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In databases there are two approaches for imposing integrity constraints on structured data collections: schema-on-read or schema-on-write. The schema-on-read approach assumes an implicit structure of the data where the structure is only interpreted at read level. Document databases like MongoDB or key value stores like Redis are examples for this kind of schema flexibility. In contrast, the schema-on-write approach has an explicit assumption of the data model where the database ensures that all data written is conform to a defined data model. The traditional relational databases like PostgreSQL as well as modern column-based databases like Cassandra fall under this category.

Datahike supports both approaches which can be chosen at creation time but can not be changed afterwards.

Have a look at the core-namespace in the examples/store-intro folder for example configuration and transactions.


By inheriting most of the code from Datascript the default approach was schema-on-read where you could add any arbitrary Clojure data structures to the database with a small set of helper definitions that added information about references and cardinality. Even though Datahike's API moved to a schema-on-write approach, the schema-less behavior is still supported. On database creation you may opt out by setting the :schema-flexibility parameter to :read.

(require '[datahike.api :as d])

(def cfg {:store {:backend :mem :id "schemaless"} :schema-flexibility :read})

(d/create-database cfg)

(def conn (d/connect cfg))

;; now you can add any arbitrary data
(d/transact conn {:tx-data [{:any "Data"}]})


With the release of version 0.2.0 Datahike enforces by default an explicit schema where you have to define your expected data shapes in advance. The schema itself is present in the database index, so you can simply transact it like any other datom.

(require '[datahike.api :as d])

;; since the :write approach is the default value we may also skip the setting
(def cfg {:store {:backend :mem :id "schema-on-write"} :schema-flexibility :write})

(d/create-database cfg)

(def conn (d/connect cfg))

;; define a simple schema
(def schema [{:db/ident :name :db/valueType :db.type/string :db/cardinality :db.cardinality/one}])

;; transact it
(d/transact conn {:tx-data schema})

;; now we can transact data based on the provided schema
(d/transact conn {:tx-data [{:name "Alice"}]})

The schema definition is for the most part compliant with Datomic's approach. Required are three attributes:

  • :db/ident: the name of the attribute, defined as a keyword with optional namespace, e.g. :user/name
  • :db/valueType: the type of the value associated with an attribute, e.g. db.type/string, see below for supported types
  • :db/cardinality: the cardinality of the value, whether the value is a single value or a set of values, can be either :db.cardinality/one or db.cardinality/many

Additionally, the following optional attributes are supported:

  • db/doc: the documentation for the attribute as a string
  • db/unique: a uniqueness constraint on the attribute for a given value, can be either db.unique/value (only one entity with this attribute can have the same value) or db.unique/identity(only one entity can have the value for this attribute with upsert enabled)
  • db/index: indicates whether an index for the attribute's value should be created as a boolean
  • db/isComponent: indicates that an attribute of type :db.type/ref references a subcomponent of the entity that has the attribute (for cascading retractions)
  • if :db/valueType is :db.type/tuple, one of:

Supported value types

The following types are currently support in datahike:

Value TypeCorresponding Type
db.type/floatDouble or Float

The schema is validated using clojure.spec. See src/datahike/schema.cljc for the implementation details.


Updating an existing schema is discouraged as it may lead to inconsistencies in your data. Therefore, only schema updates for db.cardinality and db.unique are supported. Rather than updating an existing attribute, it is recommended to create a new attribute and migrate data accordingly. Alternatively, if you want to maintain your old attribute names, export your data except the schema, transform it to the new schema, create a new database with the new schema, and import the transformed data.

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