DBMS > Datastax Enterprise vs. GBase vs. Qdrant

System Properties Comparison Datastax Enterprise vs. GBase vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

Primary database model

Wide column store

Relational DBMS

Vector DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	3.94
Rank	#67	Overall
	#4	Wide column stores

Score	1.49
Rank	#142	Overall
	#64	Relational DBMS

Score	2.23
Rank	#106	Overall
	#8	Vector DBMS

Website

www.datastax.com/products/datastax-enterprise

www.gbase.cn

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

docs.datastax.com

qdrant.tech/documentation

Developer

DataStax

General Data Technology Co., Ltd.

Qdrant

Initial release

2011

2004

2021

Current release

6.8, April 2020

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, Python

Rust

Server operating systems

Linux
OS X

Linux

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

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

SQL-like DML and DDL statements (CQL); Spark SQL

Standard with numerous extensions

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

ADO.NET
C API
JDBC
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

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

Access rights for users can be defined per object

yes

Key-based authentication

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More resources

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