问题
I have been using Play framework 2.0 for about 6 months, I have been wondering why they use so many boilerplate code to parse from my SQL query returns, like below:
case class Journal_accountDetail(amount: Double, states: Boolean)
val Journal_AccountParser: RowParser[Journal_accountDetail] = {
get[Double] ("amount") ~
get[Boolean] ("states") map({
case amount~states => Journal_accountDetail(amount,states)
})
}
Is it something that boost Play framework performance ??
回答1:
The parsing API can seem a bit tedious at first, but it's quite powerful when you start combining and re-using the parsers, and much less ugly than pattern matching in every function that returns an SQL result.
Imagine something like this:
case class User(id: Int, name: String, address: Address)
case class Address(id: Int, street: String, city: String, state: State, country: Country)
case class State(id: Int, abbrev: String, name: String)
case class Country(id: Int, code: String, name: String)
To construct the User
you need to parse a result with multiple JOIN
s. Rather than having one large parser, we construct one for each class in it's companion object:
object User {
val parser: RowParser[User] = {
get[Int]("users.id") ~
get[String]("users.name") ~
Address.parser map {
case id~name~address => User(id, name, address)
}
}
}
object Address {
val parser: RowParser[Address] = {
get[Int]("address.id") ~
get[String]("address.street) ~
get[String]("address.city") ~
State.parser ~
Country.parser map {
case id~street~city~state~country => Address(id, street, city, state, country)
}
}
}
object State {
val parser: RowParser[State] = {
get[Int]("states.id") ~
get[String]("states.abbrev") ~
get[String]("states.name") map {
case id~abbrev~name => State(id, abbrev, name)
}
}
}
object Country {
val parser: RowParser[Country] = {
get[Int]("countries.id") ~
get[String]("countries.code") ~
get[String]("countries.name") map {
case id~code~name => Country(id, code, name)
}
}
}
Note how I'm using the full table space in the parsers, in order to avoid column name collisions.
Altogether, this looks like a lot of code, but for each source file it's only a small footprint. And the largest benefit is that our User
parser is quite clean despite it's complex structure. Let's say in User
the address
is actually Option[Address]
. Then accounting for that change is as simple as changing Address.parser
in the User
parser to (Address.parser ?)
.
For parsing simple queries, yes it does seem like a lot of boilerplate. But I'm quite thankful for the parsing API when it comes to parsing examples like the one above (and much more complex ones).
回答2:
anorm.SqlParser also provides convinent parser functions, like .str
, .int
, .float
, .double
, ... instead of .get[String]
, .get[Int]
, .get[Float]
, .get[Double]
. Best regards.
来源:https://stackoverflow.com/questions/24384169/using-anorm-rowparser