SDP（9）：MongoDB-Scala - data access and modeling
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MongoDB是一种文件型数据库，对数据格式没有硬性要求，所以可以实现灵活多变的数据存储和读取。MongoDB又是一种分布式数据库，与传统关系数据库不同的是，分布式数据库不支持table-join，所以在设计数据库表结构方面与关系数据库有很大的不同。分布式数据库有一套与传统观念不同的数据模式，在设计库表结构时必须从满足各种数据抽取的需要为主要目的。关系数据库设计要求遵循范式模式（normalization）库表结构，在抽取数据时再通过table-join联结关系表。因为分布式数据库不支持table-join，在读取跨表数据时就需要多次抽取，影响数据处理的效率。MongoDB作为文件型数据库最大的特点就是容许嵌入Document：我们可以把相关联的Document嵌入在另一个关联Document中，这样就可以一次性读取全部数据，实现反范式（denormalization）的数据模式了。这方面MongoDB比Cassandra更加优胜。MongoDB支持灵活多样的索引方式，使它成为提供高效数据读取的分布式数据库最佳选择。另外，MongoDB还通过提供sort、aggregation、map-reduce来支持丰富强大的大数据统计功能。
在使用MongoDB前我们必须熟悉它的数据模式和设计理念：在大数据时代的今天，数据的产生和使用发生了质的变化，传统关系数据库数据模式已经无法满足现代信息系统的要求。比如，在设计个人信息表时要考虑有些人有两个地址，有些甚至没有地址，又有些有传真号，还有这个那个的其它特点等等。在关系数据库模式设计中我们必须作出取舍，牺牲一些属性。但MongoDB的文件类数据库特点容许不同的数据格式，能实现完整的数据采集与储存。下面是一个采购单的Document设计：
val po1 = Document (
"ponum" -> "po18012301",
"vendor" -> "The smartphone compay",
"podate" -> podate1,
"remarks" -> "urgent, rush order",
"handler" -> pic,
"podtl" -> Seq(
Document("item" -> "sony smartphone", "price" -> 2389.00, "qty" -> 1239, "packing" -> "standard"),
Document("item" -> "ericson smartphone", "price" -> 897.00, "qty" -> 1000, "payterm" -> "30 days")
)
)
val po2 = Document (
"ponum" -> "po18022002",
"vendor" -> "The Samsung compay",
"podate" -> podate2,
"podtl" -> Seq(
Document("item" -> "samsung galaxy s8", "price" -> 2300.00, "qty" -> 100, "packing" -> "standard"),
Document("item" -> "samsung galaxy s7", "price" -> 1897.00, "qty" -> 1000, "payterm" -> "30 days"),
Document("item" -> "apple iphone7", "price" -> 6500.00, "qty" -> 100, "packing" -> "luxury")
)
)
po1和po2都在podtl键嵌入了多条采购项目Document。首先，po1与po2有结构上的不同：po1多出了remarks、handler这两个键。嵌入的Document各自也有不同的结构。在这个例子里我特别加了date、binary、array类型的使用示范：
val ca = Calendar.getInstance()
ca.set(2011,10,23)
val podate1 = ca.getTime
ca.set(2012,12,23)
val podate2 = ca.getTime
val pic = FileToByteArray("/users/tiger-macpro/sample.png",3 seconds)
MongoDB的Date是java.util.Date，可以用Calendar来操作。再看看下面类型转换中的数据类型对应：
case
class
PO (
ponum: String,
podate: java.util.Date,
vendor: String,
remarks: Option[String],
podtl: Option[BsonArray],
handler: Option[BsonBinary]
)
def toPO(doc: Document): PO = {
val ks = doc.keySet
PO(
ponum = doc.getString("ponum"),
podate = doc.getDate("podate"),
vendor = doc.getString("vendor"),
remarks = {
if (ks.contains("remarks"))
Some(doc.getString("remarks"))
else
None
},
podtl = {
if (ks.contains("podtl"))
doc.get("podtl").asInstanceOf[Option[BsonArray]]
else
None
},
handler = {
if (ks.contains("handler"))
doc.get("handler").asInstanceOf[Option[BsonBinary]]
else
None
}
)
}
caseclass PODTL(
item: String,
price: Double,
qty: Int,
packing: Option[String],
payTerm: Option[String]
)
def toPODTL(podtl: Document): PODTL = {
val ks = podtl.keySet
PODTL(
item = podtl.getString("item"),
price = podtl.getDouble("price"),
qty = podtl.getInteger("qty"),
packing = {
if (ks.contains("packing"))
Some(podtl.getString("packing"))
else None
},
payTerm = {
if(ks.contains("payterm"))
Some(podtl.getString("payterm"))
