DBMS > CouchDB vs. Machbase Neo vs. MySQL vs. Solr vs. Spark SQL

System Properties Comparison CouchDB vs. Machbase Neo vs. MySQL vs. Solr vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

TimeSeries DBMS for AIoT and BigData

Widely used open source RDBMS

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

Spatial DBMS

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.couchdb.org/en/stable

machbase.com/dbms

dev.mysql.com/doc

solr.apache.org/resources.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Machbase

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2005

2013

1995

2006

2014

Current release

3.3.3, December 2023

V8.0, August 2023

8.4.0, April 2024

9.6.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free test version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Erlang

C and C++

Java

Scala

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

Linux
OS X
Windows

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

via views

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

Solr Parallel SQL Interface

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

View functions in JavaScript

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

atomic operations within a single document possible

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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.

yes

volatile and lookup table

yes

User concepts

Access control

Access rights for users can be defined per database

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

yes

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