DBMS > Apache Doris vs. Google BigQuery vs. Quasardb vs. TimesTen vs. Vitess

System Properties Comparison Apache Doris vs. Google BigQuery vs. Quasardb vs. TimesTen vs. Vitess

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Large scale data warehouse service with append-only tables

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

cloud.google.com/bigquery

quasar.ai

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

vitess.io

Technical documentation

github.com/apache/doris/wiki

cloud.google.com/bigquery/docs

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

vitess.io/docs

Developer

Apache Software Foundation, originally contributed from Baidu

Google

quasardb

Oracle, TimesTen Performance Software, HP

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

The Linux Foundation, PlanetScale

Initial release

2017

2010

2009

1998

2013

Current release

1.2.2, February 2023

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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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Implementation language

Java

C++

Server operating systems

Linux

hosted

BSD
Linux
OS X
Windows

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

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
MySQL client

RESTful HTTP/JSON API

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C++
Java
PL/SQL

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

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

consistent hashing

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

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 using LevelDB

yes

by means of logfiles and checkpoints

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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