DBMS > Apache Impala vs. Apache IoTDB vs. Elasticsearch vs. Microsoft SQL Server

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Elasticsearch vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

impala.apache.org

iotdb.apache.org

www.elastic.co/elasticsearch

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

Technical documentation

impala.apache.org/impala-docs.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Elastic

Microsoft

Initial release

2013

2018

2010

1989

Current release

4.1.0, June 2022

1.1.0, April 2023

8.6, January 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Elastic License

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux
Windows

Data scheme

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

APIs and other access methods

JDBC
ODBC

JDBC
Native API

Java API
RESTful HTTP/JSON API

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

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.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

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

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Integration with Hadoop and Spark

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

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

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

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

fine grained access rights according to SQL-standard

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