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Architecture

Scaling Kim Kardashian to 100 Million Page Views

The team at PAPER estimated their article (NSFW) containing pictures of a very naked Kim Kardashian would quickly receive over 100 million page views. The very definition of bursty viral driven traffic.

As a comparison in 2013 it was estimated Google processed over 500 million searches a day. So a nude Kim Kardashian is worth one-fifth of a Google. Strangely, I can believe it.

How did they handle this traffic gold mine? A complete recounting of the unusual behind the scenes story is told by Paul Ford in How PAPER Magazine’s web engineers scaled their back-end for Kim Kardashian (SFW).  (BTW, only one butt pun was made intentionally in this story, all others are serendipity).

What can we learn from the experience? I know what you are thinking. This is just a single static page with a few static pictures. It’s not a complex problem like search or a social network. Shouldn’t any decent CDN be enough to handle that? And you would be correct, but that’s not the whole of the story:

Categories: Architecture

The Great Love Quotes Collection Revamped

A while back I put together a comprehensive collection of love quotes.   It’s a combination of the wisdom of the ages + modern sages.   In the spirit of Valentine’s Day, I gave it a good revamp.  Here it is:

The Great Love Quotes Collection

It's a serious collection of love quotes and includes lessons from the likes of Lucille Ball, Shakespeare, Socrates, and even The Princess Bride.

How I Organized the Categories for Love Quotes

I organized the quotes into a set of buckets:
Beauty
Broken Hearts and Loss
Falling in Love
Fear and Love
Fun and Love
Kissing
Love and Life
Significance and Meaning
The Power of Love
True Love

I think there’s a little something for everyone among the various buckets.   If you walk away with three new quotes that make you feel a little lighter, put a little skip in your step, or help you see love in a new light, then mission accomplished.

Think of Love as Warmth and Connection

If you think of love like warmth and connection, you can create more micro-moments of love in your life.

This might not seem like a big deal, but if you knew all the benefits for your heart, brain, bodily processes, and even your life span, you might think twice.

You might be surprised by how much your career can be limited if you don’t balance connection with conviction.  It’s not uncommon to hear a lot of turning points in the careers of developers, program managers, IT leaders, and business leaders that changed their game, when they changed their heart.

In fact, on one of the teams I was on, the original mantra was “business before technology”, but people in the halls started to say, “people before business, business before technology” to remind people of what makes business go round.

When people treat each other better, work and life get better.

Love Quotes Help with Insights and Actions

Here are a few of my favorite love quotes from the collection …

“Love is like heaven, but it can hurt like hell.” – Unknown

“Love is not a feeling, it’s an ability.” — Dan in Real Life

“There is a place you can touch a woman that will drive her crazy. Her heart.” — Milk Money

“Hearts will be practical only when they are made unbreakable.”  – The Wizard of Oz

“Things are beautiful if you love them.” – Jean Anouilh

“Life is messy. Love is messier.” – Catch and Release

“To the world you may be just one person, but to one person you may be the world.” – Unknown

For many more quotes, explore The Great Love Quotes Collection.

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Happiness Quotes Revamped

My Story of Personal Transformation

The Great Leadership Quotes Collection Revamped

The Great Personal Development Quotes Collection Revamped

The Great Productivity Quotes Collection

Categories: Architecture, Programming

Stuff The Internet Says On Scalability For February 13th, 2015

Hey, it's HighScalability time:


