DBMS > Apache Impala vs. Cachelot.io vs. eXtremeDB vs. MySQL vs. Trino

System Properties Comparison Apache Impala vs. Cachelot.io vs. eXtremeDB vs. MySQL vs. Trino

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

In-memory caching system

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

Widely used open source RDBMS

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Relational DBMS

Key-value store

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	5.00
Rank	#66	Overall
	#36	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

www.mcobject.com/docs/extremedb.htm

dev.mysql.com/doc

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

McObject

Oracle

since 2010, originally MySQL AB, then Sun

Trino Software Foundation

Initial release

2013

2015

2001

1995

2012

2020 rebranded from PrestoSQL

Current release

4.1.0, June 2022

8.2, 2021

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Simplified BSD License

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

AIX
HP-UX
Linux
macOS
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

for devlopment

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.

support of XML interfaces available

yes

Secondary indexes

yes

depending on connected data-source

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with the option: eXtremeSQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

Memcached protocol

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

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

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

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

yes, depending on connected data-source

Triggers

yes

by defining events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning / sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

depending on connected data-source

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

depending on connected data-source

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

Users with fine-grained authorization concept

no user groups or roles

SQL standard access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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High performance analtyics and data processing of very large data sets Powerful ANSI...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
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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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33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
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Licensing and pricing models

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.