else None
}
)
}
注意BsonBinary和BsonArray这两个类型和它们的使用方法。我们可以用嵌入Document的键作为查询条件：
poCollection.find(equal("podtl.qty",100)).toFuture().onComplete {
case Success(docs) => docs.map(toPO).foreach (showPO)
println("-------------------------------")
case Failure(e) => println(e.getMessage)
}
我们可以用toPO和toPODTL把po,podtl对应到case class，然后用强类型方式来使用它们：
def showPO(po: PO) = {
println(s"po number: ${po.ponum}")
println(s"po date: ${po.podate.toString}")
println(s"vendor: ${po.vendor}")
if (po.remarks != None)
println(s"remarks: ${po.remarks.get}")
po.podtl match {
case Some(barr) =>
val docs = barr.getValues.asScala.toList
docs.map { dc =>
toPODTL(dc.asInstanceOf[org.bson.BsonDocument])
}.foreach { doc: PODTL =>
print(s"==>Item: ${doc.item} ")
print(s"price: ${doc.price} ")
print(s"qty: ${doc.qty} ")
doc.packing.foreach(pk => print(s"packing: ${pk} "))
doc.payTerm.foreach(pt => print(s"payTerm: ${pt} "))
println("")
}
case _ =>
}
po.handler match {
case Some(bs) =>
val fileName = s"/users/tiger-macpro/${po.ponum}.png"
ByteArrayToFile(bs.getData,fileName)
println(s"picture saved to ${fileName}")
case None => println("no picture provided")
}
}
poCollection.find(equal("podtl.qty",100)).toFuture().onComplete {
case Success(docs) => docs.map(toPO).foreach (showPO)
println("------------------------------")
case Failure(e) => println(e.getMessage)
}
poCollection.find().toFuture().onComplete {
case Success(docs) => docs.map(toPO).foreach (showPO)
println("-------------------------------")
case Failure(e) => println(e.getMessage)
}
试运行显示结果如下：
po number: po18022002
po date: Wed Jan
23
11:57:50 HKT 2013
vendor: The Samsung compay
==>Item: samsung galaxy s8 price: 2300.0 qty: 100 packing: standard
==>Item: samsung galaxy s7 price: 1897.0 qty: 1000 payTerm: 30 days
==>Item: apple iphone7 price: 6500.0 qty: 100 packing: luxury
no picture provided
-------------------------------
po number: po18012301
po date: Wed Nov 2311:57:50 HKT 2011
vendor: The smartphone compay
remarks: urgent, rush order
==>Item: sony smartphone price: 2389.0 qty: 1239 packing: standard
==>Item: ericson smartphone price: 897.0 qty: 1000 payTerm: 30 days
picture saved to /users/tiger-macpro/po18012301.png
po number: po18022002
po date: Wed Jan 2311:57:50 HKT 2013
vendor: The Samsung compay
==>Item: samsung galaxy s8 price: 2300.0 qty: 100 packing: standard
==>Item: samsung galaxy s7 price: 1897.0 qty: 1000 payTerm: 30 days
==>Item: apple iphone7 price: 6500.0 qty: 100 packing: luxury
no picture provided
------------------------------
下面是本次示范的源代码：
build.sbt
name := "learn-mongo"
version := "0.1"
scalaVersion := "2.12.4"
libraryDependencies := Seq(
"org.mongodb.scala" %% "mongo-scala-driver" % "2.2.1",
"com.lightbend.akka" %% "akka-stream-alpakka-mongodb" % "0.17"
)
FileStreaming.scala
import java.nio.file.Paths
import akka.stream.{Materializer}
import akka.stream.scaladsl.{FileIO, StreamConverters}
import scala.concurrent.{Await}
import akka.util._
import scala.concurrent.duration._
object
FileStreaming {
def FileToByteBuffer(fileName: String, timeOut: FiniteDuration)(
