DBMS > Amazon CloudSearch vs. RocksDB vs. Sphinx vs. VictoriaMetrics

System Properties Comparison Amazon CloudSearch vs. RocksDB vs. Sphinx vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

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

Score	1.81
Rank	#137	Overall
	#12	Search engines

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Website

aws.amazon.com/cloudsearch

rocksdb.org

sphinxsearch.com

victoriametrics.com

Technical documentation

docs.aws.amazon.com/cloudsearch

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Amazon

Facebook, Inc.

Sphinx Technologies Inc.

VictoriaMetrics

Initial release

2012

2013

2001

2018

Current release

9.2.1, May 2024

3.5.1, February 2023

v1.91, May 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 2.0

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++

Server operating systems

hosted

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

FreeBSD
Linux
macOS
OpenBSD

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

yes

all search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

HTTP API

C++ API
Java API

Proprietary protocol

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

none

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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.

yes

User concepts

Access control

authentication via encrypted signatures

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