DBMS > Apache Impala vs. H2GIS vs. MonetDB vs. ReductStore

System Properties Comparison Apache Impala vs. H2GIS vs. MonetDB vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Spatial extension of H2

A relational database management system that stores data in columns

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Secondary database models

Document store

Relational DBMS

Document store
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

impala.apache.org

www.h2gis.org

www.monetdb.org

github.com/reductstore
www.reduct.store

Technical documentation

impala.apache.org/impala-docs.html

www.h2gis.org/docs/home

www.monetdb.org/Documentation

www.reduct.store/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

CNRS

MonetDB BV

ReductStore LLC

Initial release

2013

2004

2023

Current release

4.1.0, June 2022

Dec2023 (11.49), December 2023

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

LGPL 3.0

Open Source

Mozilla Public License 2.0

Open Source

Business Source License 1.1

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++

Java

C++, Rust

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
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

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java

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

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

based on H2

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

based on H2

none

Source-replica replication available in experimental status

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

yes, multi-version concurrency control (MVCC)

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

yes

based on H2

fine grained access rights according to SQL-standard

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More resources

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