A Real-Time Reactive Platform for Data Integration and Event Stream Processing
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Akka Java Documentation Release 2.2.5
Akka Java Documentation Release 2.2.5 Typesafe Inc February 19, 2015 CONTENTS 1 Introduction 1 1.1 What is Akka?............................................1 1.2 Why Akka?..............................................3 1.3 Getting Started............................................3 1.4 The Obligatory Hello World.....................................7 1.5 Use-case and Deployment Scenarios.................................8 1.6 Examples of use-cases for Akka...................................9 2 General 10 2.1 Terminology, Concepts........................................ 10 2.2 Actor Systems............................................ 12 2.3 What is an Actor?.......................................... 14 2.4 Supervision and Monitoring..................................... 16 2.5 Actor References, Paths and Addresses............................... 19 2.6 Location Transparency........................................ 25 2.7 Akka and the Java Memory Model.................................. 26 2.8 Message Delivery Guarantees.................................... 28 2.9 Configuration............................................. 33 3 Actors 65 3.1 Actors................................................ 65 3.2 Typed Actors............................................. 84 3.3 Fault Tolerance............................................ 88 3.4 Dispatchers.............................................. 103 3.5 Mailboxes.............................................. 106 3.6 Routing................................................ 111 3.7 Building Finite State Machine -
Parsing with First-Class Derivatives
Parsing with First-Class Derivatives Jonathan Immanuel Brachthauser¨ Tillmann Rendel Klaus Ostermann rtifact A Comple * t * te n * A * te is W E s A e n C l l L o D C S University of Tubingen,¨ Germany o * * c P u e m s E u O e e n v R t e O o d t a y * s E a * fbrachthaeuser, rendel, [email protected] l u d a e t Abstract often reducing the modularity of their parser because these ad- Brzozowski derivatives, well known in the context of reg- hoc additions are cross-cutting with respect to the otherwise ular expressions, have recently been rediscovered to give a context-free syntactic structure of the language. simplified explanation to parsers of context-free languages. Parser combinators [10, 10, 15, 21, 22] are a parsing We add derivatives as a novel first-class feature to a standard approach that is well-suited for such ad-hoc additions. With parser combinator language. First-class derivatives enable an parser combinators, a parser is described as a first-class inversion of the control flow, allowing to implement modular entity in some host language, and parsers for smaller subsets parsers for languages that previously required separate pre- of a language are combined into parsers of larger subsets processing steps or cross-cutting modifications of the parsers. using built-in or user-defined combinators such as sequence, We show that our framework offers new opportunities for alternative, or iteration. The fact that parsers are first-class reuse and supports a modular definition of interesting use entities at runtime allows an elegant way of structuring cases of layout-sensitive parsing. -
Warp: a Haskell Web Server
The Functional Web Warp: A Haskell Web Server Michael Snoyman • Suite Solutions oughly two years ago, I began work on about this runtime is its lightweight threads. As the Yesod Web framework. I originally a result of this feature, Warp simply spawns a R intended FastCGI for my deployment strat- new thread for each incoming connection, bliss- egy, but I also created a simple HTTP server for fully unaware of the gymnastics the runtime is local testing, creatively named SimpleServer. performing under the surface. Because Yesod targets the Web Application Part of this abstraction involves converting Interface (WAI), a standard interface between synchronous Haskell I/O calls into asynchro- Haskell Web servers and Web applications, it nous system calls. Once again, Warp reaps the was easy to switch back ends for testing and benefits by calling simple functions like recv production. and send, while GHC does the hard work. It didn’t take long before I started getting Up through GHC 6.12, this runtime sys- feature requests for SimpleServer: slowly but tem was based on the select system call. This surely, features such as chunked transfer encod- worked well enough for many tasks but didn’t ing and sendfile-based file serving made their scale for Web servers. One big feature of the way in. The tipping point was when Matt Brown GHC 7 release was a new event manager, written of Soft Mechanics made some minor tweaks by Google’s Johan Tibell and Serpentine’s Bryan to SimpleServer and found that it was already O’Sullivan. This new runtime uses different sys- the fastest Web server in Haskell (see Figure 1). -
Interleaving Data and Effects"
