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Jeremy Edberg, the first paid employee at reddit, teaches us a lot about how to create a successful social site in a really good talk he gave at the RAMP conference. Watch it here at Scaling Reddit from 1 Million to 1 Billion–Pitfalls and Lessons.

Jeremy uses a virtue and sin approach. Examples of the mistakes made in scaling reddit are shared and it turns out they did a lot of good stuff too. Somewhat of a shocker is that Jeremy is now a Reliability Architect at Netflix, so we get a little Netflix perspective thrown in for free.

Some of the lessons that stood out most for me: 

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Original author: 
Todd Hoff

This is a guest post by Yelp's Jim Blomo. Jim manages a growing data mining team that uses Hadoop, mrjob, and oddjob to process TBs of data. Before Yelp, he built infrastructure for startups and Amazon. Check out his upcoming talk at OSCON 2013 on Building a Cloud Culture at Yelp.

In Q1 2013, Yelp had 102 million unique visitors (source: Google Analytics) including approximately 10 million unique mobile devices using the Yelp app on a monthly average basis. Yelpers have written more than 39 million rich, local reviews, making Yelp the leading local guide on everything from boutiques and mechanics to restaurants and dentists. With respect to data, one of the most unique things about Yelp is the variety of data: reviews, user profiles, business descriptions, menus, check-ins, food photos... the list goes on.  We have many ways to deal data, but today I’ll focus on how we handle offline data processing and analytics.

In late 2009, Yelp investigated using Amazon’s Elastic MapReduce (EMR) as an alternative to an in-house cluster built from spare computers.  By mid 2010, we had moved production processing completely to EMR and turned off our Hadoop cluster.  Today we run over 500 jobs a day, from integration tests to advertising metrics.  We’ve learned a few lessons along the way that can hopefully benefit you as well.

Job Flow Pooling

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New submitter NewYork writes with this chestnut from an article about the role of age in the high-tech workplace: 'The shelf life of a software engineer today is no more than that of a cricketer — about 15 years,' says V R Ferose, MD of German software major SAP's India R&D Labs that has over 4,500 employees . 'The 20-year-old guys provide me more value than the 35-year-olds do.'" The article features similar sentiments from Mukund Mohan, CEO of Microsoft's India-based startup initiative.


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The tech unit's sign, autographed by its members.

The reelection of Barack Obama was won by people, not by software. But in a contest as close as last week's election, software may have given the Obama for America organization's people a tiny edge—making them by some measures more efficient, better connected, and more engaged than the competition.

That edge was provided by the work of a group of people unique in the history of presidential politics: Team Tech, a dedicated internal team of technology professionals who operated like an Internet startup, leveraging a combination of open source software, Web services, and cloud computing power. The result was the sort of numbers any startup would consider a success. As Scott VanDenPlas, the head of the Obama technology team's DevOps group, put it in a tweet:

4Gb/s, 10k requests per second, 2,000 nodes, 3 datacenters, 180TB and 8.5 billion requests. Design, deploy, dismantle in 583 days to elect the President. #madops

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When Sega discontinued production of the Dreamcast console in 2001 and withdrew from the domestic hardware market, it marked the conclusion of one of the most tumultuous and error-strewn periods in the company's 72-year history. Sega Enterprises' spectacular fall from grace during the course of the 1990s remains a tragic spectacle of overconfidence and woefully misguided business practice.

At the start of the decade, Sega stood astride the gaming world like a colossus; it had smashed Nintendo's vice-like stranglehold in the US and conquered Europe with its street-smart marketing. But by the close of the '90s, the company's reputation was in tatters, its user-base had all but collapsed and it was driven dangerously close to the yawning abyss of insolvency.

This is the story of how The House That Sonic Built came crashing down in spectacular fashion.

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