Stunning depiction of every space mission over the past 50 years. (Max Roser)
  • 700 billion: Apple's valuation; 1: Number of lines of code it takes to bring down UK air traffic control; 20: how old that line of code was in years; 2: the problem was of course a never been seen before double failure; 1: atom-thin silicon transistors may mean super-fast computing; a few: how many data points it takes to identify you
  • Quotable Quotes:
    • @EpicureanDeal: The Uber model is everywhere: using the internet to connect infrequent, consumers of ad hoc, spot market services with fragmented suppliers.
    • @awendt: “I’m sorry you learned about transactions and joins in college, but you’ll have to de-normalize for #microservices” – @adrianco #microxchg
    • @samnewman: @adrianco "JSON was 10x faster than XML. Protobufs 10x faster than JSON. Avro same speed as Protobufs, but half the size"
    • @RichardWarburto: Premature Optimization isn't the root of all evil: misunderstood domain models are.
    • @MichaelPisula: Says @ewolff at #microxchg: start big with your microservices, splitting is easier than joining and your architecture will be wrong anyway
    • @MJFKlewitz: "With vertical scaling the problem is you end up giving a lot of money to Larry Ellison" #greatquote @crichardson #microxchg
    • Jenny Rood: Species of ants which differ in size can coexist peacefully, but the insects will chase away similarly sized competing species.
    • Steven Levy: The nonlinear gains that Moore predicted are so mind-bending that it is no wonder that very few were able to bend their minds around it.
    • ntoshev: There seems to be a fundamental trade-off between latency and throughput, with stream processors optimizing for latency and batch processors optimizing for throughput.
    • Sam Altman: Nobody cares if you’re using an Intel Edison or a 555 to blink the LED in the prototype you show them: people care about whether you’ve made something that they want.
    • @swardley: 30-50 years from genesis to industrialisation is about the average these days
    • Alex Clemmer: 84% of a single-threaded 1KB write in Redis is spent in the kernel
    • @allspaw: Psst: while lots of folks hope for fully "autonomous" tech to solve all the world's ills, I'll just be over here getting some work done.
    • @alejandrocrosa: “The database you read from is just a cached view of the event log”
    • @viktorklang: Optimizing for latency (as in "time to serve") will also yield higher throughput. Thank you, Mr Little.
    • rakoo: using GOMAXPROCS doesn't automagically turn your program into a parallel one
    • fluidcruft: Data science manifesto: The purpose of computing is numbers.

  • Is the golden age of the cheap startup over? The Rising Costs Of Scaling A Startup. In San Francisco it is. Twice as expensive in 2014 than it was in 2009. Wages have doubled. Op-ex has doubled. And thus startup round sizes have increased. People and place costs dwarf compute infrastructure cost savings.

  • Just in case you are of the fashionable opinion Perl code must look like line noise, take a gander: Real measurement is hard. Nice, eh?

  • Magic tricks for algorithms. This may prove helpful in your new job as Algorithm Profiler...Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images:  It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects. 

  • Here's the rare Docker nocker. Many reasons why you should stop using Docker. One side: Idea good, implementation leaves something to be desired. Other side: it tastes great and is less filling. Interesting from dacjames: In general, I agree 100%. With the case of Docker, the concept of containerization is more important than the current project. Decoupling the application environment from the infrastructure environment is an immensely valuable paradigm.

  • Bounty Hunter. A job title that conjures up romantic images and dreams of the never was. You can still be one in the digital age. And make some money too. 11 Essential Bug Bounty Programs of 2015. Hundreds of thousands of dollars are available. Good hunting.

  • When algorithms rule the world you are just one weighting factor away from insignificance. Apple,Apps and Algorithmic Glitches. Divination used to be how we attempted to contol the future. Now we attempt to penetrate to the unknowable heart of opaque algorithms with something different...data.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

Categories: Architecture

Rescuing an Outsourced Project from Collapse: 8 Problems Found and 8 Lessons Learned

If you are one of those people that think most of the products featured on HighScalability use way too many servers then you'll love this story: 130 VMs serving less than 10,000 users daily were chopped down to just one machine.

Here's the setup. A smallish website was having problems. Users were unhappy. In the balance was not only the product, but the company. The site was built using Angular, Symfony2, Postgres, Redis, Centos, 8 HP blades with 128 G RAM each, two racks, a very large HP 3par storage array, a 1Gbps uplink, and VMWare.

More than enough power for the task at hand. Yet the system couldn't handle the load. What would you do?

That's the story Jacques Mattheij tells in his very entertaining and educational Saving a Project and a Company article.

