DBMS > AgensGraph vs. IBM Db2 warehouse vs. NSDb vs. TimesTen

System Properties Comparison AgensGraph vs. IBM Db2 warehouse vs. NSDb vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Cloud-based data warehousing service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.17
Rank	#320	Overall
	#29	Graph DBMS
	#143	Relational DBMS

Score	1.26
Rank	#162	Overall
	#73	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

bitnine.net/agensgraph

www.ibm.com/products/db2/warehouse

nsdb.io

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

bitnine.net/documentation

nsdb.io/Architecture

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Bitnine Global Inc.

IBM

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2014

2017

1998

Current release

2.1, December 2018

Release 22.1

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2.0

commercial

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.

Implementation language

Java, Scala

Server operating systems

Linux
OS X
Windows

hosted

Linux
macOS

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

depending on used data model

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

.NET Client API
JDBC
ODBC
OLE DB

gRPC
HTTP REST
WebSocket

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C
Java
JavaScript
Python

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Scala

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

PL/SQL, SQL PL

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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 Apache Lucene

yes

by means of logfiles and checkpoints

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

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More resources

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