implicit mat: Materializer):ByteBuffer = {
val fut = FileIO.fromPath(Paths.get(fileName)).runFold(ByteString()) { case (hd, bs) =>
hd ++ bs
}
(Await.result(fut, timeOut)).toByteBuffer
}
def FileToByteArray(fileName: String, timeOut: FiniteDuration)(
implicit mat: Materializer): Array[Byte] = {
val fut = FileIO.fromPath(Paths.get(fileName)).runFold(ByteString()) { case (hd, bs) =>
hd ++ bs
}
(Await.result(fut, timeOut)).toArray
}
def FileToInputStream(fileName: String, timeOut: FiniteDuration)(
implicit mat: Materializer): InputStream = {
val fut = FileIO.fromPath(Paths.get(fileName)).runFold(ByteString()) { case (hd, bs) =>
hd ++ bs
}
val buf = (Await.result(fut, timeOut)).toArray
new ByteArrayInputStream(buf)
}
def ByteBufferToFile(byteBuf: ByteBuffer, fileName: String)(
implicit mat: Materializer) = {
val ba = new Array[Byte](byteBuf.remaining())
byteBuf.get(ba,0,ba.length)
val baInput = new ByteArrayInputStream(ba)
val source = StreamConverters.fromInputStream(() => baInput) //ByteBufferInputStream(bytes))
source.runWith(FileIO.toPath(Paths.get(fileName)))
}
def ByteArrayToFile(bytes: Array[Byte], fileName: String)(
implicit mat: Materializer) = {
val bb = ByteBuffer.wrap(bytes)
val baInput = new ByteArrayInputStream(bytes)
val source = StreamConverters.fromInputStream(() => baInput) //ByteBufferInputStream(bytes))
source.runWith(FileIO.toPath(Paths.get(fileName)))
}
def InputStreamToFile(is: InputStream, fileName: String)(
implicit mat: Materializer) = {
val source = StreamConverters.fromInputStream(() => is)
source.runWith(FileIO.toPath(Paths.get(fileName)))
}
}
MongoScala103.scala
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import java.util.Calendar
import org.bson.BsonBinary
import scala.util._
import FileStreaming._
import scala.concurrent.duration._
import org.mongodb.scala._
import org.mongodb.scala.bson.{BsonArray, BsonDocument}
import scala.collection.JavaConverters._
import org.mongodb.scala.connection.ClusterSettings
import org.mongodb.scala.model.Filters._
object
MongoScala103 extends App {
import Helpers._
val clusterSettings = ClusterSettings.builder()
.hosts(List(new ServerAddress("localhost:27017")).asJava).build()
val clientSettings = MongoClientSettings.builder().clusterSettings(clusterSettings).build()
val client = MongoClient(clientSettings)
implicit val system = ActorSystem()
implicit val mat = ActorMaterializer()
implicit val ec = system.dispatcher
val db: MongoDatabase = client.getDatabase("testdb")
val poOrgCollection: MongoCollection[Document] = db.getCollection("po")
poOrgCollection.drop.headResult()
val poCollection: MongoCollection[Document] = db.getCollection("po")
val ca = Calendar.getInstance()
ca.set(2011,10,23)
val podate1 = ca.getTime
ca.set(2012,12,23)
val podate2 = ca.getTime
val pic = FileToByteArray("/users/tiger-macpro/sample.png",3 seconds)
val po1 = Document (
"ponum" -> "po18012301",
"vendor" -> "The smartphone compay",
"podate" -> podate1,
"remarks" -> "urgent, rush order",
"handler" -> pic,
"podtl" -> Seq(
