DBMS > GridGain vs. Qdrant vs. SWC-DB vs. Trafodion

System Properties Comparison GridGain vs. Qdrant vs. SWC-DB vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

A high performance, scalable Wide Column DBMS

Transactional SQL-on-Hadoop DBMS

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Vector DBMS

Wide column store

Relational DBMS

Secondary database models

Time Series DBMS

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

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

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

www.gridgain.com

github.com/qdrant/qdrant
qdrant.tech

github.com/kashirin-alex/swc-db
www.swcdb.org

trafodion.apache.org

Technical documentation

www.gridgain.com/docs/index.html

qdrant.tech/documentation

trafodion.apache.org/documentation.html

Developer

GridGain Systems, Inc.

Qdrant

Alex Kashirin

Apache Software Foundation, originally developed by HP

Initial release

2007

2021

2020

2014

Current release

GridGain 8.5.1

0.5, April 2021

2.3.0, February 2019

License

Commercial or Open Source

commercial, open source

Open Source

Apache Version 2.0

Open Source

GPL V3

Open Source

Apache 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, Python, REST, SQL

Rust

C++

C++, Java

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Docker
Linux
macOS
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

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
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C++

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

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

Java Stored Procedures

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)

Collection-level replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Role-based access control
Security Hooks for custom implementations

Key-based authentication

fine grained access rights according to SQL-standard

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