DBMS > Fujitsu Enterprise Postgres vs. Lovefield vs. SingleStore vs. Snowflake vs. Stardog

System Properties Comparison Fujitsu Enterprise Postgres vs. Lovefield vs. SingleStore vs. Snowflake vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Embeddable relational database for web apps written in pure JavaScript

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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Trend Chart

Score	0.37
Rank	#278	Overall
	#128	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.postgresql.fastware.com

google.github.io/lovefield

www.singlestore.com

www.snowflake.com

www.stardog.com

Technical documentation

www.postgresql.fastware.com/product-manuals

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.singlestore.com

docs.snowflake.net/manuals/index.html

docs.stardog.com

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Google

SingleStore Inc.

Snowflake Computing Inc.

Stardog-Union

Initial release

2014

2013

2014

2010

Current release

Fujitsu Enterprise Postgres 14, January 2022

2.1.12, February 2017

8.5, January 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free developer edition available

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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

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Implementation language

JavaScript

C++, Go

Java

Server operating systems

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

64 bit version required

hosted

Linux
macOS
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

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

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

but no triggers and foreign keys

yes

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

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

CLI Client
JDBC
ODBC

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

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Using read-only observers

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

none

Sharding

hash partitioning

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

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Licensing and pricing models

F ree Tier and Enterprise Edition
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