DBMS > Elasticsearch vs. Heroic vs. OpenQM vs. SAP HANA

System Properties Comparison Elasticsearch vs. Heroic vs. OpenQM vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#10	Multivalue DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Website

www.elastic.co/elasticsearch

github.com/spotify/heroic

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.sap.com/products/hana.html

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

spotify.github.io/heroic

help.sap.com/hana

Developer

Elastic

Spotify

Rocket Software, originally Martin Phillips

SAP

Initial release

2010

2014

1993

2010

Current release

8.6, January 2023

3.4-12

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Appliance or cloud-service

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

with some exceptions

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

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

JDBC
ODBC

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency
Immediate Consistency

Immediate 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

In-memory capabilities

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

Memcached and Redis integration

yes

User concepts

Access control

Access rights can be defined down to the item level

yes

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