DBMS > Apache Phoenix vs. BigObject vs. InterSystems Caché vs. Machbase Neo vs. Postgres-XL

System Properties Comparison Apache Phoenix vs. BigObject vs. InterSystems Caché vs. Machbase Neo vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Analytic DBMS for real-time computations and queries

A multi-model DBMS and application server

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

phoenix.apache.org

bigobject.io

www.intersystems.com/products/cache

machbase.com

www.postgres-xl.org

Technical documentation

phoenix.apache.org

docs.bigobject.io

docs.intersystems.com

machbase.com/dbms

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

BigObject, Inc.

InterSystems

Machbase

Initial release

2014

2015

1997

2013

2014

since 2012, originally named StormDB

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

2018.1.4, May 2020

V8.0, August 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

free test version available

Open Source

Mozilla public license

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
Unix
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux
macOS

Data scheme

yes

late-bound, schema-on-read capabilities

yes

depending on used data model

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC

fluentd
ODBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
RESTful HTTP API

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

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

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C#
C++
Java

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

user defined functions

Lua

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

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

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights for users, groups and roles

simple password-based access control

fine grained access rights according to SQL-standard

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