DBMS > Badger vs. GeoMesa vs. NSDb

System Properties Comparison Badger vs. GeoMesa vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Primary database model

Key-value store

Spatial DBMS

Time Series DBMS

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

Score	0.12
Rank	#338	Overall
	#50	Key-value stores

Score	0.80
Rank	#208	Overall
	#4	Spatial DBMS

Score	0.00
Rank	#378	Overall
	#40	Time Series DBMS

Website

github.com/dgraph-io/badger

www.geomesa.org

nsdb.io

Technical documentation

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

www.geomesa.org/documentation/stable/user/index.html

nsdb.io/Architecture

Developer

DGraph Labs

CCRi and others

Initial release

2017

2014

2017

Current release

5.0.0, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java, Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

gRPC
HTTP REST
WebSocket

Supported programming languages

Java
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Recent citations in the news

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