DBMS > Elasticsearch vs. IBM Db2 warehouse vs. MonetDB vs. SAP HANA vs. Sphinx

System Properties Comparison Elasticsearch vs. IBM Db2 warehouse vs. MonetDB vs. SAP HANA vs. Sphinx

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

Cloud-based data warehousing service

A relational database management system that stores data in columns

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Search engine

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial 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	132.83
Rank	#7	Overall
	#1	Search engines

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

www.elastic.co/elasticsearch

www.ibm.com/products/db2/warehouse

www.monetdb.org

www.sap.com/products/hana.html

sphinxsearch.com

Technical documentation

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

www.monetdb.org/Documentation

help.sap.com/hana

sphinxsearch.com/docs

Developer

Elastic

IBM

MonetDB BV

SAP

Sphinx Technologies Inc.

Initial release

2010

2014

2004

2010

2001

Current release

8.6, January 2023

Dec2023 (11.49), December 2023

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

Mozilla Public License 2.0

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

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

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.

Import/export of XML data possible

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

yes

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Proprietary protocol

Supported programming languages

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

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

PL/SQL, SQL PL

yes, in SQL, C, R

SQLScript, R

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

yes

none

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

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

The original contents of fields are not stored in the Sphinx index.

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

fine grained access rights according to SQL-standard

yes

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