FunDA（14）－ 示范：并行运算，并行数据库读取 - parallel data loading

   FunDA的并行数据库读取功能是指在多个线程中同时对多个独立的数据源进行读取。这些独立的数据源可以是在不同服务器上的数据库表，又或者把一个数据库表分成几个独立部分形成的独立数据源。当然，并行读取的最终目的是提高程序的运算效率。在FunDA中具体的实现方式是对多个独立的数据流进行并行读取形成一个统一综合的数据流。我们还是用上次示范所产生的表AQMRPT作为样板数据。在这次示范里我们需要把AQMRPT表中的STATENAME,COUNTYNAME字段抽取出来形成两个独立的表STATE和COUNTY。这两个表结构如下：

case class StateModel(id: Int, name: String) extends FDAROW
class StateTable(tag: Tag) extends Table[StateModel](tag,"STATE") {
def id = column[Int]("ID",O.AutoInc,O.PrimaryKey)
def name = column[String]("NAME",O.Length(32))
def * = (id,name)<>(StateModel.tupled,StateModel.unapply)
}
val StateQuery = TableQuery[StateTable]

case class CountyModel(id: Int, name: String) extends FDAROW
case class CountyTable(tag: Tag) extends Table[CountyModel](tag,"COUNTY") {
def id = column[Int]("ID",O.AutoInc,O.PrimaryKey)
def name = column[String]("NAME",O.Length(64))
def * = (id,name)<>(CountyModel.tupled,CountyModel.unapply)
}
val CountyQuery = TableQuery[CountyTable]

首先我们用一些铺垫代码把这两个表结构创建出来：

//assume two distinct db objects
val db_a = Database.forConfig("h2db")
//another db object
val db_b = Database.forConfig("h2db")

//create STATE table
val actionCreateState = Models.StateQuery.schema.create
val futCreateState = db_a.run(actionCreateState).andThen {
case Success(_) => println("State Table created successfully!")
case Failure(e) => println(s"State Table may exist already! Error: ${e.getMessage}")
}
//would carry on even fail to create table
Await.ready(futCreateState,Duration.Inf)

//create COUNTY table
val actionCreateCounty = Models.CountyQuery.schema.create
val futCreateCounty = db_a.run(actionCreateCounty).andThen {
case Success(_) => println("County Table created successfully!")
case Failure(e) => println(s"County Table may exist already! Error: ${e.getMessage}")
}
//would carry on even fail to create table
Await.ready(futCreateCounty,Duration.Inf)

下一步我们把STATENAME从AQMRPT表里抽取出来形成一个数据源（data-source）：

//define query for extracting State names from AQMRPT
val qryStates = AQMRPTQuery.map(_.state).distinct.sorted  //     .distinctOn(r => r)
case class States(name: String) extends FDAROW
implicit def toStates(row: String) = States(row)
val stateLoader = FDAStreamLoader(slick.jdbc.H2Profile)(toStates _)
val statesStream = stateLoader.fda_typedStream(qryStates.result)(db_a)(64,64)()

由于COUNTYNAME比较多，我们可以把AQMRPT表按STATENAME拆成三部分A-K、K-P、P-Z。然后把这三部分构建成三个独立的数据源：

//define query for extracting County names from AQMRPT in separate chunks
//query with state name >A and <K
val qryCountiesA_K = AQMRPTQuery.filter(r => (r.state.toUpperCase > "A" &&
r.state.toUpperCase < "K")).map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

//query with state name >K and <P
val qryCountiesK_P = AQMRPTQuery.filter(r => (r.state.toUpperCase > "K" &&
r.state.toUpperCase < "P")).map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

//query with state name >P
val qryCountiesP_Z = AQMRPTQuery.filter(r => r.state.toUpperCase > "P")
.map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

case class Counties(state: String, name: String) extends FDAROW
implicit def toCounties(row: (String,String)) = Counties(row._1,row._2)
val countyLoader = FDAStreamLoader(slick.jdbc.H2Profile)(toCounties _)
//3 separate streams to extract county names from the same database table AQMRPT
val countiesA_KStream = countyLoader.fda_typedStream(qryCountiesA_K.result)(db_b)(64,64)()
val countiesK_PStream = countyLoader.fda_typedStream(qryCountiesK_P.result)(db_b)(64,64)()
val countiesP_ZStream = countyLoader.fda_typedStream(qryCountiesP_Z.result)(db_b)(64,64)()

