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mysql like jdbc_ScalikeJDBC,操作mysql数据,API

周奇
2023-12-01

一、构建maven项目,添加pom.xml依赖

2.11.8

3.3.2

org.scalikejdbc

scalikejdbc_2.11

${scalikejdbc.version}

org.scalikejdbc

scalikejdbc-config_2.11

${scalikejdbc.version}

二、resource文件下创建application.conf文件,并配置以下内容

# JDBC settings

db.default.driver="com.mysql.jdbc.Driver"

db.default.url="jdbc:mysql://localhost:3306//spark?characterEncoding=uft-8"

db.default.user="root"

db.default.password="123456"

# Connection Pool settings

db.default.poolInitialSize=10

db.default.poolMaxSize=20

db.default.connectionTimeoutMillis=1000

# Connection Pool settings

db.default.poolInitialSize=5

db.default.poolMaxSize=7

db.default.poolConnectionTimeoutMillis=1000

db.default.poolValidationQuery="select 1 as one"

db.default.poolFactoryName="commons-dbcp2"

db.legacy.driver="org.h2.Driver"

db.legacy.url="jdbc:h2:file:./db/db2"

db.legacy.user="foo"

db.legacy.password="bar"

# MySQL example

db.default.driver="com.mysql.jdbc.Driver"

db.default.url="jdbc:mysql://localhost/scalikejdbc"

# PostgreSQL example

db.default.driver="org.postgresql.Driver"

db.default.url="jdbc:postgresql://localhost:5432/scalikejdbc"

三、操作mysql数据库实例

import scalikejdbc.{ConnectionPool, DB, SQL}

import scalikejdbc.config.DBs

case class User(id: Int, name: String, age: Int)

object ScalaLikeJdbc {

def main(args: Array[String]): Unit = {

// 加载驱动

classOf[com.mysql.jdbc.Driver]

// Class.forName("com.mysql.jdbc.Driver")

//解析application.conf的文件

DBs.setup()

// createTable()

// println("User2表创建完毕")

// val userLists = List(User(1, "zhangsan", 18), User(2, "lisi", 20), User(3, "wangwu", 35))

// insert(userLists)

// println("批量插入完毕")

// println(selectAll())

// println(selectByID(2))

// updateByID(2,60)

// println(selectByID(2))

deleteByID(2)

println(selectAll())

DBs.close()

}

def createTable(): Unit = {

DB.autoCommit { implicit session =>

SQL("create table user2(\nid int not null auto_increment,\nname varchar(100) not null,\nage int,\nprimary key ( id )\n)engine=innodb default charset=utf8; ")

.execute.apply()

}

}

def insert(users: List[User]): Unit = {

DB.localTx { implicit session =>

for (user

SQL("insert into user2(id,name,age) values(?,?,?)")

.bind(user.id, user.name, user.age)

.update().apply()

}

}

}

//3、查询所有

def selectAll(): List[User] = {

val list: List[User] = DB.readOnly {

implicit session =>

SQL("SELECT * from user2").map(rs => User(rs.int(1), rs.string(2), rs.int(3))).list().apply()

}

list

}

def selectByID(id: Int): Option[User] = {

val list: Option[User] = DB.readOnly {

implicit session =>

SQL(s"select id,name,age from user2 where id = ${id}").map(rs => User(rs.int(1), rs.string(2), rs.int(3))).single.apply()

}

list

}

def updateByID(id: Int, age: Int): Unit = {

DB.localTx {

implicit session =>

SQL(s"update user2 set age = ? where id = ${id}").bind(age).update().apply()

}

}

def deleteByID(id: Int): Unit = {

DB.localTx {

implicit session =>

SQL(s"delete from user2 where id = ${id}").update().apply()

}

}

}

四、直接在代码中进行连接初始化,省去(二)

import scalikejdbc.config._

import scalikejdbc._

import scala.collection.mutable.ListBuffer

object ScalikejdbcApp {

Class.forName("com.mysql.jdbc.Driver")

ConnectionPool.singleton("jdbc:mysql://192.168.xx.xx:3306/spark","root","123456")

implicit val session = AutoSession

def main(args: Array[String]): Unit = {

create

//insert(1,"ruoruo")

//highlevelinsert(List(3,4),List("JJ","星星"))//顺序不连续没关系,但是id有重复就会报错

//update(4,"xingxing")

println(select())

delete()

ConnectionPool.close()//用完连接池要关闭

}

def create = {

implicit val session = AutoSession

sql"""

CREATE TABLE IF NOT EXISTS Person(

id int PRIMARY KEY NOT NULL auto_increment,

name varchar(64),

created_time timestamp not null DEFAULT current_timestamp

)ENGINE=InnoDB DEFAULT CHARSET=utf8 AUTO_INCREMENT=1

""".execute.apply()

