DBMS > Apache Phoenix vs. eXtremeDB vs. Lovefield vs. Sphinx vs. Yanza

System Properties Comparison Apache Phoenix vs. eXtremeDB vs. Lovefield vs. Sphinx vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Embeddable relational database for web apps written in pure JavaScript

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

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

Time Series DBMS

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

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	0.73
Rank	#227	Overall
	#104	Relational DBMS
	#18	Time Series DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

phoenix.apache.org

www.mcobject.com

google.github.io/lovefield

sphinxsearch.com

yanza.com

Technical documentation

phoenix.apache.org

www.mcobject.com/docs/extremedb.htm

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

sphinxsearch.com/docs

Developer

Apache Software Foundation

McObject

Google

Sphinx Technologies Inc.

Yanza

Initial release

2014

2001

2014

2001

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

8.2, 2021

2.1.12, February 2017

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

JavaScript

C++

Server operating systems

Linux
Unix
Windows

AIX
HP-UX
Linux
macOS
Solaris
Windows

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

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.

support of XML interfaces available

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

with the option: eXtremeSQL

SQL-like query language

via JavaScript builder pattern

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC

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

Proprietary protocol

HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

JavaScript

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

by defining events

Using read-only observers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning / sharding

none

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual 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

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

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

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more

Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more

Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
» more

Licensing and pricing models

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

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

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