Dr. ChuckCartledgeDr. ChuckCartledgeDr. ChuckCartledge Dr ...ccartled/Teaching/2015... · Miscellanea Data presentation DB comparisons Origins and history Data model CRUDy stuﬀ

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Miscellanea Data presentation DB comparisons Origins and history Data model CRUDy stuff Assignment #4 Midterm Conclusion

CS-695 NoSQL DatabaseMongoDB (part 1 of 2)

Dr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck CartledgeDr. Chuck Cartledge

8 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 20158 Oct. 2015

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Table of contents I

1 Miscellanea

2 Data presentation

3 DB comparisons

4 Origins and history

5 Data model

6 CRUDy stuff

7 Assignment #4

8 Midterm

9 Conclusion

10 References

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Corrections and additions since last lecture.

Corrected typos in lecture006

Assignment #04 is available.

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Homework #3

How to present wide ranging data

These comments apply to all axis(x, y, z, . . . ):

All data is important

Range of values is such thatsmall values are “lost”

Small values are important

In example, max. Y value“swamps” rest of Y values.

Sample data from homework #3.

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Homework #3

Solution is to change how data are scaled.

Simple case is when all data is greaterthan 0. Only slightly more complexwhen there are 0s.

If there are 0 values, fudge themjust a little

Plot the logarithms of the data.

Add custom labels to the axis.

Inform the reader that the data isplotted using logarithmic scales.

Now large and small values are visible.

Sample data from homework #3.

A less obvious technique is to normalize data, can lead to confusing

interpretations.

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How different DBs compare to a RDBMS

We have some terms to compare now[2]

RDBMS K/V Columnar Doc.

DB. instance cluster cluster instancedatabase — namespace —table bucket table collectionrow key-value row documentrowid key — idcol. — col. fam. —schema — — databasejoin — — DBRef

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Where it came from and why.

Initially developed in 2007as part of a “platform as aservice” project

Shifted to open source in2009

Currently used by Craigslist,eBay, and Foursquare

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Where it came from and why.

MongoDB’s view in the universe

Many things are outside ofMongoDB’s concern andcontrol

Administrators establish andmaintain an environment

Once a client has beenauthenticated, the client istrusted to “do the rightthing”

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What is in the backend?

JSON is our friend.

Document databases storedocuments in the value partof a key-value store

Document databases havekey-value stores where thevalue part is examinable

The JSON attributes arewhere the data lives

Image from [2].

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JSON is our friend.

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Sharding and replications

Different types ofauto-sharding[1]:

Range-based according to ashard key value

Hash-based according toMD5 hash of shard key

Location-based controlled bythe administrator

Sharding partitions a very largedatabases the into smaller, faster,more easily managed parts.

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Availability1

A mongoDB installation isavailable for most commonmodern operating systems andlanguages:

Various *nix packages

Windows after Windows XP

Mac OSX 10.6+

Language drivers availablefor: Java, .NET, Ruby, PHP,JavaScript, node.js, Python,Perl, PHP, Scala, andothers.

There is a mongoDB shell.

1http://docs.mongodb.org/manual/installation/

http://docs.mongodb.org/manual/installation/

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Pre-CRUDy stuff

The mongoDB already exists

The database is static andavailable at all times

The collection is a group ofidentifiers

Anyone can create anddelete a collection

No security.

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Pre-CRUDy stuff

How to access things:

MongoDB shell:mongo

curl commands:It appears that the curlinterface isn’t working. Thismay be an unintentionalconsequence of mulitpleconflicting uses of apache onport 8080.

