DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. AnzoGraph DB vs. EsgynDB vs. OrigoDB vs. Riak KV

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. AnzoGraph DB vs. EsgynDB vs. OrigoDB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fully ACID in-memory object graph database

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Document store
Object oriented DBMS

Key-value store

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

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

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

Website

www.alibabacloud.com/product/hybriddb-postgresql

cambridgesemantics.com/anzograph

www.esgyn.cn

origodb.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

origodb.com/docs

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

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Cambridge Semantics

Esgyn

Robert Friberg et al

OpenSource, formerly Basho Technologies

Initial release

2016

2018

2015

2009

under the name LiveDB

2009

Current release

2.3, January 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Erlang

Server operating systems

hosted

Linux

Linux
Windows

Linux
OS X

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

ADO.NET
JDBC
ODBC

.NET Client API
HTTP API
LINQ

HTTP API
Native Erlang Interface

Supported programming languages

C
Java
Perl
Python
R

C++
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

.Net

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

yes

user defined functions and aggregates

Java Stored Procedures

yes

Erlang

Triggers

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic sharding

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication in MPP-Cluster

Multi-source replication between multi datacenters

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not needed in graphs

yes

depending on model

links between data sets can be stored

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

Write ahead log

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Role based authorization

yes, using Riak Security

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