DBMS > Badger vs. GridGain vs. Netezza vs. SiriDB vs. SpaceTime

System Properties Comparison Badger vs. GridGain vs. Netezza vs. SiriDB vs. SpaceTime

Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

Data warehouse and analytics appliance part of IBM PureSystems

Open Source Time Series DBMS

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

github.com/dgraph-io/badger

www.gridgain.com

www.ibm.com/products/netezza

siridb.com

www.mireo.com/spacetime

Technical documentation

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

www.gridgain.com/docs/index.html

docs.siridb.com

Developer

DGraph Labs

GridGain Systems, Inc.

IBM

Cesbit

Mireo

Initial release

2017

2007

2000

2017

2020

Current release

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT License

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++, .Net

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux

included in appliance

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

SQL

Support of SQL

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

yes

A subset of ANSI SQL is implemented

APIs and other access methods

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

JDBC
ODBC
OLE DB

HTTP API

RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

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

C#
C++
Python

Server-side scripts

Stored procedures

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

yes

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

Source-replica replication

yes

Real-time block device replication (DRBD)

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

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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

Security Hooks for custom implementations

Users with fine-grained authorization concept

simple rights management via user accounts

yes

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More resources

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