DBMS > Google BigQuery vs. Memcached

System Properties Comparison Google BigQuery vs. Memcached

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

In-memory key-value store, originally intended for caching

Primary database model

Relational DBMS

Key-value store

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Trend Chart

Score	62.17
Rank	#19	Overall
	#13	Relational DBMS

Score	19.16
Rank	#35	Overall
	#4	Key-value stores

Website

cloud.google.com/bigquery

www.memcached.org

Technical documentation

cloud.google.com/bigquery/docs

github.com/memcached/memcached/wiki

Developer

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Initial release

2010

2003

Current release

1.6.22, October 2023

License

Commercial or Open Source

commercial

Open Source

BSD license

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Server operating systems

hosted

FreeBSD
Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Repcached, a Memcached patch, provides this functionallity

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

using SASL (Simple Authentication and Security Layer) protocol

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