Jacques says much was right about the website, but time pressure and mismanagement created big problems at the system level. "A single clueless person in a position of trust with non technical management, an outsourced project and a huge budget, what could possibly go wrong?" Sound familiar? 

Problem 1: Virtualization Gone Crazy
Categories: Architecture

Random thoughts on big data

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I began blogging in 2005, back then I managed to post something new almost everyday. Now, 10 years after, I hardly post anything. I was beginning to think I don’t have anything left to say but I recently noticed I have quite a few posts in various states of “draft”. I guess that  I am spending too much thinking about how to get a polished idea out there, rather than just go on and write what’s on my mind. This post is an attempt to change that by putting some thought I have (on big data in this case) without worrying too much on how complete and polished they are.

Anyway, here we go:

  1. All data is time-series – When data is added to the big data store (Hadoop or otherwise) it is already historical i.e. it is being imported from a transactional system, even if it is being streamed into the platform. If you treat the data as historical and somehow version it (most simplistic is  adding timestamp) before you store it it would enable you to see how this data changes over time  – and when you relate it to other data you’d be able to see both the way the system was at the time of that data particular data (e.g. event) was created as well as getting the latest version and see its state now. Essentially  treating all data as slow changing dimensions gives you enhanced capabilities when you’d want to analyse the data later.
  2. Enrich data with “foreign keys” before persisting it (or close to it). Usually a data source is not stand alone and it can be related to other data – either form the same source or otherwise. Solving some of these correlations when the data is fresh and context is known can save a lot of time doing them later both because you’d probably do these correlations multiple times instead of once as well as because when the data is ingested the context and relations are more obvious than when, say, a year later you try to make sense of the data and recall its relations to other data.
  3. Land data in a queue – This ties nicely to the previous two points as the type of enrichments mentioned above are suited well for handling in streaming. if you lend all data in a queue you can gain a unified ingestion pipeline for both batch and streaming data. Naturally not all the computations can be handled in streaming, but you’d be able to share at least some of the pipeline.
  4. Lineage is important (and doesn’t get enough attention) – raw data is just that but to get insights you need to process it , enrich and aggregate it – a lot of times this creates a disconnect between the end result and the original data. Understanding how insights were generated is important both for debugging problems as well as ensuring compliance (for actions that demand that).
  5. Not everything is big data – Big data is all the rage today but a lot of problems don’t require that. Not only that when you make the move to a distributed system you both complicate the solution and more importantly slow the processing (until, of course, you hit the threshold where the data can’t be handled by a single machine). This is even truer for big data systems where you have a lot of distributed nodes so the management (coordination etc.) is more costly (back at Nice we referred to  the initialization time for Hadoop jobs as “waking the elephant”  and we wanted to make sure we need to wait it).
  6. Don’t underestimate scale-up – A related point to the above. Machines today are quite powerful and when the problem is only “bigish” it might be that a scale-up solution would solve it better and cheaper. Read for example “Scalability! But at what COST?” by Frank McSherry and “Command-line tools can be 235x faster than your Hadoop cluster” by Adam Drake as two examples for points 5 & 6

This concludes this batch of thoughts. Comments and questions are welcomed

image by Nilay Gandhi

Categories: Architecture

Vinted Architecture: Keeping a busy portal stable by deploying several hundred times per day

This is guest post by Nerijus Bendžiūnas and Tomas Varaneckas of Vinted.

Vinted is a peer-to-peer marketplace to sell, buy and swap clothes. It allows members to communicate directly and has the features of a social networking service.

Started in 2008 as a small community for Lithuanian girls, it developed into a worldwide project that serves over 7 million users in 8 different countries, and is growing non-stop, handling over 200 M requests per day.

