Shahzad Bhatti Welcome to my ramblings and rants!

November 17, 2020

Structured Concurrency in modern programming languages – Part-IV

Filed under: Computing,Kotlin,Languages,Swift,Technology — admin @ 12:58 pm

In this fourth part of the series on structured concurrency (Part-I, Part-II, Part-III), I will review Kotlin and Swift languages for writing concurrent applications and their support for structured concurrency:

Kotlin

Kotlin is a JVM language that was created by JetBrains with improved support of functional and object-oriented features such as extension functions, nested functions, data classes, lambda syntax, etc. Kotlin also uses optional types instead of null references similar to Rust and Swift to remove null-pointer errors. Kotlin provides native OS-threads similar to Java and coroutines with async/await syntax similar to Rust. Kotlin brings first-class support for structured concurrency with its support for concurrency scope, composition, error handling, timeout/cancellation and context for coroutines.

Structured Concurrency in Kotlin

Kotlin provides following primitives for concurrency support:

suspend functions

Kotlin adds suspend keyword to annotate a function that will be used by coroutine and it automatically adds continuation behavior when code is compiled so that instead of return value, it calls the continuation callback.

Launching coroutines

A coroutine can be launched using launch, async or runBlocking, which defines scope for structured concurrency. The lifetime of children coroutines is attached to this scope that can be used to cancel children coroutines. The async returns a Deferred (future) object that extends Job. You use await method on the Deferred instance to get the results.

Dispatcher

Kotlin defines CoroutineDispatcher to determine thread(s) for running the coroutine. Kotlin provides three types of dispatchers: Default – that are used for long-running asynchronous tasks; IO – that may use IO/network; and Main – that uses main thread (e.g. on Android UI).

Channels

Kotlin uses channels for communication between coroutines. It defines three types of channels: SendChannel, ReceiveChannel, and Channel. The channels can be rendezvous, buffered, unlimited or conflated where rendezvous channels and buffered channels behave like GO’s channels and suspend send or receive operation if other go-routine is not ready or buffer is full. The unlimited channel behave like queue and conflated channel overwrites previous value when new value is sent. The producer can close send channel to indicate end of work.

Using async/await in Kotlin

Following code shows how to use async/await to build the toy web crawler:

package concurrency

import concurrency.domain.Request
import concurrency.domain.Response
import concurrency.utils.CrawlerUtils
import kotlinx.coroutines.*
import org.slf4j.LoggerFactory
import java.util.concurrent.atomic.AtomicInteger

class CrawlerWithAsync(val maxDepth: Int, val timeout: Long) : Crawler {
    private val logger = LoggerFactory.getLogger(CrawlerWithCoroutines::class.java)
    val crawlerUtils = CrawlerUtils(maxDepth)

    // public method for crawling a list of urls using async/await
    override fun crawl(urls: List<String>): Response {
        var res = Response()
        // Boundary for concurrency and it will not return until all
        // child URLs are crawled up to MAX_DEPTH limit.
        runBlocking {
            res.childURLs = crawl(urls, 0).childURLs
        }
        return res
    }

    suspend private fun crawl(urls: List<String>, depth: Int): Response {
        var res = Response()
        if (depth >= maxDepth) {
            return res.failed("Max depth reached")
        }
        var size = AtomicInteger()

        withTimeout(timeout) {
            val jobs = mutableListOf<Deferred<Int>>()
            for (u in urls) {
                jobs.add(async {
                    val childURLs = crawlerUtils.handleCrawl(Request(u, depth))
                    // shared
                    size.addAndGet(crawl(childURLs, depth + 1).childURLs + 1)
                })
            }
            for (j in jobs) {
                j.await()
            }
        }
        return res.completed(size.get())
    }
}

In above example, CrawlerWithAsync class defines timeout parameter for crawler. The crawl function takes list of URLs to crawl and defines high-level scope of concurrency using runBlocking. The private crawl method is defined as suspend so that it can be used as continuation. It uses async with timeout to start background tasks and uses await to collect results. This method recursively calls handleCrawl to crawl child URLs.

Following unit tests show how to test above crawl method:

package concurrency

import org.junit.Test
import org.slf4j.LoggerFactory
import kotlin.test.assertEquals

class CrawlerAsynTest {
    private val logger = LoggerFactory.getLogger(CrawlerWithCoroutinesTest::class.java)
    val urls = listOf("a.com", "b.com", "c.com", "d.com", "e.com", "f.com",
            "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com")

    @Test
    fun testCrawl() {
        val crawler = CrawlerWithAsync(4, 1000L)
        val started = System.currentTimeMillis()
        val res = crawler.crawl(urls);
        val duration = System.currentTimeMillis() - started
        logger.info("CrawlerAsync - crawled %d urls in %d milliseconds".format(res.childURLs, duration))
        assertEquals(19032, res.childURLs)
    }

    @Test(expected = Exception::class)
    fun testCrawlWithTimeout() {
        val crawler = CrawlerWithAsync(1000, 100L)
        crawler.crawl(urls);
    }
}

You can download the full source code from https://github.com/bhatti/concurency-katas/tree/main/kot_pool.

Following are major benefits of using this approach to implement crawler and its support of structured concurrency:

  • The main crawl method defines high level scope of concurrency and it waits for the completion of child tasks.
  • Kotlin supports cancellation and timeout APIs and the crawl method will fail with timeout error if crawling exceeds the time limit.
  • The crawl method captures error from async response and returns so that client code can perform error handling.
  • The async syntax in Kotlin allows easy composition of asynchronous code.
  • Kotlin allows customized dispatcher for more control on the asynchronous behavior.

Following are shortcomings using this approach for structured concurrency and general design:

  • As Kotlin doesn’t enforce immutability by default, you will need synchronization to protect shared state.
  • Async/Await support is still new in Kotlin and lacks stability and proper documentation.
  • Above design creates a new coroutine for crawling each URL and it can strain expensive network and IO resources so it’s not practical for real-world implementation.

Using coroutines in Kotlin

Following code uses coroutine syntax to implement the web crawler:

package concurrency

import concurrency.domain.Request
import concurrency.domain.Response
import concurrency.utils.CrawlerUtils
import kotlinx.coroutines.coroutineScope
import kotlinx.coroutines.runBlocking
import kotlinx.coroutines.withTimeout
import org.slf4j.LoggerFactory
import java.util.concurrent.atomic.AtomicInteger

class CrawlerWithCoroutines(val maxDepth: Int, val timeout: Long) : Crawler {
    private val logger = LoggerFactory.getLogger(CrawlerWithCoroutines::class.java)
    val crawlerUtils = CrawlerUtils(maxDepth)

    // public method for crawling a list of urls using coroutines
    override fun crawl(urls: List<String>): Response {
        var res = Response()
        // Boundary for concurrency and it will not return until all
        // child URLs are crawled up to MAX_DEPTH limit.
        runBlocking {
            res.childURLs = crawl(urls, 0).childURLs
        }
        return res
    }

    suspend private fun crawl(urls: List<String>, depth: Int): Response {
        var res = Response()
        if (depth >= maxDepth) {
            return res.failed("Max depth reached")
        }
        var size = AtomicInteger()
        withTimeout(timeout) {
            for (u in urls) {
                coroutineScope {
                    val childURLs = crawlerUtils.handleCrawl(Request(u, depth))
                    // shared
                    size.addAndGet(crawl(childURLs, depth + 1).childURLs + 1)
                }
            }
        }
        return res.completed(size.get())
    }
}

Above example is similar to async/await but uses coroutine syntax and its behavior is similar to async/await implementation.

Following example shows how async coroutines can be cancelled:

package concurrency

import concurrency.domain.Request
import concurrency.domain.Response
import concurrency.utils.CrawlerUtils
import kotlinx.coroutines.Deferred
import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking
import kotlinx.coroutines.withTimeout
import org.slf4j.LoggerFactory
import java.util.concurrent.atomic.AtomicInteger

class CrawlerCancelable(val maxDepth: Int, val timeout: Long) : Crawler {
    private val logger = LoggerFactory.getLogger(CrawlerWithCoroutines::class.java)
    val crawlerUtils = CrawlerUtils(maxDepth)

    // public method for crawling a list of urls to show cancel operation
    // internal method will call cancel instead of await so this method will
    // fail.
    override fun crawl(urls: List<String>): Response {
        var res = Response()
        // Boundary for concurrency and it will not return until all
        // child URLs are crawled up to MAX_DEPTH limit.
        runBlocking {
            res.childURLs = crawl(urls, 0).childURLs
        }
        return res
    }

    ////////////////// Internal methods
    suspend private fun crawl(urls: List<String>, depth: Int): Response {
        var res = Response()
        if (depth >= maxDepth) {
            return res.failed("Max depth reached")
        }
        var size = AtomicInteger()

        withTimeout(timeout) {
            val jobs = mutableListOf<Deferred<Int>>()
            for (u in urls) {
                jobs.add(async {
                    val childURLs = crawlerUtils.handleCrawl(Request(u, depth))
                    // shared
                    size.addAndGet(crawl(childURLs, depth + 1).childURLs + 1)
                })
            }
            for (j in jobs) {
                j.cancel()
            }
        }
        return res.completed(size.get())
    }
}

You can download above examples from https://github.com/bhatti/concurency-katas/tree/main/kot_pool.

