Datomic Cloud Documentation
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Supported Operations

Datomic provides the following set of operations:

Database Operations

The Datomic Client API can create, list, and delete databases.

ACID Transactions

Databases are indelible, and all new information is added by fully serialized ACID transactions. The transact operation takes as its arguments

  • a connection to a database
  • a collection that can include raw assertions, raw retractions, and transaction functions for conditional operations.

For example, to increase an item's price by 10%, you would pass a transaction function to transact, and two new datoms would be added to the database:

  • a retraction of the previous price
  • an assertion of the new price

For more information, see transactions.

Installing Schema

Schema attributes are ordinary entities in a database, and are added via ACID transactions just like any other data. Schema attributes must be defined in a transaction prior to any transaction that uses them.

For more information, see schema.

Datalog

Datomic's query and rules system is an extended form of Datalog. Datalog is a deductive query system, typically consisting of:

  • A database of facts
  • A set of rules for deriving new facts from existing facts
  • a query processor that, given some partial specification of a fact or rule:
    • finds all instances of that specification implied by the database and rules
    • i.e. all the matching facts

Typically a Datalog system would have a global fact database and set of rules. Datomic's query engine instead takes databases (or other data sources) as fact sources and rule sets as inputs.

Datomic Datalog is simple, declarative, and logic-based.

Simple

Datalog is simple. The basic component of Datalog is a clause, which is a list that either begins with the name of a rule, or is a data pattern. These clauses can contain variables (symbols beginning with a ?). The query engine finds all combinations of values of the variables that satisfy all of the clauses. There is no complex syntax to learn.

Declarative

Like SQL and other good query languages, Datalog is declarative. That is, you specify what you want to know and not how to find it. Declarative programs are:

  • More evident - it is easier to tell what their purpose is, both for programmers and stakeholders.
  • More readily optimized - the query engine is free to reorder and parallelize operations to a degree not normally taken on by application programs.
  • Simpler - and thus, more robust.

Logic-based

Even SQL, while fundamentally declarative, still includes many operations that go beyond the query itself, like specifying joins explicitly. Because Datalog is based upon logical implication, joins are implicit, and the query engine figures out when they are needed.

Time

Datomic is indelible, remembering the history of all information. The db operation returns the current value of a database. This value can then be filtered:

  • the as-of operation filters a database back to a past point in time
  • the since operation filters a database to include only datoms after a point in time
  • the history operations returns an unfiltered view of all present and past information.

Datomic transactions are reified, that is, transactions are themselves entities in a database. You can use query and pull to retrieve information about transactions associated with an entity. Or you can go in the opposite direction, retrieving datoms from transactions directly via the tx-range operation.

For more information, see time.

Raw Indexes

Datomic provides direct iteration on indexes. Most applications will not use this, and you should prefer instead the higher-level query and pull operations.

The datoms operation can seek to a point in any index and iterate from there, and the index-range operation can provide a value range from the AVET index.

For more information, see indexes.