Programming with Python
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Declarative Languages for Big Streaming Data a Database Perspective
Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data. -
The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 Minutes)
EuroPython 2007 Contribution ID: 62 Type: not specified The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 minutes) This is a re-worked, actualized and improved version of my talk at PyCon 2007. Repeating the abstract: As a surprise for people who think they know Stackless, we present the new Stackless implementation For PyPy, which has led to a significant amount of new insight about parallel programming and its possible implementations. We will isolate the known Stackless as a special case of a general concept. This is a Stackless, not a PyPy talk. But the insights presented here would not exist without PyPy’s existance. Summary Stackless has been around for a long time now. After several versions with different goals in mind, the basic concepts of channels and tasklets turned out to be useful abstractions, and since many versions, Stackless is only ported from version to version, without fundamental changes to the principles. As some spin-off, Armin Rigo invented Greenlets at a Stackless sprint. They are some kind of coroutines and a bit of special semantics. The major benefit is that Greenlets can runon unmodified CPython. In parallel to that, the PyPy project is in its fourth year now, and one of its goals was Stackless integration as an option. And of course, Stackless has been integrated into PyPy in a very nice and elegant way, much nicer than expected. During the design of the Stackless extension to PyPy, it turned out, that tasklets, greenlets and coroutines are not that different in principle, and it was possible to base all known parallel paradigms on one simple coroutine layout, which is as minimalistic as possible. -
Method, 224 Vs. Typing Module Types
Index A migration metadata, 266 running migrations, 268, 269 Abstract base classes, 223 setting up a new project, 264 @abc.abstractmethod decorator, 224 using constants in migrations, 267 __subclasshook__(...) method, 224 __anext__() method, 330 vs. typing module types, 477 apd.aggregation package, 397, 516, 524 virtual subclasses, 223 clean functions (see clean functions) Actions, 560 database, 254 analysis process, 571, 572 get_data_by_deployment(...) config file, 573, 575 function, 413, 416 DataProcessor class, 561 get_data(...) function, 415 extending, 581 plot_sensor(...) function, 458 IFTTT service, 566 plotting data, 429 ingesting data, 567, 571 plotting functions, 421 logs, 567 query functions, 417 trigger, 563, 564 apd.sensors package, 106 Adafruit, 42, 486 APDSensorsError, 500 Adapter pattern, 229 DataCollectionError, 500 __aenter__() method, 331, 365 directory structure, 108 __aexit__(...) method, 331, 365 extending, 149 Aggregation process, 533 IntermittentSensorFailureError, 500 aiohttp library, 336 making releases, 141 __aiter__() method, 330 sensors script, 32, 36, 130, 147, 148, Alembic, 264 153, 155, 156, 535 ambiguous changes, 268 UserFacingCLIError, 502 creating a new revision, 265 apd.sunnyboy_solar package, 148, current version, 269 155, 173 downgrading, 268, 270 API design, 190 irreversible, migrations, 270 authentication, 190 listing migrations, 269 versioning, 240, 241, 243 merging, migrations, 269 589 © Matthew Wilkes 2020 M. Wilkes, Advanced Python Development, https://doi.org/10.1007/978-1-4842-5793-7 INDEX AssertionError, -
Python Concurrency
Python Concurrency Threading, parallel and GIL adventures Chris McCafferty, SunGard Global Services Overview • The free lunch is over – Herb Sutter • Concurrency – traditionally challenging • Threading • The Global Interpreter Lock (GIL) • Multiprocessing • Parallel Processing • Wrap-up – the Pythonic Way Reminder - The Free Lunch Is Over How do we get our free lunch back? • Herb Sutter’s paper at: • http://www.gotw.ca/publications/concurrency-ddj.htm • Clock speed increase is stalled but number of cores is increasing • Parallel paths of execution will reduce time to perform computationally intensive tasks • But multi-threaded development has typically been difficult and fraught with danger Threading • Use the threading module, not thread • Offers usual helpers for making concurrency a bit less risky: Threads, Locks, Semaphores… • Use logging, not print() • Don’t start a thread in module import (bad) • Careful importing from daemon threads Traditional management view of Threads Baby pile of snakes, Justin Guyer Managing Locks with ‘with’ • With keyword is your friend • (compare with the ‘with file’ idiom) import threading rlock = threading.RLock() with