Swift

Swift was developed by Apple to replace Objective-C and offer modern features such as closures, optionals instead of null-pointers (similar to Rust and Kotlin), optionals chaining, guards, value types, generics, protocols, algebraic data types, etc. It uses same runtime system as Objective-C and uses automatic-reference-counting (ARC) for memory management, grand-central-dispatch for concurrency and provides integration with Objective-C code and libraries.

Structured Concurrency in Swift

I discussed concurrency support in Objective-C in my old blog [1685] such as NSThread, NSOperationQueue, Grand Central Dispatch (GCD), etc and since then GCD has improved launching asynchronous tasks using background queues with timeout/cancellation support. However, much of the Objective-C and Swift code still suffers from callbacks and promises hell discussed in Part-I. Chris Lattner and Joe Groff wrote a proposal to add async/await and actor-model to Swift and provide first-class support for structured concurrency. As this work is still in progress, I wasn’t able to test it but here are major features of this proposal:

Coroutines

Swift will adapt coroutines as building blocks of concurrency and asynchronous code. It will add syntactic sugar for completion handlers using async or yield keywords.

Async/Await

Swift will provide async/await syntactic sugar on top of coroutines to mark asynchronous behavior. The async code will use continuations similar to Kotlin so that it suspends itself and schedules execution by controlling context. It will use Futures (similar to Deferred in Kotlin) to await for the results (or errors). This syntax will work with normal error handling in Swift so that errors from asynchronous code are automatically propagated to the calling function.

Actor model

Swift will adopt actor-model with value based messages (copy-on-write) to manage concurrent objects that can receive messages asynchronously and the actor can keep internal state and eliminate race conditions.

Kotlin and Swift are very similar in design and both have first-class support of structured concurrency such as concurrency scope, composition, error handling, cancellation/timeout, value types, etc. Both Kotlin and Swift use continuation for async behavior so that async keyword suspends the execution and passes control to the execution context so that it can be executed asynchronously and control is passed back at the end of execution.

Structured Concurrency Comparison

Following table summarizes support of structured concurrency discussed in this blog series:

FeatureTypescript (NodeJS)ErlangElixirGORustKotlinSwift
Structured scopeBuilt-inmanuallymanuallymanuallyBuilt-inBuilt-inBuilt-in
Asynchronous CompositionYesNoNoNoYesYesYes
Error HandlingNatively using ExceptionsManually storing errors in ResponseManually storing errors in ResponseManually storing errors in ResponseManually using Result ADTNatively using ExceptionsNatively using Exceptions
CancellationCooperative CancellationBuilt-in Termination or Cooperative CancellationBuilt-in Termination or Cooperative CancellationBuilt-in Cancellation or Cooperative CancellationBuilt-in Cancellation or Cooperative CancellationBuilt-in Cancellation or Cooperative CancellationBuilt-in Cancellation or Cooperative Cancellation
TimeoutNoYesYesYesYesYesYes
Customized Execution ContextNoNoNoNoNoYesYes
Race ConditionsNo due to NodeJS architectureNo due to Actor modelNo due to Actor modelPossible due to shared stateNo due to strong ownershipPossible due to shared statePossible due to shared state
Value TypesNoYesYesYesYesYesYes
Concurrency paradigmsEvent loopActor modelActor modelGo-routine, CSP channelsOS-Thread, coroutineOS-Thread, coroutine, CSP channelsOS-Thread,
GCD queues, coroutine, Actor model
Type CheckingStaticDynamicDynamicStatic but lacks genericsStrongly static types with genericsStrongly static types with genericsStrongly static types with generics
Suspends Async code using Continuations NoNoNoNoYesYesYes
Zero-cost based abstraction ( async)NoNoNoNoYesNoNo
Memory ManagementGCGC
(process-scoped)
GC (process-scoped)GC(Automated) Reference counting, BoxingGCAutomated reference counting

Performance Comparison

Following table summarizes runtime of various implementation of web crawler when crawling 19K URLs that resulted in about 76K messages to asynchronous methods/coroutines/actors discussed in this blog series:

LanguageDesignRuntime (secs)
TypescriptAsync/Await0.638
ErlangSpawning Process4.636
ErlangPMAP4.698
ElixirSpawning OTP Children43.5
ElixirTask async/await187
ElixirWorker-pool with queue97
GOGo-routine/channels1.2
RustAsync/Await4.3
KotlinAsync/Await0.736
KotlinCoroutine0.712
Note: The purpose of above results was not to run micro-benchmarks but to show rough cost of spawning thousands of asynchronous tasks.

Summary

Overall, Typescript/NodeJS provides a simpler model for concurrency but lacks proper timeout/cancellation support and it’s not suitable for highly concurrent applications that require blocking APIs. The actor based concurrency model in Erlang/Elixir supports high-level of concurrency, error handling, cancellation and isolates process state to prevent race conditions but it lacks composing asynchronous behavior natively. Though, you can compose Erlang processes with parent-child hierarchy and easily start or stop these processes. GO supports concurrency via go-routines and channels with built-in cancellation and timeout APIs but GO statements are considered harmful by structured concurrency and it doesn’t protect against race conditions due to mutable state. Erlang and GO are the only languages that were designed from ground-up with support for actors and coroutines and their schedulers have strong support for asynchronous IO and non-blocking APIs. Erlang also offers process-scoped garbage collection to clean up related data easily as opposed to global GC in other languages. The async/await support in Rust is still immature and lacks proper support of cancellation but strong ownership properties of Rust eliminate race conditions and allows safe concurrency. Rust, Kotlin and Swift uses continuation for async/await that allows composition for multiple async/await chained together. For example, instead of using await (await download()).parse() in Javascript/Typescript/C#, you can use await download().parse(). The async/await changes are still new in Kotlin and lack stability whereas Swift has not yet released these changes as part of official release. As Kotlin, Rust, and Swift built coroutines or async/await on top of existing runtime and virtual machine, their green-thread schedulers are not as optimal as schedulers in Erlang or GO and may exhibit limitations on concurrency and scalability.

Finally, structured concurrency helps your code structure with improved data/control flow, concurrency scope, error handling, cancellation/timeout and composition but it won’t solve data races if multiple threads/coroutines access mutable shared data concurrently so you will need to rely on synchronization mechanisms to protect the critical section.

Note: You can download all examples in this series from https://github.com/bhatti/concurency-katas.

November 10, 2020

Structured Concurrency in modern programming languages – Part-III

Filed under: Computing,Languages,Technology — admin @ 4:23 pm

In this third part of the series on structured concurrency (Part-I, Part-II, Part-IV), I will review GO and Rust languages for writing concurrent applications and their support for structured concurrency:

GO

GO language was created by Rob Pike, and Ken Thompson and uses light-weight go-routines for asynchronous processing. Go uses channels for communication that are designed after Tony Hoare’s rendezvous style communicating sequential processes (CSP) where the sender cannot send the message until receiver is ready to accept it. Though, GO supports buffering for channels so that sender/receiver don’t have to wait if buffer is available but channels are not designed to be used as mailbox or message queue. The channels can be shared by multiple go-routines and the messages can be transmitted by value or by reference. GO doesn’t protect against race conditions and shared state must be protected when it’s accessed in multiple go-routines. Also, if a go-routine receives a message by reference, it must be treated as transfer of ownership otherwise it can lead to race conditions. Also, unlike Erlang, you cannot monitor lifetime of other go-routines so you won’t be notified if a go-routine exits unexpectedly.

Following is high-level architecture of scheduling and go-routines in GO process:

Using go-routines/channels in GO

GO doesn’t support async/await syntax but it can be simulated via go-routine and channels. As the cost of each go-routine is very small, you can use them for each background task.

Following code shows how to use go-routines and channels to build the toy web crawler:

package async

import (
	"context"
	"errors"
	"fmt"
	"time"

	"github.com/google/uuid"
)

type AsyncHandler func(ctx context.Context, payload interface{}) (interface{}, error)

type AsyncAwaiter interface {
	Await(ctx context.Context, timeout time.Duration) (interface{}, error)
}

// Future encapsulates request to process asynchronously
type Future struct {
	id      string
	payload interface{}
	outQ    chan Result
}

// Result encapsulates results
type Result struct {
	id      string
	payload interface{}
	err     error
}

// AsyncTask - processes data asynchronously
type AsyncTask struct {
	handler AsyncHandler
}

func New(handler AsyncHandler) *AsyncTask {
	async := &AsyncTask{handler: handler}
	return async
}

func (a *AsyncTask) Async(ctx context.Context, payload interface{}) AsyncAwaiter {
	future := &Future{id: uuid.New().String(), payload: payload, outQ: make(chan Result, 1)}
	go future.run(ctx, a.handler) // run handler asynchronously
	return future
}

func (f Future) run(ctx context.Context, handler AsyncHandler) {
	go func() {
		payload, err := handler(ctx, f.payload)
		f.outQ <- Result{id: f.id, payload: payload, err: err} // out channel is buffered by 1
		close(f.outQ)
	}()
}

func (f Future) Await(ctx context.Context, timeout time.Duration) (payload interface{}, err error) {
	payload = nil
	select {
	case <-ctx.Done():
		err = errors.New("async_cancelled")
	case res := <-f.outQ:
		payload = res.payload
		err = res.err
	case <-time.After(timeout):
		err = errors.New(fmt.Sprintf("async_timedout %v", timeout))
	}

	return
}

In above example, Async method takes a function to invoke in background and creates a channel for reply. It then executes the function and sends back reply to the channel. The client uses the future object return by Async method to wait for the response. The Await method provides timeout to specify the max wait time for response. Note: The Await method listens to ctx.Done() in addition to the response channel that notifies it if client canceled the task or if it timed out by high-level settings.

