DBMS > Cubrid vs. Hypertable vs. Lovefield vs. TimescaleDB vs. WakandaDB

System Properties Comparison Cubrid vs. Hypertable vs. Lovefield vs. TimescaleDB vs. WakandaDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

An open source BigTable implementation based on distributed file systems such as Hadoop

Embeddable relational database for web apps written in pure JavaScript

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Secondary database models

Relational DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

cubrid.com (korean)
cubrid.org (english)

google.github.io/lovefield

www.timescale.com

wakanda.github.io

Technical documentation

cubrid.org/manuals

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.timescale.com

wakanda.github.io/doc

Developer

CUBRID Corporation, CUBRID Foundation

Hypertable Inc.

Google

Timescale

Wakanda SAS

Initial release

2008

2009

2014

2017

2012

Current release

11.0, January 2021

0.9.8.11, March 2016

2.1.12, February 2017

2.15.0, May 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU version 3. Commercial license available

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

AGPLv3, extended commercial license available

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++, Java

C++

JavaScript

C++, JavaScript

Server operating systems

Linux
Windows

Linux
OS X
Windows

an inofficial Windows port is available

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

full PostgreSQL SQL syntax

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

C++ API
Thrift

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

Supported programming languages

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

C++
Java
Perl
PHP
Python
Ruby

JavaScript

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor on file system level

none

Source-replica replication with hot standby and reads on replicas

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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