInserters.fromObject
import org.springframework.web.reactive.function.client.ClientResponse
import org.springframework.web.reactive.function.client.WebClient
import org.springframework.web.reactive.function.client.bodyToMono
import org.springframework.web.reactive.function.server.ServerRequest
import org.springframework.web.reactive.function.server.ServerResponse
import org.springframework.web.reactive.function.server.bodyToMono
import reactor.core.publisher.Mono

class PassThroughHandler(private val webClient: WebClient) {

    fun handle(serverRequest: ServerRequest): Mono<ServerResponse> {
        val messageMono = serverRequest.bodyToMono<Message>()

        return messageMono.flatMap { message ->
            passThrough(message)
                    .flatMap { messageAck ->
                        ServerResponse.ok().body(fromObject(messageAck))
                    }
        }
    }

    fun passThrough(message: Message): Mono<MessageAck> {
        return webClient.post()
                .uri("/messages")
                .header(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
                .header(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
                .body(fromObject<Message>(message))
                .exchange()
                .flatMap { response: ClientResponse ->
                    response.bodyToMono<MessageAck>()
                }
    }
}

Details of the Perfomance Test

The test is simple, for different sets of concurrent users (300, 1000, 1500, 3000, 5000), I send a message with the delay attribute set to 300 ms, each user repeats the scenario 30 times with a delay between 1 to 2 seconds between requests. I am using the excellent Gatling tool to generate this load.

Results

These are the results as captured by Gatling:

300 concurrent users:

Boot 1	Boot 2

1000 concurrent users:

Boot 1	Boot 2

1500 concurrent users:

Boot 1	Boot 2

3000 concurrent users:

Boot 1	Boot 2

5000 concurrent users:

Boot 1	Boot 2

Reference

The sample application and the load scripts are available in my github repo - https://github.com/bijukunjummen/boot2-load-demo.

Kata - implementing a functional List data structure in Kotlin

I saw an exercise in chapter 3 of the excellent Functional Programming in Scala book which deals with defining functional data structures and uses the linked list as an example on how to go about developing such a datastructure. I wanted to try this sample using Kotlin to see to what extent I can replicate the sample.

A scala skeleton of the sample is available in the companion code to the book here and my attempt in Kotlin is heavily inspired (copied!) by the answerkey in the repository.

Basic

This is what a basic List representation in Kotlin looks like:

sealed class List<out A> {

    abstract val head: A

    abstract val tail: List<A>
}

data class Cons<out T>(override val head: T, override val tail: List<T>) : List<T>()

object Nil : List<Nothing>() {
    override val head: Nothing
        get() {
            throw NoSuchElementException("head of an empty list")
        }

    override val tail: List<Nothing>
        get() {
            throw NoSuchElementException("tail of an empty list")
        }
}

the List has been defined as a sealed class, this means that all subclasses of the sealed class will be defined in the same file. This is useful for pattern matching on the type of an instance and will come up repeatedly in most of the functions.

There are two implementations of this List -
1. Cons a non-empty list consisting of a head element and a tail List,
2. Nil an empty List

This is already very useful in its current form, consider the following which constructs a List and retrieves elements from it:

val l1:List<Int> = Cons(1, Cons(2, Cons(3, Cons(4, Nil))))
assertThat(l1.head).isEqualTo(1)
assertThat(l1.tail).isEqualTo(Cons(2, Cons(3, Cons(4, Nil))))


val l2:List<String> = Nil

Pattern Matching with "when" expression

Now to jump onto implementing some methods of List. Since List is a sealed class it allows for some good pattern matching, say to get the sum of elements in the List:

fun sum(l: List<Int>): Int {
    return when(l) {
        is Cons -> l.head + sum(l.tail)
        is Nil -> 0
    }
}

The compiler understands that Cons and Nil are the only two paths to take for the match on a list instance.

A little more complex operation, "drop" some number of elements from the beginning of the list and "dropWhile" which takes in a predicate and drops elements from the beginning matching the predicate:

fun drop(n: Int): List<A> {
    return if (n <= 0)
        this
    else when (this) {
        is Cons -> tail.drop(n - 1)
        is Nil -> Nil
    }
}

val l = list(4, 3, 2, 1)
assertThat(l.drop(2)).isEqualTo(list(2, 1))

fun dropWhile(p: (A) -> Boolean): List<A> {
    return when(this) {
        is Cons -> if (p(this.head)) this.tail.dropWhile(p) else this
        is Nil -> Nil
    }
}

val l = list(1, 2, 3, 5, 8, 13, 21, 34, 55, 89)
assertThat(l.dropWhile({e -> e < 20})).isEqualTo(list(21, 34, 55, 89))

These show off the power of pattern matching with the "when" expression in Kotlin.

