DBMS > GBase vs. Ignite vs. KeyDB vs. PostGIS vs. SiteWhere

System Properties Comparison GBase vs. Ignite vs. KeyDB vs. PostGIS vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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.

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

Spatial extension of PostgreSQL

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Spatial DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

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

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

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

www.gbase.cn

ignite.apache.org

github.com/Snapchat/KeyDB
keydb.dev

postgis.net

github.com/sitewhere/sitewhere

Technical documentation

apacheignite.readme.io/docs

docs.keydb.dev

postgis.net/documentation

sitewhere1.sitewhere.io/index.html

Developer

General Data Technology Co., Ltd.

Apache Software Foundation

EQ Alpha Technology Ltd.

SiteWhere

Initial release

2004

2015

2019

2005

2010

Current release

GBase 8a, GBase 8s, GBase 8c

Apache Ignite 2.6

3.4.2, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD-3

Open Source

GPL v2.0

Open Source

Common Public Attribution License Version 1.0

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

C, Java, Python

C++, Java, .Net

C++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

predefined scheme

Typing

predefined data types such as float or date

yes

partial

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

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

SQL

Support of SQL

Standard with numerous extensions

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

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

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

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP REST

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

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

Lua

user defined functions

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

yes

based on PostgreSQL

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

User concepts

Access control

yes

Security Hooks for custom implementations

simple password-based access control and ACL

yes

based on PostgreSQL

Users with fine-grained authorization concept

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More resources

DB-Engines blog posts

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