DBMS > atoti vs. Ehcache vs. GeoMesa vs. Heroic vs. Vertica

System Properties Comparison atoti vs. Ehcache vs. GeoMesa vs. Heroic vs. Vertica

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A widely adopted Java cache with tiered storage options

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

atoti.io

www.ehcache.org

www.geomesa.org

github.com/spotify/heroic

www.vertica.com

Technical documentation

docs.atoti.io

www.ehcache.org/documentation

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

spotify.github.io/heroic

vertica.com/documentation

Developer

ActiveViam

Terracotta Inc, owned by Software AG

CCRi and others

Spotify

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2009

2014

2005

Current release

3.10.0, March 2022

4.0.5, February 2024

12.0.3, January 2023

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache License 2.0

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Java

C++

Server operating systems

All OS with a Java VM

Linux

Data scheme

schema-free

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Multidimensional Expressions (MDX)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JCache

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Python

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Cache Event Listeners

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

by using Terracotta Server

depending on storage layer

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

depending on storage layer

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

depending on storage layer

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

In-memory capabilities

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

yes

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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