Stats
Categories: Architecture

The Great Productivity Quotes Collection Revamped

A while back I put together a comprehensive collection of personal productivity quotes.   It’s a combination of the wisdom of the ages + modern sages.   It was time for a revamp.  Here it is:

The Great Productivity Quotes Collection

It's a serious collection of personal productivity quotes and includes lessons from the likes of Benjamin Franklin, Bruce Lee, Peter Drucker, Tony Robbins Voltaire, and more.

Productivity is Value Divided by Time

My favorite definition is a simple formula from Steve Pavlina:

Productivity = Value / Time

I like the formula because of it’s simplicity and because of the insight it provides.  If you want to increase your productivity, then you can increase the value or reduce the time it takes, or both.

One of the things I remind my colleagues in the halls of Microsoft is that value is the ultimate short-cut.  If you know what’s valued, you can eliminate or reduce all the waste in between.

Productivity Hot Spots

To organize the productivity quotes, I use a simple frame of productivity Hot Spots:

Action, Approach, Efficiency / Effectiveness, Energy Management, Failure, Focus, Goals, Improvement, Motivation and Mindset, Planning, Opportunity, Self-Awareness, and Time Management.

I find these buckets are useful for organizing principles, patterns, practices, and even quotes.   There are a lot of productivity quotes, so using this frame helps group the quotes into more manageable themes.

The Ultimate Formula for Personal Productivity

It sounds so simple when I say it now, but it took me a while to figure out the ultimate formula for personal productivity.   Here’s the formula:

Work on the right things, at the right time, the right way, with the right energy.

In other words, start with the right things.  Trim your tree of opportunity and focus on the right branches and leaves that will bear the most fruit.   Work on these things at the right time.  It’s easy to miss windows of opportunity and time changes what’s valued.   We also have better times in the day to work on things than others.   Work on things the right way.  This is really about using better techniques.  If have the wrong technique, then throwing hours and effort at something will just waste your time.  Lastly, work on things with the right energy.  Your energy is your force multiplier since you won’t get more time in the day.

A simple way to think of the way to optimize your productivity is to use your best energy for your best results.

I share a simple system and a comprehensive set of proven practices for personal productivity in my book, Getting Results the Agile Way. (It’s been a best seller in time management, and it helps you master productivity, time management, and work-life balance.)

Productivity Strategies

Believe it or not, quotes are not just neat and fun little sayings.   The right quotes are actually pithy ways to share strategies and insights.  They can completely change your game.

Here are a handful of some of the strategies that I’ve learned for improving productivity and many of these strategies are echoed in The Great Productivity Quotes Collection:

Less is more.

Focus on quality.

Quotas and quantity can help you achieve quality.  (If you learn from your process and apply it.)

Value is in the eye of the beholder and the stakeholder.

Find better techniques to multiply your results.

Enjoy the process.

Spend more time in your strengths, and less time in your weaknesses.

Pair up to amplify your talents and capabilities.

Focus on continuous improvement.

Manage your energy, not time.

Reduce the time you spend and you’ll innovate in your process.

Use timeboxing to invest time more intelligently. (Set limits and boundaries so you don’t over-spend in the wrong areas of your life.)

Work smarter, not harder.

Change your approach when it’s not working.

Test your results.

Productivity is Power

There are many ways to think about productivity.   I like to think of productivity as power.  I think of power as the ability to take action.   When you exercise your ability to take action and concentrate your effort and your focus, you can make amazing things happen in work and life.

Productivity is a powerful tool in your toolbox for personal empowerment.

As with anything, make sure you use the right tool for the job.  And that’s why I continue to fill my toolbox with several tools.  Otherwise, if all you have is a hammer, then everything looks like a nail.

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The Great Personal Development Quotes Collection Revamped

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Categories: Architecture, Programming

The Great Personal Development Quotes Collection Revamped

“Knowing others is intelligence.  Knowing yourself is true wisdom.  Mastering others is strength.  Mastering yourself is true power.” -- Lao Tzu

A while back I put together a comprehensive collection of personal development quotes.   It’s a combination of the wisdom of the ages + modern sages.   It was time for a revamp.  Here it is:

The Great Personal Development Quotes Collection

It's a serious collection of personal development quotes and includes lessons from the likes of Buddha, Covey, Emerson,  Gandhi, Robbins, Ziglar, and more.