Following code shows how crawler can use these primitives to define background tasks for crawler:

package crawler

import (
	"context"
	"errors"
	"sync/atomic"
	"time"

	"plexobject.com/crawler/async"
	"plexobject.com/crawler/domain"
	"plexobject.com/crawler/utils"
)

const MAX_DEPTH = 4
const MAX_URLS = 11

// Crawler is used for crawing URLs
type Crawler struct {
	crawlTask     *async.AsyncTask
	downloader    *async.AsyncTask
	jsrenderer    *async.AsyncTask
	indexer       *async.AsyncTask
	totalMessages uint64
}

// Instantiates new crawler
func New(ctx context.Context) *Crawler {
	crawler := &Crawler{totalMessages: 0}
	crawlHandler := func(ctx context.Context, payload interface{}) (interface{}, error) {
		req := payload.(*domain.Request)
		return crawler.handleCrawl(ctx, req)
	}
	downloader := func(ctx context.Context, payload interface{}) (interface{}, error) {
		// TODO check robots.txt and throttle policies
		// TODO add timeout for slow websites and linearize requests to the same domain to prevent denial of service attack
		return utils.RandomString(100), nil
	}
	renderer := func(ctx context.Context, payload interface{}) (interface{}, error) {
		// for SPA apps that use javascript for rendering contents
		return utils.RandomString(100), nil
	}
	indexer := func(ctx context.Context, payload interface{}) (interface{}, error) {
		return 0, nil
	}
	crawler.crawlTask = async.New(crawlHandler)
	crawler.downloader = async.New(downloader)
	crawler.jsrenderer = async.New(renderer)
	crawler.indexer = async.New(indexer)
	return crawler
}

// Crawls list of URLs with specified depth
func (c *Crawler) Crawl(ctx context.Context, urls []string, timeout time.Duration) (int, error) {
	// Boundary for concurrency and it will not return until all
	// child URLs are crawled up to MAX_DEPTH limit.
	return c.crawl(ctx, urls, 0, timeout)
}

func (c *Crawler) TotalMessages() uint64 {
	return c.totalMessages
}

// handles crawl
func (c *Crawler) handleCrawl(ctx context.Context, req *domain.Request) (*domain.Result, error) {
	atomic.AddUint64(&c.totalMessages, 1)
	timeout := time.Duration(req.Timeout * time.Second)
	res := domain.NewResult(req)
	if contents, err := c.downloader.Async(ctx, req.URL).Await(ctx, timeout); err != nil {
		res.Failed(err)
	} else {
		if newContents, err := c.jsrenderer.Async(ctx, [...]string{req.URL, contents.(string)}).Await(ctx, timeout); err != nil {
			res.Failed(err)
		} else {
			if hasContentsChanged(ctx, req.URL, newContents.(string)) && !isSpam(ctx, req.URL, newContents.(string)) {
				c.indexer.Async(ctx, [...]string{req.URL, newContents.(string)}).Await(ctx, timeout)
				urls := parseURLs(ctx, req.URL, newContents.(string))
				if childURLs, err := c.crawl(ctx, urls, req.Depth+1, req.Timeout); err != nil {
					res.Failed(err)
				} else {
					res.Succeeded(childURLs + 1)
				}
			} else {
				res.Failed(errors.New("contents didn't change"))
			}
		}
	}

	return res, nil
}

/////////////////// Internal private methods ///////////////////////////
// Crawls list of URLs with specified depth
func (c *Crawler) crawl(ctx context.Context, urls []string, depth int, timeout time.Duration) (int, error) {
	if depth < MAX_DEPTH {
		futures := make([]async.AsyncAwaiter, 0)
		for i := 0; i < len(urls); i++ {
			futures = append(futures, c.crawlTask.Async(ctx, domain.NewRequest(urls[i], depth, timeout)))
		}
		sum := 0
		var savedError error
		for i := 0; i < len(futures); i++ {
			res, err := futures[i].Await(ctx, timeout)
			if err != nil {
				savedError = err // returning only a single error
			}
			if res != nil {
				sum += res.(*domain.Result).ChildURLs
			}
		}

		return sum, savedError
	} else {
		return 0, nil
	}
}

func parseURLs(ctx context.Context, url string, contents string) []string {
	// tokenize contents and extract href/image/script urls
	urls := make([]string, 0)
	for i := 0; i < MAX_URLS; i++ {
		urls = append(urls, utils.RandomChildUrl(url))
	}
	return urls
}

func hasContentsChanged(ctx context.Context, url string, contents string) bool {
	return true
}

func isSpam(ctx context.Context, url string, contents string) bool {
	return false
}

In above implementation, crawler defines background tasks for crawling, downloading, rendering and indexing. The Crawl defines concurrency boundary and waits until all child tasks are completed. Go provides first class support for cancellation and timeout via context.Context, but you have to listen special ctx.Done() channel.

Following unit tests show examples of cancellation, timeout and normal processing:

package crawler

import (
	"context"
	"log"
	"testing"
	"time"
)

const EXPECTED_URLS = 19032

func TestCrawl(t *testing.T) {
	rootUrls := []string{"https://a.com", "https://b.com", "https://c.com", "https://d.com", "https://e.com", "https://f.com", "https://g.com", "https://h.com", "https://i.com", "https://j.com", "https://k.com", "https://l.com", "https://n.com"}
	started := time.Now()
	timeout := time.Duration(8 * time.Second)
	ctx, cancel := context.WithTimeout(context.Background(), timeout)
	defer cancel()
	crawler := New(ctx)
	received, err := crawler.Crawl(ctx, rootUrls, timeout)
	elapsed := time.Since(started)
	log.Printf("Crawl took %s to process %v messages -- %v", elapsed, received, crawler.TotalMessages())
	if crawler.totalMessages != EXPECTED_URLS {
		t.Errorf("Expected %v urls but was %v", EXPECTED_URLS, crawler.totalMessages)
	}
	if err != nil {
		t.Errorf("Unexpected error %v", err)
	} else if EXPECTED_URLS != received {
		t.Errorf("Expected %v urls but was %v", EXPECTED_URLS, received)
	}
}

func TestCrawlWithTimeout(t *testing.T) {
	started := time.Now()
	timeout := time.Duration(4 * time.Millisecond)
	ctx, cancel := context.WithTimeout(context.Background(), timeout)
	defer cancel()
	crawler := New(ctx)
	received, err := crawler.Crawl(ctx, []string{"a.com", "b.com", "c.com", "d.com", "e.com", "f.com", "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com"}, timeout)
	if err == nil {
		t.Errorf("Expecting timeout error")
	}
	elapsed := time.Since(started)
	log.Printf("Timedout took %s to process %v messages -- %v - %v", elapsed, received, crawler.TotalMessages(), err)
}

func TestCrawlWithCancel(t *testing.T) {
	started := time.Now()
	timeout := time.Duration(3 * time.Second)
	ctx, cancel := context.WithTimeout(context.Background(), timeout)
	crawler := New(ctx)
	var err error
	var received int
	go func() {
		// calling asynchronously
		received, err = crawler.Crawl(ctx, []string{"a.com", "b.com", "c.com", "d.com", "e.com", "f.com", "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com"}, timeout)
	}()
	time.Sleep(5 * time.Millisecond)
	cancel()
	time.Sleep(50 * time.Millisecond)
	if err == nil {
		t.Errorf("Expecting cancel error")
	}
	elapsed := time.Since(started)
	log.Printf("Cancel took %s to process %v messages -- %v - %v", elapsed, received, crawler.TotalMessages(), err)
}

You can download the full source code from https://github.com/bhatti/concurency-katas/tree/main/go_pool.

Following are major benefits of using this approach to implement crawler and its support of structured concurrency:

  • The main Crawl method defines high level scope of concurrency and it waits for the completion of child tasks.
  • Go supports cancellation and timeout APIs and the Crawl method passes timeout parameter so that the crawling all URLs must complete with the time period.
  • The Crawl method captures error from async response and returns so that client code can perform error handling.

