/*

Overview
--------

To run a query using anorm you need to do three things:

 1. Connect to the database (with or without a transaction)
 2. Create an instance of `anorm.SqlQuery` using the `SQL` string interpolator
 3. Call one of the methods on `SqlQuery` to actually run the query
 4. In some cases, pass a `ResultSetParser` as an argument to the method in step 3

In some cases step 3 involves creating a `ResultSetParser` to dictate how to
parse the results. We'll come to that below.

Step 1: Connect to the database
-------------------------------

There are a few ways of doing this. We can connect with or without a transaction,
and we can connect to any of the databases we have defined in `conf/application.conf`.

We give each database in `application.conf` a name:

    db.databaseid.driver=org.postgresql.Driver
    db.databaseid.url="jdbc:postgresql://localhost:5432/databasename"

Play expects us to define a "default" database:

    db.default.driver=...
    db.default.url=...

but we can give a database any ID we like:

    db.test.driver=...
    db.test.url=...

We can define as many databases as we want. See the documentation here for full instructions:
  
    https://www.playframework.com/documentation/2.3.x/ScalaDatabase

*/

import play.api.db._

// Option A. Connect to the "default" database without a transaction:
DB.withConnection { implicit conn =>
  // query code goes here...
}

// Option B. Connect to another database without a transaction:
DB.withConnection("databaseid") { implicit conn =>
  // query code goes here...
}

// Option A. Connect to the "default" database with a transaction:
DB.withTransaction { implicit conn =>
  // query code goes here...
}

// Option B. Connect to another database with a transaction:
DB.withTransaction("databaseid") { implicit conn =>
  // query code goes here...
}

/*

Step 2. Create an SqlQuery
--------------------------

Anorm gives us a way to write raw SQL and create a Scala object of type `anorm.SqlQuery`.

`SqlQuery` has mehods to execute the query and return various types of result:

*/

import anorm._

// We create the query using the `SQL""` string interpolator:
val query = SQL"select * from mytable;"

// We can embed Scala values in the SQL using the standard `${}` syntax.
// Anorm escapes interpolated values appropriately:

val value = // some scala value
val query = SQL"select * from mytable where mycolumn = ${value};"

/*

Step 3. Call a method to execute the query
------------------------------------------

`SqlQuery` has several methods to actually start the query.
Each method accepts an implicit `Connection` as a parameter,
so we can only call them within a call to `withConnection` or
`withTransaction`:

*/

// Option A. `query.execute()` executes a query and returns nothing:
DB.withConnection { implicit conn =>
  SQL"""
    update mytable set col1 = $value where col2 = $anotherValue;
  """.execute()
  
  // etc...
}

// Option B. `query.as(...)` executes a query and returns results.
// The argument to `as(...)` is a `ResultSetParser`... see the next step:
DB.withConnection { implicit conn =>
  val rsParser: ResultSetParser[List[MyType]] = // ...

  val results: List[MyType] = SQL"""
    select * from mytable;
  """.as(rsParser)

  // etc...
}

// Option C. `query.executeInsert()` returns an `Option[Long]` primary key...
// assuming your table is set up with an auto-incrementing integer primary key:
DB.withConnection { implicit conn =>
  val newPK: Option[Long] = SQL"""
    insert into mytable (...) values (...);
  """.executeInsert()
  
  // etc...
}

// Option D. `query.executeInsert(...)` returns a primary key of another type.
// The argument to `executeInsert(...)` is a `ResultSetParser` to parse the keys:
DB.withConnection { implicit conn =>
  val rsParser: ResultSetParser[List[MyType]] = // ...

  val newPKs: List[MyType] = SQL"""
    insert into mytable (...) values (...);
  """.executeInsert(rsParser)

  // etc...
}

/*

Step 4. Create a `ResultSetParser` to use with the method from Step 3
---------------------------------------------------------------------

If you're retrieving results from the database using `as(...)`, you 
need to supply a `ResultSetParser` to tell Anorm how to parse the results 
of the query. This is also true for the result of an `executeInsert()`.