rlock: print "code that can only be executed while we acquire rlock" #lock is released at end of code block, regardless of exceptions Atomic Operations in Python • Some operations can be pre-empted by another thread • This can lead to bad data or deadlocks • Some languages offer constructs to help • CPython has a set of atomic operations due to the operation of something called the GIL and the way the underlying C code is implemented • This is a fortuitous implementation detail – ideally use RLocks to future-proof your code CPython Atomic Operations • reading or replacing a single instance attribute • reading or replacing a single global variable • fetching an item from a list • modifying a list in place (e.g. -
Thread-Level Parallelism II
Great Ideas in UC Berkeley UC Berkeley Teaching Professor Computer Architecture Professor Dan Garcia (a.k.a. Machine Structures) Bora Nikolić Thread-Level Parallelism II Garcia, Nikolić cs61c.org Languages Supporting Parallel Programming ActorScript Concurrent Pascal JoCaml Orc Ada Concurrent ML Join Oz Afnix Concurrent Haskell Java Pict Alef Curry Joule Reia Alice CUDA Joyce SALSA APL E LabVIEW Scala Axum Eiffel Limbo SISAL Chapel Erlang Linda SR Cilk Fortan 90 MultiLisp Stackless Python Clean Go Modula-3 SuperPascal Clojure Io Occam VHDL Concurrent C Janus occam-π XC Which one to pick? Garcia, Nikolić Thread-Level Parallelism II (3) Why So Many Parallel Programming Languages? § Why “intrinsics”? ú TO Intel: fix your #()&$! compiler, thanks… § It’s happening ... but ú SIMD features are continually added to compilers (Intel, gcc) ú Intense area of research ú Research progress: 20+ years to translate C into good (fast!) assembly How long to translate C into good (fast!) parallel code? • General problem is very hard to solve • Present state: specialized solutions for specific cases • Your opportunity to become famous! Garcia, Nikolić Thread-Level Parallelism II (4) Parallel Programming Languages § Number of choices is indication of ú No universal solution Needs are very problem specific ú E.g., Scientific computing/machine learning (matrix multiply) Webserver: handle many unrelated requests simultaneously Input / output: it’s all happening simultaneously! § Specialized languages for different tasks ú Some are easier to use (for some problems) -
Uwsgi Documentation Release 1.9
uWSGI Documentation Release 1.9 uWSGI February 08, 2016 Contents 1 Included components (updated to latest stable release)3 2 Quickstarts 5 3 Table of Contents 11 4 Tutorials 137 5 Articles 139 6 uWSGI Subsystems 141 7 Scaling with uWSGI 197 8 Securing uWSGI 217 9 Keeping an eye on your apps 223 10 Async and loop engines 231 11 Web Server support 237 12 Language support 251 13 Release Notes 317 14 Contact 359 15 Donate 361 16 Indices and tables 363 Python Module Index 365 i ii uWSGI Documentation, Release 1.9 The uWSGI project aims at developing a full stack for building (and hosting) clustered/distributed network applica- tions. Mainly targeted at the web and its standards, it has been successfully used in a lot of different contexts. Thanks to its pluggable architecture it can be extended without limits to support more platforms and languages. Cur- rently, you can write plugins in C, C++ and Objective-C. The “WSGI” part in the name is a tribute to the namesake Python standard, as it has been the first developed plugin for the project. Versatility, performance, low-resource usage and reliability are the strengths of the project (and the only rules fol- lowed). Contents 1 uWSGI Documentation, Release 1.9 2 Contents CHAPTER 1 Included components (updated to latest stable release) The Core (implements configuration, processes management, sockets creation, monitoring, logging, shared memory areas, ipc, cluster membership and the uWSGI Subscription Server) Request plugins (implement application server interfaces for various languages and platforms: WSGI, PSGI, Rack, Lua WSAPI, CGI, PHP, Go ...) Gateways (implement load balancers, proxies and routers) The Emperor (implements massive instances management and monitoring) Loop engines (implement concurrency, components can be run in preforking, threaded, asynchronous/evented and green thread/coroutine modes. -
Goless Documentation Release 0.6.0
