A quick overview of CAVE, a managed service for monitoring infrastructure, platform, and application metrics, to provide visibility into your system's performance and operational levels. CAVE is built at GILT, using Scala, Play and Akka.
My name is Neta Barkay , and I'm a data scientist at LivePerson.
I'd like to share with you a talk I presented at the Underscore Scala community on "Efficient MapReduce using Scalding".
In this talk I reviewed why Scalding fits big data analysis, how it enables writing quick and intuitive code with the full functionality vanilla MapReduce has, without compromising on efficient execution on the Hadoop cluster. In addition, I presented some examples of Scalding jobs which can be used to get you started, and talked about how you can use Scalding's ecosystem, which includes Cascading and the monoids from Algebird library.
Read more & Video: https://connect.liveperson.com/community/developers/blog/2014/02/25/scalding-reaching-efficient-mapreduce
Martin Fowler's Refactoring Techniques Quick ReferenceSeung-Bum Lee
Martin Fowler's Refactoring Techniques Summary. This includes categorization and simple descriptions as well as some sample code and class diagram for better understanding
Xlab #1: Advantages of functional programming in Java 8XSolve
Presentation from xlab workshop about functional programming components introduced to the Java 8. How to operate the streams and lambdas in theory and practice.
My name is Neta Barkay , and I'm a data scientist at LivePerson.
I'd like to share with you a talk I presented at the Underscore Scala community on "Efficient MapReduce using Scalding".
In this talk I reviewed why Scalding fits big data analysis, how it enables writing quick and intuitive code with the full functionality vanilla MapReduce has, without compromising on efficient execution on the Hadoop cluster. In addition, I presented some examples of Scalding jobs which can be used to get you started, and talked about how you can use Scalding's ecosystem, which includes Cascading and the monoids from Algebird library.
Read more & Video: https://connect.liveperson.com/community/developers/blog/2014/02/25/scalding-reaching-efficient-mapreduce
Martin Fowler's Refactoring Techniques Quick ReferenceSeung-Bum Lee
Martin Fowler's Refactoring Techniques Summary. This includes categorization and simple descriptions as well as some sample code and class diagram for better understanding
Xlab #1: Advantages of functional programming in Java 8XSolve
Presentation from xlab workshop about functional programming components introduced to the Java 8. How to operate the streams and lambdas in theory and practice.
Scalding - Hadoop Word Count in LESS than 70 lines of codeKonrad Malawski
Twitter Scalding is built on top of Cascading, which is built on top of Hadoop. It's basically a very nice to read and extend DSL for writing map reduce jobs.
Programiści aplikacji Mobilnych na Androida, uwięzieni w czasach Java 1.7 od pewnego czasu eksperymentowali z innymi językami programowania. Żaden nie zdobył do tej pory takiej popularności jak Kotlin. Ale czy faktycznie jest to coś rewolucyjnego? Przecież getery, settery i konstruktory wygenerujemy za pomocą Lomboka. Używając Retrolamby zyskamy wsparcie dla dopełnień. A dodatkowo od niedawna Android ma wsparcie dla Javy 8.
Zatem co decyduje o sile Kotlina, które konstrukcje i właściwości języka powodują, że warto zastosować go w swoim projekcie? Jaki wpływ będzie to miało na architekturę aplikacji i wydajność? Kotlin jest tylko ciekawostką czy spowoduje, że będziesz kodował efektywniej? Z tej prezentacji wyniesiesz pełen zestaw informacji pozwalający odpowiedzieć na wszystkie te pytania.
Apache Spark - Key-Value RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sewz2m
This CloudxLab Key-Value RDD tutorial helps you to understand Key-Value RDD in detail. Below are the topics covered in this tutorial:
1) Spark Key-Value RDD
2) Creating Key-Value Pair RDDs
3) Transformations on Pair RDDs - reduceByKey(func)
4) Count Word Frequency in a File using Spark
String Function
1. charAt():
This method returns the character from the specified index.Characters in a string are indexed from left to right. The index of the first character is 0, and the index of the last character in a string called stringName is stringName.length - 1.
Syntax:
string.charAt(index);
Return Value:
Returns the character from the specified index.
