DBMS > Elasticsearch vs. Kinetica vs. Memcached vs. MySQL vs. Newts

System Properties Comparison Elasticsearch vs. Kinetica vs. Memcached vs. MySQL vs. Newts

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

Fully vectorized database across both GPUs and CPUs

In-memory key-value store, originally intended for caching

Widely used open source RDBMS

Time Series DBMS based on Cassandra

Primary database model

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Website

www.elastic.co/elasticsearch

www.kinetica.com

www.memcached.org

www.mysql.com

opennms.github.io/newts

Technical documentation

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

docs.kinetica.com

github.com/memcached/memcached/wiki

dev.mysql.com/doc

github.com/OpenNMS/newts/wiki

Developer

Elastic

Kinetica

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Oracle

since 2010, originally MySQL AB, then Sun

OpenNMS Group

Initial release

2010

2012

2003

1995

2014

Current release

8.6, January 2023

7.1, August 2021

1.6.27, May 2024

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

BSD license

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

C and C++

Java

Server operating systems

All OS with a Java VM

Linux

FreeBSD
Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

Java API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST
Java API

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java

Server-side scripts

Stored procedures

yes

user defined functions

yes

proprietary syntax

Triggers

yes

by using the 'percolation' feature

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

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 or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

yes

using SASL (Simple Authentication and Security Layer) protocol

Users with fine-grained authorization concept

no user groups or roles

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