DBMS > Databend vs. DolphinDB vs. HugeGraph vs. Oracle Rdb

System Properties Comparison Databend vs. DolphinDB vs. HugeGraph vs. Oracle Rdb

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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

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 fast-speed and highly-scalable Graph DBMS

Primary database model

Secondary database models

Relational DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

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

www.dolphindb.com

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

docs.databend.com

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

hugegraph.apache.org/docs

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

Developer

Databend Labs

DolphinDB, Inc

Baidu

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2021

2018

1984

Current release

1.0.59, April 2023

v2.00.4, January 2022

0.9

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

Open Source

Apache Version 2.0

commercial

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

C++

Java

Server operating systems

hosted
Linux
macOS

Linux
Windows

Linux
macOS
Unix

HP Open VMS

Data scheme

yes

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

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

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

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

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

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

yes

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

User concepts

Access control

Users with fine-grained authorization concept, user roles

Administrators, Users, Groups

Users, roles and permissions

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