As more and more web applications integrate with third-party APIs and other external data, processing those external resources in the background more and more important. A simple job runner is a great start, however as your load increases, you very quickly outgrew that simplistic queuing system. We will cover where getting started using Resque and Redis, how to test your jobs, when it makes sense to use Resque, implementations of Resque in other languages, and look how I've used Resque.
Resque is a background processing library for Ruby that allows processing of jobs asynchronously via a queue using Redis. It uses workers that listen for jobs on queues and process them, and includes plugins like resque-lock to prevent duplicate jobs and resque-loner to relaunch failed jobs. The document discusses setting up Resque with God for monitoring workers, handling signals, and restarting workers gracefully on the bookandgolf.com project which synchronizes golf course data from multiple APIs into Redis.
This document discusses using the Jesque framework and Redis for distributed job processing. It provides an example of configuring jobs in Jesque that run performance tests using Grails services. Workers pull jobs from the Redis queue in a distributed manner across multiple JVM processes and nodes. Results are saved back to the database. Dashboards can display performance test results.
For many years Capistrano has been the defacto deployment tool, but many organisations have yet to realise the benefits of automating their deployment process. Automated Deployments are fast, less error prone, easier to rollback and you can dish out the keys to other team members so anyone can deploy.
During this talk we’ll look at how to “capify” a simple PHP project and deploy it in a few minutes. And, as Capistrano is a “remote server automation and deployment tool”, we’ll also look at some of the other things Capistrano can do for you such as restarting apache or grepping server log (and more). We’ll also take a look at the various plug-ins available and see how easy it can be to write your own.
If you are deploying using ssh / git pull / apache restart? Then it’s time to make a change: automate all the things and live in a world of “repeatable success”.
Open Source Saturday - How can I contribute to Ruby on Rails?Pravin Mishra
Making your first contribution to an open source library can be very daunting. If you’re like me, I was/am nagged by self-doubt and a fear that I would/will “do it wrong.” I worry about the mocking of other developers, all solidified by years of open source contributions.
If you are stuck in the self-doubt phase, but want to jump in, you may be asking “What’s the first step?” or “How do I contribute?” Well, We aim to answer those kinds of questions by walking you through steps.
Learn how to use Capistrano to automate the deployment of your Ruby on Rails applications. Apply best practices and add-ons for customizing Capistrano.
This document provides instructions for deploying a Rails application using Capistrano. It includes steps to set up Capistrano, configure the deploy.rb file, generate SSH keys, add the deploy key to GitHub, run Capistrano tasks to deploy the application, and make subsequent deploys when code changes. The application is deployed to a server at 192.168.255.54 running Mongrel and uses Git for version control.
Celery for internal API in SOA infrastructureRoman Imankulov
The document discusses using Celery as an internal API for service-oriented architectures, describing how Celery can provide a low-level interface that defines only task names and handles serialization, routing, and error handling through message passing via a broker like RabbitMQ. It also outlines the benefits of Celery for building asynchronous and parallel APIs, while noting its complexity and that the broker should never be exposed publicly for security reasons.
Celery is an asynchronous task queue that allows tasks to be handled outside of HTTP requests. For example, a web application could use Celery to poll an API every 10 minutes and store the results in a database without blocking the HTTP response. Celery distributes tasks by passing messages, allowing tasks to run across multiple worker processes. It uses brokers like Redis to manage queues and tasks. Developers define tasks as functions that are executed asynchronously by Celery workers.
Cachopo - Scalable Stateful Services - Madrid Elixir MeetupAbel Muíño
This is an introduction to building our services in a different way, where state is moved out of the database and into the services (as opposed to mainstream stateless servers).
It also describes one particular proof-of-concept tool that Cabify built during its annual offsite.
Performance Tuning Your Puppet Infrastructure - PuppetConf 2014Puppet
The document discusses ways to monitor and tune Puppet infrastructure using the same techniques used for applications. It describes instrumenting the Puppet master and database with New Relic to monitor performance. It also discusses collecting logs and reports from Puppet agents and masters and sending them to Elasticsearch for analysis in Kibana.
This document discusses kRouter, a CoffeeScript routing library for AngularJS applications. It introduces kRouter and its key features like handling routes with history.pushState() and window.onpopstate. The document also provides an overview of kRouter's code structure, including common, directive, module, and provider CoffeeScript files. It concludes by explaining how to install CoffeeScript, clone the kRouter git repository, and get started using the library.