Personal Development is a Way to Realize Your Potential

Personal development is a process for life where you improve your awareness, your skills, your abilities, and your potential.  Personal development shapes your growth by developing your strengths, reducing your liabilities, and expanding what you’re capable of.

You improve your potential through self-awareness, habits, practice, and feedback.

Awareness is Half the Battle

A big part of personal development is simply awareness.  For example, when you know your Myers & Briggs Personality Type, you gain insight into whether you outwardly or inwardly focused, how you prefer to take in information, how you prefer to make decisions, and how you prefer to live your outer life.

Aside from better understanding your own patterns, you can also use it to understand other people’s behavior preferences, and you can adapt your style.  If you see somebody staring blankly at you during your presentation, it doesn’t mean they aren’t engaged.  They might just be an introvert processing the information in their own quiet way.

If you know your Conflict Style, you can tailor and adapt it to the situation, as well as better understand the mode that others are operating in.

There are many models and tools for self-awareness, but the goal is the same:  learn how to be more effective in more situations based on your individual strengths, abilities, and experience.

Action is the Other Half

Personal development is a verb.   You need to take action.  All the knowledge in the world doesn’t matter if you don’t apply it.  Even thoughts are habits that we haven’t learned how to measure.  When you apply what you learn, you can adjust what you learn based on feedback and results.

If you keep in mind that personal development is about continuously improving your thinking, feeling, and doing, then it’s easier to stay focused and to evaluate your results.

You can also approach personal development in a number of ways.  Just like martial arts, there are hard-styles and there are soft-styles.  In my experience, it helps to balance and blend hard-core skill building along with building the soft skills, especially interpersonal skills and your emotional intelligence.

Personal Development Requires a Growth Mindset

If you want to grow, you have to believe you can.

If you adopt a Growth Mindset, you can create a love of learning and a resilience that is the basis of great accomplishment in every area of work and life.

In the book, Mindset: The New Psychology of Success, author Carol Dweck shares a lot of science and stories around how our mindset limits or enables our growth.  If we believe that our abilities are fixed traits, and that we are either good or bad at something, then we have a Fixed Mindset.

If, on the other hand, you believe that you can get better through skills development, then you have a Growth Mindset.

If you’ve ever been in any sort of elite training, or specialized skills development or had a great mentor that provides deep feedback, it should be more than obvious to you how much growth and greatness is possible.

Adapting is the Key to Personal Development Success

So if you have a Growth Mindset, and you practice personal development, and you develop your self-awareness, then what will hold you back?

Simple.   The inability or lack of willingness for you to change your approach.

Darwin taught us that nature favors the flexible and Einstein said that doing the same thing over and over again and expecting different results is the definition of insanity.

And yet, how many people get stuck in a rut or hold themselves back through limiting thought patterns or behaviors?

One of the greatest things you can possible do for your future success is to learn how to change your approach with skill.

I could say so much more about personal development but at this point, I’d rather share what some of the greatest giants in personal development have had to say.  

Use The Great Personal Development Quotes Collection to stand on the shoulders of giants, and see further, as you look inward and upward.

And if you want a jumpstart in agile personal development, check out my best-selling book on productivity:  Getting Results the Agile Way.   It’s a simple system for meaningful results, and  it’s a way to use personal development to think better, feel better, and do better in all areas of your life.

