DBMS > Elasticsearch vs. EsgynDB vs. Riak TS vs. TimesTen vs. Yanza

System Properties Comparison Elasticsearch vs. EsgynDB vs. Riak TS vs. TimesTen 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 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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

In-Memory RDBMS compatible to Oracle

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.elastic.co/elasticsearch

www.esgyn.cn

www.oracle.com/database/technologies/related/timesten.html

yanza.com

Technical documentation

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

www.tiot.jp/riak-docs/riak/ts/latest

docs.oracle.com/database/timesten-18.1

Developer

Elastic

Esgyn

Open Source, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Yanza

Initial release

2010

2015

1998

2015

Current release

8.6, January 2023

3.0.0, September 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

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++, Java

Erlang

Server operating systems

All OS with a Java VM

Linux

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Windows

Data scheme

schema-free

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

yes

schema-free

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.

Secondary indexes

yes

All search fields are automatically indexed

yes

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

yes, limited

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Java
PL/SQL

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Erlang

PL/SQL

Triggers

yes

by using the 'percolation' feature

yes

pre-commit hooks and post-commit hooks

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

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

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

by means of logfiles and checkpoints

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

fine grained access rights according to SQL-standard

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More resources

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2 January 2018, Paul Andlinger, Matthias Gelbmann

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2 March 2016, Paul Andlinger

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