If you're using plain `execute()` you don't need this.

We create `ResultSetParsers` in two steps:

 A. Create a `RowParser` to specify what information is stored in each row.
 B. Call one of four methods on `RowParser` to convert it to a `ResultSetParser`
    that parses more than one row.

Step 4A. Create a `RowParser`
-----------------------------

A `RowParser` is a bit like a `Reads` -- it specifies a transformation from untyped data
(in this case a `java.sql.ResultSet`) to typed Scala data (an `Option` or `List` of domain objects).

*/

// Here is the syntax for creating a `RowParser`:
val myCaseClassParser: RowParser[MyCaseClass] = (
  SqlParser.somemethod1("columnname1") ~
  SqlParser.somemethod2("columnname2") ~
  SqlParser.somemethod3("columnname3") ~
  SqlParser.somemethod4("columnname4") ~
  SqlParser.somemethod5("columnname5") // etc...
) map {
  case columnvalue1 ~ columnvalue2 ~ columnvalue3 ~ columnvalue4 ~ columnvalue5 => // etc...
    MyCaseClass(columnvalue1, columnvalue2, columnvalue3, columnvalue4, columnvalue5) // etc...
}

/*

Let's look at the individual parts. First, the `SqlParser.somemethod(...)` parts.
`anorm.SqlParser` is an object with a bunch of methods for parsing column data types:

 - `SqlParser.str("columnname")`  creates a `RowParser[String]`  that parses "columname" as a `String`;
 - `SqlParser.int("columnname")`  creates a `RowParser[Int]`     that parses "columname" as a `Int`;
 - `SqlParser.long("columnname")` creates a `RowParser[Long]`    that parses "columname" as a `Long`;
 - `SqlParser.date("columnname")` creates a `RowParser[Date]`    that parses "columname" as a `Date`;
 - `SqlParser.bool("columnname")` creates a `RowParser[Boolean]` that parses "columname" as a `Boolean`;
 - and so on...

also...

 - `SqlParser.scalar[Type]` creates a `RowParser[Type]` that parses a single-column row,
   no matter what the column name is (useful for "select count(*) ..." style queries).

See https://www.playframework.com/documentation/2.3.x/api/scala/index.html#anorm.SqlParser$
for a complete list of methods.

We can combine `RowParsers` together to create bigger `RowParsers` that parse more than one column:

*/

val rowParser1: RowParser[Int]    = SqlParser.int("column1") // parses "column1" as an `Int`
val rowParser2: RowParser[String] = SqlParser.str("column2") // parses "column2" as a `String`

val rowParser3: RowParser[~[Int, String]] = rowParser1 ~ rowParser2

/*

What's going on with this last line of code? There are two things called `~`:

First, `anorm.~` is a pair-like case class that holds two values. See:
https://www.playframework.com/documentation/2.3.x/api/scala/index.html#anorm.$tilde

*/

val pairLikeThing: ~[Int, String] = ~(123, "456")

/*

It's called `~` because Scala lets us write binary (two-argument) types and patterns
using an infix syntax:

*/

val pairLikeThing: Int ~ String = ~(123, "456")

pairLikeThing match {
  case a ~ b =>
    assert(a == 123)
    assert(b == "456")
}

/*

The other use of `~` is a method on `RowParser`:

*/

rowParser1 ~ rowParser2

/*

This method combines the `RowParsers` together into a single `RowParser` that extracts
a values of type `~(a, b)` from the result-set. Its type is written like this:

*/

val rowParser3: RowParser[~[A, B]] = rowParser1 ~ rowParser2

/*

or we can write it using infix syntax:

*/

val rowParser3: RowParser[A ~ B] = rowParser1 ~ rowParser2

/*

We can `map` over a `RowParser` to produce a new parser with a different output:

