DBMS > Citus vs. Sadas Engine vs. SiriDB vs. Snowflake vs. Solr

System Properties Comparison Citus vs. Sadas Engine vs. SiriDB vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store

Spatial DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

Technical documentation

docs.citusdata.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

SADAS s.r.l.

Cesbit

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2010

2006

2017

2014

2006

Current release

8.1, December 2018

8.0

9.6.0, April 2024

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

free trial version available

Open Source

MIT License

commercial

Open Source

Apache Version 2

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++

Java

Server operating systems

Linux

AIX
Linux
Windows

Linux

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

Numeric data

yes

supports customizable data types and automatic typing

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

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

standard, with numerous extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol

HTTP API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

Java plugins

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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