§Anorm, simple SQL data access
Play includes a simple data access layer called Anorm that uses plain SQL to interact with the database and provides an API to parse and transform the resulting datasets.
Anorm is Not an Object Relational Mapper
In the following documentation, we will use the MySQL world sample database .
If you want to enable it for your application, follow the MySQL website instructions, and enable it for your application by adding the following configuration line in your
conf/application.conffile:db.default.driver= com.mysql.jdbc.Driver db.default.url="jdbc:mysql://localhost/world" db.default.user=root db.default.password=secret
§Overview
It can feel strange to return to plain old SQL to access an SQL database these days, especially for Java developers accustomed to using a high-level Object Relational Mapper like Hibernate to completely hide this aspect.
Although we agree that these tools are almost required in Java, we think that they are not needed at all when you have the power of a higher-level programming language like Scala. On the contrary, they will quickly become counter-productive.
§Using JDBC is a pain, but we provide a better API
We agree that using the JDBC API directly is tedious, particularly in Java. You have to deal with checked exceptions everywhere and iterate over and over around the ResultSet to transform this raw dataset into your own data structure.
We provide a simpler API for JDBC; using Scala you don’t need to bother with exceptions, and transforming data is really easy with a functional language. In fact, the goal of the Play Scala SQL access layer is to provide several APIs to effectively transform JDBC data into other Scala structures.
§You don’t need another DSL to access relational databases
SQL is already the best DSL for accessing relational databases. We don’t need to invent something new. Moreover the SQL syntax and features can differ from one database vendor to another.
If you try to abstract this point with another proprietary SQL like DSL you will have to deal with several ‘dialects’ dedicated for each vendor (like Hibernate ones), and limit yourself by not using a particular database’s interesting features.
Play will sometimes provide you with pre-filled SQL statements, but the idea is not to hide the fact that we use SQL under the hood. Play just saves typing a bunch of characters for trivial queries, and you can always fall back to plain old SQL.
§A type safe DSL to generate SQL is a mistake
Some argue that a type safe DSL is better since all your queries are checked by the compiler. Unfortunately the compiler checks your queries based on a meta-model definition that you often write yourself by ‘mapping’ your data structure to the database schema.
There are no guarantees that this meta-model is correct. Even if the compiler says that your code and your queries are correctly typed, it can still miserably fail at runtime because of a mismatch in your actual database definition.
§Take Control of your SQL code
Object Relational Mapping works well for trivial cases, but when you have to deal with complex schemas or existing databases, you will spend most of your time fighting with your ORM to make it generate the SQL queries you want.
Writing SQL queries yourself can be tedious for a simple ‘Hello World’ application, but for any real-life application, you will eventually save time and simplify your code by taking full control of your SQL code.
§Executing SQL queries
To start you need to learn how to execute SQL queries.
First, import anorm._, and then simply use the SQL object to create queries. You need a Connection to run a query, and you can retrieve one from the play.api.db.DB helper:
import anorm._
DB.withConnection { implicit c =>
val result: Boolean = SQL("Select 1").execute()
}
The execute() method returns a Boolean value indicating whether the execution was successful.
To execute an update, you can use executeUpdate(), which returns the number of rows updated.
val result: Int = SQL("delete from City where id = 99").executeUpdate()
If you are inserting data that has an auto-generated Long primary key, you can call executeInsert(). If you have more than one generated key, or it is not a Long, executeInsert can be passed a ResultSetParser to return the correct key.
val id: Int = SQL("insert into City(name, country) values ({name}, {country}")
.on("Cambridge", "New Zealand").executeInsert()
Since Scala supports multi-line strings, feel free to use them for complex SQL statements:
val sqlQuery = SQL(
"""
select * from Country c
join CountryLanguage l on l.CountryCode = c.Code
where c.code = 'FRA';
"""
)
If your SQL query needs dynamic parameters, you can declare placeholders like {name} in the query string, and later assign a value to them:
SQL(
"""
select * from Country c
join CountryLanguage l on l.CountryCode = c.Code
where c.code = {countryCode};
"""
).on("countryCode" -> "FRA")
§Retrieving data using the Stream API
The first way to access the results of a select query is to use the Stream API.
When you call apply() on any SQL statement, you will receive a lazy Stream of Row instances, where each row can be seen as a dictionary:
// Create an SQL query
val selectCountries = SQL("Select * from Country")
// Transform the resulting Stream[Row] to a List[(String,String)]
val countries = selectCountries().map(row =>
row[String]("code") -> row[String]("name")
).toList
In the following example we will count the number of Country entries in the database, so the result set will be a single row with a single column:
// First retrieve the first row
val firstRow = SQL("Select count(*) as c from Country").apply().head
// Next get the content of the 'c' column as Long
val countryCount = firstRow[Long]("c")
§Using Pattern Matching
You can also use Pattern Matching to match and extract the Row content. In this case the column name doesn’t matter. Only the order and the type of the parameters is used to match.
The following example transforms each row to the correct Scala type:
case class SmallCountry(name:String)
case class BigCountry(name:String)
case class France
val countries = SQL("Select name,population from Country")().collect {
case Row("France", _) => France()
case Row(name:String, pop:Int) if(pop > 1000000) => BigCountry(name)
case Row(name:String, _) => SmallCountry(name)
}
Note that since collect(…) ignores the cases where the partial function isn’t defined, it allows your code to safely ignore rows that you don’t expect.