*/

val rowParser4: RowParser[String] = rowParser3.map { valueFromRowParser3 =>
  valueFromRowParser3 match {
    case ~(valueFromRowParser1, valueFromRowParser2) =>
      s"I extracted the integer $valueFromRowParser1 and the string $valueFromRowParser2"
  }
}

/*

We can reduce this to the code we wrote above in two steps. First, write the pattern 
using infix syntax:

*/

val rowParser4: RowParser[String] = rowParser3.map { valueFromRowParser3 =>
  valueFromRowParser3 match {
    case valueFromRowParser1 ~ valueFromRowParser2 =>
      s"I extracted the integer $valueFromRowParser1 and the string $valueFromRowParser2"
  }
}

/*

Second, write the mapping function as a partial function:

*/

val rowParser4: RowParser[String] = rowParser3.map {
  case valueFromRowParser1 ~ valueFromRowParser2 =>
    s"I extracted the integer $valueFromRowParser1 and the string $valueFromRowParser2"
}

/*

All three versions of the code are semantically identical -- we're just using convenient
syntax to cut down on typing.

The final intuition we need to understand `RowParsers` is that instances of `~(a, b)`
can be nested:

*/

val nestedTildes: ~[A, ~[B, C]] = ~(a, ~(b, c))

/*

Again, we can write the types infix to make them easier to read:

*/

val nestedTildes: A ~ B ~ C = ~(a, ~(b, c))

/*

We can pattern match using infix syntax too:

*/

nestedTildes match {
  case a ~ b ~ c =>
    // etc...
}

/*

Bring it all back home, this gives us all the knowledge we need to understand the 
`RowParser` pattern from above.

We're extracting a bunch of values from individual columns, combining them into a set
of nested instances of `~(a, b)`, and pattern matching to pull them apart again and
turn them into something sane... in this case `MyCaseClass`:

*/

val myCaseClassParser: RowParser[MyCaseClass] = (
  SqlParser.somemethod1("columnname1") ~
  SqlParser.somemethod2("columnname2") ~
  SqlParser.somemethod3("columnname3") ~
  SqlParser.somemethod4("columnname4") ~
  SqlParser.somemethod5("columnname5") // etc...
) map {
  case columnvalue1 ~ columnvalue2 ~ columnvalue3 ~ columnvalue4 ~ columnvalue5 => // etc...
    MyCaseClass(columnvalue1, columnvalue2, columnvalue3, columnvalue4, columnvalue5) // etc...
}

/*

Confusing as this all may seem, it's just syntax. Syntax we don't see very often,
but syntax nonetheless.

Step 4B. Turn our `RowParser` into a `ResultSetParser`
------------------------------------------------------

Our `RowParser` states how we want to parse a single row from our results. We can use one
of four methods to turn that into a `ResultSetParser` that parses an entire set of results:

*/

// `rowParser.*` creates a `ResultSetParser` that parses all rows and returns a `List`:
val allRowsParser: ResultSetParser[List[MyCaseClass]] = myCaseClassParser.*

// `rowParser.single` creates a `ResultSetParser` that parses a single row
// (and fails if the result set is empty);
val singleRowParser: ResultSetParser[MyCaseClass] = myCaseClassParser.single

// `rowParser.singleOpt` creates a `ResultSetParser` that parses 0 or 1 rows and returns an `Option`:
val optionalRowParser: ResultSetParser[Option[MyCaseClass]] = myCaseClassParser.singleOpt

// We can pass any of these `ResultSetParsers` to `as(...)` or `executeInsert(...)` to receive
// the relevant results:

val allRows: List[MyCaseClass] = 
  SQL"""select * from mytable;""".
  as(allRowsParser)

val firstRow: Option[MyCaseClass] = 
  SQL"""select * from mytable limit 1;""".
  as(optionalRowParser)

val countRows: Int = 
  SQL"""select count(*) from mytable;""".
  as(SqlParser.scalar[Int].single)

/*

That's it! That's all you need to know to use Anorm.

*/