DBMS > Apache Kylin vs. DuckDB vs. Hypertable vs. Microsoft SQL Server vs. OceanBase

System Properties Comparison Apache Kylin vs. DuckDB vs. Hypertable vs. Microsoft SQL Server vs. OceanBase

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

An embeddable, in-process, column-oriented SQL OLAP RDBMS

An open source BigTable implementation based on distributed file systems such as Hadoop

Microsofts flagship relational DBMS

A distributed, high available RDBMS compatible with Oracle and MySQL

Primary database model

Relational DBMS

Wide column store

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	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Website

kylin.apache.org

duckdb.org

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

en.oceanbase.com

Technical documentation

kylin.apache.org/docs

duckdb.org/docs

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

en.oceanbase.com/docs/oceanbase-database

Developer

Apache Software Foundation, originally contributed from eBay Inc

Hypertable Inc.

Microsoft

OceanBase

previously Alibaba and Ant Group

Initial release

2015

2018

2009

1989

2010

Current release

3.1.0, July 2020

0.10, February 2024

0.9.8.11, March 2016

SQL Server 2022, November 2022

4.3.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

Open Source

Commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux

server-less

Linux
OS X
Windows

an inofficial Windows port is available

Linux
Windows

Linux

Data scheme

yes

schema-free

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

C++ API
Thrift

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

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

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

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

none

Sharding

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

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor on file system level

yes, but depending on the SQL-Server Edition

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

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

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