DBMS > BigchainDB vs. GBase vs. IBM Cloudant vs. PostgreSQL vs. Spark SQL

System Properties Comparison BigchainDB vs. GBase vs. IBM Cloudant vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Database as a Service offering based on Apache CouchDB

Widely used open source RDBMS

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

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	0.79
Rank	#212	Overall
	#36	Document stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.bigchaindb.com

www.gbase.cn

www.ibm.com/products/cloudant

www.postgresql.org

spark.apache.org/sql

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.ibm.com/docs/Cloudant

www.postgresql.org/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

General Data Technology Co., Ltd.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

PostgreSQL Global Development Group

www.postgresql.org/developer

Apache Software Foundation

Initial release

2016

2004

2010

1989

1989: Postgres, 1996: PostgreSQL

2014

Current release

GBase 8a, GBase 8s, GBase 8c

16.3, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

C, Java, Python

Erlang

Scala

Server operating systems

Linux

hosted

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

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

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

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

RESTful HTTP/JSON API

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

JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

View functions (Map-Reduce) in JavaScript

user defined functions

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

none

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

ACID

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

More information provided by the system vendor

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