Unsafe Variance!

To touch on a wrinkle, see how the List is defined with a type parameter that is declared as "out T", this is called the "declaration site variance" which in this instance makes List co-variant on type T. Declaration site variance is explained beautifully with the Kotlin documentation. With the way List is declared, it allows me to do something like this:

val l:List<Int> = Cons(1, Cons(2, Nil))
val lAny: List<Any> = l

Now, consider an "append" function which appends another list:

fun append(l: List<@UnsafeVariance A>): List<A> {
    return when (this) {
        is Cons -> Cons(head, tail.append(l))
        is Nil -> l
    }
}

here a second list is taken as a parameter to the append function, however Kotlin would flag the parameter - this is because it is okay to return a co-variant type but not to take it as a parameter. However since we know the List in its current form is immutable, I can get past this by marking the type parameter with "@UnsafeVariance" annotation.

Folding

Folding operations allow the list to be "folded" into a result based on some aggregation on individual elemnents in it.

Consider foldLeft:

fun <B> foldLeft(z: B, f: (B, A) -> B): B {
    tailrec fun foldLeft(l: List<A>, z: B, f: (B, A) -> B): B {
        return when (l) {
            is Nil -> z
            is Cons -> foldLeft(l.tail, f(z, l.head), f)
        }
    }

    return foldLeft(this, z, f)
}

If a list were to consist of elements (2, 3, 5, 8) then foldLeft is equivalent to "f(f(f(f(z, 2), 3),5),8)"

With this higher order function in place, the sum function can expressed this way:

val l = Cons(1, Cons(2, Cons(3, Cons(4, Nil))))
assertThat(l.foldLeft(0, {r, e -> r + e})).isEqualTo(10)

foldRight looks like the following in Kotlin:

fun <B> foldRight(z: B, f: (A, B) -> B): B {
    return when(this) {
        is Cons -> f(this.head, tail.foldRight(z, f))
        is Nil -> z
    }
}

If a list were to consist of elements (2, 3, 5, 8) then foldRight is equivalent to "f(2, f(3, f(5, f(8, z))))"

This version of the foldRight, though cooler looking is not tail recursive, a more stack friendly version can be implemented using the previously defined tail recursive foldLeft by simply reversing the List and calling foldLeft internally the following way:

fun reverse(): List<A> {
    return foldLeft(Nil as List<A>, { b, a -> Cons(a, b) })
}

fun <B> foldRightViaFoldLeft(z: B, f: (A, B) -> B): B {
    return reverse().foldLeft(z, { b, a -> f(a, b) })
}

map and flatMap

map is a function which transforms the element of this list:

fun <B> map(f: (A) -> B): List<B> {
    return when (this) {
        is Cons -> Cons(f(head), tail.map(f))
        is Nil -> Nil
    }
}

An example of using this function is the following:

val l = Cons(1, Cons(2, Cons(3, Nil)))
val l2 = l.map { e -> e.toString() }
assertThat(l2).isEqualTo(Cons("1", Cons("2", Cons("3", Nil))))

A variation of map where the transforming function returns another list, and the final results flattens everything, best demoed using an example after the implementation:

fun <B> flatMap(f: (a: A) -> List<@UnsafeVariance B>): List<B> {
    return flatten(map { a -> f(a) })
}

companion object {
    fun <A> flatten(l: List<List<A>>): List<A> {
        return l.foldRight(Nil as List<A>, { a, b -> a.append(b) })
    }
}


val l = Cons(1, Cons(2, Cons(3, Nil)))

val l2 = l.flatMap { e -> list(e.toString(), e.toString()) }

assertThat(l2)
        .isEqualTo(
                Cons("1", Cons("1", Cons("2", Cons("2", Cons("3", Cons("3", Nil)))))))

This covers the basics involved in implementing a functional list datastructure using Kotlin, there were a few rough edges when compared to the scala version but I think it mostly works. Admittedly the sample can likely be improved drastically, if you have any observations on how to improve the code please do send me a PR at my github repo for this sample or as comment to this post.