Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge ...ccartled/Teaching/2019-Spring/DataWrangli… · Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge,

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Intro. Amdahl BD Processing Languages Q & A Conclusion References Concepts

Big Data: Data Wrangling Boot CampBD Tools and Techniques

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

8 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 20198 Feburary 2019

c©Old Dominion University

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Table of contents (1 of 1)1 Intro.

2 AmdahlA little math

3 BD ProcessingProgramming paradigmsAn overview

4 LanguagesEach is different for areason

5 Q & A

6 Conclusion

7 References8 Concepts

ChaosSelf referential curvesKoch curvesMandelbrot curvesHow applicable to BigData?


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What are we going to cover?

We’re going to talk about:

Why it is important to understandyour problem

What are single and multithreadedprograms

What are different tools, andframeworks to support BDprocessing

What languages and programmingparadigms fit the BD world

A passing appreciation of BD andChaos concepts


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A little math

Amdahl’s Law [2]

Time for serial executiondef.== T (1)

Portion that can NOT beparalyzed

def.== B ∈ [0, 1]

Number of parallel resourcesdef.== n

T (n) = T (1)∗(B+ 1n (1−B))

Speed updef.== S(n)

S(n) = T (1)T (n) = 1

B+ 1n

(1−B)Dr. Gene Amdahl (circa 1960)


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A little math

Amdahl’s Law (A summary)

Division and measurement of serial and parallel operations appearstime and again. (Shades of Mandelbrot.)

“Make the common fast.”

“Make the fast common.”

Understand what parts haveto be done serially

Understand what parts canbe done in parallel

Need to factor in “overhead” costs when computing speed up.


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A little math

Some questions are easily stated, . . .

Which of these are paralizable(and why)?

1 a[i ] = b[i ] + c[i ]

2 a[i ] = f (b)

3 a[i ] = a[i − 1] + b[i − 1]

4 a = b + c


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Programming paradigms

Single thread vs. multithreads

Single-threaded process –has full access to CPU andRAM

Multithreaded process –shares access to CPU andRAM

Multithreaded makes sensewith independent tasks

Multithreaded may share thesame memory space(language dependent) Image from [3].

Coordination across multiple threads can be tricky.c©Old Dominion University

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Programming paradigms

Hadoop multithreading hidden from view.

Image from [5].

Hadoop infrastructure hides lots of complexity.


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An overview

Vocabulary

Data Sources – where data comesfromIngestion – how data ispre-processed for acceptanceData Sea/Lake – where data livesProcessing – how data isprocessed prior to storageData warehouse – transition fromSQL to NoSQLAnalysis – extracting informationfrom dataUser interface – how the userinteracts with the information Image from [1].


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An overview

Same image.

Image from [1].c©Old Dominion University

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An overview

Another collection of Open Source BD tools

Tools partitioned differently:

Big Data searchBusiness IntelligenceData aggregationData Analysis &PlatformsDatabases / DatawarehousingData aggregationData MiningDocument StoreGraph databasesGrid Solutions

In-MemoryComputingKeyValueMultidimensionalMultimodelMultivalue databaseObject databasesOperationalProgrammingSocialXML Databases

Image from [10].


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An overview

Image from [10].

The reference has links to each piece of software.c©Old Dominion University

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Each is different for a reason

Hammer and nails . . .

“. . . it is tempting, ifthe only tool you have isa hammer, to treat ev-erything as if it were anail.”

Abraham H. Maslow [8]

Image from [11].


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A simple comparison of some languages

Languages are created by people to solve certain types of problems.

C# – declarative, functional,generic, imperative,object-oriented (class-based)Java – client side, compiled,concurrent, curly-bracket,impure, imperative,object-oriented (class-based),procedural, reflectivePython – compiled, extension,

functional, imperative, impure,interactive mode, interpreted,iterative, metaprogramming,object-oriented (class-based),reflective, scriptingR – array, impure, interpreted,interactive mode, list-based,object-oriented prototype-based,scripting

Categorizations from [12].


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Vocabulary (1 of 2)[12].

array – generalize operations onscalars to apply transparently tovectors, matrices, andhigher-dimensional arrays.client side – languages are limitedby the abilities of the browser orintended client.compiled – languages typicallyprocessed by compilers, thoughtheoretically any language can becompiled or interpreted.concurrent – languages providelanguage constructs forconcurrency.curly-bracket – languages have asyntax that defines statementblocks using the curly bracket orbrace charactersdeclarative – languages describe a

problem rather than defining asolution.extension – languages embeddedinto another program and used toharness its features in extensionscripts.functional – languages defineprograms and subroutines asmathematical functions.generic – language is applicable tomany domains.imperative – languages may bemulti-paradigm and appear in otherclassifications.impure – languages containingimperative features.interactive mode – languages act asa kind of shell


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Vocabulary (2 of 2)[12].

interpreted – languages areprogramming languages in whichprograms may be executed fromsource code form, by an interpreter.iterative – languages are builtaround or offering generators.list-based – languages are a type ofdata-structured language that arebased upon the list data structure.metaprogramming – hat write ormanipulate other programs (orthemselves) as their data or that dopart of the work that is otherwisedone at run time during compiletime.object-oriented (class-based) –

support objects defined by theirclass.object-oriented prototype-based –languages are object-orientedlanguages where the distinctionbetween classes and instances hasbeen removedprocedural – languages are basedon the concept of the unit andscopereflective – languages let programsexamine and possibly modify theirhigh level structure at runtime.scripting – another term forinterpreted


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Each reflects/supports a programming paradigm

A plethora of programmingparadigms:

ActionAgent-orientedAutomata-basedConcurrentData-drivenDeclarativeFunctional

DynamicEvent-drivenGenericImperativeLanguage-orientedParallelSemanticStructured

Image from [13].