§Special data types
§Clobs
CLOBs/TEXTs can be extracted as so:
SQL("Select name,summary from Country")().map {
case Row(name: String, summary: java.sql.Clob) => name -> summary
}
Here we specifically chose to use map, as we want an exception if the row isn’t in the format we expect.
§Binary
Extracting binary data is similarly possible:
SQL("Select name,image from Country")().map {
case Row(name: String, image: Array[Byte]) => name -> image
}
§Dealing with Nullable columns
If a column can contain Null values in the database schema, you need to manipulate it as an Option type.
For example, the indepYear of the Country table is nullable, so you need to match it as Option[Int]:
SQL("Select name,indepYear from Country")().collect {
case Row(name:String, Some(year:Int)) => name -> year
}
If you try to match this column as Int it won’t be able to parse Null values. Suppose you try to retrieve the column content as Int directly from the dictionary:
SQL("Select name,indepYear from Country")().map { row =>
row[String]("name") -> row[Int]("indepYear")
}
This will produce an UnexpectedNullableFound(COUNTRY.INDEPYEAR) exception if it encounters a null value, so you need to map it properly to an Option[Int], as:
SQL("Select name,indepYear from Country")().map { row =>
row[String]("name") -> row[Option[Int]]("indepYear")
}
This is also true for the parser API, as we will see next.
§Using the Parser API
You can use the parser API to create generic and reusable parsers that can parse the result of any select query.
Note: This is really useful, since most queries in a web application will return similar data sets. For example, if you have defined a parser able to parse a
Countryfrom a result set, and anotherLanguageparser, you can then easily compose them to parse both Country and Language from a join query.First you need to
import anorm.SqlParser._
First you need a RowParser, i.e. a parser able to parse one row to a Scala value. For example we can define a parser to transform a single column result set row, to a Scala Long:
val rowParser = scalar[Long]
Then we have to transform it into a ResultSetParser. Here we will create a parser that parse a single row:
val rsParser = scalar[Long].single
So this parser will parse a result set to return a Long. It is useful to parse to result produced by a simple SQL select count query:
val count: Long = SQL("select count(*) from Country").as(scalar[Long].single)
Let’s write a more complicated parser:
str("name") ~ int("population"), will create a RowParser able to parse a row containing a String name column and an Integer population column. Then we can create a ResultSetParser that will parse as many rows of this kind as it can, using *:
val populations:List[String~Int] = {
SQL("select * from Country").as( str("name") ~ int("population") * )
}
As you see, this query’s result type is List[String~Int] - a list of country name and population items.
You can also rewrite the same code as:
val result:List[String~Int] = {
SQL("select * from Country").as(get[String]("name")~get[Int]("population")*)
}
Now what about the String~Int type? This is an Anorm type that is not really convenient to use outside of your database access code. You would rather have a simple tuple (String, Int) instead. You can use the map function on a RowParser to transform its result to a more convenient type:
str("name") ~ int("population") map { case n~p => (n,p) }
Note: We created a tuple
(String,Int)here, but there is nothing stopping you from transforming theRowParserresult to any other type, such as a custom case class.
Now, because transforming A~B~C types to (A,B,C) is a common task, we provide a flatten function that does exactly that. So you finally write:
val result:List[(String,Int)] = {
SQL("select * from Country").as(
str("name") ~ int("population") map(flatten) *
)
}
Now let’s try with a more complicated example. How to parse the result of the following query to retrieve the country name and all spoken languages for a country code?
select c.name, l.language from Country c
join CountryLanguage l on l.CountryCode = c.Code
where c.code = 'FRA'
Let’s start by parsing all rows as a List[(String,String)] (a list of name,language tuple):
var p: ResultSetParser[List[(String,String)] = {
str("name") ~ str("language") map(flatten) *
}
Now we get this kind of result:
List(
("France", "Arabic"),
("France", "French"),
("France", "Italian"),
("France", "Portuguese"),
("France", "Spanish"),
("France", "Turkish")
)
We can then use the Scala collection API, to transform it to the expected result:
case class SpokenLanguages(country:String, languages:Seq[String])
languages.headOption.map { f =>
SpokenLanguages(f._1, languages.map(_._2))
}
Finally, we get this convenient function:
case class SpokenLanguages(country:String, languages:Seq[String])
def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
val languages: List[(String, String)] = SQL(
"""
select c.name, l.language from Country c
join CountryLanguage l on l.CountryCode = c.Code
where c.code = {code};
"""
)
.on("code" -> countryCode)
.as(str("name") ~ str("language") map(flatten) *)
languages.headOption.map { f =>
SpokenLanguages(f._1, languages.map(_._2))
}
}
To continue, let’s complicate our example to separate the official language from the others:
case class SpokenLanguages(
country:String,
officialLanguage: Option[String],
otherLanguages:Seq[String]
)
def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
val languages: List[(String, String, Boolean)] = SQL(
"""
select * from Country c
join CountryLanguage l on l.CountryCode = c.Code
where c.code = {code};
"""
)
.on("code" -> countryCode)
.as {
str("name") ~ str("language") ~ str("isOfficial") map {
case n~l~"T" => (n,l,true)
case n~l~"F" => (n,l,false)
} *
}
languages.headOption.map { f =>
SpokenLanguages(
f._1,
languages.find(_._3).map(_._2),
languages.filterNot(_._3).map(_._2)
)
}
}
If you try this on the world sample database, you will get:
$ spokenLanguages("FRA")
> Some(
SpokenLanguages(France,Some(French),List(
Arabic, Italian, Portuguese, Spanish, Turkish
))
)