DBMS > Alibaba Cloud Table Store vs. Apache Impala vs. Elasticsearch vs. Microsoft SQL Server vs. Spark SQL

System Properties Comparison Alibaba Cloud Table Store vs. Apache Impala vs. Elasticsearch vs. Microsoft SQL Server 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

Analytic DBMS for Hadoop

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store

Document store
Spatial DBMS
Vector 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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.alibabacloud.com/product/table-store

impala.apache.org

www.elastic.co/elasticsearch

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

spark.apache.org/sql

Technical documentation

www.alibabacloud.com/help/en/tablestore

impala.apache.org/impala-docs.html

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

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

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

Developer

Alibaba

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Elastic

Microsoft

Apache Software Foundation

Initial release

2016

2013

2010

1989

2014

Current release

4.1.0, June 2022

8.6, January 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Elastic License

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java

C++

Scala

Server operating systems

hosted

Linux

All OS with a Java VM

Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

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

JDBC
ODBC

Supported programming languages

Java
Python

All languages supporting JDBC/ODBC

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

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

Triggers

yes

by using the 'percolation' feature

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication factor

yes

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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.

Memcached and Redis integration

yes

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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