Example:
<html>
<head>
<title>JavaScript String charAt() Method</title>
</head>
<body>
</body>
</html>
Output:
str.charAt(0) is:T
2. concat():
Description:
This method adds two or more strings and returns a new single string.
Syntax:
string.concat(string2, string3[, ..., stringN]);
parameters:
string2...stringN : These are the strings to be concatenated.
Return Value:
Returns a single concatenated string.
Example:
<html>
<head>
<title>JavaScript String concat() Method</title>
</head>
<body>
</body>
</html>
Output:
Concatenated String :This is string oneThis is string two.
3. indexOf():
Description:
This method returns the index within the calling String object of the first occurrence of the specified value, starting the search at fromIndex or -1 if the value is not found.
Syntax:
string.indexOf(searchValue[, fromIndex])
Parameters:
searchValue : A string representing the value to search for.
fromIndex : The location within the calling string to start the search from. It can be any integer between 0 and the length of the string. The default value is 0.
Return Value:
Returns the index of the found occurrence otherwise -1 if not found.
Example:
<html>
<head>
<title>JavaScript String indexOf() Method</title>
</head>
<body>
<br />");
var index = str1.indexOf( "one" );
document.write("indexOf found String :" + index );
</body></html>
Oputput:
indexOf found String :8
indexOf found String :15
4. lastIndexOf():
Description:
This method returns the index within the calling String object of the last occurrence of the specified value, starting the search at fromIndex or -1 if the value is not found.
Syntax:
string.lastIndexOf(searchValue[, fromIndex])
Parameters:
searchValue : A string representing the value to search for.
fromIndex : The location within the calling string to start the search from. It can be any integer between 0 and the length of the string. The default value is 0.
Return Value:
Returns the index of the last found occurrence otherwise -1 if not found.
Example:
<html>
<head>
<title>JavaScri
Apache Spark - Key Value RDD - Transformations | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm5Ekd
This CloudxLab Key-Value RDD Transformations tutorial helps you to understand Key-Value RDD transformations in detail. Below are the topics covered in this tutorial:
1) Transformations on Key-Value Pair RDD - keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), cogroup(), countByKey() and lookup()
JAVA 8 : Migration et enjeux stratégiques en entrepriseSOAT
La sortie de Java 8 est une véritable révolution dont l’enjeu dépasse de loin la simple évolution d’un langage et de ses APIs. Rdv sur notre chaîne Youtube pour revoir la conférence :
Après une version 7 peu convaincante, la version 8 replace Java au premier rang des langages objets actuels.
En parfaite adéquation avec les besoins des projets et les possibilités offertes par les environnements matériels actuels, cette nouvelle version apporte une modernisation du langage et de ses API, un suivi des performances des processeurs et des améliorations de la JVM.
Quels sont les nouveaux concepts introduits par Java 8 ? En quoi les expressions lambdas et l’API Stream représentent une avancée majeure de la plateforme ? Quelle stratégie adopter pour migrer vers Java 8 en toute sécurité et en diminuant au maximum sa dette technique ?
Big Data Everywhere Chicago: Unleash the Power of HBase Shell (Conversant) BigDataEverywhere
Jayesh Thakrar, Senior Systems Engineer, Conversant
The venerable HBase shell is often regarded as a simple utility to perform basic DDL and maintenance activities. However, it is in fact a powerful, interactive programming environment, primarily due to the JRuby engine under the covers. In this presentation, I'll describe its JRuby heritage and show some of the things that can be done with the "ird" (interactive ruby shell), as well as show how to exploit JRuby and Java integration via concrete working examples. In addition, I will demonstrate how the "shell" can be used in Hadoop streaming to quickly perform complex and large volume batch jobs.
Building a JavaScript Module Framework at GiltEric Shepherd
For modules to function within a large-scale system and on third-party sites, they need to be self-contained units with minimal dependencies. They also need to keep their hands off of other modules and library code. Gilt's module framework manages multiple independent components, providing them with what they need, and only what they need, to do their jobs.
Scalding - Hadoop Word Count in LESS than 70 lines of codeKonrad Malawski
Twitter Scalding is built on top of Cascading, which is built on top of Hadoop. It's basically a very nice to read and extend DSL for writing map reduce jobs.
