DBMS > Machbase Neo vs. MySQL vs. Riak KV vs. Solr vs. SQream DB

System Properties Comparison Machbase Neo vs. MySQL vs. Riak KV vs. Solr vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

TimeSeries DBMS for AIoT and BigData

Widely used open source RDBMS

Distributed, fault tolerant key-value store

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

Technical documentation

machbase.com/dbms

dev.mysql.com/doc

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

solr.apache.org/resources.html

docs.sqream.com

Developer

Machbase

Oracle

since 2010, originally MySQL AB, then Sun

OpenSource, formerly Basho Technologies

Apache Software Foundation

SQream Technologies

Initial release

2013

1995

2009

2006

2017

Current release

V8.0, August 2023

8.4.0, April 2024

3.2.0, December 2022

9.6.1, May 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free test version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

C and C++

Erlang

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

All OS with a Java VM

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

Linux

Data scheme

yes

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, ANSI Standard SQL Types

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

Solr Parallel SQL Interface

yes

APIs and other access methods

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

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

.Net
JDBC
ODBC

Supported programming languages

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

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

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

Erlang

Java plugins

user defined functions in Python

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

no "single point of failure"

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

links between data sets can be stored

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking

ACID

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.

yes

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

yes, using Riak Security

yes

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