Each paradigm is a result of a problem domain.c©Old Dominion University

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Same image.

Image from [13].

Each paradigm is a result of a problem domain.


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What does the future hold?

“If languages are not defined by taxonomies, how arethey constructed? They are aggregations of features.Rather than study extant languages as a whole, which con-flates the essential with the accidental, it is more instruc-tive to decompose them into constituent features, whichin turn can be studied individually. The student then hasa toolkit of features that they can re-compose per theirneeds.”

S. Krishnamurthi [6]

New languages will be created all the time to fit needs.


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Q & A time.

Q: Do you know what the deathrate around here is?A: One per person.


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What have we covered?

Looked at how Amdahl’s Law canimprove performanceLooked at single and multithreadedprogramsLooked at some of the many OpenSource Big Data tools that areavailableLooked at how and why somelanguages are better than othersfor a particular application

Next: Getting Twitter developer accounts


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References (1 of 4)

[1] Josef Adersberger, Big Data Landscape Q3/2016, email,2016.

[2] Gene M Amdahl,Validity of the single processor approach to achieving large scale computing capabilities,Proceedings of the Spring Joint Computer Conference, ACM,1967, pp. 483–485.

[3] John T. Bell, Threads, https://www.cs.uic.edu/~jbell/CourseNotes/OperatingSystems/4_Threads.html, 2013.

[4] James Gleick, Chaos: Making a New Science, Random House,1997.

[5] Ricky Ho, How Hadoop Map/Reduce works, 2008.


https://www.cs.uic.edu/~jbell/CourseNotes/OperatingSystems/4_Threads.html

https://www.cs.uic.edu/~jbell/CourseNotes/OperatingSystems/4_Threads.html

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References (2 of 4)

[6] Shriram Krishnamurthi,Teaching Programming Languages in a Post-Linnaean Age,SIGPLAN Notices 43 (2008), no. 11, 81–83.

[7] Benoit B Mandelbrot, How long is the coast of Britain,Science 156 (1967), no. 3775, 636–638.

[8] Abraham H. Maslow, The Psychology of Science, HenryRegency, 1966.

[9] Lewis F. Richardson, The Problem of Contiguity, GeneralSystems Yearbook 6 (1961), 139–187.


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References (3 of 4)

[10] DataFloq Staff, The Big Data Open Source Tools Landscape,https://datafloq.com/big-data-open-source-tools/

os-home/, 2014.

[11] Happiness Staff, Abraham Maslow,http://www.pursuit-of-happiness.org/history-of-

happiness/abraham-maslow/, 2016.

[12] Wikipedia Staff, List of programming languages by type,https://en.wikipedia.org/wiki/List_of_

programming_languages_by_type, 2106.


https://datafloq.com/big-data-open-source-tools/os-home/

https://datafloq.com/big-data-open-source-tools/os-home/

http://www.pursuit-of-happiness.org/history-of-happiness/abraham-maslow/

http://www.pursuit-of-happiness.org/history-of-happiness/abraham-maslow/

https://en.wikipedia.org/wiki/List_of_programming_languages_by_type

https://en.wikipedia.org/wiki/List_of_programming_languages_by_type

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References (4 of 4)

[13] Peter Van Roy et al.,Programming Paradigms for Dummies: What Every Programmer Should Know,New Computational Paradigms for Computer Music 104(2009).


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Chaos

How long is the coast of the Britain?

Question raised byRichardson [9]

Popularized by Mandelbrot[7]

Foundational question inChaos Theory [4]

Varies from ≈ 2,400 to ≈ 3,400 km depending on your yardstick[7]


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Self referential curves

Curves that look like themselves.

Richardson derived:L(G ) = MG 1−D

It was ignored

D is the dimensionalcharacteristic [7]


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Koch curves

Simple algorithms yield things of beauty.


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Mandelbrot curves

In 2 and 3D.

Mandelbrot’s equation:zn+1 = z2

n + cwhere c is complex

Mandelbrot curve is selfreferential


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How applicable to Big Data?

Big data problems are addressed in your computer.

With Koch and Mandelbrot, we were looking deeper and deeper.What happens if we go higher instead of deeper?

Concept Computer Big Data

Paralizable Cores Processing nodes

Data locality Cache (L1, L2, etc.) HDFS

Coordination OS Hadoop

Output RAM HDFS

We will be bringing these ideas out into the open.


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Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge ...ccartled/Teaching/2019-Spring/DataWrangli… · Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge,