SORM API was designed to avoid scatterring accross multiple components. It is centralized in a single class Instance - you use this class both to access the database and to initialize SORM.

There aren’t many things you’ll need to configure in SORM, and most of them are quite obvious, so first off we’ll start with a complete example of SORM initialization.


case class Artist ( name : String, genres : Set[Genre] )
case class Genre ( name : String ) 

import sorm._

object Db extends Instance (
  entities = Set() + Entity[Artist]() + Entity[Genre](unique = Set() + Seq("name")),
  url = "jdbc:h2:mem:test",
  user = "",
  password = "",
  initMode = InitMode.Create,
  poolSize = 12

In the code above you can see a data model declaration, which is followed by SORM instance configured to work with case classes Artist and Genre. This instance will create an appropriate unique key for a Genre property name. It will connect to an in-memory H2 database named test without specifying user or password and will generate schema tables if they don’t already exist. It will also manage a pool of 12 connections, allowing you to utilize as many to connect to your db parallelly.


As you already know SORM works with data models designed with case classes, which are also called entities. In order for SORM to generate a correct database schema and to know how to work with your model generally, you need to register all entities of your model with it. You do that with a help of a sorm.Entity case class, in a type-argument of which you specify the case class and in optional arguments unique and indexed you specify the keys.


#### Types of keys There are two types of keys in SORM:

  • Unique - a sequence of names of entity properties which together form a unique value. When possible this one gets mapped to a standard sql UNIQUE KEY.

  • Index - a sequence of names of entity properties which are planned to be used together in queries which require optimization. By “being used” it is implied that they will get specified in either a “where” or an “order”-clause of a query. Usually this one maps to a standard sql INDEX.


Entity[Task](unique = Set() + Seq("name"),
             indexed = Set() + Seq("started") + Seq("closed", "outdated", "started"))

You can see a declaration of a unique key for a single property “name” and two indexes - for a single property “started” and for a group of properties “closed”, “outdated” and “started”.

What happens if a property is impossible to map to a key on the DB side?

Nothing happens. The key just doesn’t get set, but no exceptions get thrown. Your application remains in a perfectly working state, but just not optimized for that specific key. The same happens when the DB adapter doesn’t support a key.

Pool size

SORM is designed to support connection pooling out of the box. This allows your multithreaded application to effectively utilize multiple connections to your datasource.

This feature becomes essential when your application puts a 100% load on a db connection. Since a db connection is executed on a single thread it will only let you utilize the power of a single processor at max, therefor for such applications multiple connections are essential. Luckily SORM doesn’t just provide you with the ability to have multiple connections, it also automatically manages them for you. All you need to do is just specify how many connections you want to be opened at max with the poolSize parameter.

Initialization mode

Each time you launch your application SORM performs initialization. For purposes of running your application in different environments (dev, production) multiple initialization modes were implemented. You specify them with an initMode parameter. * DropAllCreate - wipe out all the contents of the db and generate the new tables. Useful for development. Complete data loss risks - be very careful not to specify this option in production environment * DropCreate - drop only the tables which have conflicting names with the ones to be generated and generate the new ones. Useful for development when there are some tables unrelated to SORM present in the same database. Complete data loss risks - be very careful not to specify this option in production environment * Create - try to generate the tables if they don’t already exist. No dataloss risks, but when you change your domain, incompatibility errors may arise. Safe to use in production. * DoNothing - no changes get applied to schema. Safe to use in production.

Persisted trait and ids

All the entities returned from SORM have a Persisted trait with an appropriate value of id mixed in. This is what lets SORM decide whether to INSERT rows or UPDATE them (and which ones) when the save operation is called. This also provides you with access to its generated id.

Since the id property value is meant to be generated by database, it is protected from the user of being able to manually specify it as well as letting the case classes have such a property.

So, instead of

case class Artist ( id : Long, name : String )

you should use

case class Artist ( name : String )

and let SORM take care of id property management for you.

