DBMS > Alibaba Cloud Table Store vs. Hypertable vs. Kinetica vs. Microsoft SQL Server vs. Oracle Berkeley DB

System Properties Comparison Alibaba Cloud Table Store vs. Hypertable vs. Kinetica vs. Microsoft SQL Server vs. Oracle Berkeley DB

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 fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Widely used in-process key-value store

Primary database model

Wide column store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.31
Rank	#297	Overall
	#11	Wide column stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

www.alibabacloud.com/product/table-store

www.kinetica.com

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

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.kinetica.com

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

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Alibaba

Hypertable Inc.

Kinetica

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2016

2009

2012

1989

1994

Current release

0.9.8.11, March 2016

7.1, August 2021

SQL Server 2022, November 2022

18.1.40, May 2020

License

Commercial or Open Source

commercial

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

yes

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Implementation language

C++

C, C++

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP API

C++ API
Thrift

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

Java
Python

C++
Java
Perl
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

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

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication factor on file system level

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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