DBMS > Cubrid vs. IBM Db2 warehouse vs. Splice Machine vs. TimescaleDB vs. YDB

System Properties Comparison Cubrid vs. IBM Db2 warehouse vs. Splice Machine vs. TimescaleDB vs. YDB

Editorial information provided by DB-Engines

Name

Description

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

Cloud-based data warehousing service

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	1.34
Rank	#166	Overall
	#76	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Score	0.34
Rank	#285	Overall
	#43	Document stores
	#130	Relational DBMS

Website

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

www.ibm.com/products/db2/warehouse

splicemachine.com

www.timescale.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

cubrid.org/manuals

splicemachine.com/how-it-works

docs.timescale.com

ydb.tech/en/docs

Developer

CUBRID Corporation, CUBRID Foundation

IBM

Splice Machine

Timescale

Yandex

Initial release

2008

2014

2017

2019

Current release

11.0, January 2021

3.1, March 2021

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache 2.0

Open Source

Apache 2.0; commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Java

Java

Server operating systems

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Import/export of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

SQL-like query language (YQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
JDBC
ODBC
OLE DB

JDBC
Native Spark Datasource
ODBC

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

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

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

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

PL/SQL, SQL PL

yes

Java

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

yes, across time and space (hash partitioning) attributes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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More resources

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