This is so funny I laughed until I cried! Definitely NSFW. OMG it's hilarious, but it's also not a bad overview of the issues. Especially loved: You read the latest post on HighScalability.com and think you are a f*cking Google and architect and parrot slogans like Web Scale and Sharding but you have no idea what the f*ck you are talking about. There are so many more gems like that.
Thanks to Alex Popescu for posting this on MongoDB is Web Scale. Whoever made this deserves a Webby.

With summer almost gone, it's time to fall into some good links...
The need for IT consolidation is most evident in two types of organizations. In the first group, IT grew organically with business over the decades, and survived changes of strategy, management, staff and vendor orientation. The second group of businesses capital groups are characterized by rapid growth through acquisitions (followed by attempts to integrate radically different IT environments). In both groups, their IT infrastructures have typically been pieced together over the past 20 (or more) years.
Read more on BigDataMatters.com
Consistent Hashing is a specific implementation of hashing that is well suited for many of today’s web-scale load balancing problems. Specifically, it can be seen in use in various caching solutions like Memcached and is applicable to NoSQL solutions as well. Consistent Hashing is used particularly because it provides a solution for the typical “hashcode mod n” method of distributing keys across a series of servers. It does this by allowing servers to be added or removed without significantly upsetting the distribution of keys, nor does it require that all keys be rehashed to accommodate the change in the number of servers.
You can read the full store here.
In this post I'll cover the difference between multi-core concurrency that is often referred to as Scale-Up and distributed computing that is often referred to as Scale-Out mode.
more..
Source: Scale-out vs Scale-up (http://www.dzone.com/links/r/scaleout_vs_scaleup.html) by Nati Shalom
Can you have your ACID cake and eat your distributed database too? Yes explains Daniel Abadi, Assistant Professor of Computer Science at Yale University, in an epic post, The problems with ACID, and how to fix them without going NoSQL, coauthored with Alexander Thomson, on their paper The Case for Determinism in Database Systems. We've already seen VoltDB offer the best of both worlds, this sounds like a completely different approach.
The solution, they propose, is:

Pomegranate is a novel distributed file system built over distributed tabular storage that acts an awful lot like a NoSQL system. It's targeted at increasing the performance of tiny object access in order to support applications like online photo and micro-blog services, which require high concurrency, high throughput, and low latency. Their tests seem to indicate it works:
We have demonstrate that file system over tabular storage performs well for highly concurrent access. In our test cluster, we observed linearly increased more than 100,000 aggregate read and write requests served per second (RPS).
Rather than sitting atop the file system like almost every other K-V store, Pomegranate is baked into file system. The idea is that the file system API is common to every platform so it wouldn't require a separate API to use. Every application could use it out of the box.
The features of Pomegranate are:
Can Ma, who leads the research on Pomegranate, was kind enough to agree to a short interview.

The Silicon Valley Cloud Computing Group had a meetup Wednesday on OpenStack, whose tag line is the open source, open standards cloud. I was shocked at the large turnout. 287 people registered and it looked like a large percentage of them actually showed up. I wonder, was it the gourmet pizza, the free t-shirts, or are people really that interested in OpenStack? And if they are really interested, why are they that interested? On the surface an open cloud doesn't seem all that sexy a topic, but with contributions from NASA, from Rackspace, and from a very avid user community, a lot of interest there seems to be.
The brief intro blurb to OpenStack is:

This a follow-up post to an earlier post on the Scaling Rails Screencast Series by Gregg Pollack, when there were only 13 screencasts, now there are 21. Eight more have been added on topics like load testing and database scaling. This series is of surprisingly high quality. While I didn't view every screencast, I sampled a large set and found them to have solid content and high production values. In fact, how did they make these things? The instructor moves around in a little box while the content flows around him. A very cool effect. But that wouldn't matter if the content didn't deliver, here's what's new:

This is a guest post by Steffen Konerow, author of the High Performance Blog.
Learning how to scale isn’t easy without any prior experience. Nowadays you have plenty of websites like highscalability.com to get some inspiration, but unfortunately there is no solution that fits all websites and needs. You still have to think on your own to find a concept that works for your requirements. So did I.
A few years ago, my bosses came to me and said “We’ve got a new project for you. It’s the relaunch of a website that has already 1 million users a month. You have to build the website and make sure we’ll be able to grow afterwards”. I was already an experienced coder, but not in these dimensions, so I had to start learning how to scale – the hard way.

Lots of good links this week...
Misco: A MapReduce Framework for Mobile Systems is a very exciting paper to me because it's really one of the first explorations of some of the ideas in Building Super Scalable Systems: Blade Runner Meets Autonomic Computing in the Ambient Cloud. What they are trying to do is efficiently distribute work across a set cellphones using a now familiar MapReduce interface. Usually we think of MapReduce as working across large data center hosted clusters. Here, the cluster nodes are cellphones not contained in any data center, but compute nodes potentially distributed everywhere.
I talked briefly with Adam Dou, one of the paper's authors, and he said they don't see cellphone clusters replacing dedicated computer clusters, primarily because of the power required for both network communication and the map-reduce computations. Large multi-terabyte jobs aren't in the cards...yet. Adam estimates computationally that cellphones are performing similarly to desktops of ten years ago. Instead, they want to focus on the unique characteristics of the mobile devices--camera, microphone, GPS and other directly collectable data--so the data can be processed where collected.
MapReduce was selected as the programming interface because it is familiar to programmers, it transparently supports programming multiple devices, and can be implemented--especially using Python---in such a way that programmers are freed from all the underlying details like concurrency, data distribution, and code management. A very smart move in my estimation.
It's interesting to contrast the economics of the ambient cloud to the economics of the data center cloud. The goal of a data center cloud is 100 percent utilization. Use every possible CPU cycle or money is being wasted money on unused equipment. In an ambient cloud the idea is more parasitic, deploy to more resources yet leave the primary function of the device unaffected. It's a different perspective that may lead to different architectures.
A quick introduction to Misco from the abstract:

