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

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

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Editorial information provided by DB-Engines

Name

Description

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

TimeSeries DBMS for AIoT and BigData

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Time Series 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	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

www.gridgain.com

machbase.com

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

victoriametrics.com

Technical documentation

www.gridgain.com/docs/index.html

machbase.com/dbms

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

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

GridGain Systems, Inc.

Machbase

Oracle, originally developed by Digital Equipment Corporation (DEC)

VictoriaMetrics

Initial release

2007

2013

1984

2018

Current release

GridGain 8.5.1

V8.0, August 2023

7.4.1.1, 2021

v1.91, May 2023

License

Commercial or Open Source

commercial

free test version available

commercial

Open Source

Apache Version 2.0

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

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

HP Open VMS

FreeBSD
Linux
macOS
OpenBSD

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 query language

yes

APIs and other access methods

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

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

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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)

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

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

simple password-based access control

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More resources

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