DBMS > Microsoft Azure Cosmos DB vs. ObjectBox vs. Riak KV vs. Sphinx

System Properties Comparison Microsoft Azure Cosmos DB vs. ObjectBox vs. Riak KV vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Object oriented 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

Time Series DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

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

Website

azure.microsoft.com/services/cosmos-db

objectbox.io

sphinxsearch.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.objectbox.io

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

sphinxsearch.com/docs

Developer

Microsoft

ObjectBox Limited

OpenSource, formerly Basho Technologies

Sphinx Technologies Inc.

Initial release

2014

2017

2009

2001

Current release

3.2.0, December 2022

3.5.1, February 2023

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

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++

Erlang

C++

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

yes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary native API

HTTP API
Native Erlang Interface

Proprietary protocol

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

JavaScript

Erlang

Triggers

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

no "single point of failure"

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

online/offline synchronization between client and server

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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.

In-memory capabilities

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

User concepts

Access control

Access rights can be defined down to the item level

yes

yes, using Riak Security

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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