DBMS > Google BigQuery vs. Hive vs. Percona Server for MongoDB vs. PouchDB vs. Quasardb

System Properties Comparison Google BigQuery vs. Hive vs. Percona Server for MongoDB vs. PouchDB vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

JavaScript DBMS with an API inspired by CouchDB

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store

Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

cloud.google.com/bigquery

hive.apache.org

www.percona.com/mongodb/software/percona-server-for-mongodb

pouchdb.com

quasar.ai

Technical documentation

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.percona.com/percona-distribution-for-mongodb

pouchdb.com/guides

doc.quasar.ai/master

Developer

Google

Apache Software Foundation

initially developed by Facebook

Percona

Apache Software Foundation

quasardb

Initial release

2010

2012

2015

2012

2009

Current release

3.1.3, April 2022

3.4.10-2.10, November 2017

7.1.1, June 2019

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL Version 2

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

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Implementation language

Java

C++

JavaScript

C++

Server operating systems

hosted

All OS with a Java VM

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.

Secondary indexes

yes

via views

yes

with tags

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

proprietary protocol using JSON

HTTP REST

only for PouchDB Server
JavaScript API

HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

JavaScript

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

JavaScript

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

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

query execution via MapReduce

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

via In-Memory Engine

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

Access rights for users and roles

Cryptographically strong user authentication and audit trail

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