//如果你不想字段为 NULL 可以设置字段的属性为 NOT NULL, 在操作数据库时如果输入该字段的数据为NULL ,就会报错。

//PRIMARY KEY关键字用于定义列为主键。 您可以使用多列来定义主键,列间以逗号分隔

//AUTO_INCREMENT定义列为自增的属性,一般用于主键,数值会自动加1

//ENGINE 设置存储引擎,CHARSET 设置编码

}

//插入一条数据

def insert(id:Int,name:String ): Unit ={

implicit val session=AutoSession

sql"""insert into Person(id,name)values (${id},${name})""".update.apply()

}

//插入两条数据。

def highlevelinsert(id:List[Int],name:List[String])={

sql"""insert into Person(id,name)values(${id(0)},${name(0)}),(${id(1)},${name(1)}) """.update().apply()

println(s"${id}(0),${name(0)}")//List(3, 4)(0),JJ

}

//更新数据

def update(id:Int,name:String)={

implicit val session=AutoSession

sql"update Person set name=${name}where id =${id}".update().apply()

}

//查询数据

def select()={

implicit val session=AutoSession

//sql"select * from Person".map(x=>x.string("name")).list().apply()//List(ruoruo, J?, xingxing)

//sql"select * from Person where Person.id=4".map(x=>x.string("name")).single().apply()//Some(xingxing)

// sql"select * from Person where Person.id=4".map(x=>x.string("name")).single().apply().get//xingxing

sql"select * from Person".map(x=>(x.string("id"),x.string("name"))).list().apply()//List((1,ruoruo), (3,J?), (4,xingxing))

}

//删除数据

def delete={

//sql"delete from Person where person.id=3".update()//删除id=3,name=J总这条数据

//sql"delete from Person".update()//删除Person这张表里面的所有数据,但是该表依然存在

sql"drop table if exists person".update()//删除整张表

}

}

五、ScalikeJDBC操作API

5.1 查询API

ScalikeJDBC中有多种查询API,包括single, first, list 和foreach,他们内部都是调用java.sql.PreparedStatement#executeQuery()实现的。

single查询

single函数返回匹配到的单行数据,并且封装成Option值。如果single函数匹配到多行,那么在运行的时候会抛出异常。使用single函数如下:

import scalikejdbc._

val id = 123

// simple example

val name: Option[String] = DB readOnly { implicit session =>

sql"select name from emp where id = ${id}".map(rs => rs.string("name")).single.apply()

}

// defined mapper as a function

val nameOnly = (rs: WrappedResultSet) => rs.string("name")

val name: Option[String] = DB readOnly { implicit session =>

sql"select name from emp where id = ${id}".map(nameOnly).single.apply()

}

// define a class to map the result

case class Emp(id: String, name: String)

val emp: Option[Emp] = DB readOnly { implicit session =>

sql"select id, name from emp where id = ${id}"

.map(rs => Emp(rs.string("id"), rs.string("name"))).single.apply()

}

// QueryDSL

object Emp extends SQLSyntaxSupport[Emp] {

def apply(e: ResultName[Emp])(rs: WrappedResultSet): Emp = new Emp(id = rs.get(e.id), name = rs.get(e.name))

}

val e = Emp.syntax("e")

val emp: Option[Emp] = DB readOnly { implicit session =>

withSQL { select.from(Emp as e).where.eq(e.id, id) }.map(Emp(e.resultName)).single.apply()

}

返回多行结果中的第一行

first函数返回多行结果中的第一行结果,而且返回的类型也是Option封装的。

val name: Option[String] = DB readOnly { implicit session =>

sql"select name from emp".map(rs => rs.string("name")).first.apply()

}

val e = Emp.syntax("e")

val name: Option[String] = DB readOnly { implicit session =>

withSQL { select(e.result.name).from(Emp as e) }.map(_.string(e.name)).first.apply()

}

返回List的结果

list函数将匹配到的多行存储在scala.collection.immutable.List中:

val name: List[String] = DB readOnly { implicit session =>

sql"select name from emp".map(rs => rs.string("name")).list.apply()

}

val e = Emp.syntax("e")

val name: Option[String] = DB readOnly { implicit session =>

withSQL { select(e.result.name).from(Emp as e) }.map(_.string(e.name)).list.apply()

}

Foreach操作

foreach函数允许你在iterations中进行一些有副作用的计算,这个函数在ResultSet含有大量的返回值情况下特别有用。

DB readOnly { implicit session =>

sql"select name from emp".foreach { rs =>

out.write(rs.string("name"))

