DBMS > Derby vs. DolphinDB vs. Qdrant vs. SAP HANA

System Properties Comparison Derby vs. DolphinDB vs. Qdrant vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

db.apache.org/derby

www.dolphindb.com

github.com/qdrant/qdrant
qdrant.tech

www.sap.com/products/hana.html

Technical documentation

db.apache.org/derby/manuals/index.html

docs.dolphindb.cn/en/help200/index.html

qdrant.tech/documentation

help.sap.com/hana

Developer

Apache Software Foundation

DolphinDB, Inc

Qdrant

SAP

Initial release

1997

2018

2021

2010

Current release

10.17.1.0, November 2023

v2.00.4, January 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community version available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Rust

Server operating systems

All OS with a Java VM

Linux
Windows

Docker
Linux
macOS
Windows

Appliance or cloud-service

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

yes

SQL-like query language

yes

APIs and other access methods

JDBC

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

JDBC
ODBC

Supported programming languages

Java

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Collection-level replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

fine grained access rights according to SQL-standard

Administrators, Users, Groups

Key-based authentication

yes

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