DBMS > Datomic vs. SQream DB vs. VelocityDB vs. XTDB

System Properties Comparison Datomic vs. SQream DB vs. VelocityDB vs. XTDB

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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

a GPU-based, columnar RDBMS for big data analytics workloads

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Graph DBMS
Object oriented DBMS

Document store

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.datomic.com

sqream.com

velocitydb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.datomic.com

docs.sqream.com

velocitydb.com/UserGuide

www.xtdb.com/docs

Developer

Cognitect

SQream Technologies

VelocityDB Inc

Juxt Ltd.

Initial release

2012

2017

2011

2019

Current release

1.0.6735, June 2023

2022.1.6, December 2022

7.x

1.19, September 2021

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

MIT License

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++, CUDA, Haskell, Java, Scala

Clojure

Server operating systems

All OS with a Java VM

Linux

Any that supports .NET

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

yes, extensible-data-notation format

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.

Secondary indexes

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC

.Net

HTTP REST
JDBC

Supported programming languages

Clojure
Java

C++
Java
JavaScript (Node.js)
Python

.Net

Clojure
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions in Python

Triggers

By using transaction functions

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal and vertical partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

User concepts

Access control

Based on Windows Authentication

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