DBMS > BigchainDB vs. Ignite vs. InfinityDB vs. MySQL vs. Netezza

System Properties Comparison BigchainDB vs. Ignite vs. InfinityDB vs. MySQL vs. Netezza

Editorial information provided by DB-Engines

Name

Description

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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A Java embedded Key-Value Store which extends the Java Map interface

Widely used open source RDBMS

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Document store

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

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	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

www.ibm.com/products/netezza

Technical documentation

bigchaindb.readthedocs.io/en/latest

apacheignite.readme.io/docs

boilerbay.com/infinitydb/manual

dev.mysql.com/doc

Developer

Apache Software Foundation

Boiler Bay Inc.

Oracle

since 2010, originally MySQL AB, then Sun

IBM

Initial release

2016

2015

2002

1995

2000

Current release

Apache Ignite 2.6

4.0

8.4.0, April 2024

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Python

C++, Java, .Net

Java

C and C++

Server operating systems

Linux

Linux
OS X
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux

included in appliance

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

yes

APIs and other access methods

CLI Client
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
OLE DB

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes (replicated cache)

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

In-memory capabilities

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

yes

User concepts

Access control

yes

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

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