The document discusses designing a Perl module called Net::AWS::Glacier for interacting with Amazon Glacier, a cloud storage service. It proposes a two-level design with a low-level API module that mirrors the Glacier API and a high-level module that provides a more object-oriented interface. The high-level module would use a "Vault" object to manage downloads and access to archive data, hiding low-level details like iterating through job lists. An inheritance-based approach is described where derived classes can customize initialization while reusing a common object structure and initialization logic.
Matthew Eernisse gave a presentation on NodeJS at the Toster Conference in 2011. He discussed what NodeJS is, how it uses asynchronous and event-driven JavaScript, and some examples of using NodeJS at Yammer for applications like a file upload service and real-time document editing. He also covered challenges like debugging asynchronous code, developing for NodeJS, and operations considerations like monitoring and logging.
1. The document discusses continuous delivery which is the practice of automating the process of shipping code changes to production frequently by integrating testing into the deployment pipeline.
2. It recommends automating acceptance tests, deploying to production whenever code is ready, and having multiple versions deployed to allow quick switching of code.
3. Challenges discussed include the expense of manual operations, fixing requirements, changing environments, and handling hotfixes and rollbacks. The document provides examples of handling schema changes incrementally.
- Ansible 2 includes an architecture overhaul, new YAML parsing engine, and 100% backwards compatibility for playbooks. It introduces task blocks for error handling and dynamic includes. Execution strategies allow running tasks linearly or in parallel. Many new modules were added.
- Galaxy 2 provides better metrics, role management, and Travis CI integration. It allows importing roles from organizations and individuals. The ansible-galaxy CLI was improved with features like role scaffolding and authentication.
This document discusses using Celery and RabbitMQ to handle asynchronous and distributed tasks in Python. It provides an overview of Celery and RabbitMQ and how they can be used together. Examples are given of defining and running tasks, including periodic tasks, task sets, routing, retries, and interacting with results. The benefits of using Celery for slow, memory-intensive, or externally-dependent tasks are highlighted.
Celery is a Python-based distributed task queue that uses message queues like AMQP to asynchronously execute tasks across multiple machines. It provides features like task scheduling, retries, error handling, and integration with Django. Tasks are defined as Python functions decorated with the "@task" decorator. Workers process tasks from the queue and results are sent back via messages. Celery simplifies building asynchronous workflows in Python applications.
This document discusses using infrastructure as code to automate server setup and deployment using Ansible. It outlines benefits like reducing human error, allowing developers and operations teams to work seamlessly through continuous integration and testing. Specifics of the infrastructure as code stack used are covered, including Ansible for configuration management, Docker for containers, and Vagrant for local virtual machine provisioning. Challenges and lessons learned around testing, debugging, and migrating existing systems are also presented. The overall message is that infrastructure as code takes time to implement but results in major improvements to deployment speed, reliability, and ability to rapidly iterate.
The Puppet Master on the JVM - PuppetConf 2014Puppet
Puppet Server is a new component of Puppet Enterprise that improves performance, scalability, and availability. It uses a Service-Oriented Architecture and the Trapperkeeper framework, which allows for better extensibility. Puppet Server provides significantly faster catalog compilation times, agent run times, and request response times compared to the previous Apache/Passenger architecture. It can also handle more agents per master as it continues to be optimized.
This document discusses integrating test automation and code coverage for web service applications. It introduces Postman for calling web services and testing responses, and Jenkins for build automation and tracking test results over time. It then demonstrates setting up a test automation workflow using these tools on a sample Laravel application, including starting and stopping coverage collection, running tests from Postman and PHPUnit, and merging the results. Some best practices and philosophies around test automation and code coverage are also discussed.
Data processing with celery and rabbit mqJeff Peck
This document summarizes a presentation about using Python, Celery, and RabbitMQ for data processing. It describes using Celery to efficiently process large amounts of data from multiple sources in parallel and deploy the results to different targets. It provides a practical example of using Celery to parse 500,000 emails and load them into a MySQL database and Elasticsearch index. The example code demonstrates setting up Celery, defining tasks, and using Fabric to start workers and process files.
