我刚刚开始使用Anorm和解析器组合器。似乎有很多样板代码。例如,我有
case class Model(
id:Int,
field1:String,
field2:Int,
// a bunch of fields omitted
)
val ModelParser:RowParser[RegdataStudentClass] = {
int("id") ~
str("field1") ~
int("field2") ~
// a bunch of fields omitted
map {
case id ~ field1 ~ field2 //more omissions
=> Model(id, field1, field2, // still more omissions
)
}
}
在定义整个事物之前,每个数据库字段重复四(!)次。似乎解析器应该能够从case类中半自动推导出来。建议减少这里涉及的工作的任何工具或其他技术?
感谢您的任何指示。
答案 0 :(得分:3)
嗯,你实际上根本不需要重复任何事情。您可以使用flatten
创建一个元组,然后从该元组中创建模型实例:
(int("id") ~ str("field1") ~ int("field2"))
.map(flatten)
.map { tuple => (Model apply _).tupled(tuple) }
但是,如果你需要进行一些进一步的转换,你需要以某种方式修改元组:
(int("id") ~ str("field1") ~ int("field2"))
.map(flatten)
.map { tuple => (Model apply _).tupled(tuple.copy(_1=..., _2=....) }
答案 1 :(得分:3)
这是我最终开发的解决方案。我目前在Play项目中将其作为课程;它可以(应该!)变成一个独立的工具。要使用它,请将tableName
val更改为表的名称。然后使用课程底部的main
运行它。它将打印案例类和解析器组合器的框架。大多数时候,这些骷髅只需要很少的调整。
拜伦
package tools
import scala.sys.process._
import anorm._
/**
* Generate a parser combinator for a specified table in the database.
* Right now it's just specified with the val "tableName" a few lines
* down.
*
* 20121024 bwbecker
*/
object ParserGenerator {
val tableName = "uwdata.uwdir_person_by_student_id"
/**
* Convert the sql type to an equivalent Scala type.
*/
def fieldType(field:MetaDataItem):String = {
val t = field.clazz match {
case "java.lang.String" => "String"
case "java.lang.Boolean" => "Boolean"
case "java.lang.Integer" => "Int"
case "java.math.BigDecimal" => "BigDecimal"
case other => other
}
if (field.nullable) "Option[%s]" format (t)
else t
}
/**
* Drop the schema name from a string (tablename or fieldname)
*/
def dropSchemaName(str:String):String =
str.dropWhile(c => c != '.').drop(1)
def formatField(field:MetaDataItem):String = {
"\t" + dropSchemaName(field.column) + " : " + fieldType(field)
}
/**
* Derive the class name from the table name: drop the schema,
* remove the underscores, and capitalize the leading letter of each word.
*/
def deriveClassName(tableName:String) =
dropSchemaName(tableName).split("_").map(w => w.head.toUpper + w.tail).mkString
/**
* Query the database to get the metadata for the given table.
*/
def getFieldList(tableName:String):List[MetaDataItem] = {
val sql = SQL("""select * from %s limit 1""" format (tableName))
val results:Stream[SqlRow] = util.Util.DB.withConnection { implicit connection => sql() }
results.head.metaData.ms
}
/**
* Generate a case class definition with one data member for each field in
* the database table.
*/
def genClassDef(className:String, fields:List[MetaDataItem]):String = {
val fieldList = fields.map(formatField(_)).mkString(",\n")
""" case class %s (
%s
)
""" format (className, fieldList )
}
/**
* Generate a parser for the table.
*/
def genParser(className:String, fields:List[MetaDataItem]):String = {
val header:String = "val " + className.take(1).toLowerCase() + className.drop(1) +
"Parser:RowParser[" + className + "] = {\n"
val getters = fields.map(f =>
"\tget[" + fieldType(f) + "](\"" + dropSchemaName(f.column) + "\")"
).mkString(" ~ \n")
val mapper = " map {\n case " + fields.map(f => dropSchemaName(f.column)).mkString(" ~ ") +
" =>\n\t" + className + "(" + fields.map(f => dropSchemaName(f.column)).mkString(", ") + ")\n\t}\n}"
header + getters + mapper
}
def main(args:Array[String]) = {
val className = deriveClassName(tableName)
val fields = getFieldList(tableName)
println( genClassDef(className, fields) )
println( genParser(className, fields))
}
}