DBMS > BoltDB vs. DolphinDB vs. ReductStore vs. SWC-DB

System Properties Comparison BoltDB vs. DolphinDB vs. ReductStore 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.

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store

Time Series DBMS

Wide column store

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

github.com/boltdb/bolt

www.dolphindb.com

github.com/reductstore
www.reduct.store

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

Technical documentation

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

www.reduct.store/docs

Developer

DolphinDB, Inc

ReductStore LLC

Alex Kashirin

Initial release

2013

2018

2023

2020

Current release

v2.00.4, January 2022

1.9, March 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

MIT License

commercial

free community version available

Open Source

Business Source License 1.1

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++, Rust

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Docker
Linux
macOS
Windows

Linux

Data scheme

schema-free

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

APIs and other access methods

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

HTTP API

Proprietary protocol
Thrift

Supported programming languages

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

C++
JavaScript (Node.js)
Python
Rust

C++

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

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