DBMS > eXtremeDB vs. Lovefield vs. SAP HANA vs. SiriDB vs. Solr

System Properties Comparison eXtremeDB vs. Lovefield vs. SAP HANA vs. SiriDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Natively in-memory DBMS with options for persistency, high-availability and clustering

Embeddable relational database for web apps written in pure JavaScript

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

Open Source Time Series DBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.mcobject.com

google.github.io/lovefield

www.sap.com/products/hana.html

siridb.com

solr.apache.org

Technical documentation

www.mcobject.com/docs/extremedb.htm

github.com/google/lovefield/blob/master/docs/spec_index.md

help.sap.com/hana

docs.siridb.com

solr.apache.org/resources.html

Developer

McObject

Google

SAP

Cesbit

Apache Software Foundation

Initial release

2001

2014

2010

2017

2006

Current release

8.2, 2021

2.1.12, February 2017

2.0 SPS07 (April 4, 2023), April 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

MIT License

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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

C and C++

JavaScript

Java

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Appliance or cloud-service

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

Numeric data

yes

supports customizable data types and automatic typing

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.

support of XML interfaces available

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with the option: eXtremeSQL

SQL-like query language

via JavaScript builder pattern

yes

Solr Parallel SQL Interface

APIs and other access methods

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

JDBC
ODBC

HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Java
Lua
Python
Scala

JavaScript

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

Java plugins

Triggers

yes

by defining events

Using read-only observers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

yes

simple rights management via user accounts

yes

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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