DBMS > Geode vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. Teradata

System Properties Comparison Geode vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Globally distributed, horizontally scalable, multi-model database service

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Website

geode.apache.org

azure.microsoft.com/services/cosmos-db

www.postgres-xl.org

www.teradata.com

Technical documentation

geode.apache.org/docs

learn.microsoft.com/azure/cosmos-db

www.postgres-xl.org/documentation

docs.teradata.com

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Microsoft

Teradata

Initial release

2002

2014

since 2012, originally named StormDB

1984

Current release

1.1, February 2017

10 R1, October 2018

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Mozilla public license

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
macOS

hosted
Linux

Data scheme

schema-free

yes

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language (OQL)

SQL-like query language

yes

distributed, parallel query execution

yes

SQL 2016 + extensions

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

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

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

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

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

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

JavaScript

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Cache Event Listeners

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

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

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes, on a single node

Multi-item ACID transactions with snapshot isolation within a partition

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Access rights per client and object definable

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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