DBMS > BigObject vs. Greenplum vs. HugeGraph vs. Infobright vs. KairosDB

System Properties Comparison BigObject vs. Greenplum vs. HugeGraph vs. Infobright vs. KairosDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

A fast-speed and highly-scalable Graph DBMS

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Website

bigobject.io

greenplum.org

github.com/hugegraph
hugegraph.apache.org

ignitetech.com/softwarelibrary/infobrightdb

github.com/kairosdb/kairosdb

Technical documentation

docs.bigobject.io

docs.greenplum.org

hugegraph.apache.org/docs

kairosdb.github.io

Developer

BigObject, Inc.

Pivotal Software Inc.

Baidu

Ignite Technologies Inc.; formerly InfoBright Inc.

Initial release

2015

2005

2018

2005

2013

Current release

7.0.0, September 2023

0.9

1.2.2, November 2018

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

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

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
macOS
Unix

Linux
Windows

Linux
OS X
Windows

Data scheme

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

since Version 4.2

Secondary indexes

yes

also supports composite index and range index

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

C
Java
Perl
Python
R

Groovy
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

edges in graph

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

Users, roles and permissions

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

simple password-based access control

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More resources

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