DBMS > Apache Impala vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. SwayDB vs. Tkrzw

System Properties Comparison Apache Impala vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. SwayDB vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Globally distributed, horizontally scalable, multi-model database service

Microsofts flagship relational DBMS

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

Secondary database models

Document store

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

impala.apache.org

azure.microsoft.com/services/cosmos-db

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

swaydb.simer.au

dbmx.net/tkrzw

Technical documentation

impala.apache.org/impala-docs.html

learn.microsoft.com/azure/cosmos-db

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

Simer Plaha

Mikio Hirabayashi

Initial release

2013

2014

1989

2018

2020

Current release

4.1.0, June 2022

SQL Server 2022, November 2022

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

GNU Affero GPL V3.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Scala

C++

Server operating systems

Linux

hosted

Linux
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Java
Kotlin
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

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

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Atomic execution of operations

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

using specific database classes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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