goless Documentation Release 0.6.0 Rob Galanakis July 11, 2014 Contents 1 Intro 3 2 Goroutines 5 3 Channels 7 4 The select function 9 5 Exception Handling 11 6 Examples 13 7 Benchmarks 15 8 Backends 17 9 Compatibility Details 19 9.1 PyPy................................................... 19 9.2 Python 2 (CPython)........................................... 19 9.3 Python 3 (CPython)........................................... 19 9.4 Stackless Python............................................. 20 10 goless and the GIL 21 11 References 23 12 Contributing 25 13 Miscellany 27 14 Indices and tables 29 i ii goless Documentation, Release 0.6.0 • Intro • Goroutines • Channels • The select function • Exception Handling • Examples • Benchmarks • Backends • Compatibility Details • goless and the GIL • References • Contributing • Miscellany • Indices and tables Contents 1 goless Documentation, Release 0.6.0 2 Contents CHAPTER 1 Intro The goless library provides Go programming language semantics built on top of gevent, PyPy, or Stackless Python. For an example of what goless can do, here is the Go program at https://gobyexample.com/select reimplemented with goless: c1= goless.chan() c2= goless.chan() def func1(): time.sleep(1) c1.send(’one’) goless.go(func1) def func2(): time.sleep(2) c2.send(’two’) goless.go(func2) for i in range(2): case, val= goless.select([goless.rcase(c1), goless.rcase(c2)]) print(val) It is surely a testament to Go’s style that it isn’t much less Python code than Go code, but I quite like this. Don’t you? 3 goless Documentation, Release 0.6.0 4 Chapter 1. Intro CHAPTER 2 Goroutines The goless.go() function mimics Go’s goroutines by, unsurprisingly, running the routine in a tasklet/greenlet. -
Kodai: a Software Architecture and Implementation for Segmentation
University of Rhode Island DigitalCommons@URI Open Access Master's Theses 2017 Kodai: A Software Architecture and Implementation for Segmentation Rick Rejeleene University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Rejeleene, Rick, "Kodai: A Software Architecture and Implementation for Segmentation" (2017). Open Access Master's Theses. Paper 1107. https://digitalcommons.uri.edu/theses/1107 This Thesis is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Master's Theses by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. KODAI: A SOFTWARE ARCHITECTURE AND IMPLEMENTATION FOR SEGMENTATION BY RICK REJELEENE A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF RHODE ISLAND 2017 MASTER OF SCIENCE THESIS OF RICK REJELEENE APPROVED: Thesis Committee: Major Professor: Joan Peckham Lisa DiPippo Ruby Dholakia Nasser H Zawia DEAN OF GRADUATE COMMITTEE UNIVERSITY OF RHODE ISLAND 2017 ABSTRACT The purpose of this thesis is to design and implement a software architecture for segmen- tation models to improve revenues for a supermarket. This tool supports analysis of su- permarket products and generates results to interpret consumer behavior, to give busi- nesses deeper insights into targeted consumer markets. The software design developed is named as Kodai. Kodai is horizontally reusable and can be adapted across various indus- tries. This software framework allows testing a hypothesis to address the problem of in- creasing revenues in supermarkets. Kodai has several advantages, such as analyzing and visualizing data, and as a result, businesses can make better decisions. -
DARPA and Data: a Portfolio Overview
DARPA and Data: A Portfolio Overview William C. Regli Special Assistant to the Director Defense Advanced Research Projects Agency Brian Pierce Director Information Innovation Office Defense Advanced Research Projects Agency Fall 2017 Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 1 DARPA Dreams of Data • Investments over the past decade span multiple DARPA Offices and PMs • Information Innovation (I2O): Software Systems, AI, Data Analytics • Defense Sciences (DSO): Domain-driven problems (chemistry, social science, materials science, engineering design) • Microsystems Technology (MTO): New hardware to support these processes (neuromorphic processor, graph processor, learning systems) • Products include DARPA Program testbeds, data and software • The DARPA Open Catalog • Testbeds include those in big data, cyber-defense, engineering design, synthetic bio, machine reading, among others • Multiple layers and qualities of data are important • Important for reproducibility; important as fuel for future DARPA programs • Beyond public data to include “raw” data, process/workflow data • Data does not need to be organized to be useful or valuable • Software tools are getting better eXponentially, ”raw” data can be processed • Changing the economics (Forensic Data Curation) • Its about optimizing allocation of attention in human-machine teams Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 2 Working toward Wisdom Wisdom: sound judgment - governance Abstraction Wisdom (also Understanding: -
Continuations and Stackless Python
