DBMS > Axibase vs. Heroic vs. MaxDB vs. SpatiaLite

System Properties Comparison Axibase vs. Heroic vs. MaxDB vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Spatial extension of SQLite

Primary database model

Time Series DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

axibase.com/docs/atsd/finance

github.com/spotify/heroic

maxdb.sap.com

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

spotify.github.io/heroic

maxdb.sap.com/documentation

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Axibase Corporation

Spotify

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

Alessandro Furieri

Initial release

2013

2014

1984

2008

Current release

15585

7.9.10.12, February 2024

5.0.0, August 2020

License

Commercial or Open Source

commercial

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

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

Java

C++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

server-less

Data scheme

yes

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
WebDAV

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
C#
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate 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

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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