Archived version from NCDOCKS Institutional Repository http://libres.uncg.edu/ir/asu/ Interleaving data and effects By: Robert Atkey and Patricia Johann Abstract: The study of programming with and reasoning about inductive datatypes such as lists and trees has benefited from the simple categorical principle of initial algebras. In initial algebra semantics, each inductive datatype is represented by an initial f-algebra for an appropriate functor f. The initial algebra principle then supports the straightforward derivation of definitional principles and proof principles for these datatypes. This technique has been expanded to a whole methodology of structured functional programming, often called origami programming.In this article we show how to extend initial algebra semantics from pure inductive datatypes to inductive datatypes interleaved with computational effects. Inductive datatypes interleaved with effects arise naturally in many computational settings. For example, incrementally reading characters from a file generates a list of characters interleaved with input/output actions, and lazily constructed infinite values can be represented by pure data interleaved with the possibility of non-terminating computation. Straightforward application of initial algebra techniques to effectful datatypes leads either to unsound conclusions if we ignore the possibility of effects, or to unnecessarily complicated reasoning because the pure and effectful concerns must be considered simultaneously. We show how pure and effectful concerns can be separated using the abstraction of initial f-and-m-algebras, where the functor f describes the pure part of a datatype and the monad m describes the interleaved effects. Because initial f-and-m-algebras are the analogue for the effectful setting of initial f- algebras, they support the extension of the standard definitional and proof principles to the effectful setting. -
On the Duality of Streams How Can Linear Types Help to Solve the Lazy IO Problem?
Draft: do not distribute On the Duality of Streams How Can Linear Types Help to Solve the Lazy IO Problem? Jean-Philippe Bernardy Josef Svenningsson Chalmers University of Technology and University of Gothenburg bernardy,josefs at chalmers.se Abstract sired behavior does not always happen. Indeed, a necessary con- We present a novel stream-programming library for Haskell. As dition is that the production pattern of f matches the consumption other coroutine-based stream libraries, our library allows syn- pattern of g; otherwise buffering occurs. In practice, this means that chronous execution, which implies that effects are run in lockstep a seemingly innocuous change in either of the function definitions and no buffering occurs. may drastically change the memory behavior of the composition, A novelty of our implementation is that it allows to locally in- without warning. If one cares about memory behavior, this means troduce buffering or re-scheduling of effects. The buffering require- that the compositionality principle touted by Hughes breaks down. ments (or re-scheduling opportunities) are indicated by the type- Second, lazy evaluation does not extend nicely to effectful pro- system. cessing. That is, if (say) an input list is produced by reading a file lazily, one is exposed to losing referential transparency (as ? has Our library is based on a number of design principles, adapted 1 from the theory of Girard’s Linear Logic. These principles are shown). For example, one may rightfully expect that both follow- applicable to the design of any Haskell structure where resource ing programs have the same behavior: management (memory, IO, ...) is critical. -
A Companion Booklet to "Functional Programming in Scala"
A companion booklet to ”Functional Programming in Scala” Chapter notes, errata, hints, and answers to exercises Rúnar Óli Bjarnason This book is for sale at http://leanpub.com/fpinscalacompanion This version was published on 2015-03-05 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. ©2015 Manning Publications Co., Rúnar Óli Bjarnason, and other contributors Contents About this booklet ......................................... 2 Why make this? ......................................... 2 License .............................................. 2 Errata ................................................ 3 Chapter notes ........................................... 6 Getting answers to your questions ............................... 6 Notes on chapter 1: What is functional programming? .................... 6 Notes on chapter 2: Getting started ............................... 7 Notes on chapter 3: Functional data structures ........................ 9 Notes on chapter 4: Handling errors without exceptions ................... 12 Notes on chapter 5: Strictness and laziness .......................... 14 Notes on chapter 6: Purely functional state .......................... 16 Notes on chapter 7: Purely functional parallelism ....................... 20 Notes on chapter 8: Property-based testing ......................... -
A Brief Overview of Functional Programming Languages
