DBMS > Amazon Redshift vs. Bangdb vs. Machbase Neo vs. Riak KV vs. Solr

System Properties Comparison Amazon Redshift vs. Bangdb vs. Machbase Neo vs. Riak KV vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Converged and high performance database for device data, events, time series, document and graph

TimeSeries DBMS for AIoT and BigData

Distributed, fault tolerant key-value store

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Key-value store

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

Search engine

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

bangdb.com

machbase.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.bangdb.com

machbase.com/dbms

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

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Sachin Sinha, BangDB

Machbase

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2012

2013

2009

2006

Current release

BangDB 2.0, October 2021

V8.0, August 2023

3.2.0, December 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD 3

commercial

free test version available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Erlang

Java

Server operating systems

hosted

Linux

Linux
macOS
Windows

Linux
OS X

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

yes

supports customizable data types and automatic typing

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

restricted

yes

secondary, composite, nested, reverse, geospatial

yes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL like support with command line tool

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

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

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

Erlang

Java plugins

Triggers

yes, Notifications (with Streaming only)

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

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor, Knob for CAP (enterprise version only)

selectable replication factor

yes

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

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

links between data sets can be stored

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

yes

volatile and lookup table

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

simple password-based access control

yes, using Riak Security

yes

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