DBMS > Alibaba Cloud MaxCompute vs. GigaSpaces vs. Microsoft SQL Server vs. Spark SQL vs. Sphinx

System Properties Comparison Alibaba Cloud MaxCompute vs. GigaSpaces vs. Microsoft SQL Server vs. Spark SQL vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Microsofts flagship relational DBMS

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Search engine

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.alibabacloud.com/product/maxcompute

www.gigaspaces.com

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

spark.apache.org/sql

sphinxsearch.com

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.gigaspaces.com/latest/landing.html

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

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

sphinxsearch.com/docs

Developer

Alibaba

Gigaspaces Technologies

Microsoft

Apache Software Foundation

Sphinx Technologies Inc.

Initial release

2016

2000

1989

2014

2001

Current release

15.5, September 2020

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Scala

C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-99 for query and DML statements

yes

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

Fluentd
Flume
MaxCompute Console

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

JDBC
ODBC

Proprietary protocol

Supported programming languages

Java

.Net
C++
Java
Python
Scala

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

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Java

yes

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

Triggers

yes, event driven architecture

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

yes, utilizing Spark Core

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

yes

The original contents of fields are not stored in the Sphinx index.

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 for users and roles

Role-based access control

fine grained access rights according to SQL-standard

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