问题
After playing with F# agents I tried to do a map reduce using them.
The basic structure I use is:
- map supervisor which queues up all the work to do in its state and receives work request from map workers
- reduce supervisor does the same thing as map supervisor for reduce work
- a bunch of map and reduce workers that map and reduce, if one fails its work it sends it back to the respective supervisr to be reprocessed.
The questions I wonder about is:
- does this make any sense compared to a more traditional (yet very nice) map reduce like (http://tomasp.net/blog/fsharp-parallel-aggregate.aspx) that uses PSeq ?
- the way I implemented the map and reduce workers seems ugly is there a better way ?
- it seems like I can create a 1000 000 map workers and 1000 0000 reduce workers lol, how should I choose these numbers, the more the better ?
Thanks a lot,
type Agent<'T> = MailboxProcessor<'T>
//This is the response the supervisor
//gives to the worker request for work
type 'work SupervisorResponse =
| Work of 'work //a piece of work
| NoWork//no work left to do
//This is the message to the supervisor
type 'work WorkMsg =
| ToDo of 'work //piles up work in the Supervisor queue
| WorkReq of AsyncReplyChannel<SupervisorResponse<'work>> //'
//The supervisor agent can be interacted with
type AgentOperation =
| Stop //stop the agent
| Status //yield the current status of supervisor
type 'work SupervisorMsg =
| WorkRel of 'work WorkMsg
| Operation of AgentOperation
//Supervises Map and Reduce workers
module AgentSupervisor=
let getNew (name:string) =
new Agent<SupervisorMsg<'work>>(fun inbox -> //'
let rec loop state = async {
let! msg = inbox.Receive()
match msg with
| WorkRel(m) ->
match m with
| ToDo(work) ->
let newState = work:state
return! loop newState
| WorkReq(replyChannel) ->
match state with
| [] ->
replyChannel.Reply(NoWork)
return! loop []
| [item] ->
replyChannel.Reply(Work(item))
return! loop []
| (item::remaining) ->
replyChannel.Reply(Work(item))
return! loop remaining
| Operation(op) ->
match op with
| Status ->
Console.WriteLine(name+" current Work Queue "+
string (state.Length))
return! loop state
| Stop ->
Console.WriteLine("Stoppped SuperVisor Agent "+name)
return()
}
loop [] )
let stop (agent:Agent<SupervisorMsg<'work>>) = agent.Post(Operation(Stop))
let status (agent:Agent<SupervisorMsg<'work>>) =agent.Post(Operation(Status))
//Code for the workers
type 'success WorkOutcome =
| Success of 'success
| Fail
type WorkerMsg =
| Start
| Stop
| Continue
module AgentWorker =
type WorkerSupervisors<'reduce,'work> =
{ Map:Agent<SupervisorMsg<'work>> ; Reduce:Agent<SupervisorMsg<'reduce>> }
let stop (agent:Agent<WorkerMsg>) = agent.Post(Stop)
let start (agent:Agent<WorkerMsg>) = agent.Start()
agent.Post(Start)
let getNewMapWorker( map, supervisors:WorkerSupervisors<'reduce,'work> ) =
new Agent<WorkerMsg>(fun inbox ->
let rec loop () = async {
let! msg = inbox.Receive()
match msg with
| Start -> inbox.Post(Continue)
return! loop ()
| Continue ->
let! supervisorOrder =
supervisors.Map.PostAndAsyncReply(
fun replyChannel ->
WorkRel(WorkReq(replyChannel)))
match supervisorOrder with
| Work(work) ->
let! res = map work
match res with
| Success(toReduce) ->
supervisors.Reduce
.Post(WorkRel(ToDo(toReduce)))
| Fail ->
Console.WriteLine("Map Fail")
supervisors.Map
.Post(WorkRel(ToDo(work)))
inbox.Post(Continue)
| NoWork ->
inbox.Post(Continue)
return! loop ()
| Stop ->
Console.WriteLine("Map worker stopped")
return ()
}
loop () )
let getNewReduceWorker(reduce,reduceSupervisor:Agent<SupervisorMsg<'work>>)=//'
new Agent<WorkerMsg>(fun inbox ->
let rec loop () = async {