Continuations and Stackless Python Or "How to change a Paradigm of an existing Program" Christian Tismer Virtual Photonics GmbH mailto:[email protected] Abstract 2 Continuations In this paper, an implementation of "Stackless Python" (a Python which does not keep state on the C 2.1 What is a Continuation? stack) is presented. Surprisingly, the necessary changes affect just a small number of C modules, and Many attempts to explain continuations can be found a major rewrite of the C library can be avoided. The in the literature[5-10], more or less hard to key idea in this approach is a paradigm change for the understand. The following is due to Jeremy Hylton, Python code interpreter that is not easy to understand and I like it the best. Imagine a very simple series of in the first place. Recursive interpreter calls are statements: turned into tail recursion, which allows deferring evaluation by pushing frames to the frame stack, x = 2; y = x + 1; z = x * 2 without the C stack involved. In this case, the continuation of x=2 is y=x+1; By decoupling the frame stack from the C stack, we z=x*2. You might think of the second and third now have the ability to keep references to frames and assignments as a function (forgetting about variable to do non-local jumps. This turns the frame stack into scope for the moment). That function is the a tree, and every leaf of the tree can now be a jump continuation. In essence, every single line of code has target. -
Faster Cpython Documentation Release 0.0
Faster CPython Documentation Release 0.0 Victor Stinner January 29, 2016 Contents 1 FAT Python 3 2 Everything in Python is mutable9 3 Optimizations 13 4 Python bytecode 19 5 Python C API 21 6 AST Optimizers 23 7 Old AST Optimizer 25 8 Register-based Virtual Machine for Python 33 9 Read-only Python 39 10 History of Python optimizations 43 11 Misc 45 12 Kill the GIL? 51 13 Implementations of Python 53 14 Benchmarks 55 15 PEP 509: Add a private version to dict 57 16 PEP 510: Specialized functions with guards 59 17 PEP 511: API for AST transformers 61 18 Random notes about PyPy 63 19 Talks 65 20 Links 67 i ii Faster CPython Documentation, Release 0.0 Contents: Contents 1 Faster CPython Documentation, Release 0.0 2 Contents CHAPTER 1 FAT Python 1.1 Intro The FAT Python project was started by Victor Stinner in October 2015 to try to solve issues of previous attempts of “static optimizers” for Python. The main feature are efficient guards using versionned dictionaries to check if something was modified. Guards are used to decide if the specialized bytecode of a function can be used or not. Python FAT is expected to be FAT... maybe FAST if we are lucky. FAT because it will use two versions of some functions where one version is specialised to specific argument types, a specific environment, optimized when builtins are not mocked, etc. See the fatoptimizer documentation which is the main part of FAT Python. The FAT Python project is made of multiple parts: 3 Faster CPython Documentation, Release 0.0 • The fatoptimizer project is the static optimizer for Python 3.6 using function specialization with guards. -
STORM: Refinement Types for Secure Web Applications
STORM: Refinement Types for Secure Web Applications Nico Lehmann Rose Kunkel Jordan Brown Jean Yang UC San Diego UC San Diego Independent Akita Software Niki Vazou Nadia Polikarpova Deian Stefan Ranjit Jhala IMDEA Software Institute UC San Diego UC San Diego UC San Diego Abstract Centralizing policy specification is not a new idea. Several We present STORM, a web framework that allows developers web frameworks (e.g., HAILS [4], JACQUELINE [5], and to build MVC applications with compile-time enforcement of LWEB [6]) already do this. These frameworks, however, have centrally specified data-dependent security policies. STORM two shortcomings that have hindered their adoption. First, ensures security using a Security Typed ORM that refines the they enforce policies at run-time, typically using dynamic (type) abstractions of each layer of the MVC API with logical information flow control (IFC). While dynamic enforcement assertions that describe the data produced and consumed by is better than no enforcement, dynamic IFC imposes a high the underlying operation and the users allowed access to that performance overhead, since the system must be modified to data. To evaluate the security guarantees of STORM, we build a track the provenance of data and restrict where it is allowed formally verified reference implementation using the Labeled to flow. More importantly, certain policy violations are only IO (LIO) IFC framework. We present case studies and end-to- discovered once the system is deployed, at which point they end applications that show how STORM lets developers specify may be difficult or expensive to fix, e.g., on applications diverse policies while centralizing the trusted code to under 1% running on IoT devices [7].