Programiści aplikacji Mobilnych na Androida, uwięzieni w czasach Java 1.7 od pewnego czasu eksperymentowali z innymi językami programowania. Żaden nie zdobył do tej pory takiej popularności jak Kotlin. Ale czy faktycznie jest to coś rewolucyjnego? Przecież getery, settery i konstruktory wygenerujemy za pomocą Lomboka. Używając Retrolamby zyskamy wsparcie dla dopełnień. A dodatkowo od niedawna Android ma wsparcie dla Javy 8.
Zatem co decyduje o sile Kotlina, które konstrukcje i właściwości języka powodują, że warto zastosować go w swoim projekcie? Jaki wpływ będzie to miało na architekturę aplikacji i wydajność? Kotlin jest tylko ciekawostką czy spowoduje, że będziesz kodował efektywniej? Z tej prezentacji wyniesiesz pełen zestaw informacji pozwalający odpowiedzieć na wszystkie te pytania.
Apache Spark - Key-Value RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sewz2m
This CloudxLab Key-Value RDD tutorial helps you to understand Key-Value RDD in detail. Below are the topics covered in this tutorial:
1) Spark Key-Value RDD
2) Creating Key-Value Pair RDDs
3) Transformations on Pair RDDs - reduceByKey(func)
4) Count Word Frequency in a File using Spark
String Function
1. charAt():
This method returns the character from the specified index.Characters in a string are indexed from left to right. The index of the first character is 0, and the index of the last character in a string called stringName is stringName.length - 1.
Syntax:
string.charAt(index);
Return Value:
Returns the character from the specified index.
Example:
<html>
<head>
<title>JavaScript String charAt() Method</title>
</head>
<body>
</body>
</html>
Output:
str.charAt(0) is:T
2. concat():
Description:
This method adds two or more strings and returns a new single string.
Syntax:
string.concat(string2, string3[, ..., stringN]);
parameters:
string2...stringN : These are the strings to be concatenated.
Return Value:
Returns a single concatenated string.
Example:
<html>
<head>
<title>JavaScript String concat() Method</title>
</head>
<body>
</body>
</html>
Output:
Concatenated String :This is string oneThis is string two.
3. indexOf():
Description:
This method returns the index within the calling String object of the first occurrence of the specified value, starting the search at fromIndex or -1 if the value is not found.
Syntax:
string.indexOf(searchValue[, fromIndex])
Parameters:
searchValue : A string representing the value to search for.
fromIndex : The location within the calling string to start the search from. It can be any integer between 0 and the length of the string. The default value is 0.
Return Value:
Returns the index of the found occurrence otherwise -1 if not found.
Example:
<html>
<head>
<title>JavaScript String indexOf() Method</title>
</head>
<body>
<br />");
var index = str1.indexOf( "one" );
document.write("indexOf found String :" + index );
</body></html>
Oputput:
indexOf found String :8
indexOf found String :15
4. lastIndexOf():
Description:
This method returns the index within the calling String object of the last occurrence of the specified value, starting the search at fromIndex or -1 if the value is not found.
Syntax:
string.lastIndexOf(searchValue[, fromIndex])
Parameters:
searchValue : A string representing the value to search for.
fromIndex : The location within the calling string to start the search from. It can be any integer between 0 and the length of the string. The default value is 0.
Return Value:
Returns the index of the last found occurrence otherwise -1 if not found.
Example:
<html>
<head>
<title>JavaScri
Apache Spark - Key Value RDD - Transformations | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm5Ekd
This CloudxLab Key-Value RDD Transformations tutorial helps you to understand Key-Value RDD transformations in detail. Below are the topics covered in this tutorial:
1) Transformations on Key-Value Pair RDD - keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), cogroup(), countByKey() and lookup()
JAVA 8 : Migration et enjeux stratégiques en entrepriseSOAT
La sortie de Java 8 est une véritable révolution dont l’enjeu dépasse de loin la simple évolution d’un langage et de ses APIs. Rdv sur notre chaîne Youtube pour revoir la conférence :
Après une version 7 peu convaincante, la version 8 replace Java au premier rang des langages objets actuels.
En parfaite adéquation avec les besoins des projets et les possibilités offertes par les environnements matériels actuels, cette nouvelle version apporte une modernisation du langage et de ses API, un suivi des performances des processeurs et des améliorations de la JVM.
