DBMS > GeoMesa vs. Graph Engine vs. Heroic vs. Hive

System Properties Comparison GeoMesa vs. Graph Engine vs. Heroic vs. Hive

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Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Spatial DBMS

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

www.geomesa.org

www.graphengine.io

github.com/spotify/heroic

hive.apache.org

Technical documentation

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

www.graphengine.io/docs/manual

spotify.github.io/heroic

cwiki.apache.org/confluence/display/Hive/Home

Developer

CCRi and others

Microsoft

Spotify

Apache Software Foundation

initially developed by Facebook

Initial release

2014

2010

2014

2012

Current release

5.0.0, May 2024

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache Version 2

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

.NET and C

Java

Server operating systems

.NET

All OS with a Java VM

Data scheme

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

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC
Thrift

Supported programming languages

C#
C++
F#
Visual Basic

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency
Immediate Consistency

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights for users, groups and roles

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More resources

DB-Engines blog posts

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