DBMS > Apache Impala vs. Drizzle vs. IRONdb vs. STSdb vs. Trino

System Properties Comparison Apache Impala vs. Drizzle vs. IRONdb vs. STSdb vs. Trino

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Primary database model

Secondary database models

Document store

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	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	5.00
Rank	#66	Overall
	#36	Relational DBMS

Website

impala.apache.org

www.circonus.com/solutions/time-series-database/

github.com/STSSoft/STSdb4

trino.io

Technical documentation

impala.apache.org/impala-docs.html

docs.circonus.com/irondb/category/getting-started

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

Social network pages

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

Circonus LLC.

STS Soft SC

Trino Software Foundation

Initial release

2013

2008

2017

2011

2012

2020 rebranded from PrestoSQL

Current release

4.1.0, June 2022

7.2.4, September 2012

V0.10.20, January 2018

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

commercial

Open Source

GPLv2, commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

C++

C and C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux

Windows

Linux
macOS

for devlopment

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

depending on connected data-source

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

JDBC
ODBC

JDBC

HTTP API

.NET Client API

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
Java

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in Lua

yes, depending on connected data-source

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

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

Immediate consistency per node, eventual consistency across nodes

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

SQL standard access control

More information provided by the system vendor

Specific characteristics

Trino is the fastest open source, massively parallel processing SQL query engine...
» more

Competitive advantages

High performance analtyics and data processing of very large data sets Powerful ANSI...
» more

Typical application scenarios

Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
» more

Key customers

Trino is widely adopted across the globe as freely-available open source software....
» more

Market metrics

33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
» more

Licensing and pricing models

Trino is an open source project and usage is therefore free. Commercial offerings...
» more

News

Big names round out the Trino Fest 2024 lineup
8 May 2024

59: Querying Trino with Java and jOOQ
24 April 2024

A sneak peek of Trino Fest 2024
15 April 2024

Time travel in Delta Lake connector
11 April 2024

58: Understanding your users with Trino and Mitzu
4 April 2024

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

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