Concept introduction series

Anand, 26-Feb-2015

What this deck aims to convey and what it doesn’t?

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Captures some attributes of RDBMS (SQL DB!) with the intent of providing the context behind non-relational database models

Does not attempt to capture the pro-s or con-s of RDBMS. Does not attempt to explain RDBMS’ role in modern server architectures etc.

Some attributes and their implications

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{

id: 223091

job: “test.feature_x”

status: “complete”

events:{

{id: 220923232, type: started, time: 13:20:21, date: 03-06-2016}

{id: 220923439, type: restarted, time: 13:20:30, date: 03-06-2016}

{id: 220923439, type: finished, time: 12:24:31, date: 03-06-2016}}

{description: {console_output: {blah blah}}}

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{

id: 223091

job: “test.feature_x”

status: “complete”

events:{

{id: 220923232, type: started, time: 13:20:21, date: 03-06-2016}

{id: 220923439, type: restarted, time: 13:20:30, date: 03-06-2016}

{id: 220923439, type: finished, time: 12:24:31, date: 03-06-2016}}

{description: {console_output: {blah blah}}}

Jobs

Job Definition

Events

Status

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An event in job table must already have an entry in event table (create an entry in event table and then attach it to a job table to which it belongs). Schema changes (version) management is not trivial either.

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Thinking in tables needs a different skillset than creating a Job class. In other words, memory data structures (classes/objects in your heap or in a in-mem db) rarely resemble relational tables. Keeping

them in-synch while rapid development/ prototyping is costly.

Jobs

Job Definition

Events

Status

=

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Clustering (sharding,

replicating etc.) data is a necessity

with constantly evolving

relationships (=schema) and exponentially

increasing volumes. Doing

that in RDBMS is complicated to say

the least

TYPES

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SELECT db_name_text

FROM db_table

WHERE data_relationship_model <>

‘TABULAR’;

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KEY VALUE

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

DOCUMENT

GRAPH

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{Event Id: <id>Event:{

Lat: 12.952772Long: 77.644305Type: <location alert>

}}

COLUMN FAMILY

a

c

b

x

z

y

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= A Key to a value mapping storage

Think of java.util.Map<K,V>

The DB knows only about the keys

Values could be anything; they are just stored and retrieved, never processed

Use cases:– User -> Profile Data– User -> Preference Data– User -> Session Data

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{User Id: <id>User:{

Call Name: XAddress: YGenres: [“action”, “noir”]

}}

{Event Id: <id>Event:{

Lat: 12.952772Long: 77.644305Type: <location alert>

}}

= A (implicit) Key (id) to a document (value) mapping storage

Think of filing a document in a folder

Values are supplied in readable formats – json, bson, XML

Values are visible to the database and typically indexed

Use cases:– Rapidly evolving data (application of schema like thing can

be delayed till maturity)– Event processing (self contained documents for future

computation; mostly ‘read’ or ‘created’ and rarely ‘updated’)

= Column is the unit of data; Stores columns as groups (‘families’)

Think of it as a ‘sparse matrix’

Every row (key) may have data only against a sub-set of columns

Use cases:– Live analytics, Iteratively computed outcomes

{column family

‘likes’

user1

user2

userX

userY

= Graphs with data in both nodes and edges

Think of it as a, well, just graph!

Node data represent what we normally think as ‘data’

Edge data represent the ‘relationship’ between the connected nodes

Use cases:– Recommendations, Relationship traversal & analytics

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c

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No SQL Distilled

The Definitive Guide to MongoDB

Plenty of references in WWW

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DB Schema: http://commons.wikimedia.org/wiki/File:Mediawiki-database-schema.png

Internet per Minute: http://upload.wikimedia.org/wikipedia/commons/5/54/Internet_Minute_Infographic.jpg

NoSQL Boom: From Ramana under CC2.0