DBMS > Amazon DocumentDB vs. KairosDB vs. Sphinx vs. STSdb

System Properties Comparison Amazon DocumentDB vs. KairosDB vs. Sphinx vs. STSdb

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Distributed Time Series DBMS based on Cassandra or H2

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

aws.amazon.com/documentdb

github.com/kairosdb/kairosdb

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

aws.amazon.com/documentdb/resources

kairosdb.github.io

sphinxsearch.com/docs

Developer

Sphinx Technologies Inc.

STS Soft SC

Initial release

2019

2013

2001

2011

Current release

1.2.2, November 2018

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, 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

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Graphite protocol
HTTP REST
Telnet API

Proprietary protocol

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java
JavaScript

Node.js
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

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

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 for users and roles

simple password-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Unlock the power of parallel indexing in Amazon DocumentDB
19 June 2024, AWS Blog

AWS announces Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service
16 May 2024, AWS Blog

Reduce cost and improve performance by migrating to Amazon DocumentDB 5.0
15 April 2024, AWS Blog

Update your Amazon DocumentDB TLS certificates: Expiring in 2024
28 March 2024, AWS Blog

Unlock the power of Amazon DocumentDB text search with real-world use cases
5 March 2024, AWS Blog

provided by Google News

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

provided by Google News

Switching From Sphinx to MkDocs Documentation — What Did I Gain and Lose?
2 February 2024, Towards Data Science

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

Beyond the Concert Hall: 5 Organizations Making a Difference in Classical Music in 2018
22 December 2018, WQXR Radio

Here’s Why Russia Is Cracking Down on Google
23 February 2015, TIME

The Pirate Bay was recently down for over a week due to a DDoS attack
29 October 2019, The Hacker News

provided by Google News

Share this page