DBMS > Badger vs. HEAVY.AI vs. Heroic vs. mSQL vs. SpatiaLite

System Properties Comparison Badger vs. HEAVY.AI vs. Heroic vs. mSQL vs. SpatiaLite

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Spatial extension of SQLite

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

github.com/dgraph-io/badger

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

github.com/spotify/heroic

hughestech.com.au/products/msql

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.heavy.ai

spotify.github.io/heroic

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

Developer

DGraph Labs

HEAVY.AI, Inc.

Spotify

Hughes Technologies

Alessandro Furieri

Initial release

2017

2016

2014

1994

2008

Current release

5.10, January 2022

4.4, October 2021

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

free licenses can be provided

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

C++ and CUDA

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

server-less

Data scheme

schema-free

yes

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

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

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

none

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

Spatial database management systems
6 April 2021, Matthias Gelbmann

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