DBMS > Google BigQuery vs. OceanBase vs. PouchDB vs. Quasardb

System Properties Comparison Google BigQuery vs. OceanBase vs. PouchDB vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed, high available RDBMS compatible with Oracle and MySQL

JavaScript DBMS with an API inspired by CouchDB

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store
Wide column store

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.97
Rank	#119	Overall
	#58	Relational DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Website

cloud.google.com/bigquery

en.oceanbase.com

pouchdb.com

quasar.ai

Technical documentation

cloud.google.com/bigquery/docs

en.oceanbase.com/docs/oceanbase-database

pouchdb.com/guides

doc.quasar.ai/master

Developer

Google

OceanBase

previously Alibaba and Ant Group

Apache Software Foundation

quasardb

Initial release

2010

2012

2009

Current release

4.3.0, April 2024

7.1.1, June 2019

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Commercial license available

Open Source

commercial

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

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

JavaScript

C++

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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.

yes

Secondary indexes

yes

via views

yes

with tags

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

HTTP REST

only for PouchDB Server
JavaScript API

HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

JavaScript

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

Cryptographically strong user authentication and audit trail

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