Celery is an asynchronous task queue/job queue based on distributed message passing. It allows tasks to be executed asynchronously (in the background) outside of the main request-response cycle. Some key uses of Celery include running long-running or expensive queries, interacting with external APIs, and scheduling periodic or recurring tasks as an alternative to cron jobs. Celery uses message brokers like RabbitMQ or Redis to ensure tasks are only executed once and are distributed across worker servers. Tasks can be defined as Python functions that are decorated to make them visible to the Celery system. Periodic and recurring tasks can also be defined for scheduling purposes.
Celery is a distributed task queue that allows long-running processes to be executed asynchronously outside of the main request-response cycle. It uses message brokers like RabbitMQ to distribute jobs to worker nodes for processing. This improves request performance and allows tasks to be distributed across multiple machines. Common use cases include asynchronous tasks like email sending, long database operations, image/video processing, and external API calls.
Controlling multiple VMs with the power of PythonYurii Vasylenko
This document discusses using Python and related libraries to remotely control and manage multiple virtual machines. It recommends using PySphere and related tools to connect to VMWare servers, power on/off VMs, create snapshots, execute commands and programs on VMs, and more. The document provides instructions for setting up the necessary Python environment and configuring connections to VMWare servers. It also includes code examples for performing basic tasks like connecting to a VM, managing snapshots, running long-running tasks asynchronously, and executing programs on VMs with graphical interfaces.
This document discusses various computing concepts related to resources and performance in PHP applications. It covers topics like data storage technologies, areal storage density of hard drives and solid state drives, streams as a way to access input and output generically in PHP, using filters to perform operations on stream data, common issues like running out of memory and how to address them through better programming practices, limitations of CPU and how to distribute load through job queuing, and basics of networking like IP addresses, TCP, and using sockets. The key advice is to assume large amounts of data and potential failures, use appropriate data storage, avoid unnecessary processing in memory, optimize code through profiling, and offload work to other systems when possible.
Celery for internal API in SOA infrastructureRoman Imankulov
The document discusses using Celery as an internal API for service-oriented architectures, describing how Celery can provide a low-level interface that defines only task names and handles serialization, routing, and error handling through message passing via a broker like RabbitMQ. It also outlines the benefits of Celery for building asynchronous and parallel APIs, while noting its complexity and that the broker should never be exposed publicly for security reasons.
Celery is an asynchronous task queue that allows tasks to be handled outside of HTTP requests. For example, a web application could use Celery to poll an API every 10 minutes and store the results in a database without blocking the HTTP response. Celery distributes tasks by passing messages, allowing tasks to run across multiple worker processes. It uses brokers like Redis to manage queues and tasks. Developers define tasks as functions that are executed asynchronously by Celery workers.
Cachopo - Scalable Stateful Services - Madrid Elixir MeetupAbel Muíño
This is an introduction to building our services in a different way, where state is moved out of the database and into the services (as opposed to mainstream stateless servers).
It also describes one particular proof-of-concept tool that Cabify built during its annual offsite.
Performance Tuning Your Puppet Infrastructure - PuppetConf 2014Puppet
The document discusses ways to monitor and tune Puppet infrastructure using the same techniques used for applications. It describes instrumenting the Puppet master and database with New Relic to monitor performance. It also discusses collecting logs and reports from Puppet agents and masters and sending them to Elasticsearch for analysis in Kibana.
This document discusses kRouter, a CoffeeScript routing library for AngularJS applications. It introduces kRouter and its key features like handling routes with history.pushState() and window.onpopstate. The document also provides an overview of kRouter's code structure, including common, directive, module, and provider CoffeeScript files. It concludes by explaining how to install CoffeeScript, clone the kRouter git repository, and get started using the library.
The document discusses designing a Perl module called Net::AWS::Glacier for interacting with Amazon Glacier, a cloud storage service. It proposes a two-level design with a low-level API module that mirrors the Glacier API and a high-level module that provides a more object-oriented interface. The high-level module would use a "Vault" object to manage downloads and access to archive data, hiding low-level details like iterating through job lists. An inheritance-based approach is described where derived classes can customize initialization while reusing a common object structure and initialization logic.
Matthew Eernisse gave a presentation on NodeJS at the Toster Conference in 2011. He discussed what NodeJS is, how it uses asynchronous and event-driven JavaScript, and some examples of using NodeJS at Yammer for applications like a file upload service and real-time document editing. He also covered challenges like debugging asynchronous code, developing for NodeJS, and operations considerations like monitoring and logging.
