DBMS > Amazon Redshift vs. ObjectBox vs. Riak KV vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon Redshift vs. ObjectBox vs. Riak KV vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Extremely fast embedded database for small devices, IoT and Mobile

Distributed, fault tolerant key-value store

Open source search engine for searching in data from different sources, e.g. relational databases

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store

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

Search engine

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/redshift

objectbox.io

sphinxsearch.com

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.objectbox.io

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

sphinxsearch.com/docs

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

ObjectBox Limited

OpenSource, formerly Basho Technologies

Sphinx Technologies Inc.

Splice Machine

Initial release

2012

2017

2009

2001

2014

Current release

3.2.0, December 2022

3.5.1, February 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

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 and C++

Erlang

C++

Java

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

restricted

yes

restricted

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

Proprietary native API

HTTP API
Native Erlang Interface

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

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++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

no "single point of failure"

Sharding

Partitioning is done manually, search queries against distributed index is supported

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

online/offline synchronization between client and server

selectable replication factor

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

links between data sets 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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

yes, using Riak Security

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

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