DBMS > Apache Pinot vs. BigchainDB vs. Cubrid vs. PostgreSQL vs. Sequoiadb

System Properties Comparison Apache Pinot vs. BigchainDB vs. Cubrid vs. PostgreSQL vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

pinot.apache.org

www.bigchaindb.com

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

www.postgresql.org

www.sequoiadb.com

Technical documentation

docs.pinot.apache.org

bigchaindb.readthedocs.io/en/latest

cubrid.org/manuals

www.postgresql.org/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation and contributors

CUBRID Corporation, CUBRID Foundation

PostgreSQL Global Development Group

www.postgresql.org/developer

Sequoiadb Ltd.

Initial release

2015

2016

2008

1989

1989: Postgres, 1996: PostgreSQL

2013

Current release

1.0.0, September 2023

11.0, January 2021

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL v3

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Python

C, C++, Java

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

Linux
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC

CLI Client
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

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

proprietary protocol using JSON

Supported programming languages

Go
Java
Python

Go
Haskell
Java
JavaScript
Python
Ruby

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

User concepts

Access control

yes

fine grained access rights according to SQL-standard

simple password-based access control

More information provided by the system vendor

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