let! msg = inbox.Receive()
match msg with
| Start -> inbox.Post(Continue)
return! loop()
| Continue ->
let! supervisorOrder =
reduceSupervisor.PostAndAsyncReply(fun replyChannel ->
WorkRel(WorkReq(replyChannel)))
match supervisorOrder with
| Work(work) ->
let! res = reduce work
match res with
| Success(toReduce) -> inbox.Post(Continue)
| Fail ->
Console.WriteLine("ReduceFail")
reduceSupervisor.Post(WorkRel(ToDo(work)))
inbox.Post(Continue)
| NoWork -> inbox.Post(Continue)
return! loop()
|Stop ->Console.WriteLine("Reduce worker stopped"); return ()
}
loop() )
open AgentWorker
type MapReduce<'work,'reduce>( numberMap:int ,
numberReduce: int,
toProcess:'work list,
map:'work->Async<'reduce WorkOutcome>,
reduce:'reduce-> Async<unit WorkOutcome>) =
let mapSupervisor= AgentSupervisor.getNew("MapSupervisor")
let reduceSupervisor = AgentSupervisor.getNew("ReduceSupervisor")
let workerSupervisors = {Map = mapSupervisor ; Reduce = reduceSupervisor }
let mapWorkers =
[for i in 1..numberMap ->
AgentWorker.getNewMapWorker(map,workerSupervisors) ]
let reduceWorkers =
[for i in 1..numberReduce ->
AgentWorker.getNewReduceWorker(reduce,workerSupervisors.Reduce) ]
member this.Start() =
//Post work to do
toProcess
|>List.iter(fun elem -> mapSupervisor.Post( WorkRel(ToDo(elem))))
//Start supervisors
mapSupervisor.Start()
reduceSupervisor.Start()
//start workers
List.iter( fun mapper -> mapper |>start) mapWorkers
List.iter( fun reducer ->reducer|>start) reduceWorkers
member this.Status() = (mapSupervisor|>AgentSupervisor.status)
(reduceSupervisor|>AgentSupervisor.status)
member this.Stop() =
List.map2(fun mapper reducer ->
mapper |>stop; reducer|>stop) mapWorkers reduceWorkers
//Run some tests
let map = function (n:int64) -> async{ return Success(n) }
let reduce = function (toto: int64) -> async{ return Success() }
let mp = MapReduce<int64,int64>( 1,1,[for i in 1L..1000000L->i],map,reduce)
mp.Start()
mp.Status()
mp.Stop()
回答1:
I like to use MailboxProcessor for the reduce part of the algorithm, and async block that's invoked with Async.Parallel for the map part. It makes things more explicit, giving you finer control over exception handling, timeouts, and cancellation.
The following code was designed with Brian's help, and with the help of his excellent F# block highlighting "F# Depth Colorizer" plug-in for VS2010.
This code is meant to pull RSS feeds from yahoo weather server in a map-reduce pattern. It demonstrates how we can control execution flow from the outside of actual algorithm.
fetchWeather is the map part, and mailboxLoop is the reduce part of the algorithm.
#r "System.Xml.Linq.dll"
#r "FSharp.PowerPack.dll"
open System
open System.Diagnostics
open System.IO
open System.Linq
open System.Net
open System.Xml.Linq
open Microsoft.FSharp.Control.WebExtensions
type Weather (city, region, temperature) = class
member x.City = city
member x.Region = region
member x.Temperature : int = temperature
override this.ToString() =
sprintf "%s, %s: %d F" this.City this.Region this.Temperature
end
type MessageForActor =
| ProcessWeather of Weather
| ProcessError of int
| GetResults of (Weather * Weather * Weather list) AsyncReplyChannel
let parseRss woeid (rssStream : Stream) =
let xn str = XName.Get str
let yweather elementName = XName.Get(elementName, "http://xml.weather.yahoo.com/ns/rss/1.0")
let channel = (XDocument.Load rssStream).Descendants(xn "channel").First()
let location = channel.Element(yweather "location")
let condition = channel.Element(xn "item").Element(yweather "condition")
// If the RSS server returns error, condition XML element won't be available.
