DBMS > OrientDB vs. Prometheus vs. Quasardb vs. SAP SQL Anywhere vs. SQLite

System Properties Comparison OrientDB vs. Prometheus vs. Quasardb vs. SAP SQL Anywhere vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Multi-model DBMS (Document, Graph, Key/Value)

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

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

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

orientdb.org

prometheus.io

quasar.ai

www.sap.com/products/technology-platform/sql-anywhere.html

www.sqlite.org

Technical documentation

www.orientdb.com/docs/last/index.html

prometheus.io/docs

doc.quasar.ai/master

help.sap.com/docs/SAP_SQL_Anywhere

www.sqlite.org/docs.html

Developer

OrientDB LTD; CallidusCloud; SAP

quasardb

SAP

formerly Sybase

Dwayne Richard Hipp

Initial release

2010

2015

2009

1992

2000

Current release

3.2.29, March 2024

3.14.1, January 2024

17, July 2015

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Public Domain

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

C++

Server operating systems

All OS with a Java JDK (>= JDK 6)

Linux
Windows

BSD
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

server-less

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Numeric data only

yes

integer and binary

yes

not rigid because of 'dynamic typing' concept.

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.

Import of XML data possible

yes

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like query language, no joins

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

HTTP API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Java, Javascript

yes, in C/C++, Java, .Net or Perl

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

by Federation

Source-replica replication with selectable replication factor

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Access rights for users and roles; record level security configurable

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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