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

System Properties Comparison AgensGraph vs. Axibase 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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

Time Series DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

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	0.29
Rank	#292	Overall
	#25	Time Series DBMS

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

axibase.com/docs/atsd/finance

www.leanxcale.com

machbase.com

www.ibm.com/products/netezza

Technical documentation

bitnine.net/documentation

machbase.com/dbms

Developer

Bitnine Global Inc.

Axibase Corporation

LeanXcale

Machbase

IBM

Initial release

2016

2013

2015

2013

2000

Current release

2.1, December 2018

15585

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

free test version available

commercial

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
OS X
Windows

Linux

Linux
macOS
Windows

Linux

included in appliance

Data scheme

depending on used data model

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

SQL

Support of SQL

yes

SQL-like query language

yes

through Apache Derby

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

Go
Java
PHP
Python
R
Ruby

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Foreign keys

Referential integrity

yes

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

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

simple password-based access control

Users with fine-grained authorization concept

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