然后对这四个数据源进行并行读取：

//obtain a combined stream with parallel loading with max of 4 open computation
val combinedStream = fda_par_load(statesStream,countiesA_KStream,countiesK_PStream,countiesP_ZStream)(4)


现在这个组合的数据流里最少有两种不同的数据元素，分别是：case class States和case class Counties。我们可以在combinedStream上连接两个用户自定义函数（user-defined-task）分别截取States和Counties数据行并且把它们转化成各自的插入数据指令行（ActionRow）：
//define separate rows for different actions
case class StateActionRow(action: FDAAction) extends FDAROW
case class CountyActionRow(action: FDAAction) extends FDAROW
val actionRunner = FDAActionRunner(slick.jdbc.H2Profile)

//user-task to catch rows of States type and transform them into db insert actions
def processStates: FDAUserTask[FDAROW] = row => {
row match {
//catch states row and transform it into insert action
case States(stateName) => //target row type
println(s"State name: ${stateName}")
val action = StateQuery += StateModel(0,stateName)
fda_next(StateActionRow(action))
case others@ _ => //pass other types to next user-defined-tasks
fda_next(others)
}
}
//user-task to catch rows of Counties type and transform them into db insert actions
def processCounties: FDAUserTask[FDAROW] = row => {
row match {
//catch counties row and transform it into insert action
case Counties(stateName,countyName) => //target row type
println(s"County ${countyName} of ${stateName}")
val action = CountyQuery += CountyModel(0,countyName+ " of "+stateName)
fda_next(CountyActionRow(action))
case others@ _ => //pass other types to next user-defined-tasks
fda_next(others)
}
}


经过processStates和processCounties两个自定义函数处理后combinedStream里又多了两种不同的元素：StateActionRow和CountyActionRow。同样，我们可以用两个自定义函数来运算这两种动作行：
//user-task to catch States insert action rows and run them
def runStateAction: FDAUserTask[FDAROW] = row => {
row match {
case StateActionRow(action) => //this is a state action row type
println(s"runstate: ${action}")
actionRunner.fda_execAction(action)(db_a) //run this query with db_a context
fda_skip
case others@ _ => //otherwise pass alone to next user-defined-tasks
fda_next(others)
}
}

//user-task to catch Counties insert action rows and run them
def runCountyAction: FDAUserTask[FDAROW] = row => {
row match {
case CountyActionRow(action) => //this is a county action row type
actionRunner.fda_execAction(action)(db_b) //run this query with db_b context
fda_skip
case others@ _ => //otherwise pass alone to next user-defined-tasks
fda_next(others)
}
}


好了，现在我们可以把这四个自定义函数在combinedStream上组合起来成为一个完整功能的程序：
combinedStream.appendTask(processStates)
.appendTask(processCounties)
.appendTask(runStateAction)
.appendTask(runCountyAction)
.startRun


然后用startRun来正式运算这个程序了。
下面就是本次示范的源代码：

import com.bayakala.funda._
import api._
import scala.language.implicitConversions
import slick.jdbc.H2Profile.api._
import scala.concurrent.duration._
import scala.concurrent.{Await, Future}
import scala.util.{Failure, Success}
import Models._
import scala.concurrent.ExecutionContext.Implicits.global

object ParallelLoading extends App {

//assume two distinct db objects
val db_a = Database.forConfig("h2db")
//another db object
val db_b = Database.forConfig("h2db")

//create STATE table
val actionCreateState = Models.StateQuery.schema.create
val futCreateState = db_a.run(actionCreateState).andThen {
case Success(_) => println("State Table created successfully!")
case Failure(e) => println(s"State Table may exist already! Error: ${e.getMessage}")
}
//would carry on even fail to create table
Await.ready(futCreateState,Duration.Inf)

//create COUNTY table
val actionCreateCounty = Models.CountyQuery.schema.create
val futCreateCounty = db_a.run(actionCreateCounty).andThen {
case Success(_) => println("County Table created successfully!")
case Failure(e) => println(s"County Table may exist already! Error: ${e.getMessage}")
}
//would carry on even fail to create table
Await.ready(futCreateCounty,Duration.Inf)

