DBMS > BigObject vs. Cachelot.io vs. DolphinDB vs. MonetDB

System Properties Comparison BigObject vs. Cachelot.io vs. DolphinDB vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

In-memory caching system

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 relational database management system that stores data in columns

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

Technical documentation

docs.bigobject.io

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

www.monetdb.org/Documentation

Developer

BigObject, Inc.

DolphinDB, Inc

MonetDB BV

Initial release

2015

2018

2004

Current release

v2.00.4, January 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

commercial

free community version available

Open Source

Mozilla Public License 2.0

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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 DML and DDL statements

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

Lua

yes

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

Source-replica replication available in experimental status

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

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

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