Upload
chibochibo
View
284
Download
1
Embed Size (px)
Citation preview
Blocking & Hyper Context Switching Pattern
ScalaMatsuri 2017
ブロッキング&ハイパーコンテキストスイッチパターン
Agenda
● Introduction
● How did it happen?
● Importance
● Conclusions
アジェンダ
About Me
● Takako Shimamoto (@chibochibo03)
● BizReach, Inc. CTO office
● Scala Warrior: Planning / Illustration
自己紹介
Introduction
はじめに
Notes
● In this session, I don't mention the following:
○ Specifications
○ Selection of libraries
● Instead, we'll use a snippet that demonstrates a
failure pattern
仕様や使用ライブラリの議論はしません
コードは再現したものです
What is it?def delete(id: Long): Future[Unit] = {
val action = for {
_ <- repository.deleteCategory(id)
...
} yield ()
Try { Await.result(db.run(action.transactionally), Duration.Inf) } match { case Success(_) => repository.writeLog()
case Failure(e) => Future.failed(e)
}
}
quite a lot generators
returns DBIO[Unit]
returns Future[Unit]
本日のお題
What is it?
● This method is called multiple times per request
● Inject the default Play execution context
このメソッドは1リクエストで複数回呼ばれる
Oh boy!
● The number of users was small
● But response speed worsened gradually
利用者が少ないにも関わらず徐々にレスポンス速度が悪化
Dangers
● Resources were not under stress
○ database connections
○ slow queries
○ access log
● Infrastructure monitoring showed well
外からの監視で異常を検知できない
How did it happen?
何が起きたのか?
The one problem is ...def delete(id: Long): Future[Unit] = {
val action = for {
_ <- repository.deleteCategory(id)
...
} yield () Try { Await.result(db.run(action.transactionally), Duration.Inf) } match { case Success(_) => repository.writeLog()
case Failure(e) => Future.failed(e)
}
}
quite a lot generators
1つ目の問題は無駄なスイッチング
The precise meaning
The precise meaning of generators and guards is defined by translation to invocations of four methods: map, withFilter, flatMap, and foreach.
"6.19 For Comprehensions and For Loops". Scala Language Specification. https://www.scala-lang.org/files/archive/spec/2.12/, (参照 2017-01-03)
for式は4つのメソッド呼び出しに変換
Implicit ExecutionContexts
● Provide an execution context to execute the given
functions
○ When calling map or flatMap on an action
● In short, an ExecutionContext is a ThreadPool
mapやflatMapは引数に暗黙のスレッドプールが必要
渡した関数はそこで実行
Using a metaphor
CASHIER
ファーストフード店で例える
1品ずつ注文
One Hamburger.
A small Coffee.
One French Fry.Shop attendant
What the hell !?
CASHIER
いやいや、まとめて頼めば1回ですむじゃん!
Shop attendant
Gather orders!!
Sequential Execution
● DBIO.seq
● DBIO.sequence
○ takes a number of DBIOActions
まとめて渡せるメソッドを使う
The other is ...def delete(id: Long): Future[Unit] = {
val action = for {
_ <- repository.deleteCategory(id)
...
} yield ()
Try { Await.result(db.run(action.transactionally), Duration.Inf) }
match { case Success(_) => repository.writeLog()
case Failure(e) => Future.failed(e)
}
}
もう1つの問題はブロッキング
According to Scaladoc
Await.resultはブロッキング
Although this method is blocking, the internal use of blocking ensures that the underlying ExecutionContext to properly detect blocking and ensure that there are no deadlocks.
"scala.concurrent.Await". SCALA API DOCS. http://www.scala-lang.org/api/2.12.1/scala/concurrent/index.html, (参照 2017-01-03)
Cool names
● More ominous names
○ Oni.blocking(..., Oni.forever)
○ Gachi.blocking(..., Gachi.forever)
● Just kidding! Haha!
名前がカッコよすぎ
鬼ブロック!ガチブロック!(冗談です)
Blocking is evil
● Play is not a traditional web framework
● Play’s thread pools are tuned to use fewer threads
○ IO never blocks
Playは少ないスレッドをブロックせず使い回すスタイル
The C10K problem
● The number of threads multiplies too much
● Lack of resources such as memory
● CPU not busy
クライアント1万台問題
Transformations
変換やコールバックを使う
● Future's methods:
○ map, flatMap, and so on
● Callbacks
○ onComplete, foreach, and so on
Importance
重要なこと
JDBC is synchronous
● A typical example of blocking is database access
● An asynchronous framework doesn't like JDBC
JDBCドライバは同期
Slick’s solution
● Wrap blocking code
○ Blocking happens in a different thread
● Slick has its own thread pool
○ All database actions are executed in this pool
Slickは独自でスレッドプールを持つデータベースアクションはそのプールのスレッドで実行
Play default thread pool
● It is an Akka dispatcher
● This execution context is backed by a ForkJoinPool
○ Keeping CPU busy
○ Fewer threads are always awake is desirable
AkkaはForkJoinPoolを採用
Blocking in a ForkJoinPool
ForkJoinPoolでブロッキングするとどうなる?
Await.resultをおさらい
● Let's review
Although this method is blocking, the internal use of blocking ensures that the underlying ExecutionContext to properly detect blocking and ensure that there are no deadlocks. "scala.concurrent.Await". SCALA API DOCS.
http://www.scala-lang.org/api/2.12.1/scala/concurrent/index.html, (参照 2017-01-03)
Blocking in a ForkJoinPool
● Inform one is about to block
● It compensates by starting an additional thread
○ Keep available threads for non-blocking operations
○ No upper limit for threads number!!
1つをブロックする代わりに追加のスレッドを生成上限なし!!
Conclusions
まとめ
Summary
● Await.result + Duration.Inf
● Quite a lot generators
Await.result + Duration.Inf = おやすみたくさんのジェネレータ = 東奔西走
Goodnight
zzz
Busy oneself