1. The document discusses continuous delivery which is the practice of automating the process of shipping code changes to production frequently by integrating testing into the deployment pipeline.
2. It recommends automating acceptance tests, deploying to production whenever code is ready, and having multiple versions deployed to allow quick switching of code.
3. Challenges discussed include the expense of manual operations, fixing requirements, changing environments, and handling hotfixes and rollbacks. The document provides examples of handling schema changes incrementally.
- Ansible 2 includes an architecture overhaul, new YAML parsing engine, and 100% backwards compatibility for playbooks. It introduces task blocks for error handling and dynamic includes. Execution strategies allow running tasks linearly or in parallel. Many new modules were added.
- Galaxy 2 provides better metrics, role management, and Travis CI integration. It allows importing roles from organizations and individuals. The ansible-galaxy CLI was improved with features like role scaffolding and authentication.
This document discusses using Celery and RabbitMQ to handle asynchronous and distributed tasks in Python. It provides an overview of Celery and RabbitMQ and how they can be used together. Examples are given of defining and running tasks, including periodic tasks, task sets, routing, retries, and interacting with results. The benefits of using Celery for slow, memory-intensive, or externally-dependent tasks are highlighted.
Celery is a Python-based distributed task queue that uses message queues like AMQP to asynchronously execute tasks across multiple machines. It provides features like task scheduling, retries, error handling, and integration with Django. Tasks are defined as Python functions decorated with the "@task" decorator. Workers process tasks from the queue and results are sent back via messages. Celery simplifies building asynchronous workflows in Python applications.
This document discusses using infrastructure as code to automate server setup and deployment using Ansible. It outlines benefits like reducing human error, allowing developers and operations teams to work seamlessly through continuous integration and testing. Specifics of the infrastructure as code stack used are covered, including Ansible for configuration management, Docker for containers, and Vagrant for local virtual machine provisioning. Challenges and lessons learned around testing, debugging, and migrating existing systems are also presented. The overall message is that infrastructure as code takes time to implement but results in major improvements to deployment speed, reliability, and ability to rapidly iterate.
The Puppet Master on the JVM - PuppetConf 2014Puppet
Puppet Server is a new component of Puppet Enterprise that improves performance, scalability, and availability. It uses a Service-Oriented Architecture and the Trapperkeeper framework, which allows for better extensibility. Puppet Server provides significantly faster catalog compilation times, agent run times, and request response times compared to the previous Apache/Passenger architecture. It can also handle more agents per master as it continues to be optimized.
This document discusses integrating test automation and code coverage for web service applications. It introduces Postman for calling web services and testing responses, and Jenkins for build automation and tracking test results over time. It then demonstrates setting up a test automation workflow using these tools on a sample Laravel application, including starting and stopping coverage collection, running tests from Postman and PHPUnit, and merging the results. Some best practices and philosophies around test automation and code coverage are also discussed.
Data processing with celery and rabbit mqJeff Peck
This document summarizes a presentation about using Python, Celery, and RabbitMQ for data processing. It describes using Celery to efficiently process large amounts of data from multiple sources in parallel and deploy the results to different targets. It provides a practical example of using Celery to parse 500,000 emails and load them into a MySQL database and Elasticsearch index. The example code demonstrates setting up Celery, defining tasks, and using Fabric to start workers and process files.
Celery is an asynchronous task queue/job queue based on distributed message passing. It allows tasks to be executed asynchronously (in the background) outside of the main request-response cycle. Some key uses of Celery include running long-running or expensive queries, interacting with external APIs, and scheduling periodic or recurring tasks as an alternative to cron jobs. Celery uses message brokers like RabbitMQ or Redis to ensure tasks are only executed once and are distributed across worker servers. Tasks can be defined as Python functions that are decorated to make them visible to the Celery system. Periodic and recurring tasks can also be defined for scheduling purposes.
Celery is a distributed task queue that allows long-running processes to be executed asynchronously outside of the main request-response cycle. It uses message brokers like RabbitMQ to distribute jobs to worker nodes for processing. This improves request performance and allows tasks to be distributed across multiple machines. Common use cases include asynchronous tasks like email sending, long database operations, image/video processing, and external API calls.
