The example database we'll use contains movies mostly, but not exclusively, from the 80s. You'll find information about movie titles, release year, directors, cast members etc. As the tutorial advances we'll learn more about the contents of the database and how it's organized.
The data model in Datomic is based around atomic facts called datoms. A datom is a 4-tuple consisting of
You can think of the database as a flat set of datoms of the form:
[<e-id> <attribute> <value> <tx-id>] ... [ 167 :person/name "James Cameron" 102 ] [ 234 :movie/title "Die Hard" 102 ] [ 234 :movie/year 1987 102 ] [ 235 :movie/title "Terminator" 102 ] [ 235 :movie/director 167 102 ] ...
Note that the last two datoms share the same entity ID, which means they are facts about the same movie. Note also that the last datom's value is the same as the first datom's entity ID, i.e. the value of the
:movie/director attribute is itself an entity. All the datoms in the above set were added to the database in the same transaction, so they share the same transaction ID.
A query is represented as a vector starting with the keyword
:find followed by one or more pattern variables (symbols starting with
?title). After the find clause comes the
:where clause which restricts the query to datoms that match the given data patterns.
For example, this query finds all entity-ids that have the attribute
:person/name with a value of
[:find ?e :where [?e :person/name "Ridley Scott"]]
A data pattern is a datom with some parts replaced with pattern variables. It is the job of the query engine to figure out every possible value of each of the pattern variables and return the ones that are specified in the
_ can be used as a wildcard for the parts of the data pattern that you wish to ignore. You can also elide trailing values in a data pattern. Therefore, the previous query is equivalent to this next query, because we ignore the transaction part of the datoms.
[:find ?e :where [?e :person/name "Ridley Scott" _]]