DBMS > Bangdb vs. DolphinDB vs. Faircom DB vs. Hive

System Properties Comparison Bangdb vs. DolphinDB vs. Faircom DB vs. Hive

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Website

bangdb.com

www.dolphindb.com

www.faircom.com/products/faircom-db

hive.apache.org

Technical documentation

docs.bangdb.com

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

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cwiki.apache.org/confluence/display/Hive/Home

Developer

Sachin Sinha, BangDB

DolphinDB, Inc

FairCom Corporation

Apache Software Foundation

initially developed by Facebook

Initial release

2012

2018

1979

2012

Current release

BangDB 2.0, October 2021

v2.00.4, January 2022

V12, November 2020

3.1.3, April 2022

License

Commercial or Open Source

Open Source

BSD 3

commercial

free community version available

commercial

Restricted, free version available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C, C++

C++

ANSI C, C++

Java

Server operating systems

Linux

Linux
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

All OS with a Java VM

Data scheme

schema-free

yes

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

yes, ANSI SQL with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC
Thrift

Supported programming languages

C
C#
C++
Java
Python

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

user defined functions and integration of map-reduce

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

yes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Administrators, Users, Groups

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles

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More resources

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