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InfoQ: Twitter Shifting More Code to JVM, Citing Performance and Encapsulation As Primary Drivers
http://www.infoq.com/articles/twitter-java-use"While it almost certainly remains the largest Ruby on Rails based site in the world, Twitter has gradually been moving more and more of its stack to the JVM. The change is partially motivated by oft-cited advantages of the JVM, such as performance and scalability, but is also driven by a desire for better encapsulation of individual services, and other architectural concerns."
Tags: java, ruby, twitter, scala, architecture, performance, rails, jvm, scalability, via:packrati.us Saved by: admin
InfoQ: The First Few Milliseconds of an HTTPS Connection
What happens when one clicks on "Proceed to Checkout" on a website after browsing through their offerings? This is an analysis of the first milliseconds when an HTTPS connection with Amazon is established. A new page is loaded when proceeding to checkout:
http://www.infoq.com/articles/HTTPS-Connection-Jeff-Moser
Tags: http, security, https, reference, protocol, article, ssl, web, encryption Saved by: admin
What happens when one clicks on "Proceed to Checkout" on a website after browsing through their offerings? This is an analysis of the first milliseconds when an HTTPS connection with Amazon is established. A new page is loaded when proceeding to checkout:
http://www.infoq.com/articles/HTTPS-Connection-Jeff-Moser
Tags: http, security, https, reference, protocol, article, ssl, web, encryption Saved by: admin
InfoQ: Clojure and Rails - the Secret Sauce Behind FlightCaster
FlightCaster is a new site for flight delay prediction. Its web frontend is built using Rails deployed on Heroku. The backend for processing data is written using Clojure, using Hadoop and Cascading, Cloudera and other tools. We talked to Bradford Cross of the project about the architecture powering FlightCaster, how Clojure was used to implement it, as well as tips for budding Clojure and Lisp programmers coming from OOP languages.
http://www.infoq.com/articles/flightcaster-clojure-rails
Tags: clojure, rails, hadoop, programming, distributed, cloudera, jvm, web, interview, software Saved by: admin
FlightCaster is a new site for flight delay prediction. Its web frontend is built using Rails deployed on Heroku. The backend for processing data is written using Clojure, using Hadoop and Cascading, Cloudera and other tools. We talked to Bradford Cross of the project about the architecture powering FlightCaster, how Clojure was used to implement it, as well as tips for budding Clojure and Lisp programmers coming from OOP languages.
http://www.infoq.com/articles/flightcaster-clojure-rails
Tags: clojure, rails, hadoop, programming, distributed, cloudera, jvm, web, interview, software Saved by: admin
InfoQ: Persistent Data Structures and Managed References
"Rich Hickey’ presentation is organized around a number of programming concepts: identity, state and values. He explains how to represent composite objects as values and how to deal with change and state, as it is implemented in Clojure. "
http://www.infoq.com/presentations/Value-Identity-State-Rich-Hickey
Tags: clojure, video, presentation, functional, development, programming, architecture, data, infoq, concurrency Saved by: admin
"Rich Hickey’ presentation is organized around a number of programming concepts: identity, state and values. He explains how to represent composite objects as values and how to deal with change and state, as it is implemented in Clojure. "
http://www.infoq.com/presentations/Value-Identity-State-Rich-Hickey
Tags: clojure, video, presentation, functional, development, programming, architecture, data, infoq, concurrency Saved by: admin
InfoQ: Systems that Never Stop (and Erlang)
Joe Armstrong presents 6 laws to obey in order to obtain high system reliability, Isolation, Concurrency, Failure Detection, Fault Identification, Live Code Upgrade, and Stable Storage, showing how they are respected in Erlang and followed by some examples from practice. Joe Armstrong is the principle inventor of Erlang and coined the term "Concurrency Oriented Programming". At Ericsson he developed Erlang and was chief architect of the Erlang/OTP system. In 1998 he formed Bluetail, which developed all its products in Erlang.
http://www.infoq.com/presentations/Systems-that-Never-Stop-Joe-Armstrong
Tags: erlang, concurrency, video, distributed, design, programming, reliability, videos, availability, presentation Saved by: admin
Joe Armstrong presents 6 laws to obey in order to obtain high system reliability, Isolation, Concurrency, Failure Detection, Fault Identification, Live Code Upgrade, and Stable Storage, showing how they are respected in Erlang and followed by some examples from practice. Joe Armstrong is the principle inventor of Erlang and coined the term "Concurrency Oriented Programming". At Ericsson he developed Erlang and was chief architect of the Erlang/OTP system. In 1998 he formed Bluetail, which developed all its products in Erlang.
http://www.infoq.com/presentations/Systems-that-Never-Stop-Joe-Armstrong
Tags: erlang, concurrency, video, distributed, design, programming, reliability, videos, availability, presentation Saved by: admin
InfoQ: LinkedIn's Data Infrastructure
Much of LinkedIn's important data is offline - it moves fairly slowly. So they use daily batch processing with Hadoop as an important part of their calculations. For example, they pre-compute data for their "People You May Know" product this way, scoring 120 billion relationships per day in a mapreduce pipeline of 82 Hadoop jobs that requires 16 TB of intermediate data. This job uses a statistical model to predict the probability of two people knowing each other. Interestingly they use bloom filters to speed up large joins, yielding a 10x performance improvement.
http://www.infoq.com/news/2010/08/linkedin-data-infrastructure
Tags: hadoop, linkedin, scalability, mapreduce, infrastructure, data, nosql, database, architecture, bigdata Saved by: admin
Much of LinkedIn's important data is offline - it moves fairly slowly. So they use daily batch processing with Hadoop as an important part of their calculations. For example, they pre-compute data for their "People You May Know" product this way, scoring 120 billion relationships per day in a mapreduce pipeline of 82 Hadoop jobs that requires 16 TB of intermediate data. This job uses a statistical model to predict the probability of two people knowing each other. Interestingly they use bloom filters to speed up large joins, yielding a 10x performance improvement.
http://www.infoq.com/news/2010/08/linkedin-data-infrastructure
Tags: hadoop, linkedin, scalability, mapreduce, infrastructure, data, nosql, database, architecture, bigdata Saved by: admin