Programming Language Selection
Dhananjay Nene
March 28, 2009
A PuneTech event

Why language selection ?
There is no universally superior language
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Language selection is fitment of language
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strengths and weaknesses to a context
Language selection often has long term
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implications including those of business
capability, cost and technology lock-in
It is therefore a technology + management
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decision

Dimensions of Selection
Capability : What the languages can / cannot
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do
Productivity : How efficiently can one write
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programs using the languge
Ramp Up : How easily can you get online
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Extraneous Factors
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Costs : What are the costs of using the
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language

Questions to be answered
Can the language deliver on expectations ?
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What is the cost of delivering on expectations
How long does it take to write and debug
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code ?
If I don't already have the skill sets what is the
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cost and time required to build them ?
What is the support structure available from
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community and corporate groups ?
What are the hardware and deployment costs ?
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Capability
Style Memory Usage
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Object Orientation Performance
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Function Orientation / Generic Classes
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Higher Order
Garbage Collection
Functions 
Typing Integration
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Static Error Handling
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Dynamic
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Multi-threading
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Reflection
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Library coverage and
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Metaprogramming support
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Object Orientation
Encapsulation / Information Hiding
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Inheritance
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Polymorphism
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Are all types objects ?
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Are all operations performed by sending
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messages to objects ?
Are all user defined types objects ?
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Functional Programming Elements
Higher Order Functions
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Code Blocks
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Generators (potentially infinite data, lazy
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evaluation)
List operations eg. map / reduce etc
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Closures
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Traditional : Haskell,Erlang
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Upcoming : Scala, Clojure, F#
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Static or Dynamic Types ?
In static typing, type is associated with a
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variable, in case of dynamic typing, it is
associated to the runtime value
Thus dynamic typing cannot often infer type
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until at runtime
Static typing catches more errors at compile
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time. Makes debugging easier
Dynamic types allows more flexibilities (eg

metaprogramming) and lower compile idle
times

Metaprogramming
Inspect existing classes / methods
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Instantiate classes / Invoke methods using
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dynamic class / method structures
Create new classes / functions / methods on
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the fly ?
Modify existing classes / methods on the fly ?
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Productivity
Expressiveness
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Eg Wikipedia : Comparison of Programming Langs
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C / C++ =>1, JAVA => 1.5, Perl => 6, Python => 6.5
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Speed of writing code
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LOC per hour can also vary based on language
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http://page.mi.fu-
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berlin.de/prechelt/Biblio/jccpprt2_advances2003.pdf
Compilation overheads
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IDE speeds
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Refactoring capability
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Performance / Scalability /
Reliability
Performance : How fast can the programs run
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for given hardware
Scalability : How easily / cost effectively can the

software be scaled to handle higher loads
Reliability : How fault tolerant can the resultant
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software be

Extraneous Factors
These are very important factors
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Customer Preferences
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Architecture Standards
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Frameworks and Libraries
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Community
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Deployment characteristics
Hardware Requirements
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Ease of cloud / virtualised hosting
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Hosting requirements for Small vs. Medium vs.
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Big apps
Clustering capabilities
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Adaptability / Agility
How quickly can you change based on

changing requirements / objectives
Language is only one part of the mix
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Frameworks
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Design
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Processes
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Costs
Training
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Writing and Testing code
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Development Infrastructure
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Deployment Infrastructure
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Checklist
What do my customers want
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What does my architecture body state
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Can I meet performance / memory / app

specific constraints ?
What is the performance sensitivity
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How critical is time to market
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How critical is adaptability and agility
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How critical are the budget constraints
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How quickly can I ramp up
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What is the available community
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My opinions on language futures
These are my own
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These are empirical
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These are subjective
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Languages under pressure :
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Java under pressure due to productivity issues
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PHP under pressure due to performance / hardware
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/ cormplex topologies
Python / Ruby under pressure due to smaller
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installed base and multicore concerns

Trends
Innovation in Web development is maturing
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Web and Pre-web architectures are both
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starting to get used
The VM is the new OS
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CPUs/Disks/RAM/Networks have grown fast
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enough for traditional transaction processing
Service Integration becoming critical
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Scaling, Multicore becoming important issue for

many apps

Java
The big daddy - #1. But high entry barriers
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Long time to train, high requirements to deploy
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Low development productivity
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Superb performance, scalability, community
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Multi threading powerful but tough
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Considerable risk from other languages on the
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JVM eg. Jython, JRuby, Groovy, Scala, Clojure
Likely to loose share to competition
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PHP
Language with lowest entry barriers
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Easy to learn, easy to train
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Large community and supporters
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Phenomenally wide libraries coverage
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Scaling up is feasible but costs prohibitive
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Tougher acceptance when web + non web are
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used together
Unlikely to change share much due to a defined
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niche

Python / Ruby
High productivity and capability languages
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Have small but vibrant communities. Finding

trained people can be tougher
Excellent Framework support
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Current runtimes have difficulties in multi
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threading
Lot of investments still continuing in VM
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development.
Likely to continue to grab more share.
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Functional Programming Langs
Lot of current interest. Am studying these with

great interest.
Primarily useful for computation / algorithm
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intensive apps. As yet am unable to find them
useful for typical CRUD apps
Maturing still in progress. Too much rethinking

and retraining required currently. Many items
still unclear (eg. ORMs, state maintenance,
performance)
Invest in short term only if very obvious benefits
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Suggest incremental FP usage to learn more
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