DBMS > DolphinDB vs. Heroic vs. ITTIA vs. MonetDB

System Properties Comparison DolphinDB vs. Heroic vs. ITTIA vs. MonetDB

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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.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Edge Time Series DBMS with Real-Time Stream Processing

A relational database management system that stores data in columns

Primary database model

Time Series DBMS
Vector DBMS

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	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.36
Rank	#272	Overall
	#22	Time Series DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Website

www.dolphindb.com

github.com/spotify/heroic

www.ittia.com

www.monetdb.org

Technical documentation

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

spotify.github.io/heroic

www.monetdb.org/Documentation

Developer

DolphinDB, Inc

Spotify

ITTIA L.L.C.

MonetDB BV

Initial release

2018

2014

2007

2004

Current release

v2.00.4, January 2022

8.7

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache 2.0

commercial

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++

Java

C and C++

Server operating systems

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

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

C
C++
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

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

Immediate Consistency

Eventual Consistency
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

yes, multi-version concurrency control (MVCC)

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

Database file passwords

fine grained access rights according to SQL-standard

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