DBMS > Fujitsu Enterprise Postgres vs. IBM Db2 Event Store vs. LokiJS vs. Microsoft SQL Server vs. Splice Machine

System Properties Comparison Fujitsu Enterprise Postgres vs. IBM Db2 Event Store vs. LokiJS vs. Microsoft SQL Server vs. Splice Machine

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.

Distributed Event Store optimized for Internet of Things use cases

In-memory JavaScript DBMS

Microsofts flagship relational DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.37
Rank	#278	Overall
	#128	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.postgresql.fastware.com

www.ibm.com/products/db2-event-store

github.com/techfort/LokiJS

www.microsoft.com/en-us/sql-server

splicemachine.com

Technical documentation

www.postgresql.fastware.com/product-manuals

www.ibm.com/docs/en/db2-event-store

techfort.github.io/LokiJS

learn.microsoft.com/en-US/sql/sql-server

splicemachine.com/how-it-works

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

IBM

Microsoft

Splice Machine

Initial release

2017

2014

1989

2014

Current release

Fujitsu Enterprise Postgres 14, January 2022

2.0

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

commercial

free developer edition available

Open Source

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C and C++

JavaScript

C++

Java

Server operating systems

Linux
Windows

Linux

Linux, macOS, Windows for the developer addition

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

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JavaScript API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

View functions in JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active-active shard replication

none

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a single collection possible

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

No - written data is immutable

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

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

Access rights for users, groups and roles according to SQL-standard

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