DBMS > Apache Drill vs. Apache Druid vs. Microsoft SQL Server vs. OceanBase vs. Sadas Engine

System Properties Comparison Apache Drill vs. Apache Druid vs. Microsoft SQL Server vs. OceanBase vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

Microsofts flagship relational DBMS

A distributed, high available RDBMS compatible with Oracle and MySQL

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

Primary database model

Document store
Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Wide column store

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.66
Rank	#147	Overall
	#68	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

drill.apache.org

druid.apache.org

www.microsoft.com/en-us/sql-server

en.oceanbase.com

www.sadasengine.com

Technical documentation

drill.apache.org/docs

druid.apache.org/docs/latest/design

learn.microsoft.com/en-US/sql/sql-server

en.oceanbase.com/docs/oceanbase-database

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

Developer

Apache Software Foundation

Apache Software Foundation and contributors

Microsoft

OceanBase

previously Alibaba and Ant Group

SADAS s.r.l.

Initial release

2012

1989

2010

2006

Current release

1.20.3, January 2023

29.0.1, April 2024

SQL Server 2022, November 2022

4.3.0, April 2024

8.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache license v2

commercial

restricted free version is available

Open Source

Commercial license available

commercial

free trial version available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix

Linux
Windows

Linux

AIX
Linux
Windows

Data scheme

schema-free

yes

schema-less columns are supported

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL for querying

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

JDBC
ODBC
Proprietary protocol

Supported programming languages

C++

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

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

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes, but depending on the SQL-Server Edition

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

managed by 'Learn by Usage'

User concepts

Access control

Depending on the underlying data source

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

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Key customers

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Market metrics

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Licensing and pricing models

Mulan PubL v2 license for open source community edition. Commercial license for enterprise...
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