DBMS > Citus vs. Google Cloud Bigtable vs. OpenSearch vs. SiriDB vs. Sphinx

System Properties Comparison Citus vs. Google Cloud Bigtable vs. OpenSearch vs. SiriDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Open Source Time Series DBMS

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

Primary database model

Relational DBMS

Key-value store
Wide column store

Search engine

Time Series DBMS

Search engine

Secondary database models

Document store

Vector DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.citusdata.com

cloud.google.com/bigtable

github.com/opensearch-project
opensearch.org

siridb.com

sphinxsearch.com

Technical documentation

docs.citusdata.com

cloud.google.com/bigtable/docs

opensearch.org/docs/latest

docs.siridb.com

sphinxsearch.com/docs

Developer

Google

Amazon Web Services

Cesbit

Sphinx Technologies Inc.

Initial release

2010

2015

2021

2017

2001

Current release

8.1, December 2018

2.5.0, January 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

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.

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Implementation language

Java

C++

Server operating systems

Linux

hosted

All OS with a Java VM

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

yes

Numeric data

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

yes

standard, with numerous extensions

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Java API
RESTful HTTP/JSON API

HTTP API

Proprietary protocol

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row 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.

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
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Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
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Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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