electronic Journal of Computer Science and Information Technology (eJCSIT), Vol. 6, No. 1, 2016 A Brief Overview of Functional Programming Languages Jagatheesan Kunasaikaran1, Azlan Iqbal2 1ZALORA Malaysia, Jalan Dua, Chan Sow Lin, Kuala Lumpur, Malaysia e-mail: [email protected] 2College of Computer Science and Information Technology, Universiti Tenaga Nasional, Putrajaya Campus, Selangor, Malaysia e-mail: [email protected] Abstract – Functional programming is an important Common programming paradigms are procedural, object- programming paradigm. It is based on a branch of oriented and functional. Figure 1 illustrates conceptually mathematics known as lambda calculus. In this article, we the differences between the common programming provide a brief overview, aimed at those new to the field, and language paradigms [1]. Procedural and object-oriented explain the progress of functional programming since its paradigms mutate or alter the data along program inception. A selection of functional languages are provided as examples. We also suggest some improvements and speculate execution. On the other hand, in pure functional style, the on the potential future directions of this paradigm. data does not exist by itself or independently. A composition of function calls with a set of arguments Keywords – functional, programming languages, LISP, Python; generates the final result that is expected. Each function is Javascript,Java, Elm, Haskell ‘atomic’ as it only executes operations defined in it to the input data and returns the result of the computation to the ‘callee’. I. INTRODUCTION II. FUNCTIONAL LANGUAGES Programming languages can be classified into the style of programming each language supports. There are multiple Functional programming is based on mathematical logic. -
Polarized Data Parallel Data Flow
Polarized Data Parallel Data Flow Ben Lippmeierα Fil Mackayβ Amos Robinsonγ α,β Vertigo Technology (Australia) α,γ UNSW (Australia) γ Ambiata (Australia) fbenl,fi[email protected] fbenl,[email protected] [email protected] Abstract • We use continuation passing style (CPS) to provoke the Glas- We present an approach to writing fused data parallel data flow pro- gow Haskell Compiler into applying stream fusion across grams where the library API guarantees that the client programs chunks processed by independent flow operators. (x3.1) run in constant space. Our constant space guarantee is achieved by Our target applications concern medium data, meaning data that observing that binary stream operators can be provided in several is large enough that it does not fit in the main memory of a normal polarity versions. Each polarity version uses a different combina- desktop machine, but not so large that we require a cluster of tion of stream sources and sinks, and some versions allow constant multiple physical machines. For lesser data one could simply load it space execution while others do not. Our approach is embodied in into main memory and use an in-memory array library. For greater the Repa Flow Haskell library, which we are currently using for data one needs to turn to a distributed system such as Hadoop [16] production workloads at Vertigo. or Spark [19] and deal with the unreliable network and lack of Categories and Subject Descriptors D.3.4 [Programming Lan- shared memory. Repa Flow targets the sweet middle ground. guages]: Processors—Compilers; Optimization Keywords Arrays; Fusion; Haskell 2. -
Fnc Documentation Release 0.5.2
fnc Documentation Release 0.5.2 Derrick Gilland Dec 24, 2020 Contents 1 Links 3 2 Features 5 3 Quickstart 7 4 Guide 11 4.1 Installation................................................ 11 4.2 API Reference.............................................. 11 4.2.1 Generators............................................ 12 4.2.2 Iteratees............................................. 12 4.2.3 Function Composition..................................... 14 4.2.4 Sequences............................................ 15 4.2.5 Mappings............................................ 28 4.2.6 Utilities............................................. 32 4.3 Developer Guide............................................. 40 4.3.1 Python Environments...................................... 40 4.3.2 Tooling............................................. 40 4.3.3 Workflows............................................ 40 4.3.4 CI/CD.............................................. 42 5 Project Info 43 5.1 License.................................................. 43 5.2 Versioning................................................ 43 5.3 Changelog................................................ 44 5.3.1 v0.5.2 (2020-12-24)....................................... 44 5.3.2 v0.5.1 (2020-12-14)....................................... 44 5.3.3 v0.5.0 (2020-10-23)....................................... 44 5.3.4 v0.4.0 (2019-01-23)....................................... 44 5.3.5 v0.3.0 (2018-08-31)....................................... 44 5.3.6 v0.2.0 (2018-08-24)...................................... -
A Review Article on Cryptography Using Functional Programming:Haskell