Document("item" -> "sony smartphone", "price" -> 2389.00, "qty" -> 1239, "packing" -> "standard"),
Document("item" -> "ericson smartphone", "price" -> 897.00, "qty" -> 1000, "payterm" -> "30 days")
)
)
val po2 = Document (
"ponum" -> "po18022002",
"vendor" -> "The Samsung compay",
"podate" -> podate2,
"podtl" -> Seq(
Document("item" -> "samsung galaxy s8", "price" -> 2300.00, "qty" -> 100, "packing" -> "standard"),
Document("item" -> "samsung galaxy s7", "price" -> 1897.00, "qty" -> 1000, "payterm" -> "30 days"),
Document("item" -> "apple iphone7", "price" -> 6500.00, "qty" -> 100, "packing" -> "luxury")
)
)
poCollection.insertMany(Seq(po1,po2)).headResult()
caseclass PO (
ponum: String,
podate: java.util.Date,
vendor: String,
remarks: Option[String],
podtl: Option[BsonArray],
handler: Option[BsonBinary]
)
def toPO(doc: Document): PO = {
val ks = doc.keySet
PO(
ponum = doc.getString("ponum"),
podate = doc.getDate("podate"),
vendor = doc.getString("vendor"),
remarks = {
if (ks.contains("remarks"))
Some(doc.getString("remarks"))
else
None
},
podtl = {
if (ks.contains("podtl"))
doc.get("podtl").asInstanceOf[Option[BsonArray]]
else
None
},
handler = {
if (ks.contains("handler"))
doc.get("handler").asInstanceOf[Option[BsonBinary]]
else
None
}
)
}
caseclass PODTL(
item: String,
price: Double,
qty: Int,
packing: Option[String],
payTerm: Option[String]
)
def toPODTL(podtl: Document): PODTL = {
val ks = podtl.keySet
PODTL(
item = podtl.getString("item"),
price = podtl.getDouble("price"),
qty = podtl.getInteger("qty"),
packing = {
if (ks.contains("packing"))
Some(podtl.getString("packing"))
else None
},
payTerm = {
if(ks.contains("payterm"))
Some(podtl.getString("payterm"))
else None
}
)
}
def showPO(po: PO) = {
println(s"po number: ${po.ponum}")
println(s"po date: ${po.podate.toString}")
println(s"vendor: ${po.vendor}")
if (po.remarks != None)
println(s"remarks: ${po.remarks.get}")
po.podtl match {
case Some(barr) =>
val docs = barr.getValues.asScala.toList
docs.map { dc =>
toPODTL(dc.asInstanceOf[org.bson.BsonDocument])
}.foreach { doc: PODTL =>
print(s"==>Item: ${doc.item} ")
print(s"price: ${doc.price} ")
print(s"qty: ${doc.qty} ")
doc.packing.foreach(pk => print(s"packing: ${pk} "))
doc.payTerm.foreach(pt => print(s"payTerm: ${pt} "))
println("")
}
case _ =>
}
po.handler match {
case Some(bs) =>
val fileName = s"/users/tiger-macpro/${po.ponum}.png"
ByteArrayToFile(bs.getData,fileName)
println(s"picture saved to ${fileName}")
case None => println("no picture provided")
}
}
poCollection.find().toFuture().onComplete {
case Success(docs) => docs.map(toPO).foreach (showPO)
println("------------------------------")
case Failure(e) => println(e.getMessage)
}
poCollection.find(equal("podtl.qty",100)).toFuture().onComplete {
case Success(docs) => docs.map(toPO).foreach (showPO)
println("-------------------------------")
case Failure(e) => println(e.getMessage)
}
scala.io.StdIn.readLine()
system.terminate()
}
原文：https://www.cnblogs.com/tiger-xc/p/8513685.html
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