Controlling multiple VMs with the power of PythonYurii Vasylenko
This document discusses using Python and related libraries to remotely control and manage multiple virtual machines. It recommends using PySphere and related tools to connect to VMWare servers, power on/off VMs, create snapshots, execute commands and programs on VMs, and more. The document provides instructions for setting up the necessary Python environment and configuring connections to VMWare servers. It also includes code examples for performing basic tasks like connecting to a VM, managing snapshots, running long-running tasks asynchronously, and executing programs on VMs with graphical interfaces.
This document discusses various computing concepts related to resources and performance in PHP applications. It covers topics like data storage technologies, areal storage density of hard drives and solid state drives, streams as a way to access input and output generically in PHP, using filters to perform operations on stream data, common issues like running out of memory and how to address them through better programming practices, limitations of CPU and how to distribute load through job queuing, and basics of networking like IP addresses, TCP, and using sockets. The key advice is to assume large amounts of data and potential failures, use appropriate data storage, avoid unnecessary processing in memory, optimize code through profiling, and offload work to other systems when possible.
The document discusses PHP streams. It defines a stream as a resource that exhibits a flow or succession of data. A wrapper tells a stream how to handle specific protocols and encodings. A context is a set of parameters and options that tell a stream or filter how to behave. Common built-in PHP streams include file, http, and ftp streams. Filters perform operations on stream data and can be used to modify stream contents.
Redis is being used as a message queue to asynchronously process image uploads on a website for gaming screenshots. When a user uploads images, the application server adds a message to the Redis queue containing metadata about the upload. A separate process polls the queue and processes each upload by resizing images, creating database entries, and more. This allows upload processing to happen in the background without blocking the user.
Gearman and asynchronous processing in PHP applicationsTeamskunkworks
Gearman is an open source job server that allows distributing asynchronous jobs to worker processes. It provides a way to offload CPU-intensive tasks from web servers to separate worker processes. PHP applications can use the Gearman extension to create clients that send jobs and worker scripts that receive and execute jobs. Managing many worker processes across servers can be challenging. The Gearman Agent Manager aims to address this by providing centralized management and monitoring of Gearman workers through a web API.
RestMQ is a message queue system based on Redis that allows storing and retrieving messages through HTTP requests. It uses Redis' data structures like lists, sets, and hashes to maintain queues and messages. Messages can be added to and received from queues using RESTful endpoints. Additional features include status monitoring, queue control, and support for protocols like JSON, Comet, and WebSockets. The core functionality is language-agnostic but implementations exist in Python and Ruby.
This document provides instructions for setting up a demonstration of the magic_model_generator gem. It mentions setting up the database.yml file and increasing the font size. It also provides commands for starting the postgres database, generating models from existing tables, and preparing the development environment for the demo.
This document provides an overview of the current state of PHP. It discusses PHP's history from version 3 through the current version 5.3, highlighting improvements like objects, namespaces, and closures. It also mentions some challenges with PHP like messy projects and too many frameworks. The document recommends using best practices like testing and design patterns. It discusses alternatives like JavaScript for frontend work. Overall it suggests PHP is a mature and elegant option for building businesses but also considers other technologies may be better fits depending on requirements. The author is available to answer questions.
Free The Enterprise With Ruby & Master Your Own DomainKen Collins
On the heals of Luis Lavena's RailsConf talk "Infiltrating Ruby Onto The Enterprise Death Star Using Guerilla Tactics" comes a local and frank talk about the current state of Open Source Software (OSS) participation from Windows developers. Learn what OSS is, what motivates its contributors, and how OSS can make you a stronger developer. Be prepared to fall in love with writing software again!
We will start off with a 101 introduction to both the Ruby programming language and the Ruby on Rails web application framework. You will learn about ActiveRecord, a powerful ORM that maps rich objects to your databases, and the latest components to use it with SQL Server. As a Rails core contributor and author of the SQL Server stack, I will give you a modern insight into both that will allow you to leverage your legacy data with Ruby.
Lastly, I will review the bleeding edge tools being actively created for Windows developers to ease the transition to Ruby, Rails and OSS from a POSIX driven world. Many things have changed. It is time to learn and perform some occupational maintenance.
