DBMS > Apache Impala vs. MonetDB vs. Quasardb vs. SAP HANA vs. TerminusDB

System Properties Comparison Apache Impala vs. MonetDB vs. Quasardb vs. SAP HANA vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A relational database management system that stores data in columns

Distributed, high-performance timeseries database

In-memory, column based data store. Available as appliance or cloud service

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
RDF store

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Trend Chart

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

impala.apache.org

www.monetdb.org

quasar.ai

www.sap.com/products/hana.html

terminusdb.com

Technical documentation

impala.apache.org/impala-docs.html

www.monetdb.org/Documentation

doc.quasar.ai/master

help.sap.com/hana

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

MonetDB BV

quasardb

SAP

DataChemist Ltd.

Initial release

2013

2004

2009

2010

2018

Current release

4.1.0, June 2022

Dec2023 (11.49), December 2023

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Mozilla Public License 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Prolog, Rust

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Appliance or cloud-service

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

SQL-like query language

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP API

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Sharding

consistent hashing

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication available in experimental status

Source-replica replication with selectable replication factor

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

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

by using LevelDB

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

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

Cryptographically strong user authentication and audit trail

yes

Role-based access control

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