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CRUDy nuts and bolts

Create a collection2:

mongo {create: <collection name>,

capped: <true|false>,autoIndexId: <true|false>,size: <max size>,

max: <max documents>,

flags: <0|1|2|3>,storageEngine: <document>

}db.runCommand( { create: ‘‘collection’’, capped:

true, size: 64 * 1024 } )

To see the collection:show dbs

2http://docs.mongodb.org/manual/reference/command/create/,http://docs.mongodb.org/master/reference/mongo-shell/

http://docs.mongodb.org/manual/reference/command/create/

http://docs.mongodb.org/master/reference/mongo-shell/

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Create data

All mongo shell commands are executed against/with the currentdatabase. The database “test” is created by default and alwaysavailable.To use the test database, execute: use test

db.restaurants.insert(

{"address" : {"street" : "2 Avenue",

"zipcode" : "10075",

"building" : "1480",

"coord" : [ -73.9557413, 40.7720266 ],

},"borough" : "Manhattan",

"cuisine" : "Italian",

"grades" : [

{"date" :

ISODate("2014-10-01T00:00:00Z"),

"grade" : "A",

"score" : 11

},{"date" :

ISODate("2014-01-16T00:00:00Z"),

"grade" : "B",

"score" : 17

}],

"name" : "Vella",

"restaurant id" : "41704620"

})

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Find data

Find all the documents in the collection:db.restaurants.find()

Find selected documents in the collection:<field1>: <value1>, <field2>: <value2>, ...

db.restaurants.find( “borough”: “Manhattan” )

Note: Appending the .pretty() function to the commandimproves readability.

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Update data

There is both a logical and functional difference between updatingan attribute and updating a document.db.restaurants.update(

{ "name" : "Juni" },{$set: { "cuisine": "American New" },$currentDate: { "lastModified": true } } )

db.restaurants.update(

{ "restaurant id" : "41156888" },{ $set: { "address.street": "East 31st Street" } }})db.restaurants.update(

{ "restaurant id" : "41704620" },"name" : "Vella 2",

"address" : {"coord" : [ -73.9557413, 40.7720266 ],

"building" : "1480",

"street" : "2 Avenue",

"zipcode" : "10075"

}})

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Removing a document

db.restaurants.remove( { "borough": "Manhattan" } )

db.restaurants.remove( { "borough": "Queens" },{ justOne: true } )

db.restaurants.remove( { } )

db.restaurants.drop()

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Aggregations

mongoDB supports a wide range of data aggregation operators3.db.restaurants.aggregate(

[

{ $match: { "borough": "Queens", "cuisine":

"Brazilian" },{ $group: { " id": "$address.zipcode" ,

"count": { $sum: 1 } } }];

3http://docs.mongodb.org/manual/meta/aggregation-quick-reference/,http://docs.mongodb.org/manual/reference/sql-aggregation-comparison/,http://docs.mongodb.org/manual/core/aggregation-introduction/

http://docs.mongodb.org/manual/meta/aggregation-quick-reference/

http://docs.mongodb.org/manual/reference/sql-aggregation-comparison/

http://docs.mongodb.org/manual/core/aggregation-introduction/

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Words of explanation.

The full text is available at:http://www.cs.odu.edu/

~ccartled/

Teaching/2015-Fall/NoSQL/

Assignments/04/

In general terms:

1 Parse data

2 Create document database

3 Update documents based onnumber of movies filmed in eachcity

4 Query database

5 Create list of 10 most usedlocations

6 Solve TSP with 10 cities

7 Plot route on a map

8 List locations and movies

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General notes and ideas

It will be about things wecovered in class. Including:

Information and ideas

Variouslectures/presentations

Different databasetechnologies

Open book, open notes (notopen neighbor)

It will be about Sarah and Hasta.Sarah will have an idea for Hasta.Your task will be to discuss whichDB technology to use and why.

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What have we covered?

Reviewed assignment #04Covered mongoDB CRUDYstuff

Next time: continued CRUDy exploration

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References I

[1] Mongodb architecture guide, MongoDB, 2015.

[2] Eric Redmond and Jim R Wilson, Seven databases in seven weeks,Pragmatic Bookshelf, 2012.

Documents

Dr. ChuckCartledgeDr. ChuckCartledgeDr. ChuckCartledge Dr ...ccartled/Teaching/2015... · Miscellanea Data presentation DB comparisons Origins and history Data model CRUDy stuﬀ