DBMS > Apache Impala vs. eXtremeDB vs. Microsoft Azure Synapse Analytics vs. MySQL vs. Riak TS

System Properties Comparison Apache Impala vs. eXtremeDB vs. Microsoft Azure Synapse Analytics vs. MySQL vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Natively in-memory DBMS with options for persistency, high-availability and clustering

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used open source RDBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

impala.apache.org

www.mcobject.com

azure.microsoft.com/services/synapse-analytics

www.mysql.com

Technical documentation

impala.apache.org/impala-docs.html

www.mcobject.com/docs/extremedb.htm

docs.microsoft.com/azure/synapse-analytics

dev.mysql.com/doc

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

McObject

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Open Source, formerly Basho Technologies

Initial release

2013

2001

2016

1995

2015

Current release

4.1.0, June 2022

8.2, 2021

8.4.0, April 2024

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

C++

C and C++

Erlang

Server operating systems

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

yes

schema-free

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.

support of XML interfaces available

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with the option: eXtremeSQL

yes

with proprietary extensions

yes, limited

APIs and other access methods

JDBC
ODBC

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
Java
Lua
Python
Scala

C#
Java
PHP

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transact SQL

yes

proprietary syntax

Erlang

Triggers

yes

by defining events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning / sharding

Sharding, horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

yes

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

not for MyISAM storage engine

links between datasets can be stored

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

table locks or row locks depending on storage engine

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 for users, groups and roles

based on Apache Sentry and Kerberos

yes

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

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