DBMS > Microsoft Azure Cosmos DB vs. Quasardb vs. TimesTen vs. XTDB

System Properties Comparison Microsoft Azure Cosmos DB vs. Quasardb vs. TimesTen vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

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

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

azure.microsoft.com/services/cosmos-db

quasar.ai

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

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

Technical documentation

learn.microsoft.com/azure/cosmos-db

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

www.xtdb.com/docs

Developer

Microsoft

quasardb

Oracle, TimesTen Performance Software, HP

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

Juxt Ltd.

Initial release

2014

2009

1998

2019

Current release

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

1.19, September 2021

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

MIT License

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++

Clojure

Server operating systems

hosted

BSD
Linux
OS X
Windows

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

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

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

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST
JDBC

Supported programming languages

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C++
Java
PL/SQL

Clojure
Java

Server-side scripts

Stored procedures

JavaScript

PL/SQL

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate 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

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 using LevelDB

yes

by means of logfiles and checkpoints

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

Transient mode

yes

User concepts

Access control

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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