DBMS > Citus vs. Ehcache vs. Google BigQuery vs. SiriDB vs. Sphinx

System Properties Comparison Citus vs. Ehcache vs. Google BigQuery 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

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

Open Source Time Series DBMS

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

Primary database model

Secondary database models

Document store

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

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.11
Rank	#346	Overall
	#32	Time Series DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

www.citusdata.com

www.ehcache.org

cloud.google.com/bigquery

siridb.com

sphinxsearch.com

Technical documentation

docs.citusdata.com

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

docs.siridb.com

sphinxsearch.com/docs

Developer

Terracotta Inc, owned by Software AG

Google

Cesbit

Sphinx Technologies Inc.

Initial release

2010

2009

2010

2017

2001

Current release

8.1, December 2018

3.10.0, March 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2; commercial licenses available

commercial

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.

Implementation language

Java

C++

Server operating systems

Linux

All OS with a Java VM

hosted

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

full-text index on all search fields

SQL

Support of SQL

yes

standard, with numerous extensions

yes

SQL-like query language (SphinxQL)

APIs and other access methods

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

JCache

RESTful HTTP/JSON API

HTTP API

Proprietary protocol

Supported programming languages

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

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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.

user defined functions

in JavaScript

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

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

yes

by using Terracotta Server

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

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.