Quels sont les nouveaux concepts introduits par Java 8 ? En quoi les expressions lambdas et l’API Stream représentent une avancée majeure de la plateforme ? Quelle stratégie adopter pour migrer vers Java 8 en toute sécurité et en diminuant au maximum sa dette technique ?
Big Data Everywhere Chicago: Unleash the Power of HBase Shell (Conversant) BigDataEverywhere
Jayesh Thakrar, Senior Systems Engineer, Conversant
The venerable HBase shell is often regarded as a simple utility to perform basic DDL and maintenance activities. However, it is in fact a powerful, interactive programming environment, primarily due to the JRuby engine under the covers. In this presentation, I'll describe its JRuby heritage and show some of the things that can be done with the "ird" (interactive ruby shell), as well as show how to exploit JRuby and Java integration via concrete working examples. In addition, I will demonstrate how the "shell" can be used in Hadoop streaming to quickly perform complex and large volume batch jobs.
Building a JavaScript Module Framework at GiltEric Shepherd
For modules to function within a large-scale system and on third-party sites, they need to be self-contained units with minimal dependencies. They also need to keep their hands off of other modules and library code. Gilt's module framework manages multiple independent components, providing them with what they need, and only what they need, to do their jobs.
“Get Stuff Done Faster: Why Engineers Should Work with the ‘Dark Side’ of Tech”Gilt Tech Talks
On Thursday, January 15, 2015, Gilt Director of Program Management Justin Riservato, Director of Product Andrew Chen, Senior Business Systems Manager Susan Thomas, and Senior Program Manager Myron Miller presented this talk, which focuses on the difference between Program Managers, Business Analysts and Product Managers, and why you engineers need all these managers on your team.
As one of the early adopters of Apple TV and tvOS, Gilt Groupe was recently selected to present their “Gilt on TV” app at the Apple Keynote event in September.
This presentation covers Gilt's discoveries during the process of building a tvOS app from scratch in Swift.
It was presented at iOSoho on October 12, 2015 in New York City.
Scaling Gilt: from Monolithic Ruby Application to Distributed Scala Micro-Ser...C4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/10fVilQ.
Yoni Goldberg describes some of the technological innovations that have helped Gilt to reach its current size, and highlight some of the core challenges that the company's engineering team continues to face. He also discusses what every tech team needs to consider and address before heading down the path of building a first-class micro-services architecture. Filmed at qconnewyork.com.
Since joining Gilt at 2010 as a platform engineer, Yoni Goldberg has been leading a variety of personalization efforts and other customer-facing initiatives--including the Gilt Insider loyalty program, the post-purchase experience, and SEO/optimization efforts. Prior to joining Gilt, Yoni worked at Google, where he wrote his master's thesis on Fusion Tables.
My talk at the inaugural micro services meet-up at the Engine Yard in Dublin! An honest look at how we've landed on our micro-services architecture at Gilt, and the challenges we're facing.
En vieux bourlingueur du langage Swift, Grégoire Lhotellier viendra nous présenter les séquences et les collections du nouveau langage d’Apple. Il nous briefera sur l’essentiel de ce qu’il faut en savoir et ce qu’elles changent par rapport à leurs équivalent Objective-C.
A talk I gave at Scala Days San Francisco March 2015
http://workday.github.io/scala/2015/03/17/scala-days-improving-correctness-with-types/
This talk is aimed at Scala developers with a background in object oriented programming who want to learn new ways to use types to improve the correctness of their code. It introduces the topic in a practical fashion, concentrating on the “easy wins” developers can apply to their code today.
http://event.scaladays.org/scaladays-sanfran-2015#!#schedulePopupExtras-6553
Strata Presentation: One Billion Objects in 2GB: Big Data Analytics on Small ...randyguck
Slides from my Strata+Hadoop 2015 Conference session titled: One Billion Objects in 2GB: Big Data Analytics on Small Clusters with Doradus OLAP. This talk describes the Doradus OLAP query/storage engine, which is an open source module that runs on top of the Cassandra NoSQL DB. Among the benefits of this service is fast data loading, a rich query language with full text and graph query features, and very dense data storage. See the Notes section for details on each slide.
