DBMS > BigchainDB vs. KeyDB vs. Oracle NoSQL vs. Postgres-XL

System Properties Comparison BigchainDB vs. KeyDB vs. Oracle NoSQL vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Document store

Key-value store

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

www.bigchaindb.com

github.com/Snapchat/KeyDB
keydb.dev

www.oracle.com/database/nosql/technologies/nosql

www.postgres-xl.org

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.keydb.dev

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.postgres-xl.org/documentation

Developer

EQ Alpha Technology Ltd.

Oracle

Initial release

2016

2019

2011

2014

since 2012, originally named StormDB

Current release

23.3, December 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

BSD-3

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Mozilla public license

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

Python

C++

Java

Server operating systems

Linux

Linux
Solaris SPARC/x86

Linux
macOS

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

optional

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

XML type, but no XML query functionality

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

APIs and other access methods

CLI Client
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API

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

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
C#
Go
Java
JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

configurable

ACID within a storage node (=shard)

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

yes

simple password-based access control and ACL

Access rights for users and roles

fine grained access rights according to SQL-standard

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