if not(condition = null) then
let temperature = Int32.Parse(condition.Attribute(xn "temp").Value)
ProcessWeather(new Weather(
location.Attribute(xn "city").Value,
location.Attribute(xn "region").Value,
temperature))
else
ProcessError(woeid)
let fetchWeather (actor : MessageForActor MailboxProcessor) woeid =
async {
let rssAddress = sprintf "http://weather.yahooapis.com/forecastrss?w=%d&u=f" woeid
let webRequest = WebRequest.Create rssAddress
use! response = webRequest.AsyncGetResponse()
use responseStream = response.GetResponseStream()
let weather = parseRss woeid responseStream
//do! Async.Sleep 1000 // enable this line to see amplified timing that proves concurrent flow
actor.Post(weather)
}
let mailboxLoop initialCount =
let chooseCityByTemperature op (x : Weather) (y : Weather) =
if op x.Temperature y.Temperature then x else y
let sortWeatherByCityAndState (weatherList : Weather list) =
weatherList
|> List.sortWith (fun x y -> x.City.CompareTo(y.City))
|> List.sortWith (fun x y -> x.Region.CompareTo(y.Region))
MailboxProcessor.Start(fun inbox ->
let rec loop minAcc maxAcc weatherList remaining =
async {
let! message = inbox.Receive()
let remaining = remaining - 1
match message with
| ProcessWeather weather ->
let colderCity = chooseCityByTemperature (<) minAcc weather
let warmerCity = chooseCityByTemperature (>) maxAcc weather
return! loop colderCity warmerCity (weather :: weatherList) remaining
| ProcessError woeid ->
let errorWeather = new Weather(sprintf "Error with woeid=%d" woeid, "ZZ", 99999)
return! loop minAcc maxAcc (errorWeather :: weatherList) remaining
| GetResults replyChannel ->
replyChannel.Reply(minAcc, maxAcc, sortWeatherByCityAndState weatherList)
}
let minValueInitial = new Weather("", "", Int32.MaxValue)
let maxValueInitial = new Weather("", "", Int32.MinValue)
loop minValueInitial maxValueInitial [] initialCount
)
let RunSynchronouslyWithExceptionAndTimeoutHandlers computation =
let timeout = 30000
try
Async.RunSynchronously(Async.Catch(computation), timeout)
|> function Choice1Of2 answer -> answer |> ignore
| Choice2Of2 (except : Exception) -> printfn "%s" except.Message; printfn "%s" except.StackTrace; exit -4
with
| :? System.TimeoutException -> printfn "Timed out waiting for results for %d seconds!" <| timeout / 1000; exit -5
let main =
// Should have script name, sync/async select, and at least one woeid
if fsi.CommandLineArgs.Length < 3 then
printfn "Expecting at least two arguments!"
printfn "There were %d arguments" (fsi.CommandLineArgs.Length - 1)
exit -1
let woeids =
try
fsi.CommandLineArgs
|> Seq.skip 2 // skip the script name and sync/async select
|> Seq.map Int32.Parse
|> Seq.toList
with
| except -> printfn "One of supplied arguments was not an integer: %s" except.Message; exit -2
let actor = mailboxLoop woeids.Length
let processWeatherItemsConcurrently woeids =
woeids
|> Seq.map (fetchWeather actor)
|> Async.Parallel
|> RunSynchronouslyWithExceptionAndTimeoutHandlers
let processOneWeatherItem woeid =
woeid
|> fetchWeather actor
|> RunSynchronouslyWithExceptionAndTimeoutHandlers
let stopWatch = new Stopwatch()
stopWatch.Start()
match fsi.CommandLineArgs.[1].ToUpper() with
| "C" -> printfn "Concurrent execution: "; processWeatherItemsConcurrently woeids
| "S" -> printfn "Synchronous execution: "; woeids |> Seq.iter processOneWeatherItem
| _ -> printfn "Unexpected run options!"; exit -3
let (min, max, weatherList) = actor.PostAndReply GetResults
stopWatch.Stop()
assert (weatherList.Length = woeids.Length)
printfn "{"
weatherList |> List.iter (printfn " %O")
printfn "}"
printfn "Coldest place: %O" min
printfn "Hottest place: %O" max
printfn "Completed in %d millisec" stopWatch.ElapsedMilliseconds
main
来源：https://stackoverflow.com/questions/4431743/map-reduce-with-f-agents