DBMS > Badger vs. Hazelcast vs. Netezza vs. searchxml

System Properties Comparison Badger vs. Hazelcast vs. Netezza vs. searchxml

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A widely adopted in-memory data grid

Data warehouse and analytics appliance part of IBM PureSystems

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Key-value store

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

github.com/dgraph-io/badger

hazelcast.com

www.ibm.com/products/netezza

www.searchxml.net/category/products

Technical documentation

godoc.org/github.com/dgraph-io/badger

hazelcast.org/imdg/docs

www.searchxml.net/support/handouts

Developer

DGraph Labs

Hazelcast

IBM

informationpartners gmbh

Initial release

2017

2008

2000

2015

Current release

5.3.6, November 2023

1.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux

included in appliance

Windows

Data scheme

schema-free

yes

schema-free

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
OLE DB

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

on the application server

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Replicated Map

Source-replica replication

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Role-based access control

Users with fine-grained authorization concept

Domain, group and role-based access control at the document level and for application services

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