DBMS > Badger vs. GeoMesa vs. Heroic vs. Warp 10 vs. YottaDB

System Properties Comparison Badger vs. GeoMesa vs. Heroic vs. Warp 10 vs. YottaDB

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.

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

A fast and solid embedded Key-value store

Primary database model

Secondary database models

Relational DBMS

using the Octo plugin

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

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

github.com/dgraph-io/badger

www.geomesa.org

github.com/spotify/heroic

www.warp10.io

yottadb.com

Technical documentation

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

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

spotify.github.io/heroic

www.warp10.io/content/02_Getting_started

yottadb.com/resources/documentation

Developer

DGraph Labs

CCRi and others

Spotify

SenX

YottaDB, LLC

Initial release

2017

2014

2015

2001

Current release

4.0.5, February 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

AGPL 3.0

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

Scala

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Docker
Linux

Data scheme

schema-free

yes

schema-free

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

SQL

Support of SQL

by using the Octo plugin

APIs and other access methods

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

HTTP API
Jupyter
WebSocket

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes

selectable replication factor

based on HBase

yes

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
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Mandatory use of cryptographic tokens, containing fine-grained authorizations

Users and groups based on OS-security mechanisms

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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