Following are shortcomings using this approach for structured concurrency and general design:

  • You can’t monitor life-time of go-routines and you won’t get any errors if background task dies unexpectedly.
  • The cancellation API returns without cancelling underlying operation so you will need to implement a cooperative cancellation to persist any state or clean up underlying resources.
  • Go doesn’t support specifying execution context for go-routines and all asynchronous code is automatically scheduled by GO (G0 go-routines).
  • GO go-routines are not easily composable because they don’t have any parent/child relationship as opposed to async methods that can invoke other async methods in Typescript, Rust or other languages supporting async/await.
  • As Go doesn’t enforce immutability so you will need mutex to protect shared state. Also, mutex implementation in GO is not re-entrant aware so you can’t use for any recursive methods where you are acquiring locks.
  • Above code creates a new go-routine for crawling each URL and though the overhead of each process is small but it may use other expensive resources such as network resource.

Using worker-pool in GO

As opposed to creating new go-routine, we can use worker-pool of go-routines to perform background tasks so that we can manage external resource dependencies easily.

Following code shows an implementation of worker-pool in GO:

package pool

import (
	"context"
	"errors"
	"fmt"
	"time"

	"github.com/google/uuid"
)

const BUFFER_CAPACITY = 2 // allow buffering to support asynchronous behavior  as by default sender will be blocked

type Handler func(ctx context.Context, payload interface{}) (interface{}, error)

type Awaiter interface {
	Await(ctx context.Context, timeout time.Duration) (interface{}, error)
}

// Request encapsulates request to process
type Request struct {
	id      string
	payload interface{}
	outQ    chan Result
}

// Result encapsulates results
type Result struct {
	id      string
	payload interface{}
	err     error
}

// Worker structure defines inbound channel to receive request and lambda function to execute
type Worker struct {
	id                   int
	handler              Handler
	workerRequestChannel chan *Request
}

// NewWorker creates new worker
func NewWorker(id int, handler Handler) Worker {
	return Worker{
		id:                   id,
		handler:              handler,
		workerRequestChannel: make(chan *Request),
	}
}

func (w Worker) start(ctx context.Context, workersReadyPool chan chan *Request, done chan bool) {
	go func(w Worker) {
		for {
			// register the current worker into the worker queue.
			workersReadyPool <- w.workerRequestChannel

			select {
			case <-ctx.Done():
				break
			case req := <-w.workerRequestChannel:
				payload, err := w.handler(ctx, req.payload)
				req.outQ <- Result{id: req.id, payload: payload, err: err} // out channel is buffered by 1
				close(req.outQ)
			case <-done:
				return
			}
		}
	}(w)
}

// WorkPool - pool of workers
type WorkPool struct {
	size                int
	workersReadyPool    chan chan *Request
	pendingRequestQueue chan *Request
	done                chan bool
	handler             Handler
}

// New Creates new async structure
func New(handler Handler, size int) *WorkPool {
	async := &WorkPool{
		size:                size,
		workersReadyPool:    make(chan chan *Request, BUFFER_CAPACITY),
		pendingRequestQueue: make(chan *Request, BUFFER_CAPACITY),
		done:                make(chan bool),
		handler:             handler}
	return async
}

// Start - starts up workers and internal goroutine to receive requests
func (p *WorkPool) Start(ctx context.Context) {
	for w := 1; w <= p.size; w++ {
		worker := NewWorker(w, p.handler)
		worker.start(ctx, p.workersReadyPool, p.done)
	}
	go p.dispatch(ctx)
}

// Add request to process
func (p *WorkPool) Add(ctx context.Context, payload interface{}) Awaiter {
	// Adding request to process
	req := &Request{id: uuid.New().String(), payload: payload, outQ: make(chan Result, 1)}
	go func() {
		p.pendingRequestQueue <- req
	}()
	return req
}

// Await for reply -- you can only call this once
func (r Request) Await(ctx context.Context, timeout time.Duration) (payload interface{}, err error) {
	select {
	case <-ctx.Done():
		err = errors.New("async_cancelled")
	case res := <-r.outQ:
		payload = res.payload
		err = res.err
	case <-time.After(timeout):
		payload = nil
		err = fmt.Errorf("async_timedout %v", timeout)
	}

	return
}

// Stop - stops thread pool
func (p *WorkPool) Stop() {
	close(p.pendingRequestQueue)
	go func() {
		p.done <- true
	}()
}

// Receiving requests from inbound channel and forward it to the worker's workerRequestChannel
func (p *WorkPool) dispatch(ctx context.Context) {
	for {
		select {
		case <-ctx.Done():
			return
		case <-p.done:
			return
		case req := <-p.pendingRequestQueue:
			go func(req *Request) {
				// Find next ready worker
				workerRequestChannel := <-p.workersReadyPool
				// dispatch the request to next ready worker
				workerRequestChannel <- req
			}(req)
		}
	}
}

You can download above examples from https://github.com/bhatti/concurency-katas/tree/main/go_pool.

Rust

Rust was designed by Mozilla Research to provide better performance, type safety, strong memory ownership and safe concurrency. With its strong ownership and lifetime scope, Rust minimizes race conditions because each object can only have one owner that can update the value. Further, strong typing, traits/structured-types, abstinence of null references, immutability by default eliminates most of common bugs in the code.

Rust uses OS-threads for multi-threading but has added support for coroutines and async/await recently. Rust uses futures for asynchronous behavior but unlike other languages, it doesn’t provide runtime environment for async/await. Two popular runtime systems available for Rust are https://tokio.rs/ and https://github.com/async-rs/async-std. Also, unlike other languages, async/await in Rust uses zero-cost abstraction where async just creates a future without scheduling until await is invoked. The runtime systems such as async-std and tokio provides executor that polls future until it returns a value.

Following example shows how async/await can be used to implement

extern crate rand;
use std::{error::Error, fmt};
use std::time::{Duration, SystemTime, UNIX_EPOCH};
use rand::Rng;
use rand::distributions::Alphanumeric;
use rand::seq::SliceRandom;
use futures::future::{Future, join_all, BoxFuture};
use futures::stream::{FuturesUnordered};
use futures::executor;
use async_std::{task, future};
use async_std::future::timeout;

const MAX_DEPTH: u8 = 4;
const MAX_URLS: u8 = 11;

// Request encapsulates details of url to crawl
#[derive(Debug, Clone, PartialEq)]
pub struct Request {
    pub url: String,
    pub depth: u8,
    pub timeout: Duration,
    pub created_at: u128,
}

impl Request {
    pub fn new(url: String, depth: u8, timeout: Duration) -> Request {
        let epoch = SystemTime::now().duration_since(UNIX_EPOCH).expect("epoch failed").as_millis();
        Request{url: url.to_string(), depth: depth, timeout: timeout, created_at: epoch}
    }
}

#[derive(Debug, Copy, Clone)]
pub enum CrawlError {
    Unknown,
    MaxDepthReached,
    DownloadError,
    ParseError,
    IndexError,
    ContentsNotChanged,
    Timedout,
}

impl Error for CrawlError {}

impl fmt::Display for CrawlError {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        match *self {
            CrawlError::MaxDepthReached => write!(f, "MaxDepthReached"),
            CrawlError::DownloadError => write!(f, "DownloadError"),
            CrawlError::ParseError => write!(f, "ParseError"),
            CrawlError::IndexError => write!(f, "IndexError"),
            CrawlError::ContentsNotChanged => write!(f, "ContentsNotChanged"),
            CrawlError::Timedout=> write!(f, "Timedout"),
            CrawlError::Unknown => write!(f, "Unknown"),
        }
    }
}


//////// PUBLIC METHODS
// crawling a collection of urls
pub fn crawl(urls: Vec<String>, timeout_dur: Duration) -> Result<usize, CrawlError> {
    // Boundary for concurrency and it will not return until all
    // child URLs are crawled up to MAX_DEPTH limit.
    //
    match task::block_on(
        timeout(timeout_dur, async {
            do_crawl(urls, timeout_dur, 0)
        })
    ) {
        Ok(res) => res,
        Err(_err) => Err(CrawlError::Timedout),
    }
}

//////// PRIVATE METHODS
fn do_crawl(urls: Vec<String>, timeout_dur: Duration, depth: u8) -> Result<usize, CrawlError> {
    if depth >= MAX_DEPTH {
        return Ok(0)
    }

    let mut futures = Vec::new();
    let mut size = 0;

    for u in urls {
        size += 1;
        futures.push(async move {
            let child_urls = match handle_crawl(Request::new(u, depth, timeout_dur)) {
                Ok(urls) => urls,
                Err(_err) => [].to_vec(),
            };
            if child_urls.len() > 0 {
                do_crawl(child_urls, timeout_dur, depth+1)
            } else {
                Ok(0)
            }
        });
    }
    task::block_on(
        async {
            let res: Vec<Result<usize, CrawlError>> = join_all(futures).await;
            let sizes: Vec<usize> = res.iter().map(|r| r.map_or(0, |n|n)).collect::<Vec<usize>>();
            size += sizes.iter().fold(0usize, |sum, n| n+sum);
        }
    );
    Ok(size)
}