What should you do when you need to get an id of an entity?

Just do

but for you to be able to do that the artist value must have a Persisted trait mixed in (i.e., have a type Artist with Persisted), which can happen only in three cases:

  1. When you store a value in the db: <div class="highlight"><pre>val artist ="Metallica"))</pre></div>

  2. When you fetch it from the db: <div class="highlight"><pre>val artist = Db.query[Artist].whereEqual("name", "Metallica").fetchOne().get</pre></div>

  3. When you make a copy of an already persisted entity: <div class="highlight"><pre>val artist = someOtherPersistedArtist.copy(name = "METALLICA")</pre></div>

What should you do when you need an entity by id?

Either do a standard fetch, which will return an Option:

Db.query[Artist].whereEqual("id", 234).fetchOne()

or use a special method fetchById, which will either return the appropriate entity or fail if it doesn’t exist:


What should you do when you need to specify the id yourself?

Nothing. You shouldn’t do that. The id property is supposed to be generated and managed by db only. If you need to specify some external unique identifier, like, for instance, Amazon’s ASIN, just add an appropriate field to your entity and specify it as unique on SORM instantiation.


Let’s assume you have a following model registered with SORM Instance:

case class Genre ( name : String )
case class Artist ( name : String, genres : Set[Genre] )

Storing a new entity

val metal : Genre with Persisted
val metallica : Artist with Persisted
  ="Metallica", Set(metal)))

Please note that executing"Metallica", Set(Genre("Metal")))) right away is prohibited, since the entities you want to persist may refer only to already persisted ones, and a freshly created Genre("Metal") as in this case is not yet persisted.

Updating a persisted entity

val rock : Genre with Persisted

val metallica : Option[Artist with Persisted]
  = Db.query[Artist]
      .whereEqual("name", "Metallica")
      .fetchOne()   //  fetch an entity from db
                    //  (returns `Option[Artist with Persisted]`)
      .map(a => a.copy(genres = a.genres + rock))
                    //  update the entity
      .map( //  persist the updates to entity

Please note that the types are specified only for reference.


All the querying functionality is provided through the query[T] method of a SORM instance. The principle is simple: by calling query[T] you create an immutable Querier object, then stack different “modifier” methods on it, which return copies of this object with appropriate modifications - very similar to “Builder” pattern, just the functional way. After the last modification method you call one of the fetching methods on it, which actually does emit the query and fetches the results from the db.

val artists
  = Db.query[Artist]
      .whereNotContains("genre", pop)
      .limit(3) // The order of "modifier methods" doesn't matter much
      .whereNotContains("genre", rock) // Stacking "where" clauses produces the "and" logic
      .fetch() // the sql query gets emitted only at this point

With the query above we’ve fetched three artists which have neither pop or rock in the set of its genres. The results are ordered by artist name.

Property path

All modifier methods of Querier accept a String value as a first parameter. This value specifies a path to a symbol in a tree of the accessed entity. Path is specified as dot-delimited nodes.

Path tree nodes reference

  • Property. A name of a property
  • Tuple item index. Same as in Scala: _3 will refer to a third item of a tuple
  • Range start and end
  • Option, Seq, Set item
  • Map key and value

Example path tree

Here’s a decomposed tree of an Artist object taken from the Tutorial with types explained:

  Path Tree         |   Type Tree
  *                 |   Artist
  - names           |   - Map[Locale, Seq[String]]
  | - key           |   | - Locale
  | | - code        |   | | - String
  | - value         |   | - Seq[String]
  |   - item        |   |   - String
  - genres          |   - Set[Genre]
    - item          |     - Genre
      - names       |       - Map[Locale, Seq[String]]
        - key       |         - Locale
        | - code    |         | - String
        - value     |         - Seq[String]
          - item    |           - String

Here’s the assumed model:

case class Artist ( names : Map[Locale, Seq[String]], genres : Set[Genre] )
case class Genre ( names : Map[Locale, Seq[String]] )
case class Locale ( code : String )

Keeping the above table in mind here’s how we can query for artists having “pop” among the names of their genres:

Db.query[Artist].whereEqual("genres.item.names.value.item", "pop").fetch()
// or
Db.query[Artist].whereContains("genres.item.names.value", "pop").fetch()

The “or” conditions and DSL

Stacking filters on top of each other with piped “where”-clauses produces an and logic, to introduce or conditions to your query you’ll have to use the DSL and a special where method of Querier.

