DBMS > Databend vs. GridGain vs. OpenQM vs. STSdb

System Properties Comparison Databend vs. GridGain vs. OpenQM vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Multivalue DBMS

Key-value store

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

github.com/datafuselabs/databend
www.databend.com

www.gridgain.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

github.com/STSSoft/STSdb4

Technical documentation

docs.databend.com

www.gridgain.com/docs/index.html

Developer

Databend Labs

GridGain Systems, Inc.

Rocket Software, originally Martin Phillips

STS Soft SC

Initial release

2021

2007

1993

2011

Current release

1.0.59, April 2023

GridGain 8.5.1

3.4-12

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial, open source

Open Source

GPLv2, extended commercial license available

Open Source

GPLv2, commercial license available

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

Rust

Java, C++, .Net, Python, REST, SQL

Server operating systems

hosted
Linux
macOS

Linux
OS X
Solaris
Windows
z/OS

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Windows

Data scheme

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

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

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

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

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

C#
Java

Server-side scripts

Stored procedures

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

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

yes

none

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

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Users with fine-grained authorization concept, user roles

Role-based access control
Security Hooks for custom implementations

Access rights can be defined down to the item level

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