DBMS > Amazon Redshift vs. IRONdb vs. Realm vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon Redshift vs. IRONdb vs. Realm vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

aws.amazon.com/redshift

www.circonus.com/solutions/time-series-database/

realm.io

sphinxsearch.com

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.circonus.com/irondb/category/getting-started

realm.io/docs

sphinxsearch.com/docs

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Circonus LLC.

Realm, acquired by MongoDB in May 2019

Sphinx Technologies Inc.

Splice Machine

Initial release

2012

2017

2014

2001

2014

Current release

V0.10.20, January 2018

3.5.1, February 2023

3.1, March 2021

License

Commercial or Open Source

commercial

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

C++

Java

Server operating systems

hosted

Linux

Android
Backend: server-less
iOS
Windows

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

text, numeric, histograms

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

HTTP API

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

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

yes, in Lua

runs within the applications so server-side scripts are unnecessary

yes

Java

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

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

configurable replication factor, datacenter aware

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

In-Memory realm

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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

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