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A short guide to Anorm
/*
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.
*/
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