DBMS > Couchbase vs. DolphinDB vs. HEAVY.AI vs. Prometheus vs. Tibero

System Properties Comparison Couchbase vs. DolphinDB vs. HEAVY.AI vs. Prometheus vs. Tibero

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Open-source Time Series DBMS and monitoring system

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Spatial DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

www.couchbase.com

www.dolphindb.com

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

prometheus.io

us.tmaxsoft.com/products/tibero

Technical documentation

docs.couchbase.com

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

docs.heavy.ai

prometheus.io/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Couchbase, Inc.

DolphinDB, Inc

HEAVY.AI, Inc.

TmaxSoft

Initial release

2011

2018

2016

2015

2003

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

v2.00.4, January 2022

5.10, January 2022

6, April 2015

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

free community version available

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

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++, Go and Erlang

C++

C++ and CUDA

C and Assembler

Server operating systems

Linux
OS X
Windows

Linux
Windows

Linux

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

JDBC
ODBC
Thrift
Vega

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Persistent Stored Procedure (PSM)

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

horizontal partitioning

Sharding

Round robin

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

Multi-source replication

yes

by Federation

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Ephemeral buckets

yes

planned for next version

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Administrators, Users, Groups

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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