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

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

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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 high-performance vector database with neural network or semantic-based matching

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Vector 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	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

www.gridgain.com

machbase.com

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

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

www.gridgain.com/docs/index.html

machbase.com/dbms

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

qdrant.tech/documentation

Developer

GridGain Systems, Inc.

Machbase

Oracle, originally developed by Digital Equipment Corporation (DEC)

Qdrant

Initial release

2007

2013

1984

2021

Current release

GridGain 8.5.1

V8.0, August 2023

7.4.1.1, 2021

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

Rust

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

HP Open VMS

Docker
Linux
macOS
Windows

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

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

Collection-level 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, tunable 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

yes

User concepts

Access control

Security Hooks for custom implementations

simple password-based access control

Key-based authentication

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More resources

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