Concurrency Patterns

David Wagner

In this post I’m replicating in Haskell some of the examples from the talk Go Concurrency Patterns by Rob Pike. In the talk Pike explains how Go’s built-in concurrency primitives can help writing concurrent code. I was curious to see how the presented examples would look in Haskell, a language that I’m interested in learning.

Simulating a search engine

The example we are going to play with (again, taken from Pike’s presentation) is a simulated search engine. The search engine receives a search query and returns web, image and video results.

We start with a simple implementation where the different kinds of search are performed sequentially, then we gradually add concurrency to build a better performing search engine.

First we’re going to need some imports and some data types to represent our problem.

import Control.Concurrent.Async  (mapConcurrently, race)
import Control.Concurrent        (threadDelay)
import System.Random             (getStdRandom, randomR)
import System.Timeout            (timeout)
import Text.Printf               (printf)

type SearchQuery = String

data SearchKind
    = Image
    | Web
    | Video
    deriving (Show)

We represent the search query with a simple string and the kinds of search we can perform with product-type. The role of the imported functions will be clear as we go along.

This is already enough to get us started. We can write a fake search function which takes a SearchQuery, a SearchKind and returns an IO action which, when run, yields the search result as a string:

fakeSearch :: SearchQuery -> SearchKind -> IO String
fakeSearch query kind = do
    delayMs <- getStdRandom $ randomR (1, 100)
    threadDelay $ microseconds delayMs -- simulating random work
    return $ printf "%s results for '%s' in %d ms" (show kind) query delayMs

        microseconds = (* 1000)

Our search back-end won’t get any more sophisticated than this:

  • we make the current thread sleep for a random number amount of milliseconds, then
  • we return the simulated search results.

The resulting string contains the input parameters and the time it took to serve this request. We want to print these results so we write a small helper function:

-- Helper function to print the results
printResults :: Maybe [String] -> IO ()
printResults req = case req of
    Just res -> print res
    Nothing  -> putStrLn "timed out"

This function takes an optional list of strings and prints them on the console. If the argument is Nothing it means that the search request timed out (we will see this later). At this stage this function looks more generic than it needs to be. Probably you would start with a simpler signature, something like:

printResults :: String -> IO ()

and make it more complex when needed. This would make more sense and I also started with this initially. However, for the sake of this post, we will use the more generic version above so we can re-use it in the subsequent examples.

Search 1.0

The first version of our search engine will perform the image, web and video searches sequentially:

-- Run the Web, Image, and Video searches sequentally
search10 :: SearchQuery -> IO ()
search10 query = do
    req <- mapM (fakeSearch query) [Web, Image, Video]
    printResults (Just req)

We use mapM to sequentially perform the three kinds of searches. The result of a typical search using haskell as a query would look like:

["Web results for 'haskell' in 27 ms",
 "Image results for 'haskell' in 61 ms",
 "Video results for 'haskell' in 17 ms"]

Since the searches are performed sequentially it takes 27 + 61 + 17 = 105 ms to serve the results to the user. This post won’t be about concurrency if we were to stop here.

Search 2.0

We can speed things up if we can send the three kinds of queries to our back-end servers independently and wait for the results to come back. We need to change one (!) function in our code to arrive to the next version of our search engine:

-- Run the Web, Image, and Video searches concurrently, and wait for all
-- results.
search20 :: SearchQuery -> IO ()
search20 query = do
    req <- mapConcurrently (fakeSearch query) [Web, Image, Video]
    printResults (Just req)

The mapConcurrently combinator from the async library will launch three light-weight threads and perform the three search actions concurrently. The output of the program is similar (clearly the random timings will change), but it runs faster. The running time will be limited by the slowest search query.

We get a completely different behavior, but our code has the same structure as the sequential version. We don’t have to use locks, mutexes, callbacks, etc., the code clearly expresses our intent. It feels like we got concurrency for free.

