DBMS > EXASOL vs. GridDB vs. OrientDB vs. searchxml vs. Snowflake

System Properties Comparison EXASOL vs. GridDB vs. OrientDB vs. searchxml vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

Scalable in-memory time series database optimized for IoT and Big Data

Multi-model DBMS (Document, Graph, Key/Value)

DBMS for structured and unstructured content wrapped with an application server

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.exasol.com

griddb.net

orientdb.org

www.searchxml.net/category/products

www.snowflake.com

Technical documentation

www.exasol.com/resources

docs.griddb.net

www.orientdb.com/docs/last/index.html

www.searchxml.net/support/handouts

docs.snowflake.net/manuals/index.html

Developer

Exasol

Toshiba Corporation

OrientDB LTD; CallidusCloud; SAP

informationpartners gmbh

Snowflake Computing Inc.

Initial release

2000

2013

2010

2015

2014

Current release

5.1, August 2022

3.2.29, March 2024

1.0

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache version 2

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

C++

Java

C++

Server operating systems

Linux

All OS with a Java JDK (>= JDK 6)

Windows

hosted

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language, no joins

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

RESTful HTTP API
WebDAV
XQuery
XSLT

CLI Client
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++

most other programming languages supported via APIs

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Java, Javascript

yes

on the application server

user defined functions

Triggers

yes

Hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

yes

sychronisation to multiple collections

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

ACID at container level

ACID

multiple readers, single writer

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 for users, groups and roles according to SQL-standard

Access rights for users can be defined per database

Access rights for users and roles; record level security configurable

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.