DBMS > Google BigQuery vs. MySQL vs. PouchDB vs. STSdb vs. TerminusDB

System Properties Comparison Google BigQuery vs. MySQL vs. PouchDB vs. STSdb vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Widely used open source RDBMS

JavaScript DBMS with an API inspired by CouchDB

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Key/Value like access via memcached API

Document store

Key-value store

Graph DBMS

Secondary database models

Document store
Spatial DBMS

Document store
RDF store

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

cloud.google.com/bigquery

www.mysql.com

pouchdb.com

github.com/STSSoft/STSdb4

terminusdb.com

Technical documentation

cloud.google.com/bigquery/docs

dev.mysql.com/doc

pouchdb.com/guides

terminusdb.github.io/terminusdb/#

Developer

Google

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

STS Soft SC

DataChemist Ltd.

Initial release

2010

1995

2012

2011

2018

Current release

8.4.0, April 2024

7.1.1, June 2019

4.0.8, September 2015

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPLv2, commercial license available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

JavaScript

Prolog, Rust

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

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

Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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.

yes

Secondary indexes

yes

via views

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

only for PouchDB Server
JavaScript API

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

JavaScript

C#
Java

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

with a proxy-based framework, named couchdb-lounge

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

ACID

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

by using IndexedDB, WebSQL or LevelDB as backend

yes

in-memory journaling

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)

Users with fine-grained authorization concept

no user groups or roles

Role-based access control

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