Here’s how you’d do a query to select an artist who has a genre equaling either “metal” or “rock”, but excluding “Metallica”:

import sorm.Dsl._

    ( ( "genre" equal "metal" ) or
      ( "genre" equal "rock" ) ) and
    ( "name" notEqual "Metallica" )

The above implies case class Artist ( name : String, genre : String )


Transactions allow to perform multiple db-requests in such a way that when any failure occurs all the changes that were made with transaction get cancelled. For most databases transactions also provide guarantees that nothing will get changed in between the db-requests in multithreaded applications.

All db-requests which should be executed as part of a transaction must be run on the same thread and must not contain any other side-effecting code besides the db-statements. The last requirement is caused by the way SORM works around notorious locking issues of JDBC-drivers, by sometimes repeating the same transaction and thus executing the code enclosed in the transaction block all over again.

Use transactions with care because of the overhead (table/row locking, MVCC, etc.) that the underlying database engine will have to undertake in order to synchronise concurring transactions. The best practice is to make transactions as short as possible and to perform all the intensive calculations prior to entering a transaction.

Example #1

Db.transaction { = account.balance - order.price)) = true))

This example shows a classical situation when transaction is essential. It ensures that either both save operations succeed or none of them gets performed, which guarantees that if any kind of failure happens it won’t happen so that the account will get a balance reduced without the order being marked as paid.

Example #2

val task : Option[Task] 
  = Db.transaction {
      Db.query[Task].whereEqual("busy", false).fetchOne()
        .map(_.copy(busy = true))

In this example transaction is used to ensure that the same task won’t be fetched on multiple threads.

Data Types

Case Classes

Naturally, you represent your entities with them.

All Standard Primitives

Boolean, Byte, Char, Double, Float, Int, Long, Short


Strings support has a wrapped logic: if a String property is specified as part of some kind of key (unique, index) on a relational side it is represented as a VARCHAR and is limited to have a maximum length of only 256 characters, otherwise it is represented by a CLOB which is generally limited to ~4GB of data. I hope that’s enough.


Values with up to 65 digits before and 30 after the point are supported.




Not supported. Support may come in future releases. You can speed it up by voting for it on the issue tracker.


General types of immutable collections: Seq, Set and Map - are all supported. Please note that you should always use these general types instead of specific ones, i.e. Seq instead of List, IndexedSeq or Vector and etc.


A simple range of form 1 to 21 is supported. The “until”-ranges or the ones specifying a step are unsupported.




Standard Scala enumerations are supported

Joda Time

  • org.joda.time.DateTime - to represent any given point in time of current era including timezone information with precision to milliseconds
  • org.joda.time.LocalDate - to represent any date of current era without a timezone
  • org.joda.time.LocalTime - to represent a time without a timezone

So what do you get from this “support”?

You can seamlessly persist and query even the most ridiculously complex data structures, such as the following:

case class A ( map : Map[B, List[Set[(Int, String, Range)]]] ) 
case class B ( name : String )

Tracing SQL

For debugging purposes it can be useful to see the SQL that SORM emits under the hood. For that you need to set its logging level to “debug”. For example, you can add an SLF4J SimpleLogger dependency to your project:


and set the following VM property on your project execution:


Here’s how a complete execution command can look like:

java -Dorg.slf4j.simpleLogger.log.sorm=debug -jar MyAmazingApp.jar