}

}

val e = Emp.syntax("e")

DB readOnly { implicit session =>

withSQL { select(e.name).from(Emp as e) }.foreach { rs =>

out.write(rs.string(e.name))

}

}

设置JDBC fetchSize

PostgreSQL的JDBC驱动默认情况下(fetchSize=0)将无限制地获取返回的结果,这种情况会导致内存相关的问题:

在ScalikeJDBC 2.0.5之后,我们可以设置JDBC的fetchSize值:

val e = Emp.syntax("e")

DB readOnly { implicit session =>

sql"select name from emp"

.fetchSize(1000)

.foreach { rs => out.write(rs.string("name")) }

}

或者直接在scalikejdbc.DBSession上设置fetchSize:

val (e, c) = (Emp.syntax("e"), Cmp.syntax("c"))

DB readOnly { implicit session =>

session.fetchSize(1000)

withSQL { select(e.name).from(Emp as e) }.foreach { rs =>

out.write(rs.string(e.name)

}

withSQL { select(c.name).from(Cmp as c) }.foreach { rs =>

out.write(rs.string(c.name))

}

}

实现自定义的抽取器(Extractor)

def toMap(rs: WrappedResultSet): Map[String, Any] = {

(1 to rs.metaData.getColumnCount).foldLeft(Map[String, Any]()) { (result, i) =>

val label = rs.metaData.getColumnLabel(i)

Some(rs.any(label)).map { nullableValue => result + (label -> nullableValue) }.getOrElse(result)

}

}

sql"select * from emp".map(rs => toMap(rs)).single.apply()

使用ParameterBinder

ParameterBinder[A]使得我们可以在ScalikeJDBC中自定义如何将参数和PreparedStatement进行绑定。下面的例子将展示如何在InputStream和PreparedStatement进行绑定的情况使用ResultSet#setBinaryStream:

sql"create table blob_example (id bigint, data blob)").execute.apply()

val bytes = scala.Array[Byte](1, 2, 3, 4, 5, 6, 7)

val bytesBinder = ParameterBinder[InputStream](

value = new ByteArrayInputStream(bytes),

binder = (stmt: PreparedStatement, idx: Int) => stmt.setBinaryStream(idx, in, bytes.length)

)

sql"insert into blob_example (data) values (${bytesBinder})").update.apply()

5.2 更新API

update最终运行的是java.sql.PreparedStatement#executeUpdate()

import scalikejdbc._

DB localTx { implicit session =>

sql"""insert into emp (id, name, created_at) values (${id}, ${name}, ${DateTime.now})"""

.update.apply()

val id = sql"insert into emp (name, created_at) values (${name}, current_timestamp)"

.updateAndReturnGeneratedKey.apply()

sql"update emp set name = ${newName} where id = ${id}".update.apply()

sql"delete emp where id = ${id}".update.apply()

}

val column = Emp.column

DB localTx { implicit s =>

withSQL {

insert.into(Emp).namedValues(

column.id -> id,

column.name -> name,

column.createdAt -> DateTime.now)

}.update.apply()

val id: Long = withSQL {

insert.into(Empy).namedValues(column.name -> name, column.createdAt -> sqls.currentTimestamp)

}.updateAndReturnGeneratedKey.apply()

withSQL { update(Emp).set(column.name -> newName).where.eq(column.id, id) }.update.apply()

withSQL { delete.from(Emp).where.eq(column.id, id) }.update.apply()

}

5.3 Execute API

execute最终运行的是java.sql.PreparedStatement#execute().

DB autoCommit { implicit session =>

sql"create table emp (id integer primary key, name varchar(30))".execute.apply()

}

// QueryDSL doesn't support DDL yet.

5.4 批处理(Batch)API

batch和batchByName最终运行的是java.sql.PreparedStatement#executeBatch()

import scalikejdbc._

DB localTx { implicit session =>

val batchParams: Seq[Seq[Any]] = (2001 to 3000).map(i => Seq(i, "name" + i))

sql"insert into emp (id, name) values (?, ?)".batch(batchParams: _*).apply()

}

DB localTx { implicit session =>

sql"insert into emp (id, name) values ({id}, {name})"

.batchByName(Seq(Seq('id -> 1, 'name -> "Alice"), Seq('id -> 2, 'name -> "Bob")):_*)

.apply()

}

val column = Emp.column

DB localTx { implicit session =>

val batchParams: Seq[Seq[Any]] = (2001 to 3000).map(i => Seq(i, "name" + i))

withSQL {

insert.into(Emp).namedValues(column.id -> sqls.?, column.name -> sqls.?)

}.batch(batchParams: _*).apply()

}

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