DBMS > BigObject vs. Graph Engine vs. Greenplum vs. Postgres-XL

System Properties Comparison BigObject vs. Graph Engine vs. Greenplum vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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.

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Graph DBMS
Key-value store

Relational DBMS

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	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

Technical documentation

docs.bigobject.io

www.graphengine.io/docs/manual

docs.greenplum.org

www.postgres-xl.org/documentation

Developer

BigObject, Inc.

Microsoft

Pivotal Software Inc.

Initial release

2015

2010

2005

2014

since 2012, originally named StormDB

Current release

7.0.0, September 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

free community edition available

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Mozilla public license

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

.NET and C

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

.NET

Linux

Linux
macOS

Data scheme

yes

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

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

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

Supported programming languages

C#
C++
F#
Visual Basic

C
Java
Perl
Python
R

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

Server-side scripts

Stored procedures

Lua

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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More resources

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