DBMS > Alibaba Cloud Table Store vs. Apache Phoenix vs. Microsoft SQL Server vs. Postgres-XL vs. Spark SQL

System Properties Comparison Alibaba Cloud Table Store vs. Apache Phoenix vs. Microsoft SQL Server vs. Postgres-XL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

Microsofts flagship relational DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
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	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.alibabacloud.com/product/table-store

phoenix.apache.org

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

www.postgres-xl.org

spark.apache.org/sql

Technical documentation

www.alibabacloud.com/help/en/tablestore

phoenix.apache.org

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

www.postgres-xl.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Alibaba

Apache Software Foundation

Microsoft

Apache Software Foundation

Initial release

2016

2014

1989

2014

since 2012, originally named StormDB

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

SQL Server 2022, November 2022

10 R1, October 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Mozilla public license

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Scala

Server operating systems

hosted

Linux
Unix
Windows

Linux
Windows

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

JDBC

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

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

JDBC
ODBC

Supported programming languages

Java
Python

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

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

user defined functions

Triggers

yes

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

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

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

MVCC

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

User concepts

Access control

Access rights based on subaccounts and tokens

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

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