DBMS > Apache Doris vs. Google BigQuery vs. OrigoDB vs. PlanetScale vs. Sequoiadb

System Properties Comparison Apache Doris vs. Google BigQuery vs. OrigoDB vs. PlanetScale vs. Sequoiadb

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

A fully ACID in-memory object graph database

Scalable, distributed, serverless MySQL database platform built on top of Vitess

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

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

cloud.google.com/bigquery

origodb.com

planetscale.com

www.sequoiadb.com

Technical documentation

github.com/apache/doris/wiki

cloud.google.com/bigquery/docs

origodb.com/docs

planetscale.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation, originally contributed from Baidu

Google

Robert Friberg et al

PlanetScale

Sequoiadb Ltd.

Initial release

2017

2010

2009

under the name LiveDB

2020

2013

Current release

1.2.2, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

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

Linux
Windows

Docker
Linux
macOS

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

oid, date, timestamp, binary, regex

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
MySQL client

RESTful HTTP/JSON API

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
MySQL protocol
ODBC

proprietary protocol using JSON

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

JavaScript

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication
Source-replica 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

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency

Foreign keys

Referential integrity

depending on model

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

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Write ahead log

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

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)

Role based authorization

Users with fine-grained authorization concept

no user groups or roles

simple password-based access control

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