DBMS > DolphinDB vs. HEAVY.AI vs. OpenTenBase vs. SWC-DB

System Properties Comparison DolphinDB vs. HEAVY.AI vs. OpenTenBase vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

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, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

An enterprise-level distributed HTAP open source database based on PostgreSQL

A high performance, scalable Wide Column DBMS

Primary database model

Time Series DBMS

Relational DBMS

Wide column store

Secondary database models

Relational DBMS

Spatial DBMS

Time Series DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.08
Rank	#370	Overall
	#156	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

www.dolphindb.com

github.com/heavyai/heavydb
www.heavy.ai

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

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

Technical documentation

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

docs.heavy.ai

docs.opentenbase.org
docs.opentenbase.org/en

Developer

DolphinDB, Inc

HEAVY.AI, Inc.

OpenAtom Foundation, previously Tencent

Alex Kashirin

Initial release

2018

2016

2020

Current release

v2.00.4, January 2022

5.10, January 2022

2.5, January 2024

0.5, April 2021

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD-3

Open Source

GPL V3

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

C++

C++ and CUDA

C++

Server operating systems

Linux
Windows

Linux

Data scheme

yes

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

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

Proprietary protocol
Thrift

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

C
Go
Java
PHP
Python

C++

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source 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

Foreign keys

Referential integrity

yes

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

Administrators, Users, Groups

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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