[UPDATE] – added (yet another) explanation on for
comprehensions
-
The
*
method:This returns the default projection – which is how you describe:
‘all the columns (or computed values) I am usually interested’ in.
Your table could have several fields; you only need a subset for
your default projection. The default projection must match the type
parameters of the table.Let’s take it one at a time. Without the
<>
stuff, just the*
:// First take: Only the Table Defintion, no case class: object Bars extends Table[(Int, String)]("bar") { def id = column[Int]("id", O.PrimaryKey, O.AutoInc) def name = column[String]("name") def * = id ~ name // Note: Just a simple projection, not using .? etc } // Note that the case class 'Bar' is not to be found. This is // an example without it (with only the table definition)
Just a table definition like that will let you make queries like:
implicit val session: Session = // ... a db session obtained from somewhere // A simple select-all: val result = Query(Bars).list // result is a List[(Int, String)]
the default projection of
(Int, String)
leads to aList[(Int, String)]
for simple queries such as these.// SELECT b.name, 1 FROM bars b WHERE b.id = 42; val q = for (b <- Bars if b.id === 42) yield (b.name ~ 1) // yield (b.name, 1) // this is also allowed: // tuples are lifted to the equivalent projection.
What’s the type of
q
? It is aQuery
with the projection(String, Int)
.
When invoked, it returns aList
of(String, Int)
tuples as per the projection.val result: List[(String, Int)] = q.list
In this case, you have defined the projection you want in the
yield
clause
of thefor
comprehension. -
Now about
<>
andBar.unapply
.This provides what are called Mapped Projections.
So far we’ve seen how slick allows you to express queries in Scala
that return a projection of columns (or computed values); So when executing
these queries you have to think of the result row of a query as a Scala tuple.
The type of the tuple will match the Projection that is defined (by your
for
comprehension as in the previous example, of by the default*
projection).
This is whyfield1 ~ field2
returns a projection ofProjection2[A, B]
where
A
is the type offield1
andB
is the type offield2
.q.list.map { case (name, n) => // do something with name:String and n:Int } Queury(Bars).list.map { case (id, name) => // do something with id:Int and name:String }
We’re dealing with tuples, which may be cumbersome if we have too many
columns. We’d like to think of results not asTupleN
but rather some
object with named fields.(id ~ name) // A projection // Assuming you have a Bar case class: case class Bar(id: Int, name: String) // For now, using a plain Int instead // of Option[Int] - for simplicity (id ~ name <> (Bar, Bar.unapply _)) // A MAPPED projection // Which lets you do: Query(Bars).list.map ( b.name ) // instead of // Query(Bars).list.map { case (_, name) => name } // Note that I use list.map instead of mapResult just for explanation's sake.
How does this work?
<>
takes a projectionProjection2[Int, String]
and
returns a mapped projection on the typeBar
. The two argumentsBar, Bar.unapply _
tell slick how this(Int, String)
projection must be mapped to a case class.This is a two-way mapping;
Bar
is the case class constructor, so that’s the
information needed to go from(id: Int, name: String)
to aBar
. Andunapply
if you’ve guessed it, is for the reverse.Where does
unapply
come from? This is a standard Scala method available for
any ordinary case class – just definingBar
gives you aBar.unapply
which
is an extractor that can be used to get back theid
andname
that the
Bar
was built with:val bar1 = Bar(1, "one") // later val Bar(id, name) = bar1 // id will be an Int bound to 1, // name a String bound to "one" // Or in pattern matching val bars: List[Bar] = // gotten from somewhere val barNames = bars.map { case Bar(_, name) => name } val x = Bar.unapply(bar1) // x is an Option[(String, Int)]
So your default projection can be mapped to the case class you most expect to use:
object Bars extends Table[Bar]("bar") { def id = column[Int]("id", O.PrimaryKey, O.AutoInc) def name = column[String]("name") def * = id ~ name <>(Bar, Bar.unapply _) }
Or you can even have it per-query:
case class Baz(name: String, num: Int) // SELECT b.name, 1 FROM bars b WHERE b.id = 42; val q1 = for (b <- Bars if b.id === 42) yield (b.name ~ 1 <> (Baz, Baz.unapply _))
Here the type of
q1
is aQuery
with a mapped projection toBaz
.
When invoked, it returns aList
ofBaz
objects:val result: List[Baz] = q1.list
-
Finally, as an aside, the
.?
offers Option Lifting – the Scala way of
dealing with values that may not be.(id ~ name) // Projection2[Int, String] // this is just for illustration (id.? ~ name) // Projection2[Option[Int], String]
Which, wrapping up, will work nicely with your original definition of
Bar
:case class Bar(id: Option[Int] = None, name: String) // SELECT b.id, b.name FROM bars b WHERE b.id = 42; val q0 = for (b <- Bars if b.id === 42) yield (b.id.? ~ b.name <> (Bar, Bar.unapply _)) q0.list // returns a List[Bar]
-
In response to the comment on how Slick uses
for
comprehensions:Somehow, monads always manage to show up and demand to
be part of the explanation…For comprehensions are not specific to collections only.
They may be used on any kind of Monad, and collections are
just one of the many kinds of monad types available in Scala.But as collections are familiar, they make a good starting
point for an explanation:val ns = 1 to 100 toList; // Lists for familiarity val result = for { i <- ns if i*i % 2 == 0 } yield (i*i) // result is a List[Int], List(4, 16, 36, ...)
In Scala, a for comprehension is syntactic sugar for
method (possibly nested) method calls: The above code
is (more or less) equivalent to:ns.filter(i => i*i % 2 == 0).map(i => i*i)
Basically, anything with
filter
,map
,flatMap
methods (in other words, a Monad) can be used in a
for
comprehension in place ofns
. A good example
is the Option monad. Here’s the previous example
where the samefor
statement works on both the
List
as well asOption
monads:// (1) val result = for { i <- ns // ns is a List monad i2 <- Some(i*i) // Some(i*i) is Option if i2 % 2 == 0 // filter } yield i2 // Slightly more contrived example: def evenSqr(n: Int) = { // return the square of a number val sqr = n*n // only when the square is even if (sqr % 2 == 0) Some (sqr) else None } // (2) result = for { i <- ns i2 <- evenSqr(i) // i2 may/maynot be defined for i! } yield i2
In the last example, the transformation would perhaps look
like this:// 1st example val result = ns.flatMap(i => Some(i*i)).filter(i2 => i2 %2 ==0) // Or for the 2nd example result = ns.flatMap(i => evenSqr(i))
In Slick, queries are monadic – they are just objects with
themap
,flatMap
andfilter
methods. So thefor
comprehension
(shown in the explanation of the*
method) just translates to:val q = Query(Bars).filter(b => b.id === 42).map(b => b.name ~ 1) // Type of q is Query[(String, Int)] val r: List[(String, Int)] = q.list // Actually run the query
As you can see,
flatMap
,map
andfilter
are used to
generate aQuery
by the repeated transformation ofQuery(Bars)
with each invocation offilter
andmap
. In the case of
collections these methods actually iterate and filter the collection
but in Slick they are used to generate SQL. More details here:
How does Scala Slick translate Scala code into JDBC?