WebCamp: Developer Day: DDD in PHP on example of Symfony - Олег ЗинченкоGeeksLab Odessa
DDD in PHP on example of Symfony
Олег Зинченко
В докладе речь пойдет о том, что такое DDD и почему MVC годами вводит нас в заблуждение. Как автор пришёл к мысли о том, что просто писать структурированный код недостаточно. Будут описаны слои приложения DDD, как они взаимодействуют между собой. Какие архитектурные паттерны стоят за DDD и почему они облегчают жизнь разработчика. Когда лучше использовать DDD, какие достоинства и недостатки есть у этого подхода. Примеры использованиея DDD подхода в Symfony приложениях.
This document discusses how Rails may not be the best tool for rapid prototyping and suggests alternatives that are more lightweight and collaborative. It introduces Serve, an alternative to Rails that uses a simple folder structure and views to quickly prototype ideas without models, controllers or routing. Serve works with popular front-end tools and libraries and can deploy to Heroku. The document argues that prototyping tools should empower all team members, including front-end developers, and that Rack middleware like Rack::Cascade allows reusing mockups and wireframes in a Rails app.
This document discusses DevOps practices for PHP environments. It covers PHP evolution and modern standards, Laravel features and development workflows using GitHub flow. It also discusses local development environments, continuous integration, production environments, recipes for common tasks, monitoring, security, backups and lessons learned. The presenters have many years of experience with PHP, Laravel and DevOps.
The document discusses TorqueBox, an application server for Ruby that is built on JBoss Application Server 7 and uses JRuby. It allows Ruby applications to take advantage of features typically found in Java application servers like clustering, caching, messaging, and more. Key features highlighted include support for Ruby web frameworks like Rails and Sinatra, background processing using Resque or DelayedJob, messaging using JMS, long running services, and resource injection. Web sockets support is provided using STOMP and the Stilts framework. The document outlines goals of TorqueBox and provides examples of configuration and usage.
This document discusses deployment strategies for Ruby on Rails applications. It covers common components of the "Rails stack" including databases, caching, application servers and load balancers. Popular options are mentioned like Nginx, Memcached, Mongrel and Capistrano. Performance optimization techniques are also summarized, such as caching, background processing and memory usage strategies. The document concludes with an overview of common deployment processes and challenges.
Supercharging WordPress Development in 2018Adam Tomat
Slide links:
- PHP-FIG: https://www.php-fig.org/psr
- Timber: https://www.upstatement.com/timber/
- Bedrock: https://roots.io/bedrock/
- Lumberjack: https://github.com/Rareloop/lumberjack
- Lumberjack Core: https://github.com/Rareloop/lumberjack-core
- Collections: https://laravel.com/docs/5.6/collections
- PHP-DI: http://php-di.org/
- Lumberjack Validation: https://github.com/Rareloop/lumberjack-validation
- Sessions: https://github.com/Rareloop/lumberjack-core/tree/ft-session
- Lumberjack Example Repo: https://github.com/Rareloop/lumberjack-example
- Laravel Responsable: https://laravel-news.com/laravel-5-5-responsable
Towards the end of 2015 Rareloop launched their WordPress starter theme Lumberjack, which built on open source tools such as Bedrock and Timber. We wanted to move Lumberjack forward inline with everything we have learnt over the years of using it - which meant completely re-writing it from the ground up. The new Lumberjack is now stable and ready for use!
This talk is aimed at anyone involved in working with WordPress, regardless of how technical you are. The beauty of Lumberjack is that you can use as much or as little as you like, so whether you’re new to web development or a seasoned software engineer there will be something here for you.
This document provides a fast-paced introduction to Ruby, Rails, and additional technologies. It begins with an overview of Ruby basics, syntax, and uses beyond scripts. It then covers Rails fundamentals including MVC architecture, scaffolding, models, views, controllers, and routes. Additional topics discussed include gems, Git, and deploying to Heroku. The document concludes by outlining a sample project to build a marketplace for buying and selling robot spare parts.
Ruby on Rails is an open-source web framework that is optimized for programmer happiness and productivity. It allows developers to write code more easily by favoring conventions over rigid configuration. Ruby on Rails uses conventions to make assumptions about how apps should be structured, reducing the amount of configuration code needed.
Site Performance - From Pinto to FerrariJoseph Scott
This document discusses ways to improve website performance from slow "Pinto" levels to faster "Ferrari" levels. It recommends using an opcode cache like APC to speed up PHP, optimizing databases with technologies like Memcached, using caching plugins in WordPress, and considering architectures with load balancing and replication. The goal is to identify bottlenecks and apply techniques that reduce page load times through an understanding of how websites and underlying technologies work.
