DBMS > AgensGraph vs. Amazon Redshift vs. HBase vs. Hive vs. Linter

System Properties Comparison AgensGraph vs. Amazon Redshift vs. HBase vs. Hive vs. Linter

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Large scale data warehouse service for use with business intelligence tools

Wide-column store based on Apache Hadoop and on concepts of BigTable

data warehouse software for querying and managing large distributed datasets, built on Hadoop

RDBMS for high security requirements

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Website

bitnine.net/agensgraph

aws.amazon.com/redshift

hbase.apache.org

hive.apache.org

linter.ru

Technical documentation

bitnine.net/documentation

docs.aws.amazon.com/redshift

hbase.apache.org/book.html

cwiki.apache.org/confluence/display/Hive/Home

Developer

Bitnine Global Inc.

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

initially developed by Facebook

relex.ru

Initial release

2016

2012

2008

2012

1990

Current release

2.1, December 2018

2.3.4, January 2021

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache version 2

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

depending on used data model

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

C
Java
JavaScript
Python

All languages supporting JDBC/ODBC

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
PHP
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

Coprocessors in Java

yes

user defined functions and integration of map-reduce

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication
Source-replica replication

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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