NoSQL

Jan 11, 20104 likes1,879 views

This document provides an introduction to NoSQL databases. It discusses that NoSQL is a non-relational approach to data storage that does not rely on fixed schemas and provides better scalability than traditional relational databases. Specific NoSQL examples mentioned include document databases like CouchDB and MongoDB, as well as key-value stores like Redis and Cassandra. The document outlines some of the characteristics and usage of these NoSQL solutions.

NoSQL

● umbrella term
●
non-relational data storage
● no fixed table schemas

● a fresh take on the database

technology

Relational databases have issues in
handling big volumes of data

Some companies and their
databases:
●
Digg.com - 3 TB for green badges
● Facebook - 50 TB for inbox search

●
eBay - 2 P(eta)B in total

Issues

●
horizontal scalability
● server performance

●
rigid schemas
● distribution across servers

Characteristics of NoSQL

● no ACID guarantees (Atomicity,
Consistency, Isolation, Durability)
● highly distributed

● scalable

●
better performance - they don't
have to handle relations

NoSQL databases examples:

● Google Bigtable (used intensively
by almost everything made by
Google)
●
Amazon Dynamo (used by Amazon
S3)
● Facebook Cassandra

●
Apache HBase
● LinkedIn Voldemort

Some types of databases:

●
Document Oriented databases
● JSON format, XML databases

● examples: CouchDB, BaseX

● Key - Value pairs databases
●
values can be more than strings
(set of strings)
●
examples: Redis, Cassandra

CouchDB

● created by the Apache
Foundation
●
written in Erlang
● open source

●
document oriented database
● stores data as JSON documents

collection

● queried via REST API
●
JavaScript is the default language
● also supported:

PHP, Ruby, Python and Erlang
● built-in replication features

● used by Ubuntu One

$JSON document { "_id" : "fc5e038d38a570", "_rev" : "D546012", "to" : "email@example", "subject" : "helloWorld", "body" : "some text" }$

Operations with these documents

●
HTTP requests:
● GET (select), POST (create), PUT

(update), DELETE (delete).
●
HTTP AUTH
● Aplications: curl, Futon

●
JavaScript
● any application that knows HTTP

requests

Redis

●
key - value database
● written in C

●
open source
● networked

● in-memory

●
persistent database
● similar to memcached

●
data is non-volatile

● atomic operations
●
very high performance
~100.000 operations/second
by 50 parallel clients
● all data is kept in memory -

blazing fast
●
periodic synchronization to hard-
drive
●
powerful replication

●
bindings for a lot of languages:
PHP, Ruby, Python, C, Java, etc.

SET foo bar
GET foo => bar

SET - insert
GET - select

Key - value based databases
became very popular lately

Other key-value databases:
● Facebook's Cassandra (now also

used by Digg)
● GM.T

●
MemcacheDB (a persistence
enabled variant of memcached)
●
LinkedIn Voldemort

Conclusion

● relational databases are not the
holy grail of data storage
●
scalability issues determined
large corporations to look to other
solutions
● don't believe the FUD and give

them a try

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Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results. News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?