//define query for extracting State names from AQMRPT
val qryStates = AQMRPTQuery.map(_.state).distinct.sorted // .distinctOn(r => r)
case class States(name: String) extends FDAROW
implicit def toStates(row: String) = States(row)
val stateLoader = FDAStreamLoader(slick.jdbc.H2Profile)(toStates _)
val statesStream = stateLoader.fda_typedStream(qryStates.result)(db_a)(64,64)()

//define query for extracting County names from AQMRPT in separate chunks
//query with state name >A and <K
val qryCountiesA_K = AQMRPTQuery.filter(r => (r.state.toUpperCase > "A" &&
r.state.toUpperCase < "K")).map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

//query with state name >K and <P
val qryCountiesK_P = AQMRPTQuery.filter(r => (r.state.toUpperCase > "K" &&
r.state.toUpperCase < "P")).map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

//query with state name >P
val qryCountiesP_Z = AQMRPTQuery.filter(r => r.state.toUpperCase > "P")
.map(r => (r.state,r.county))
.distinctOn(r => (r._1,r._2))
.sortBy(r => (r._1,r._2))

case class Counties(state: String, name: String) extends FDAROW
implicit def toCounties(row: (String,String)) = Counties(row._1,row._2)
val countyLoader = FDAStreamLoader(slick.jdbc.H2Profile)(toCounties _)
//3 separate streams to extract county names from the same database table AQMRPT
val countiesA_KStream = countyLoader.fda_typedStream(qryCountiesA_K.result)(db_b)(64,64)()
val countiesK_PStream = countyLoader.fda_typedStream(qryCountiesK_P.result)(db_b)(64,64)()
val countiesP_ZStream = countyLoader.fda_typedStream(qryCountiesP_Z.result)(db_b)(64,64)()

//obtain a combined stream with parallel loading with max of 4 open computation
val combinedStream = fda_par_load(statesStream,countiesA_KStream,countiesK_PStream,countiesP_ZStream)(4)

//define separate rows for different actions
case class StateActionRow(action: FDAAction) extends FDAROW
case class CountyActionRow(action: FDAAction) extends FDAROW
val actionRunner = FDAActionRunner(slick.jdbc.H2Profile)

//user-task to catch rows of States type and transform them into db insert actions
def processStates: FDAUserTask[FDAROW] = row => {
row match {
//catch states row and transform it into insert action
case States(stateName) => //target row type
println(s"State name: ${stateName}")
val action = StateQuery += StateModel(0,stateName)
fda_next(StateActionRow(action))
case others@ _ => //pass other types to next user-defined-tasks
fda_next(others)
}
}
//user-task to catch rows of Counties type and transform them into db insert actions
def processCounties: FDAUserTask[FDAROW] = row => {
row match {
//catch counties row and transform it into insert action
case Counties(stateName,countyName) => //target row type
println(s"County ${countyName} of ${stateName}")
val action = CountyQuery += CountyModel(0,countyName+ " of "+stateName)
fda_next(CountyActionRow(action))
case others@ _ => //pass other types to next user-defined-tasks
fda_next(others)
}
}

//user-task to catch States insert action rows and run them
def runStateAction: FDAUserTask[FDAROW] = row => {
row match {
case StateActionRow(action) => //this is a state action row type
println(s"runstate: ${action}")
actionRunner.fda_execAction(action)(db_a) //run this query with db_a context
fda_skip
case others@ _ => //otherwise pass alone to next user-defined-tasks
fda_next(others)
}
}

//user-task to catch Counties insert action rows and run them
def runCountyAction: FDAUserTask[FDAROW] = row => {
row match {
case CountyActionRow(action) => //this is a county action row type
actionRunner.fda_execAction(action)(db_b) //run this query with db_b context
fda_skip
case others@ _ => //otherwise pass alone to next user-defined-tasks
fda_next(others)
}
}

def showRows: FDAUserTask[FDAROW] = row => {
row match {
case States(nm) =>
println("")
println(s"State: $nm")
println("************")
fda_skip
case Counties(s,c) =>
println("")
println(s"County: $c")
println(s"state of $s")
println("------------")
fda_skip
case _ => fda_skip
}
}

combinedStream.appendTask(processStates)
.appendTask(processCounties)
.appendTask(runStateAction)
.appendTask(runCountyAction)
.startRun

}