European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 08, Issue 02, 2021 A REVIEW ARTICLE ON CRYPTOGRAPHY USING FUNCTIONAL PROGRAMMING:HASKELL Dikchha Dwivedi1* and Hari Om Sharan2 *1Department of Computer Science & Engineering, Rama University Kanpur, 208016, India E-mail: [email protected] 2Department of Computer Science & Engineering, Rama University Kanpur, 208016, IndiaE- [email protected] Abstract: A modern wave of programming technology has been at the frontline of functional languages, experiencing growing success as well as impact. Hughes published an article entitled 'Why Functional Programming Matters', that has now been one of the most recent references in the field.Safe programming defines the method used by software engineers to include multiple safety mechanism for their system.Secure programming can be broken down into two subgroups to analyse its correlation with software design: access control, secure programme initialization, input validation, cryptography, secure networking, secure random number generation, and anti-tampering. In this paper we provide a review of various functional programming in Haskell for encryption. The inkling of functional programming is to make programming more closely related to mathematics. We describe crucial features and trade-offs that has to be well-thought-out while selecting the right method for secure computation. Keywords: Secure Computation,Cryptography, Haskell, Functional programming 1. Introduction In the internet age, all the interactive and computing devices are interconnected across global and private networks. A vast majority of public and private agencies depend upon information system for their mission critical operations. Thus, network and system security are of paramount importance for efficient functioning of these systems. -
Fnc Documentation Release 0.5.2
fnc Documentation Release 0.5.2 Derrick Gilland Mar 29, 2021 Contents 1 Links 3 2 Features 5 3 Quickstart 7 4 Guide 11 4.1 Installation................................................ 11 4.2 API Reference.............................................. 11 4.2.1 Generators............................................ 12 4.2.2 Iteratees............................................. 12 4.2.3 Function Composition..................................... 14 4.2.4 Sequences............................................ 15 4.2.5 Mappings............................................ 28 4.2.6 Utilities............................................. 32 4.3 Developer Guide............................................. 40 4.3.1 Python Environments...................................... 40 4.3.2 Tooling............................................. 40 4.3.3 Workflows............................................ 40 4.3.4 CI/CD.............................................. 42 5 Project Info 43 5.1 License.................................................. 43 5.2 Versioning................................................ 43 5.3 Changelog................................................ 44 5.3.1 v0.5.2 (2020-12-24)....................................... 44 5.3.2 v0.5.1 (2020-12-14)....................................... 44 5.3.3 v0.5.0 (2020-10-23)....................................... 44 5.3.4 v0.4.0 (2019-01-23)....................................... 44 5.3.5 v0.3.0 (2018-08-31)....................................... 44 5.3.6 v0.2.0 (2018-08-24)...................................... -
Stackless Scala with Free Monads
Stackless Scala With Free Monads Runar´ Oli´ Bjarnason [email protected] Abstract The runS function takes some input state of type S and Tail call elimination (TCE) in the Scala compiler is limited outputs a value of type A together with a new state. The map to self-recursive methods, but tail calls are otherwise not and flatMap methods allow us to thread the state through eliminated. This makes functions composed of many smaller a for-comprehension, in order to write imperative-looking functions prone to stack overflows. Having a general TCE programs using State combinators such as these: mechanism would be a great benefit in Scala, particularly def getState[S]: State[S,S] = for functional programming. Trampolining is a popular tech- State(s => (s,s)) nique [6], which can be used for TCE in languages that don’t def setState[S](s: S): State[S,Unit] = support it natively. This paper gives an introduction to tram- State(_ => ((),s)) polines in Scala and expands on this solution to gain elimi- nation of any method call whatsoever, even calls that are not def pureState[S, A](a: A): State[S, A] = in tail position at all. This obviates altogether the use of the State(s => (a,s)) call stack in Scala programs. Note that pureState and flatMap together make State a monad [4]. 1. Introduction As a simple demonstration, let us write a function that uses State to number all the elements in a list. Not because Since the call stack is a limited resource of the virtual ma- this is a compelling use case for State, but because it’s chine, most programmers who have some experience with simple and it demonstrates the stack overflow.