DBMS > Google BigQuery vs. TimesTen vs. Trino vs. Vitess

System Properties Comparison Google BigQuery vs. TimesTen vs. Trino vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

In-Memory RDBMS compatible to Oracle

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Secondary database models

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

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/timesten.html

trino.io

vitess.io

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/database/timesten-18.1

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

vitess.io/docs

Social network pages

Developer

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Trino Software Foundation

The Linux Foundation, PlanetScale

Initial release

2010

1998

2012

2020 rebranded from PrestoSQL

2013

Current release

11 Release 2 (11.2.2.8.0)

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS

for devlopment

Docker
Linux
macOS

Data scheme

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.

Secondary indexes

yes

depending on connected data-source

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
RESTful HTTP API
Trino CLI

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java
PL/SQL

Go
Java
JavaScript (Node.js)
Python
R
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

yes, depending on connected data-source

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

depending on connected data-source

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

depending on connected data-source

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

depending on connected data-source

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

depending on connected data-source

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

depending on connected data-source

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

SQL standard access control

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Trino is the fastest open source, massively parallel processing SQL query engine...
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Competitive advantages

High performance analtyics and data processing of very large data sets Powerful ANSI...
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Typical application scenarios

Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
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Key customers

Trino is widely adopted across the globe as freely-available open source software....
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Market metrics

33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
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Licensing and pricing models

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