DBMS > BigObject vs. Geode vs. Postgres-XL vs. Riak KV

System Properties Comparison BigObject vs. Geode vs. Postgres-XL vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

bigobject.io

geode.apache.org

www.postgres-xl.org

Technical documentation

docs.bigobject.io

geode.apache.org/docs

www.postgres-xl.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

BigObject, Inc.

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

OpenSource, formerly Basho Technologies

Initial release

2015

2002

2014

since 2012, originally named StormDB

2009

Current release

1.1, February 2017

10 R1, October 2018

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Mozilla public license

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

the JDK (8 or later) is also required

Linux
macOS

Linux
OS X

Data scheme

yes

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

XML type, but no XML query functionality

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (OQL)

yes

distributed, parallel query execution

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API
Native Erlang Interface

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Lua

user defined functions

Erlang

Triggers

yes

Cache Event Listeners

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

links between data sets can be stored

Transaction concepts

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

yes, on a single node

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Access rights per client and object definable

fine grained access rights according to SQL-standard

yes, using Riak Security

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