Search 2.1

The concurrent version performs really well, but there might be cases where the slowest request would be very slow for some reason. Users get angry if things are slow, so we’d better display an error message if we cannot display a results within a given amount of time.

We introduce a maximum delay (80 ms in this example): if the slowest request takes longer then this, instead of the search results, we display a “timed out” message and we send our sincerest apologies to our user.

-- Don't wait for slow servers
maxDelay :: Int
maxDelay = 80 * 1000 -- us

search21 :: SearchQuery -> IO ()
search21 query = do
    req <- timeout maxDelay $
        mapConcurrently (fakeSearch query) [Web, Image, Video]
    printResults req

Again, we only have to do a small modification to get the desired behavior. The search actions are wrapped in a timeout call. The signature of this function is:

timeout :: Int           -- maximum delay in microseconds
        -> IO a          -- the action to perform
        -> IO (Maybe a)  -- (Just a) if less than the delay elapsed else Nothing

We can run this version a couple of times and we will see two kinds of outputs. If all requests take less than 80ms printResults will behave as before:

["Web results for 'haskell' in 70 ms",
 "Web results for 'haskell' in 67 ms",
 "Video results for 'haskell' in 63 ms"]

otherwise, and this is why printResults takes a Maybe [String] as input,

timed out

is printed.

Search 3.0

We can still do better. The occasional timeout messages are still annoying. We won’t be too popular if our search website times out too often. It’s also a waste to discard all the results from slow servers. Maybe only the video search is slow, but we still throw away the web and image results.

We can use replication to reduce the chance of a timeout. We will send the request to two sets of back-end servers, two replicas. If one replica has some problem and responds slowly we can still get back a response from the other.

We write a function fastest that does exactly this:

fastest :: SearchQuery -> SearchKind ->  IO String
fastest query kind = do
    req <- race (fakeSearch query kind) -- server 1
                (fakeSearch query kind) -- server 2

    return $ case req of
        Left  r -> "Server1: " ++ r
        Right r -> "Server2: " ++ r

This function have the same interface as fakesearch but internally it sends the same query two times to two different servers and keeps the result from the fastest. The race combinator from the async library:

race :: IO a -> IO b -> IO (Either a b)

helps us to achieve this. It launches the two IO actions in parallel and keeps the result from the fastest. The other action will be terminated. There is no second price in this race. The return value will indicate which action won. We prepend the server name to our search result to be able to see where it was served from.

We now replace the fakeSearch call with fastest and we get:

-- Send requests to multiple replicas and use the first response
search30 :: SearchQuery -> IO ()
search30 query = do
    req <- timeout maxDelay $
        mapConcurrently (fastest query) [Web, Image, Video]
    printResults req

Let’s see some typical results:

$ search30
["Server1: Web results for 'haskell' in 29 ms",
 "Server1: Image results for 'haskell' in 36 ms",
 "Server2: Video results for 'haskell' in 49 ms"]

$ search30
["Server2: Web results for 'haskell' in 19 ms",
 "Server2: Image results for 'haskell' in 50 ms",
 "Server1: Video results for 'haskell' in 39 ms"]

We can see that in the first run the web and image results were served by the first replica, while the video result comes from the second. In the second example the first replica served the video results and the second replica the other two. Any combination is possible, these are just results from two runs.

With the version 3.0 we can serve the results of all three searches with very high probability within 80 ms.


As Rob Pike puts it, with a few transformations we converted a

  • slow
  • sequential
  • failure-sensitive

program into one that is

  • fast
  • concurrent
  • replicated
  • robust

I was surprised to see how little the code structure changed in the Haskell versions. We significantly changed the behavior of the program without compromising the readability. We didn’t have to use locks and mutexes, but we could focus on the intent, what the program should do, thanks to the powerful libraries and the run-time system.

I recommend to watch the original talk on YouTube and to further compare the Haskell and the Go implementations. You can find the code here.