DBMS > Oracle NoSQL vs. Riak TS vs. Sphinx vs. XTDB

System Properties Comparison Oracle NoSQL vs. Riak TS vs. Sphinx vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Key-value store
Relational DBMS

Time Series DBMS

Search engine

Document store

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

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

www.oracle.com/database/nosql/technologies/nosql

sphinxsearch.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

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

sphinxsearch.com/docs

www.xtdb.com/docs

Developer

Oracle

Open Source, formerly Basho Technologies

Sphinx Technologies Inc.

Juxt Ltd.

Initial release

2011

2015

2001

2019

Current release

24.1, May 2024

3.0.0, September 2022

3.5.1, February 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

GPL version 2, commercial licence available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C++

Clojure

Server operating systems

Linux
Solaris SPARC/x86

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes, extensible-data-notation format

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

yes

restricted

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

SQL-like query language (SphinxQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

HTTP API
Native Erlang Interface

Proprietary protocol

HTTP REST
JDBC

Supported programming languages

C
C#
Go
Java
JavaScript (Node.js)
Python

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

Clojure
Java

Server-side scripts

Stored procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

Access rights for users and roles

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More resources

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