DBMS > Apache Impala vs. Cubrid vs. HEAVY.AI vs. MaxDB

System Properties Comparison Apache Impala vs. Cubrid vs. HEAVY.AI vs. MaxDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A robust and reliable RDBMS optimized to run all major SAP solutions

Primary database model

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.97
Rank	#189	Overall
	#86	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	2.15
Rank	#115	Overall
	#55	Relational DBMS

Website

impala.apache.org

cubrid.com (korean)
cubrid.org (english)

github.com/heavyai/heavydb
www.heavy.ai

maxdb.sap.com

Technical documentation

impala.apache.org/impala-docs.html

cubrid.org/manuals

docs.heavy.ai

maxdb.sap.com/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

CUBRID Corporation, CUBRID Foundation

HEAVY.AI, Inc.

SAP, acquired from Software AG (Adabas-D) in 1997

Initial release

2013

2008

2016

1984

Current release

4.1.0, June 2022

11.0, January 2021

5.10, January 2022

7.9.10.12, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

Limited community edition free

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

C++

C, C++, Java

C++ and CUDA

C++

Server operating systems

Linux

Linux
Windows

Linux

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC
WebDAV

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication

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

Eventual Consistency

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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More resources

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