// method to crawl a single url
fn handle_crawl(req: Request) -> Result<Vec<String>, CrawlError> {
    let res: Result<Vec<String>, CrawlError> = task::block_on(
        async {
            let contents = match download(&req.url).await {
                Ok(data) => data,
                Err(_err) => return Err(CrawlError::DownloadError),
            };

            if has_contents_changed(&req.url, &contents) && !is_spam(&req.url, &contents) {
                let urls = match index(&req.url, &contents).await {
                    Ok(_) =>
                        match parse_urls(&req.url, &contents) {
                            Ok(urls) => urls,
                            Err(_err) => return Err(CrawlError::ParseError),
                        },
                    Err(_err) => return Err(CrawlError::IndexError),
                };
                return Ok(urls)
            } else {
                return Err(CrawlError::ContentsNotChanged)
            }
        }
    );
    match res {
        Ok(list) => return Ok(list),
        Err(err) => return Err(err),
    }
}

async fn download(url: &str) -> Result<String, CrawlError> {
    // TODO check robots.txt and throttle policies
    // TODO add timeout for slow websites and linearize requests to the same domain to prevent denial of service attack
    // invoke jsrender to generate dynamic content
    jsrender(url, &random_string(100)).await
}

async fn jsrender(_url: &str, contents: &str) -> Result<String, CrawlError> {
    // for SPA apps that use javascript for rendering contents
    Ok(contents.to_string())
}

async fn index(_url: &str, _contents: &str) -> Result<bool, CrawlError> {
    // apply standardize, stem, ngram, etc for indexing
    Ok(true)
}

fn parse_urls(_url: &str, _contents: &str) -> Result<Vec<String>, CrawlError> {
    // tokenize contents and extract href/image/script urls
    Ok((0..MAX_URLS).into_iter().map(|i| random_url(i)).collect())
}

fn has_contents_changed(_url: &str, _contents: &str) -> bool {
    true
}

fn is_spam(_url: &str, _contents: &str) -> bool {
    false
}

fn random_string(max: usize) -> String {
    rand::thread_rng().sample_iter(&Alphanumeric).take(max).collect::<String>()
}

fn random_url(i: u8) -> String {
    let domains = vec!["ab.com", "bc.com", "cd.com", "de.com", "ef.com", "fg.com", "gh.com", "hi.com", "ij.com", "jk.com", "kl.com", "lm.com", "mn.com",
		"no.com", "op.com", "pq.com", "qr.com", "rs.com", "st.com", "tu.com", "uv.com", "vw.com", "wx.com", "xy.com", "yz.com"];
    let domain = domains.choose(&mut rand::thread_rng()).unwrap();
    format!("https://{}/{}_{}", domain, random_string(20), i)
}

The crawl method defines scope of concurrency and asynchronously crawls each URL recursively but the parent URL waits until child URLs are crawled. The async-std provides support for timeout so that asynchronous task can fail early if it’s not completed within the bounded time-frame. However, it doesn’t provide cancellation support so you have to rely on cooperative cancellation.

Following unit-test and main routine shows example of crawling a list of URLs:

use std::time::Duration;
use futures::prelude::*;
use std::time::{Instant};
use crate::crawler::crawler::*;

mod crawler;

fn main() {
    let _ = do_crawl(8000);
}

fn do_crawl(timeout: u64) -> Result<usize, CrawlError> {
    let start = Instant::now();
    let urls = vec!["a.com", "b.com", "c.com", "d.com", "e.com", "f.com", "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com"].into_iter().map(|s| s.to_string()).collect();
    let res = crawl(urls, Duration::from_millis(timeout));
    let duration = start.elapsed();
    println!("Crawled {:?} urls in () is: {:?}", res, duration);
    res
}

#[cfg(test)]
mod tests {
    use super::do_crawl;
    #[test]
    fn crawl_urls() {
        match do_crawl(8000) {
            Ok(size) => assert_eq!(size, 19032),
            Err(err) => assert!(false, format!("Unexpected error {:?}", err)),
        }
    }
}

You can download the full source code from https://github.com/bhatti/concurency-katas/tree/main/rust_async.

Following are major benefits of using this approach to implement crawler and its support of structured concurrency:

  • The main crawl method defines high level scope of concurrency and it waits for the completion of child tasks.
  • The async-std runtime environment supports timeout APIs and the crawl method takes the timeout parameter so that the crawling all URLs must complete with the time period.
  • The crawl method captures error from async response and returns the error so that client code can perform error handling.
  • The async declared methods in above implementation shows asynchronous code can be easily composed.

Following are shortcomings using this approach for structured concurrency and general design:

  • Rust async/await APIs doesn’t support native support for cancellation so you will need to implement a cooperative cancellation to persist any state or clean up underlying resources.
  • Rust async/await APIs doesn’t allow you to specify execution context for asynchronous code.
  • The async/await support in Rust is relatively new and has not matured yet. Also, it requires separate runtime environment and there are a few differences in these implementations.
  • Above design for crawler is not very practical because it creates a asynchronous task for each URL that is crawled and it may strain network or IO resources.

Overall, GO provides decent support for low-level concurrency but its complexity can create subtle bugs and incorrect use of go-routines can result in deadlocks. Also, it’s prone to data races due to mutable shared state. Just like structured programming considered GOTO statements harmful and recommended if-then, loops, and function calls for control flow, structured concurrency considers GO statements harmful and recommends parent waits for children completion and supports propagating errors from children to parent. Rust offers async/await syntax for concurrency scope and supports composition and error propagation with strong ownership that reduces chance of data races. Also, Rust uses continuations by suspending async block and async keyword just creates a future and does not start execution so it results in better performance when async code is chained together. However, async/await is still in its inception phase in Rust and lacks proper support for cancellation and customized execution context.

November 4, 2020

Structured Concurrency in modern programming languages – Part-II

Filed under: Computing,Erlang,Languages — admin @ 8:46 pm

In this second part of the series on structured concurrency (Part-I, Part-III, Part-IV), I will review Elixir and Erlang languages for writing concurrent applications and their support for structured concurrency:

Erlang

The Erlang language was created by late Joe Armstrong when he worked at Ericsson and it is designed for massive concurrency by means of very light weight processes that are based on actors. Each process has its own mailbox for storing incoming messages of various kinds. The receive block in Erlang is triggered upon new message arrival and the message is removed and executed when it matches specific pattern match. The Erlang language uses supervisors for monitoring processes and immutable functional paradigm for writing robust concurrent systems. Following is high-level architecture of Erlang system:

As the cost of each process or actor is only few hundred bytes, you can create millions of these processes for writing highly scalable concurrent systems. Erlang is a functional language where all data is immutable by default and the state within each actor is held private so there is no shared state or race conditions.

An actor keeps a mailbox for incoming messages and processes one message at a time using the receive API. Erlang doesn’t provide native async/await primitives but you can simulate async by sending an asynchronous message to an actor, which can then reply back to the sender using its process-id. The requester process can then block using receive API until reply is received. Erlang process model has better support for timeouts with receive API to exit early if it doesn’t receive response within a time period. Erlang system uses the mantra of let it crash for building fault tolerant applications and you can terminate a process and all children processes connected.

Using actor model in Erlang

Following code shows how native send and receive primitives can be used to build the toy web crawler:

-module(erlcrawler).

-export([start_link/0, crawl_urls/3, total_crawl_urls/1]).

-record(request, {clientPid, ref, url, depth, timeout, created_at=erlang:system_time(millisecond)}).
-record(result, {url, status=pending, child_urls=0, started_at=erlang:system_time(millisecond), completed_at, error}).

-define(MAX_DEPTH, 4).
-define(MAX_URL, 11).
-define(DOMAINS, [
  "ab.com",
  "bc.com",
  "cd.com",
  "de.com",
  "ef.com",
  "fg.com",
  "yz.com"]).

make_request(ClientPid, Ref, Url, Depth, Timeout) ->
    #request{clientPid=ClientPid, ref=Ref, url=Url, depth=Depth, timeout=Timeout}.

make_result(Req) ->
    Url = Req#request.url,
    #result{url=Url}.

%%% Client API
start_link() ->
    spawn_link(fun init/0).

%%%%%%%%%%%% public method for crawling %%%%%%%%%%%%
%%% calling private method for crawling
%%% Pid - process-id of actor
%%% 0 - current depth
%%% Urls - list of urls to crawl
%%% Timeout - max timeout
crawl_urls(Pid, Urls, Timeout) when is_pid(Pid), is_list(Urls)  ->
    %% Boundary for concurrency and it will not return until all
    %% child URLs are crawled up to MAX_DEPTH limit.
    do_crawl_urls(Pid, 0, Urls, [], Timeout, 0).

total_crawl_urls(Pid) when is_pid(Pid) ->
    Self = self(),
    Pid ! {total, Self},
    receive {total_reply, Self, N} ->
        N
    end.

%%% Server functions
init() ->
    {ok, DownloaderPid} = downloader:start_link(),
    {ok, IndexerPid} = indexer:start_link(),
    loop(DownloaderPid, IndexerPid, 0).

%%% Main server loop
loop(DownloaderPid, IndexerPid, N) ->
    receive
        {crawl, Req} ->
            CrawlerPid = self(),
            spawn_link(fun() -> handle_crawl(CrawlerPid, Req, DownloaderPid, IndexerPid) end),
            debug_print(N),
            loop(DownloaderPid, IndexerPid, N+1);
        {total, Pid} ->
            Pid ! {total_reply, Pid, N},
            loop(DownloaderPid, IndexerPid, N);
        terminate ->
            ok
    end.