Altitude NY 2018: Leveraging Log Streaming to Build the Best Dashboards, EverFastly
If knowing is half the battle, having the most information available is the best way to win. Using real-time log streaming and a knowledge of the data passing through the system, metrics can provide more depth and breadth in to the goings on requests as they pass through various parts of the stack. This session will cover the difference between logging and metrics, writing JSON and Influx Line Protocol in VCL, and building out dashboards to give deeper insights (and more importantly, alerting) on requests and responses at the edge.
Wprowadzenie do technologii Big Data / Intro to Big Data EcosystemSages
Introduction to Hadoop Map Reduce, Pig, Hive and Ambari technologies.
Workshop deck prepared and presented on September 5th 2015 by Radosław Stankiewicz.
During that the day participants had also the possibility to go through prepared tutorials and test their analysis on real cluster.
Deze presentatie is gegeven tijdens de KScope conferentie 2012
Spreker: Luc Bors
Titel: How to Bring Common UI Patterns to ADF
Onderwerp: Fusion Middleware - Subonderwerp: ADF
Eindgebruikers van bedrijfsapplicaties eisen dezelfde gebruikerservaring die ze kennen van bijvoorbeeld office applicaties en applicaties op het internet. Functies zoals bookmarking, favorieten en het werken met tabs wordt graag gezien in de dagelijkse werk. Het zoekmechanisme van Google, dat suggesties toont op basis van de ingevoerde tekst, is zo ´gewoon´ dat mensen dit in elke applicatie terug willen zien. Twitter en Facebook geven automatisch aan dat je nieuwe berichten hebt zonder dat je daar zelf eerst om moet vragen, dat gebruikers de normaalste zaak van de wereld vinden. Er zijn nog veel meer van deze UI patterns. In deze sessie leer je hoe een aantal van deze UI patterns in je ADF applicatie kunt inbouwen waardoor de eindgebruiker beschikking krijgt over bekende en vanzelfsprekende features. Dit zal leiden tot een snellere acceptatie van de applicatie en prettigere gebruikerservaring.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
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#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
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A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
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performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
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Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
4. What is CAVE?
A monitoring system that is:
● secure
● independent
● proprietary
● open source
5. Requirements
● horizontally scalable to millions of metrics, alerts
● multi-tenant, multi-user
● extensible HTTP-based API
● flexible metric definition
● data aggregation / multiple dimensions
● flexible and extensible alert grammar
● pluggable notification delivery system
● clean user interface for graphing and dashboarding
13. Alert Grammar
Metric has name and tags (key-value pairs)
e.g.
orders [shipTo: US]
response-time [svc: svc-important, env: prod]
14. Alert Grammar
Aggregated Metric has metric, aggregator and
period of aggregation, e.g.
orders [shipTo: US].sum.5m
response-time [svc: svc-important, env: prod].p99.5m
Supported aggregators:
count, min, max, mean, mode, median, sum
stddev, p99, p999, p95, p90
15. Alert Grammar
Alert Condition contains one expression with
two terms and an operator. Each term is a
metric, an aggregated metric or a value.
e.g.
orders [shipTo: US].sum.5m < 10
orders [shipTo: US].sum.5m < ordersPredictedLow [shipTo: US]
16. Alert Grammar
An optional number of times the threshold is
broken, e.g.
response-time [svc: svc-team, env: prod].p99.5m > 3000 at least
3 times
17. Alert Grammar
Special format for missing data
e.g.
