Data Model

Relational Key/Value Entity-Attribute-Value

Datomic stores immutable facts as datoms over time. A datom follows the form of (entity, attribute, value, time). Although Datomic doesn't require a table schema that specifies attribute columns in advance, it requires to specify properties of individual attributes. This is called universal schema.

Views

Not Supported

Storage Model

Custom

Datomic treats storage as a service, which means that Datomic only provides the ways to access underlying storage, but doesn't provide the actual storage. Storage services could also be switched easily by simply changing the connection string.

Stored Procedures

Supported

Stored procedures are represented as transaction functions in Datomic, which can be invoked during transaction processes.

Indexes

B+Tree

Datomic indexes are covering indexes. In other words, instead of storing reference to data in the index, Datomic directly reads data from index. The index trees are shallow, with at most 3 levels: root, directories and segment leaf. Datomic maintains four index trees with different sorting orders for efficient access of different queries.

System Architecture

Shared-Disk

A Datomic system consists of a computing stack, a set of storage services, and an optional query group. The computing stack could have multiple computational units for Datomic system called nodes. Each node has its own low-latency cache, transactions, and queries.

Query Compilation

JIT Compilation Stored Procedure Compilation

Foreign Keys

Supported

Foreign keys can be defined using :db.type/ref attribute, but no foreign key constraints are enforced on them automatically. User needs to specify their own database functions to impose those constraints.

Isolation Levels

Serializable

There is only one thread responsible for writing transactions, so transactions are always serializable.

Concurrency Control

Optimistic Concurrency Control (OCC)

Datomic supports optimistic concurrency control, which is made possible by its built-in compare-and-swap :db/cas.

Storage Architecture

Hybrid

Query Interface

Datalog

Datomic's query interface is an extension from Datalog. The main difference is that Datalog systems usually have a global fact database and a set of rules, but Datomic Datalog could take multiple databases and sets of rules.

Compression

Naïve (Record-Level)

Index trees contain "segments," arrays of records that are serialized and then compressed with zip. Compression allows more efficient caching and storage.