%%% Internal client functions
debug_print(N) when N rem 10000 == 0 ->
    io:format("~p...~n", [{N}]);
debug_print(_) ->
    ok.

%% Go through URLs to crawl, send asynchronous request to crawl and
%% then add request to a list to monitor that will be used to receive
%% reply back from the crawling actor.
do_crawl_urls(_, _, [], [], _, ChildURLs) ->
    ChildURLs; % all done
do_crawl_urls(_, ?MAX_DEPTH, _, _, _, _) ->
    0; % reached max depth, stop more crawling
do_crawl_urls(Pid, Depth, [Url|T], SubmittedRequests, Timeout, 0) when is_pid(Pid), is_integer(Depth), is_integer(Timeout) ->
    %%% monitoring actor so that we are notified when actor process dies
    Ref = erlang:monitor(process, Pid),
    %%% crawling next url to process
    Req = make_request(self(), Ref, Url, Depth, Timeout),
    Pid ! {crawl, Req},
    do_crawl_urls(Pid, Depth, T, SubmittedRequests ++ [Req], Timeout, 0);
do_crawl_urls(Pid, Depth, [], [Req|T], Timeout, ChildURLs) when is_pid(Pid) ->
    %%% receiving response from the requests that were previously stored
    Ref = Req#request.ref,
    receive
        {crawl_done, Ref, Res} ->
            erlang:demonitor(Ref, [flush]),
            do_crawl_urls(Pid, Depth, [], T, Timeout, Res#result.child_urls+ChildURLs+1);
        {'DOWN', Ref, process, Pid, Reason} ->
            erlang:error(Reason)
    after Timeout ->
        erlang:error({crawl_timeout, Timeout})
    end.


%%% Internal server functions called by actor to process the crawling request
handle_crawl(CrawlerPid, Req, DownloaderPid, IndexerPid) ->
    Res = make_result(Req),
    ClientPid = Req#request.clientPid,
    Url = Req#request.url,
    Ref = Req#request.ref,
    Depth = Req#request.depth,
    Timeout = Req#request.timeout,

    case downloader:download(DownloaderPid, Url) of
        {ok, Contents} ->
        {ok, Contents1} = downloader:jsrender(DownloaderPid, Url, Contents),
        Changed = has_content_changed(Url, Contents1),
        Spam = is_spam(Url, Contents1),
        if Changed and not Spam ->
            indexer:index(IndexerPid, Url, Contents1), % asynchronous call
        Urls = parse_urls(Url, Contents1),
                %% Crawling child urls synchronously before returning
                ChildURLs = do_crawl_urls(CrawlerPid, Depth+1, Urls, [], Timeout, 0) + 1,
                Res1 = Res#result{completed_at=erlang:system_time(millisecond), child_urls=ChildURLs},
                ClientPid ! {crawl_done, Ref, Res1};
            true ->
                Res1 = Res#result{completed_at=erlang:system_time(millisecond)},
                ClientPid ! {crawl_done, Ref, Res1}
            end;
        Err ->
            Res1 = Res#result{completed_at=erlang:system_time(millisecond), error = Err},
            ClientPid ! {crawl_done, Ref, Res1}
        end,
    ok.

%%%%%%%%%%%%%%% INTERNAL METHODS FOR CRAWLING %%%%%%%%%%%%%%%%
parse_urls(_Url, _Contents) ->
    % tokenize contents and extract href/image/script urls
    random_urls(?MAX_URL).

random_urls(N) ->
    [random_url() || _ <- lists:seq(1, N)].

has_content_changed(_Url, _Contents) ->
     % calculate hash digest and compare it with last digest
    true.

is_spam(_Url, _Contents) ->
     % apply standardize, stem, ngram, etc for indexing
    false.

random_url() ->
    "https://" ++ random_domain() ++ "/" ++ random_string(20).

random_domain() ->
    lists:nth(random:uniform(length(?DOMAINS)), ?DOMAINS).

random_string(Length) ->
    AllowedChars = "abcdefghijklmnopqrstuvwxyz",
    lists:foldl(fun(_, Acc) -> [lists:nth(random:uniform(length(AllowedChars)), AllowedChars)] ++ Acc end, [], lists:seq(1, Length)).

In above implementation, crawl_urls method takes list of URLs and time out and waits until all URLs are crawled. It uses spawn_link to create a process, which invokes handle_crawl method to process requests concurrently. The handle_crawl method recursively crawl the URL and its children up to MAX_DEPTH limit. This implementation uses separate Erlang OTP processes for downloading, rendering and indexing contents. The handle_crawl sends back the response with number of child URLs that it crawled.

-module(erlcrawler_test).
-include_lib("eunit/include/eunit.hrl").

-define(ROOT_URLS, ["a.com", "b.com", "c.com", "d.com", "e.com", "f.com", "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com"]).

crawl_urls_test() ->
    {spawn, {timeout,30, do_crawl_urls(10000)}}.

%% Testing timeout and by default, it will terminate the test process so we will instead convert
%% kill signal into a message using erlang:exit
crawl_urls_with_timeout_test() ->
    %%% crawling next url to process
    Started = erlang:system_time(millisecond),
    Timeout = 10, % We know that processing takes longer than 10 milliseconds
    Pid = erlcrawler:start_link(),
    process_flag(trap_exit, true),
    spawn_link(fun() ->
        erlcrawler:crawl_urls(Pid, ?ROOT_URLS, Timeout)
    end),
    {{crawl_timeout, _}, _} = receive
        {'EXIT', _, Reason} -> Reason
    after 1000 ->
        erlang:error(unexpected_timeout)
    end,
    Elapsed = erlang:system_time(millisecond) - Started,
    ?debugFmt("crawl_urls_with_timeout_test: timedout as expected in millis ~p ~n", [{Elapsed}]).

%% Testing terminate/cancellation and killing a process will kill all its children
crawl_urls_with_terminate_test() ->
    %%% crawling next url to process
    Started = erlang:system_time(millisecond),
    Pid = erlcrawler:start_link(),
    spawn_link(fun() ->
        erlcrawler:crawl_urls(Pid, ?ROOT_URLS, 1000) % crawl_urls is synchronous method so calling in another process
    end),
    receive
    after 15 -> % waiting for a bit before terminating (canceling) process
        exit(Pid, {test_terminated})
    end,
    {test_terminated} = receive
        {'EXIT', Pid, Reason} -> Reason
    after 200 ->
        erlang:error(unexpected_timeout)
    end,
    Elapsed = erlang:system_time(millisecond) - Started,
    ?debugFmt("crawl_urls_with_terminate_test: terminated as expected in millis ~p ~n", [{Elapsed}]).

do_crawl_urls(Timeout) ->
    Started = erlang:system_time(millisecond),
    Pid = erlcrawler:start_link(),
    N = erlcrawler:crawl_urls(Pid, ?ROOT_URLS, Timeout),
    N1 = erlcrawler:total_crawl_urls(Pid),
    Elapsed = erlang:system_time(millisecond) - Started,
    ?debugFmt("do_crawl_urls: Crawled URLs in millis: ~p ~n", [{N, N1, Elapsed}]),
    ?assertEqual(N1, 19032).

Above tests show three ways to try out the crawl_urls API. First test crawl_urls_test tests happy case of crawling URLs within 10 seconds. The crawl_urls_with_timeout_test tests the timeout behavior to make sure proper error message is returned and all Erlang processes are terminated. The crawl_urls_with_terminate_test tests cancellation behavior by terminating the main crawling process. You can download the full source code from https://github.com/bhatti/concurency-katas/tree/main/erl_actor.

Following are major benefits of using this process model to implement structured concurrency:

  • The main crawl_urls method defines high level scope of concurrency and it waits for the completion of child tasks.
  • crawl_urls method takes a timeout parameter so that the crawling all URLs must complete with the time period.
  • Erlang allows parent-child relationship between processes where you can monitor child processes and get notified when a child process dies. You can use this feature to cancel the asynchronous task. However, it will abruptly end all processes and all state within the process will be lost.
  • Erlang implementation captures the error within the response so the client can handle all error handling using pattern matching or other approach common in Erlang applications.

Following are shortcomings using this approach for structured concurrency:

  • The terminate API is not suitable for clean cancellation so you will need to implement a cooperative cancellation to persist any state or clean up underlying resources.
  • Though, you can combine processes in groups or parent child relationships manually but Erlang doesn’t give you a lot of flexibility to specify the context for execution.
  • Unlike async declared methods in Typescript, Erlang code is not easily composable but you can define client code to wrap send/receive messages so that high level code can be comprehended easily. Also, Erlang processes can be connected with parent-child relationships and you can manage composition via process-supervisor hierarchy.
  • Above code creates a new process for crawling each URL and though the overhead of each process is small but it may use other expensive resources such as network resource. We won’t use such approach for real crawler as it will strain the resources on the website being crawled. Instead, we may need to limit how many concurrent requests can be sent to a given website or maintain delay between successive requests.