Merb is a flexible and modular web framework for Ruby that meets enterprise needs through low cost, adaptability, and scalability. It allows developers to pick and choose components to meet their specific needs, and its modular architecture makes it fast and lightweight. Merb provides a balance of structure and flexibility that makes it suitable for a wide range of applications.
The document discusses the tools and practices used by a Ruby development team, including using RVM for managing Ruby versions and gemsets, Postgres.app for the database, Pow for local development, Git for version control, GitHub pull requests for code reviews, CircleCI for continuous integration and deployment to Heroku, Capistrano or Mina for deployment automation, and services like Rollbar and HipChat for error tracking and communication. Consistent coding styles, Sublime Text settings, and code quality practices like testing and reviews are also recommended.
This document summarizes a presentation about the future of the Rake gem and domain-specific languages (DSLs) in Ruby.
The presentation discusses:
1. How Rake works as a Make-like program implemented in Ruby syntax with tasks and dependencies. Rake files use standard Ruby syntax.
2. Examples of common patterns for building internal DSLs in Ruby using class/module methods, method definition, implicit/explicit code blocks, and instance evaluation.
3. How popular Ruby gems like Rake, Bundler, and Thor use DSL techniques and inherit from each other to provide domain-specific interfaces.
Consegi 2010 - Dicas de Desenvolvimento Web com RubyFabio Akita
Esta é a palestra que dei no Consegi 2010 em Brasília. Sobre dicas gerais sobre web, em particular implementando com Ruby on Rails. YSlow, Full Text Search e Tarefas Assíncronas.
The document discusses socket applications and real-time communication. It describes common examples like chat, live feeds, and games. It then explains the typical solution of using AJAX requests and the problems with that approach. The proposed solution is to use a multi-threaded model with asynchronous I/O and the reactor pattern to process requests while waiting for network responses. Various tools for implementing this pattern in Ruby and JavaScript are also mentioned, including Pusher, Socket.IO, and SocketStream.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company
Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
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76. how ?
jobs are queued as follows
require_once 'lib/Resque.php';
// Required if redis is located elsewhere
Resque::setBackend('localhost:6379');
$args = array(
'name' => 'Chris'
);
Resque::enqueue('default', 'My_Job', $args);
78. job class
class My_Job
{
public function setUp()
{
// ... Set up environment for this job
}
public function perform()
{
// .. Run job
}
public function tearDown()
{
// ... Remove environment for this job
}
}
#2: hello and welcome to my talk on php-resque\nbefore we proceed i have to tell you all that php-resque was developed by chris boulton\nmy name is chaitanya kuber and can be contacted via twitter\n
#3: hello and welcome to my talk on php-resque\nbefore we proceed i have to tell you all that php-resque was developed by chris boulton\nmy name is chaitanya kuber and can be contacted via twitter\n
#4: hello and welcome to my talk on php-resque\nbefore we proceed i have to tell you all that php-resque was developed by chris boulton\nmy name is chaitanya kuber and can be contacted via twitter\n
#5: standing here and doing this presentaiton is a little strange because i know everyone in the audience is smarter than myself\nthat said, i want to provide a little bit of context about our world\ntalk about what resque is and how it works\nthe move to php\nwhat we use it for @ bigcommerce\n\n
#6: standing here and doing this presentaiton is a little strange because i know everyone in the audience is smarter than myself\nthat said, i want to provide a little bit of context about our world\ntalk about what resque is and how it works\nthe move to php\nwhat we use it for @ bigcommerce\n\n
#7: standing here and doing this presentaiton is a little strange because i know everyone in the audience is smarter than myself\nthat said, i want to provide a little bit of context about our world\ntalk about what resque is and how it works\nthe move to php\nwhat we use it for @ bigcommerce\n\n
#8: standing here and doing this presentaiton is a little strange because i know everyone in the audience is smarter than myself\nthat said, i want to provide a little bit of context about our world\ntalk about what resque is and how it works\nthe move to php\nwhat we use it for @ bigcommerce\n\n
#9: context, it really is everything\nin engineering web applications its all about the milliseconds\ntasks like …. take time\nthey not only take time, often they are supporting tasks\nthings that do not directly impact servicing a request form the user\n
#10: context, it really is everything\nin engineering web applications its all about the milliseconds\ntasks like …. take time\nthey not only take time, often they are supporting tasks\nthings that do not directly impact servicing a request form the user\n
#11: context, it really is everything\nin engineering web applications its all about the milliseconds\ntasks like …. take time\nthey not only take time, often they are supporting tasks\nthings that do not directly impact servicing a request form the user\n
