DBMS > Datomic vs. Oracle vs. Qdrant vs. Stardog

System Properties Comparison Datomic vs. Oracle vs. Qdrant vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Widely used RDBMS

A high-performance vector database with neural network or semantic-based matching

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Vector DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.datomic.com

www.oracle.com/database

github.com/qdrant/qdrant
qdrant.tech

www.stardog.com

Technical documentation

docs.datomic.com

docs.oracle.com/en/database

qdrant.tech/documentation

docs.stardog.com

Developer

Cognitect

Oracle

Qdrant

Stardog-Union

Initial release

2012

1980

2021

2010

Current release

1.0.6735, June 2023

23c, September 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

C and C++

Rust

Java

Server operating systems

All OS with a Java VM

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Docker
Linux
macOS
Windows

Linux
macOS
Windows

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Import/export of XML data possible

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

with proprietary extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Clojure
Java

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

PL/SQL

also stored procedures in Java possible

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

By using transaction functions

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

Collection-level replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Key-based authentication

Access rights for users and roles

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