DBMS > GridGain vs. Hive vs. Machbase Neo vs. Oracle Rdb

System Properties Comparison GridGain vs. Hive vs. Machbase Neo vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

data warehouse software for querying and managing large distributed datasets, built on Hadoop

TimeSeries DBMS for AIoT and BigData

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

www.gridgain.com

hive.apache.org

machbase.com

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

Technical documentation

www.gridgain.com/docs/index.html

cwiki.apache.org/confluence/display/Hive/Home

machbase.com/dbms

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

GridGain Systems, Inc.

Apache Software Foundation

initially developed by Facebook

Machbase

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2007

2012

2013

1984

Current release

GridGain 8.5.1

3.1.3, April 2022

V8.0, August 2023

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free test version available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
macOS
Windows

HP Open VMS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

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

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Thrift

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

user defined functions and integration of map-reduce

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate 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

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

volatile and lookup table

User concepts

Access control

Security Hooks for custom implementations

Access rights for users, groups and roles

simple password-based access control

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More resources

DB-Engines blog posts

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