Using pmap in Erlang

We can generalize above approach into a general purpose pmap that processes an array (similar to map function in functional languages) concurrently and then waits for their response such as:

-module(pmap).

-export([pmap/3]).

pmap(F, Es, Timeout) ->
   Parent = self(),
   Running = [exec(Parent, F, E) || E <- Es],
   collect(Running, Timeout).

exec(Parent, F, E) ->
    spawn_monitor(fun() -> Parent ! {self(), F(E)} end).

collect([], _Timeout) -> [];
collect([{Pid, MRef} | Next], Timeout) ->
  receive
    {Pid, Res} ->
      erlang:demonitor(MRef, [flush]),
      [{ok, Res} | collect(Next, Timeout)];
    {'DOWN', MRef, process, Pid, Reason} ->
      [{error, Reason} | collect(Next, Timeout)]
  after Timeout ->
    erlang:error({pmap_timeout, Timeout})
  end.

You can download full pmap example from https://github.com/bhatti/concurency-katas/tree/main/erl_pmap.

Elixir

The Elixir language is built upon Erlang BEAM VM and was created by Jose Valim to improve usability of Erlang language and introduce Rubyist syntax instead of Prologist syntax in Erlang language. It also removes some of the boilerplate that you needed in Erlang language and adds higher level abstractions for writing highly concurrent, distributed and fault tolerant applications.

Using a worker-pool and OTP in Elixir

As Elixir uses Erlang VM and runtime system, the application behavior will be similar to Erlang applications but following approach uses a worker pool design where the parent process keeps a list of child-processes and delegates the crawling work to child processes in a round-robin fashion:

defmodule Crawler do
  @max_depth 4

  @moduledoc """
  Documentation for Crawler.
  """

  ## Client API
  # {:ok, pid} = Crawler.start_link(100000)
  def start_link(size) when is_integer(size) do
    GenServer.start_link(__MODULE__, size)
  end

  def total_crawl_urls(pid) when is_pid(pid) do
    GenServer.call(pid, {:total_crawl_urls}, 30000)
  end

  ### Public client APIs
  def crawl_urls(pid, urls) when is_pid(pid) and is_list(urls) do
    ## Boundary for concurrency and it will not return until all
    ## child URLs are crawled up to MAX_DEPTH limit.
    crawl_urls(pid, urls, 0, self())
  end

  ### Internal client APIs
  def crawl_urls(pid, urls, depth, clientPid) when is_pid(pid) and is_list(urls) do
    if depth < @max_depth do
      requests = urls |> Enum.map(&(Request.new(&1, depth, clientPid)))
      requests |> Enum.map(&(GenServer.cast(pid, {:crawl, &1})))
    else
      :max_depth_exceeded
    end
  end

  ## init method create pool of workers based on given size
  def init(size) when is_integer(size) do
    Process.flag(:trap_exit, true)
    pid_to_workers = 0..size |> Enum.map(&child_spec/1)
    |> Enum.map(&start_child/1)
    |> Enum.into(%{})
    pids = Map.keys(pid_to_workers)
    {:ok, {pid_to_workers, pids, 0}}
  end

  ## handles crawling
  def handle_cast({:crawl, request}, {pid_to_workers, [pid|rest], total_in}) do
    GenServer.cast(pid, {:crawl, request}) # send request to workers in round-robin fashion
    {:noreply, {pid_to_workers, rest ++ [pid], total_in+1}}
  end

  def handle_call({:total_crawl_urls}, _from, {_, _, total_in} = state) do
    {:reply, total_in, state}
  end

  ## OTP Callbacks
  def handle_info({:EXIT, dead_pid, _reason}, {pid_to_workers, _, total_in}) do
    # Start new process based on dead_pid spec
    {new_pid, child_spec} = pid_to_workers
    |> Map.get(dead_pid)
    |> start_child()

    # Remove the dead_pid and insert the new_pid with its spec
    new_pid_to_workers = pid_to_workers
    |> Map.delete(dead_pid)
    |> Map.put(new_pid, child_spec)
    pids = Map.keys(new_pid_to_workers)
    {:noreply, {new_pid_to_workers, pids, total_in}}
  end

  ## Defines spec for worker
  defp child_spec(_) do
    {Worker, :start_link, [self()]}
  end

  ## Dynamically create child
  defp start_child({module, function, args} = spec) do
    {:ok, pid} = apply(module, function, args)
    Process.link(pid)
    {pid, spec}
  end

end

The parent process in above example defines crawl_urls method for crawling URLs, which is defined as an asynchronous API (handle_cast) and forwards the request to next worker. Following is implementation of the worker:

defmodule Worker do
  @moduledoc """
  Documentation for crawling worker.
  """
  @max_url 11
  @domains [
    "ab.com",
    "bc.com",
    "cd.com",
    "de.com",
    "yz.com"]
  @allowed_chars "abcdefghijklmnopqrstuvwxyz"

  use GenServer

  # Client APIs
  def start_link(crawler_pid) when is_pid(crawler_pid) do
    {:ok, downloader_pid} = Downloader.start_link()
    {:ok, indexer_pid} = Indexer.start_link()
    GenServer.start_link(__MODULE__, {crawler_pid, downloader_pid, indexer_pid})
  end

  @doc """
  Crawls web url asynchronously
  """
  def handle_cast({:crawl, request}, {crawler_pid, downloader_pid, indexer_pid}=state) do
    handle_crawl(crawler_pid, downloader_pid, indexer_pid, request)
    {:noreply, state}
  end

  def init(crawler_pid) do
      {:ok, crawler_pid}
  end

  # Internal private methods
  defp handle_crawl(crawler_pid, downloader_pid, indexer_pid, req) do
    res = Result.new(req)
    contents = Downloader.download(downloader_pid, req.url)
    new_contents = Downloader.jsrender(downloader_pid, req.url, contents)
    if has_content_changed(req.url, new_contents) and !is_spam(req.url, new_contents) do
      Indexer.index(indexer_pid, req.url, new_contents)
      urls = parse_urls(req.url, new_contents)
      Crawler.crawl_urls(crawler_pid, urls, req.depth+1, req.clientPid)
      send req.clientPid, {:crawl_done, Result.completed(res)}
    else
      send req.clientPid, {:crawl_done, Result.failed(req, :skipped_crawl)}
    end
  end

  defp parse_urls(_Url, _Contents) do
    # tokenize contents and extract href/image/script urls
    random_urls(@max_url)
  end

  defp random_urls(n) do
    1..n |> Enum.map(&(random_url/1))
  end

  defp has_content_changed(_url, _contents) do
    # calculate hash digest and compare it with last digest
    true
  end

  defp is_spam(_url, _contents) do
    # apply standardize, stem, ngram, etc for indexing
    false
  end

  defp random_url(_) do
    "https://" <> random_domain() <> "/" <> random_string(20)
  end

  defp random_domain() do
    Enum.random(@domains)
  end

  defp random_string(n) do
    1..n
    |> Enum.reduce([], fn(_, acc) -> [Enum.random(to_charlist(@allowed_chars)) | acc] end)
    |> Enum.join("")
  end
end

The worker process starts downloader and indexer processes upon start and crawls the URL upon receiving the next request. It then sends back the response to the originator of request using process-id in the request. Following unit tests are used to test the behavior of normal processing, timeouts and cancellation:

defmodule CrawlerTest do
  use ExUnit.Case
  doctest Crawler
  @max_processes 10000
  @max_wait_messages 19032
  @root_urls ["a.com", "b.com", "c.com", "d.com", "e.com", "f.com", "g.com", "h.com", "i.com", "j.com", "k.com", "l.com", "n.com"]

  test "test crawling urls" do
    started = System.system_time(:millisecond)
    {:ok, pid} = Crawler.start_link(@max_processes)
    Crawler.crawl_urls(pid, @root_urls)
    wait_until_total_crawl_urls(pid, @max_wait_messages, started)
  end

  defp wait_until_total_crawl_urls(pid, 0, started) do
    n = Crawler.total_crawl_urls(pid)
    elapsed = System.system_time(:millisecond) - started
    IO.puts("Crawled URLs in millis: #{n} #{elapsed}")
    assert n >= @max_wait_messages
  end

  defp wait_until_total_crawl_urls(pid, max, started) do
    if rem(max, 1000) == 0 do
      IO.puts("#{max}...")
    end
    receive do
      {:crawl_done, _} -> wait_until_total_crawl_urls(pid, max-1, started)
    end
  end

end

Following are major benefits of this approach for its support of structured concurrency:

  • The crawl_urls method in parent process defines high level scope of concurrency and it waits for the completion of child tasks.
  • Above implementation also uses timeout similar to the Erlang example to ensure task is completed within given time period.
  • Above implementation also captures the error within the response similar to Erlang for error handling.
  • This approach addresses some of the shortcomings of previous approach in Erlang implementation where a new process was created for each request. Instead a pool of process is used to manage the capacity of resources.

Following are shortcomings using this approach for structured concurrency:

  • This approach also suffers the same drawbacks as Erlang approach regarding cancellation behavior and you will need to implement a cooperative cancellation to cleanup the resources properly.
  • Similar to Erlang, Elixir also doesn’t give you a lot of flexibility to specify the context for execution and it’s not easily composable.

