DBMS > Faircom DB vs. SAP SQL Anywhere vs. VoltDB vs. XTDB

System Properties Comparison Faircom DB vs. SAP SQL Anywhere vs. VoltDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.faircom.com/products/faircom-db

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

www.voltdb.com

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

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

help.sap.com/docs/SAP_SQL_Anywhere

docs.voltdb.com

www.xtdb.com/docs

Developer

FairCom Corporation

SAP

formerly Sybase

VoltDB Inc.

Juxt Ltd.

Initial release

1979

1992

2010

2019

Current release

V12, November 2020

17, July 2015

11.3, April 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

ANSI C, C++

Java, C++

Clojure

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X

for development

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

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

only a subset of SQL 99

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

ADO.NET
HTTP API
JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

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

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Source-replica replication

Database mirroring

Multi-source replication
Source-replica replication

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

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Transactions are executed single-threaded within stored procedures

ACID

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

Snapshots and command logging

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

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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