orders [shipTo: US] missing for 5m
heartbeat [svc: svc-important, env: prod] missing for 10m
18. Alert Grammar
trait AlertParser extends JavaTokenParsers {
sealed trait Source
case class ValueSource(value: Double) extends Source
case class MetricSource(
metric: String, tags: Map[String, String]) extends Source
case class AggregatedSource(
metricSource: MetricSource,
aggregator: Aggregator, duration: FiniteDuration) extends Source
sealed trait AlertEntity
case class SimpleAlert(
sourceLeft: Source, operator: Operator,
sourceRight: Source, times: Int) extends AlertEntity
case class MissingDataAlert(
metricSource: MetricSource, duration: FiniteDuration) extends AlertEntity
…
}
19. Alert Grammar
trait AlertParser extends JavaTokenParsers {
…
def valueSource: Parser[ValueSource] = decimalNumber ^^ {
case num => ValueSource(num.toDouble)
}
def word: Parser[String] = """[a-zA-Z][a-zA-Z0-9.-]*""".r
def metricTag: Parser[(String, String)] = (word <~ ":") ~ word ^^ {
case key ~ value => key -> value
}
def metricTags: Parser[Map[String, String]] = repsep(metricTag, ",") ^^ {
case list => list.toMap
}
…
}
26. Scala Collections API
case class Person(id: Int, name: String)
val list = List(Person(1, "Pawel"),
Person(2, "Val"),
Person(3, "Unknown Name"))
27. Scala Collections API
case class Person(id: Int, name: String)
val list = List(Person(1, "Pawel"),
Person(2, "Val"),
Person(3, "Unknown Name"))
list.filter(_.id > 1)
28. Scala Collections API
case class Person(id: Int, name: String)
val list = List(Person(1, "Pawel"),
Person(2, "Val"),
Person(3, "Unknown Name"))
list.filter(_.id > 1).map(_.name)
29. Scala Collections API
case class Person(id: Int, name: String)
val list = List(Person(1, "Pawel"),
Person(2, "Val"),
Person(3, "Unknown Name"))
list.filter(_.id > 1).map(_.name)
SELECT name FROM list WHERE id > 1
31. Entity mapping
/** Table description of table orgs.*/
class OrganizationsTable(tag: Tag) extends Table[OrganizationsRow](tag,"organizations") {
...
/** Database column id AutoInc, PrimaryKey */
val id: Column[Long] = column[Long]("id", O.AutoInc, O.PrimaryKey)
/** Database column name */
val name: Column[String] = column[String]("name")
/** Database column created_at */
val createdAt: Column[java.sql.Timestamp] = column[java.sql.Timestamp]("created_at")
…
/** Foreign key referencing Organizations (database name token_organization_fk) */
lazy val organizationsFk = foreignKey("token_organization_fk", organizationId,
Organizations)(r => r.id, onUpdate = ForeignKeyAction.NoAction, onDelete =
ForeignKeyAction.NoAction)
}
32. CRUD
val organizationsTable = TableQuery[OrganizationsTable]
// SELECT * FROM ORGANIZATIONS
organizationsTable.list
// SELECT * FROM ORGANIZATIONS WHERE ID > 10 OFFSET 3 LIMIT 5
organizationsTable.filter(_.id > 10).drop(3).take(5).list
// INSERT
organizationsTable += OrganizationsRow(1, "name", "email", "notificationUrl", ... , None,
None)
// UPDATE ORGANIZATIONS SET name = “new org name” WHERE ID=10
organizationsTable.filter(_.id === 10).map(_.name).update("new org name")
// DELETE FROM ORGANIZATIONS WHERE ID=10
organizationsTable.filter(_.id === 10).delete
33. Queries - JOINS
val organizationsTable = TableQuery[OrganizationsTable]
val teamsTable = TableQuery[TeamsTable]
val name = “teamName”
val result = for {
t <- teamsTable.sortBy(_.createdAt).filter(t => t.deletedAt.isEmpty)
o <- t.organization.filter(o => o.deletedAt.isEmpty && o.name === name)
} yield (t.name, o.name)
SELECT t.name, o.name FROM TEAMS t
LEFT JOIN ORGANIZATIONS o ON t.organization_id = o.id
WHERE t.deleted_at IS NULL AND o.deleted_at IS NULL AND o.name = `teamName`
ORDER BY t.created_at
34. SELECT t.name, o.name FROM TEAMS t
LEFT JOIN ORGANIZATIONS o ON t.organization_id = o.id
WHERE t.deleted_at IS NULL AND o.deleted_at IS NULL AND o.name = `teamName`
ORDER BY t.created_at
val result: List[(String, String)]
35. Connection pool and transactions
val ds = new BoneCPDataSource
val db = {
ds.setDriverClass(rdsDriver)
ds.setJdbcUrl(rdsJdbcConnectionString)
ds.setPassword(rdsPassword)
ds.setUser(rdsUser)
Database.forDataSource(ds)
}
db.withTransaction { implicit session =>
// SLICK CODE GOES HERE
}