DBMS > Apache Druid vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. Sequoiadb

System Properties Comparison Apache Druid vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Globally distributed, horizontally scalable, multi-model database service

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

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Score	0.48
Rank	#260	Overall
	#41	Document stores
	#121	Relational DBMS

Website

druid.apache.org

azure.microsoft.com/services/cosmos-db

www.sadasengine.com

www.sequoiadb.com

Technical documentation

druid.apache.org/docs/latest/design

learn.microsoft.com/azure/cosmos-db

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

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation and contributors

Microsoft

SADAS s.r.l.

Sequoiadb Ltd.

Initial release

2012

2014

2006

2013

Current release

29.0.1, April 2024

8.0

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free trial version available

Open Source

Server: AGPL; Client: Apache V2

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
OS X
Unix

hosted

AIX
Linux
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

oid, date, timestamp, binary, regex

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 properties auto-indexed by default

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
Proprietary protocol

proprietary protocol using JSON

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Implicit feature of the cloud service

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Document is locked during a transaction

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'

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights can be defined down to the item level

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

simple password-based access control

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