DBMS > Kinetica vs. Microsoft Azure Cosmos DB vs. Oracle Berkeley DB vs. TimesTen vs. TinkerGraph

System Properties Comparison Kinetica vs. Microsoft Azure Cosmos DB vs. Oracle Berkeley DB vs. TimesTen vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Widely used in-process key-value store

In-Memory RDBMS compatible to Oracle

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.kinetica.com

azure.microsoft.com/services/cosmos-db

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

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

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/cd/E17076_05/html/index.html

docs.oracle.com/database/timesten-18.1

Developer

Kinetica

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle, TimesTen Performance Software, HP

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

Initial release

2012

2014

1994

1998

2009

Current release

7.1, August 2021

18.1.40, May 2020

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

commercial license available

commercial

Open Source

Apache 2.0

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

C, C++

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

only with the Berkeley DB XML edition

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

TinkerPop 3

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Java
PL/SQL

Groovy
Java

Server-side scripts

Stored procedures

user defined functions

JavaScript

PL/SQL

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency or Eventual Consistency depending on configuration

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

optional

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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