DBMS > BigchainDB vs. KeyDB vs. MySQL vs. Quasardb vs. TempoIQ

System Properties Comparison BigchainDB vs. KeyDB vs. MySQL vs. Quasardb vs. TempoIQ

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

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

Widely used open source RDBMS

Distributed, high-performance timeseries database

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Document store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Time Series 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	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.bigchaindb.com

github.com/Snapchat/KeyDB
keydb.dev

www.mysql.com

quasar.ai

tempoiq.com (offline)

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.keydb.dev

dev.mysql.com/doc

doc.quasar.ai/master

Developer

EQ Alpha Technology Ltd.

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

TempoIQ

Initial release

2016

2019

1995

2009

2012

Current release

8.4.0, April 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

BSD-3

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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.

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Implementation language

Python

C++

C and C++

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

yes

integer and binary

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

by using the Redis Search module

yes

with tags

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

CLI Client
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

proprietary syntax

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

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

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

yes

simple password-based access control and ACL

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

simple authentication-based access control

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