DBMS > Drizzle vs. EXASOL vs. Geode vs. Kinetica vs. Microsoft Azure Cosmos DB

System Properties Comparison Drizzle vs. EXASOL vs. Geode vs. Kinetica vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

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

Website

www.exasol.com

geode.apache.org

www.kinetica.com

azure.microsoft.com/services/cosmos-db

Technical documentation

www.exasol.com/resources

geode.apache.org/docs

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

Developer

Drizzle project, originally started by Brian Aker

Exasol

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

Kinetica

Microsoft

Initial release

2008

2000

2002

2012

2014

Current release

7.2.4, September 2012

1.1, February 2017

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

C++

Java

C, C++

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

the JDK (8 or later) is also required

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language (OQL)

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC

.Net
JDBC
ODBC
WebSocket

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

C
C++
Java
PHP

Java
Lua
Python
R

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

C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

Cache Event Listeners

yes

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

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

Multi-item ACID transactions with snapshot isolation within a partition

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

GPU vRAM or System RAM

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Access rights per client and object definable

Access rights for users and roles on table level

Access rights can be defined down to the item level

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