This is a guest post by Frédéric Faure (architect at Ysance), you can follow him on twitter.
How do you scale an AWS (Amazon Web Services) infrastructure? This article will give you a detailed reply in two parts: the tools you can use to make the most of Amazon’s dynamic approach, and the architectural model you should adopt for a scalable infrastructure.
I base my report on my experience gained in several AWS production projects in casual gaming (Facebook), e-commerce infrastructures and within the mainstream GIS (Geographic Information System). It’s true that my experience in gaming (IsCool, The Game) is currently the most representative in terms of scalability, due to the number of users (over 800 thousand DAU – daily active users – at peak usage and over 20 million page views every day), however my experiences in e-commerce and GIS (currently underway) provide a different view of scalability, taking into account the various problems of availability and data management. I will therefore attempt to provide a detailed overview of the factors to take into account in order to optimise the dynamic nature of an infrastructure constructed in a Cloud Computing environment, and in this case, in the AWS environment.

I can’t even count the number of times that I’ve heard this phrase: “don’t worry about scaling your web application, worry about visitor (or customer) acquisition.” My response to this is always that you don’t need to choose one or the other, you can do both! In this post, I’m going to go over some of the strategies I’ve used to architect web applications for scalability, right from the start of the design process, in such a way that I’m prepared to scale when I need to, but not forced into doing so before its necessary. Easing the transition from small scale to large scale can be made much easier by choosing the right technologies and implementing the right coding patterns up front.
You can read the full store here.

This is an interesting tidbit from near the end of the Packet Pushers podcast Show 15 – Saving the Web With Dinky Putt Putt Firewalls. The conversation was about how SSL connections need to terminate before they can be processed by a WAF (Web Application Firewall), which inspects HTTP for security problems like SQL injection and cross-site scripting exploits. Much was made that if programmers did their job better these appliances wouldn't be necessary, but I digress.
To terminate SSL most shops run SSL connections into Intel based Linux boxes running Apache. This setup is convenient for developers, but it's not optimized for SSL, so it's slow and costly. Much of the capacity of these servers are unnecessarily consumed processing SSL.

The title of this post is a quote from Ilya Grigorik's post Weak Consistency and CAP Implications. Besides the article being excellent, I thought this idea had something to add to the great NoSQL versus RDBMS debate, where Mike Stonebraker makes the argument that network partitions are rare so designing eventually consistent systems for such rare occurrence is not worth losing ACID semantics over. Even if network partitions are rare, latency between datacenters is not rare, so the game is still on.
The rare-partition argument seems to flow from a centralized-distributed view of systems. Such systems are scale-out in that they grow by adding distributed nodes, but the nodes generally do not cross datacenter boundaries. The assumption is the network is fast enough that distributed operations are roughly homogenous between nodes.
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VoltDB Field/Community EngineerVoltDB is attracting more and more users every day. If you have a strong technical background in SQL and Linux, are experienced with production database deployments, and have a passion for customers and community, you could be just the person we are looking for. Are you excited about the prospect of working with users to develop and deploy VoltDB applications, and about helping users participate in the thriving VoltDB community? If so, read on at their job page.
Get Your High Scalability Fix at DiggInterested in working on cutting-edge high-scale infrastructure at Digg? We're making a big investment in scaling and have committed to the NoSQL (Not only SQL) path with Cassandra. We're using other open-source infrastructure to help us scale including Hadoop, RabbitMQ, Zookeeper, Thrift, HDFS and Lucene. We're rewriting Digg from the ground up and we need amazing developers to join our world-class team. If you think you are up for the challenge, or you know someone who might be, take a look at our jobs page for more information.
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More information at CloudSigma.
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RequirementsPlease email your resume (as an attachment) to employment@ezrez.com with the subject "Sr. Software Engineer" for immediate consideration. You may also contact our recruiter via Skype at JanetBourland. Please note that at this time we are not considering candidates that will require employer sponsorship to work in the United States. No calls from recruiters/agencies please. ezRez Software is an equal opportunity employer.
ezRez Software Engineer You will be part of a fun, fast-paced and highly collaborative engineering team leveraging agile methodologies to deliver new functionality in incremental iterations. You will get exposed to cutting-edge technologies including an open source stack, dependency injection (DI) frameworks, ORM, XML web services, distributed grid-based caching and the latest UI technologies. We're also currently using best-of-breed extreme programming (XP) techniques including test driven development, pair programming, continuous refactoring and continuous integration. RequirementsPlease email your resume (as an attachment) to employment@ezrez.com with the subject "Software Engineer" for immediate consideration. You may also contact our recruiter via Skype at JanetBourland. Please note that at this time we are not considering candidates that will require employer sponsorship to work in the United States. No calls from recruiters/agencies please. ezRez Software is an equal opportunity employer.
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