DBMS > AgensGraph vs. Cassandra vs. LeanXcale vs. Machbase Neo vs. Netezza

System Properties Comparison AgensGraph vs. Cassandra vs. LeanXcale vs. Machbase Neo vs. Netezza

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

TimeSeries DBMS for AIoT and BigData

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Graph DBMS
Relational DBMS

Wide column store

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

bitnine.net/agensgraph

cassandra.apache.org

www.leanxcale.com

machbase.com

www.ibm.com/products/netezza

Technical documentation

bitnine.net/documentation

cassandra.apache.org/doc/latest

machbase.com/dbms

Developer

Bitnine Global Inc.

Apache Software Foundation

Apache top level project, originally developped by Facebook

LeanXcale

Machbase

IBM

Initial release

2016

2008

2015

2013

2000

Current release

2.1, December 2018

4.1.3, July 2023

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache version 2

commercial

free test version available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Linux
macOS
Windows

Linux

included in appliance

Data scheme

depending on used data model

schema-free

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.

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

yes

SQL-like SELECT, DML and DDL statements (CQL)

yes

through Apache Derby

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

JDBC
ODBC
OLE DB

Supported programming languages

C
Java
JavaScript
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
Java
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

volatile and lookup table

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

simple password-based access control

Users with fine-grained authorization concept

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Key customers

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Market metrics

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