#12: context, it really is everything\nin engineering web applications its all about the milliseconds\ntasks like …. take time\nthey not only take time, often they are supporting tasks\nthings that do not directly impact servicing a request form the user\n
#13: context, it really is everything\nin engineering web applications its all about the milliseconds\ntasks like …. take time\nthey not only take time, often they are supporting tasks\nthings that do not directly impact servicing a request form the user\n
#14: users are fickle\ni like to think that a web application should focus on responding as fast possible to the request form its user\nif not they will leave\nso, any operation thats not directly related to the request can and probably should go elsewhere\n\n
#15: where ?\nbackground jobs … whats that\nthink asynchronous\nsupermarket shelves are stacked in the night to ensure shoppers can walk in get there things and walk out\nit would be a sucky experience to walk in and wait for them to stock the milk before you could buy it\n
#16: where ?\nbackground jobs … whats that\nthink asynchronous\nsupermarket shelves are stacked in the night to ensure shoppers can walk in get there things and walk out\nit would be a sucky experience to walk in and wait for them to stock the milk before you could buy it\n
#17: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#18: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#19: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#20: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#21: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#22: there are various systems to do background/async processing\nbackgroundjob is a zero admin background priority queue for rails\ndelayedjob is similar with the advantage that it doesn’t load the entire rails env into memory\nbeanstalk is a simple, fast work queue. its interface is generic, was designed for increase response times for high-volume web apps\ngearman is quite similar to resque as it supports persistant queues, is open source, fault tolerant and is distributed\n
#23: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#24: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#25: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#26: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#27: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#28: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#29: it was developed by the cool folks @github, it was inspired by delayedjob\nresque is written in ruby which is a redis backed library for creating background jobs, placing them in queues and processing them later\nworkers can process one or multiple queues\nworkers can be run on different servers\n
#30: resque supports prioritising jobs by prioritising queues against each other\njobs are persisted to queues as JSON objects and the queues are stored in redis as lists\na nice web interface, a sinatra app, is also included for monitoring the workers\nplugin system using a event framework for events like after_fork, before_fork etc \n
#31: resque supports prioritising jobs by prioritising queues against each other\njobs are persisted to queues as JSON objects and the queues are stored in redis as lists\na nice web interface, a sinatra app, is also included for monitoring the workers\nplugin system using a event framework for events like after_fork, before_fork etc \n
#32: resque supports prioritising jobs by prioritising queues against each other\njobs are persisted to queues as JSON objects and the queues are stored in redis as lists\na nice web interface, a sinatra app, is also included for monitoring the workers\nplugin system using a event framework for events like after_fork, before_fork etc \n
#33: resque supports prioritising jobs by prioritising queues against each other\njobs are persisted to queues as JSON objects and the queues are stored in redis as lists\na nice web interface, a sinatra app, is also included for monitoring the workers\nplugin system using a event framework for events like after_fork, before_fork etc \n
#34: resque supports prioritising jobs by prioritising queues against each other\njobs are persisted to queues as JSON objects and the queues are stored in redis as lists\na nice web interface, a sinatra app, is also included for monitoring the workers\nplugin system using a event framework for events like after_fork, before_fork etc \n
#35: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#36: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#37: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#38: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#39: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#40: this is a little tangent, but i quickly want to talk about redis\nit’s a key-value store, all its operations are atomic and O(1) meaning they will always complete in a constant amount of time\nthis means speed and its really important when you want to set and forget\n\n
#47: around 2009 when they released this library they had already processed over 10 million jobs\nwow … right\n
#48: around 2009 when they released this library they had already processed over 10 million jobs\nwow … right\n
#49: so, how does it do what it does ?\nas a user of the resque library in your app you essentially interact with redis\nwhen you ask resque to queue up a job, it will push it onto the queue in redis\na resque worker comes along at some point in time and pops a job of the queue and forks that into its own process and runs the job\n\n