DBMS > Heroic vs. NSDb vs. SpatiaLite

System Properties Comparison Heroic vs. NSDb vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Spatial extension of SQLite

Primary database model

Time Series DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	1.72
Rank	#149	Overall
	#3	Spatial DBMS

Website

github.com/spotify/heroic

nsdb.io

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

Technical documentation

spotify.github.io/heroic

nsdb.io/Architecture

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

Developer

Spotify

Alessandro Furieri

Initial release

2014

2017

2008

Current release

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

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Implementation language

Java

Java, Scala

C++

Server operating systems

Linux
macOS

server-less

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, 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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

gRPC
HTTP REST
WebSocket

Supported programming languages

Java
Scala

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual 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

Using Apache Lucene

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

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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