Using async-await in Elixir

Elixir defines abstracts Erlang process with Task when you only need to execute a single action throughout its lifetime. Here is an example that combines Task async/await with pmap implementation:

defmodule Parallel do
  def pmap(collection, func, timeout) do
    collection
    |> Enum.map(&(Task.async(fn -> func.(&1) end)))
    |> Enum.map(fn t -> Task.await(t, timeout) end)
  end
end
defmodule Crawler do
  @domains [
    "ab.com",
    "bc.com",
    "cd.com",
    "de.com",
    "ef.com",
    "yz.com"]
  @allowed_chars "abcdefghijklmnopqrstuvwxyz"
  @max_depth 4
  @max_url 11

  @moduledoc """
  Documentation for Crawler.
  """

  ## Client API
  def crawl_urls(urls, timeout) when is_list(urls) do
    ## Boundary for concurrency and it will not return until all
    ## child URLs are crawled up to MAX_DEPTH limit.
    ## Starting external services using OTP for downloading and indexing
    {:ok, downloader_pid} = Downloader.start_link()
    {:ok, indexer_pid} = Indexer.start_link()
    res = crawl_urls(urls, downloader_pid, indexer_pid, 0, timeout)
    ## Stopping external services using OTP for downloading and indexing
    Process.exit(downloader_pid, :normal)
    Process.exit(indexer_pid, :normal)
    res
  end

  def crawl_urls(urls, downloader_pid, indexer_pid, depth, timeout) when is_list(urls) and is_pid(downloader_pid) and is_pid(indexer_pid) and is_integer(depth) and is_integer(timeout) do
    if depth < @max_depth do
      requests = urls |> Enum.map(&(Request.new(&1, downloader_pid, indexer_pid, depth, timeout)))
      Parallel.pmap(requests, &(handle_crawl/1), timeout)
    else
      []
    end
  end

  # Internal private methods
  defp handle_crawl(req) do
    {:ok, contents} = Downloader.download(req.downloader_pid, req.url, req.timeout)
    {:ok, new_contents} = Downloader.jsrender(req.downloader_pid, req.url, contents, req.timeout)
    if has_content_changed(req.url, new_contents) and !is_spam(req.url, new_contents) do
      Indexer.index(req.indexer_pid, req.url, new_contents, req.timeout)
      urls = parse_urls(req.url, new_contents)
      res = Crawler.crawl_urls(urls, req.downloader_pid, req.indexer_pid, req.depth+1, req.timeout)
      Enum.reduce(res, 0, &(&1 + &2)) + 1
    else
      0
    end
  end

  defp parse_urls(_Url, _Contents) do
    # tokenize contents and extract href/image/script urls
    random_urls(@max_url)
  end

  defp random_urls(n) do
    1..n |> Enum.map(&(random_url/1))
  end

  defp has_content_changed(_url, _contents) do
    # calculate hash digest and compare it with last digest
    true
  end

  defp is_spam(_url, _contents) do
    # apply standardize, stem, ngram, etc for indexing
    false
  end

  defp random_url(_) do
    "https://" <> random_domain() <> "/" <> random_string(20)
  end

  defp random_domain() do
    Enum.random(@domains)
  end

  defp random_string(n) do
    1..n
    |> Enum.reduce([], fn(_, acc) -> [Enum.random(to_charlist(@allowed_chars)) | acc] end)
    |> Enum.join("")
  end
end

Above example is a bit shorter due to the high level Task abstraction but its design has similar pros/cons as actor and pmap implementation of Erlang example. You can download full source code for this implementation from https://github.com/bhatti/concurency-katas/tree/main/elx_pmap.

Using Queue in Elixir

Following example shows web crawler implementation using queue:

defmodule Crawler do
  @max_depth 4

  @moduledoc """
  Documentation for Crawler.
  """

  ## Client API
  def start_link(size) when is_integer(size) do
    {:ok, downloader_pid} = Downloader.start_link()
    {:ok, indexer_pid} = Indexer.start_link()
    GenServer.start_link(__MODULE__, {size, downloader_pid, indexer_pid})
  end

  ## crawl list of url
  def crawl_urls(pid, urls, timeout) when is_pid(pid) and is_list(urls) and is_integer(timeout) do
    ## Boundary for concurrency and it will not return until all
    ## child URLs are crawled up to MAX_DEPTH limit.
    crawl_urls(pid, urls, 0, self(), timeout)
  end

  # returns number of urls crawled
  def total_crawl_urls(pid, timeout) when is_pid(pid) do
    GenServer.call(pid, {:total_crawl_urls}, timeout)
  end

  ## dequeue returns pops top request from the queue and returns it
  def dequeue(pid) when is_pid(pid) do
    GenServer.call(pid, {:dequeue})
  end

  ###########################################
  ## internal api to crawl urls
  def crawl_urls(pid, urls, depth, clientPid, timeout) when is_pid(pid) and is_list(urls) and is_pid(clientPid) and is_integer(timeout) do
    if depth < @max_depth do
      requests = urls |> Enum.map(&(Request.new(&1, depth, clientPid, timeout)))
      requests |> Enum.map(&(GenServer.cast(pid, {:crawl, &1})))
    else
      :max_depth_exceeded
    end
  end

  ###########################################
  ## init method create pool of workers based on given size
  def init({size, downloader_pid, indexer_pid}) when is_integer(size) and is_pid(downloader_pid) and is_pid(indexer_pid) do
    Process.flag(:trap_exit, true)
    pid_to_workers = 0..size |> Enum.map(&child_spec/1)
    |> Enum.map(&start_child/1)
    |> Enum.into(%{})
    {:ok, {pid_to_workers, :queue.new, 0, 0, downloader_pid, indexer_pid}}
  end

  ## asynchronous server handler for adding request to crawl in the queue
  def handle_cast({:crawl, request}, {pid_to_workers, queue, total_in, total_out, downloader_pid, indexer_pid}) do
    new_queue = :queue.in(request, queue)
    {:noreply, {pid_to_workers, new_queue, total_in+1, total_out, downloader_pid, indexer_pid}}
  end

  ## synchronous server handler for returning total urls crawled
  def handle_call({:total_crawl_urls}, _from, {_, _, _total_in, total_out, _, _} = state) do
    {:reply, total_out, state}
  end

  ## synchronous server handler to pop top request from the queue and returning it
  def handle_call({:dequeue}, _from, {pid_to_workers, queue, total_in, total_out, downloader_pid, indexer_pid}) do
    {head, new_queue} = :queue.out(queue)
    if head == :empty do
      {:reply, {head, downloader_pid, indexer_pid}, {pid_to_workers, new_queue, total_in, total_out, downloader_pid, indexer_pid}}
    else
      if rem(:queue.len(queue), 1000) == 0 or rem(total_out+1, 1000) == 0do
        IO.puts("#{total_out+1}...")
      end
      {:value, req} = head
      {:reply, {req, downloader_pid, indexer_pid}, {pid_to_workers, new_queue, total_in, total_out+1, downloader_pid, indexer_pid}}
    end
  end

  ## OTP helper callbacks
  def handle_info({:EXIT, dead_pid, _reason}, {pid_to_workers, queue, total_in, total_out}) do
    # Start new process based on dead_pid spec
    {new_pid, child_spec} = pid_to_workers
    |> Map.get(dead_pid)
    |> start_child()

    # Remove the dead_pid and insert the new_pid with its spec
    new_pid_to_workers = pid_to_workers
    |> Map.delete(dead_pid)
    |> Map.put(new_pid, child_spec)

    {:noreply, {new_pid_to_workers, queue, total_in, total_out}}
  end

  ## Defines spec for worker
  defp child_spec(_) do
    {Worker, :start_link, [self()]}
  end

  ## Dynamically create child
  defp start_child({module, function, args} = spec) do
    {:ok, pid} = apply(module, function, args)
    Process.link(pid)
    {pid, spec}
  end

end

You can download full source code of this example from https://github.com/bhatti/concurency-katas/tree/main/elx_queue.

Using Actor model as Abstract Data Structure

As the cost of actors is very small, you can also use it as an abstract data structure or objects that maintains internal state. Alan Kay, the pioneer in object-oriented programming described message-passing, isolation and state encapsulation as foundation of object-oriented design and Joe Armstrong described Erlang as the only object-oriented language. For example, let’s say you need to create a cache of stock quotes using dictionary data structure, which is updated from another source and provides easy access to the latest quotes. You would need to protect access to shared data in multi-threaded environment with synchronization. However, with actor-based design, you may define an actor for each stock symbol that keeps latest value internally and provides API to access or update quote data. This design will remove the need to synchronize shared data structure and will result in better performance.

Overall, Erlang process model is a bit low-level compared to async/await syntax and lacks composition in asynchronous code but it can be designed to provide structured scope, error handling and termination. Further, immutable data structures and message passing obviates the need for locks to protect shared state. Another benefit of Erlang/Elixir is its support of distributed services so it can be used for automatically distributing tasks to remote machines seamlessly.

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