#SCALA OPTION HOW TO#

* projection is given as a type parameter to Table. Slick’s * projection does not have to match This describes what you get back when you return rows (in the form of a There is usually no reason to specify theseĮvery table requires a * method containing a default projection. Nullability is otherwise determined from the

#SCALA OPTION HOW TO#

NotNull, Nullable Explicitly mark the column as nullable or non-nullable when creating theĭDL statements for the table. Let's use the function defined in Step 1 to calculate the cost for a customer buying 5 Glazed Donuts: You should see the following output when you run your Scala application in IntelliJ: Step 2: How to call a function which has an Option parameter Calculating cost for Glazed Donut, quantity 5 Total cost 12.5. Slick will check if the return column is properly marked as AutoInc where Outside of DDL creation: Many databases do not allow non-AutoInc columns toīe returned when inserting data (often silently ignoring other columns), so Unlike the other column options, this one also has a meaning AutoInc Mark the column as an auto-incrementing key when creating the DDL

DBType(dbType: String) Use a non-standard database-specific type for the DDL statements (e.g.ĭBType('VARCHAR(20)') for a String column). This information is only used for creating DDL statements so that theĭatabase can fill in the missing information. Default(defaultValue: T) Specify a default value for inserting data into the table without this column. The following ones are defined for JdbcProfile: PrimaryKey Mark the column as a (non-compound) primary key when creating the DDL The applicable options are available through the table’s O This behaviour may change in a future major release of Slick.Īfter the column name, you can add optional column options to a columnĭefinition. Which may differ from Scala’s Option semantics. Nullable columns are represented by Option where T is one of theĬurrently all operations on Option values use the database’s null propagation semantics Numeric types: Byte, Short, Int, Long, BigDecimal, Float, Double.Theįollowing primitive types are supported out of the box for JDBC-basedĭatabases in JdbcProfile (with certain limitations imposed by the Scala type and a column name for the database (usually in upper-case). See how efficiently flatMap has done it for us, we can also do the same thing using method flatten, similarly we can also use flatMap operation wherever we flatten nested Options wrappers to flatten the structure as per our needs.Class Coffees ( tag : Tag ) extends Table ( tag, 'COFFEES' ) Īll columns are defined through the column method. So if we look at the signatures of map() and flatMap() methods in class Option they look like this: Now let’ s first look the above operations help us dealing with Option, So let’s get started with moving toward Options.

In my previous blog, map and flatMap in Scala for collections we had a quite basic but yet effective understanding how map() and flatMap() works with collections in scala, keeping in contrast with my last blog, one may find it pretty weird like the methods that works on collections can also work on Futures and Options too, but we will look how these methods put us at ease while handling Futures and Options. In this blog, we would be looking at how map() and flatMap() operations work with Option and Future of scala, literally speaking both Futures and Options are very effective features of scala, A Future lets us have a value from some task on a differnt thread and Option provides us a hand from null of java as using null in scala is seen a very bad approach in functional programming.

Scala option