Categories: Architecture, Programming

Stuff The Internet Says On Scalability For February 6th, 2015

Hey, it's HighScalability time:


What a beautiful example of Moore's law visualized through the evolution of Lara Croft! (from @silenok)
  • $1 million: per day gross of Clash of Clans
  • Quotable Quotes:
    • @dancow: In 45 minutes, the largest trader in U.S. equities went bankrupt because of bad devops
    • @bmdhacks: How to be a 10x engineer: Incur technical debt fast enough to appear 10x as productive as the ten engineers tasked with cleaning it up.
    • @CompSciFact: Scaling poorly: Performance degrades with problem size Poorly scaled: Things change far more rapidly in one direction than others
    • @mikiobraun: Before scaling out, a machine learning person would always try some approximation shortcut to achieve speed up. #cheating #orisit
    • @cshirky: 3/4 If your organization has ever made a significant and unpleasant change based on something you measured, you can probably use more data.
    • @PatrickMcFadin: Service Discovery Overview: ZooKeeper vs. Consul vs. Etcd vs. Eureka 
    • @jaykreps: TIL: Dequeuing a single item in RabbitMQ requires traversing every single item in the queue. Oh my.
    • @Carnage4Life: No single recipe 4 success. Great companies had bad habits; Apple micromanagement, Google random side projects & Facebook used fricking PHP
    • Stubbornly Persistent: although life would persist in the absence of microbes, both the quantity and quality of life would be reduced drastically.

  • At inflection points change the world must. Netflix: In the early days of Netflix streaming, circa 2008, we manually tracked hundreds of metrics, relying on humans to detect problems.  Our approach worked for tens of servers and thousands of devices, but not for the thousands of servers and millions of devices that were in our future.  Complexity and human-reliant approaches don’t scale; simplicity and algorithm-driven approaches do.

  • IBM is turning Watson into a platform, offering 5 new services: Speech to Text, Text to Speech, Visual Recognition, Concept Insights, Tradeoff Analytics. GA probable next month. Good discussion on Hacker News. Most of the services allow for training through feedback. Some question the quality of the services, but it's early days. Pricing is not set. Hopefully it won't suffer from what these next gen deep learning services tend to suffer from: expensivitis. Who can afford $1.00 per 1000 API calls for a mobile app that needs to acquire users? IBM, make it cheap, try for ubiquity. Cool stuff will happen.

  • Looking for that next step in distributed reliability? Look at TLA+. Murat has several articles on TLA+ and is using it his teaching distributed systems class. Oh, TLA stands for Temporal Logic of Actions. Leslie Lamport has many papers on TLA. James Hamilton wrote up their experiences at Amazon using TLA+: Challenges in Designing at Scale: Formal Methods in Building Robust Distributed Systems: TLA+, a formal specification language invented by ACM Turing award winner, Leslie Lamport. TLA+ is based on simple discrete math, basic set theory and predicates with which all engineers are quite familiar. A TLA+ specification simply describes the set of all possible legal behaviors (execution traces) of a system. 

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

Categories: Architecture

《程序员必读之软件架构》作者Simon Brown:架构师与程序员的区别(图灵访谈)

Coding the Architecture - Simon Brown - Thu, 02/05/2015 - 07:52

Simon Brown 是全球知名软件架构独立咨询师、讲师,创办了专门讨论软件架构问题的网站“编码架构”(CodingTheArchitecture.com)。他自称是写代码的软件架构师和明白架构的软件开发者。自2008年以来的7年时间里,Simon在全球28个国家做过有关软件架构、技术领导力及其与敏捷的平衡等主题的百余场演讲,并于2012年8月在中国举办的ArchSummit全球架构师峰会上以“郁闷的架构师”和“如何设计安全的架构”为主题发表演讲,深受与会者好评。Simon已为全球20多个国家的软件团队提供咨询和培训,他的客户既有小型技术初创企业,也不乏全球家喻户晓的品牌公司。Simon著有《程序员必读之软件架构》一书,他在这本书中打破传统的认知,模糊软件开发和架构在流程中的界限,进而为软件架构正名。

问:开发者和架构师之间最大的区别是什么?

架构师和开发者一样,也经常写代码,简单的说,开发者和架构师之间最大的区别就是技术领导力。软件架构师的角色需要理解最重要的架构驱动力是什么,他提供的设计需要考虑这些因素。架构师还要控制技术风险,在需要的时候积极演化架构,并且负责技术质量保证。从根本上讲,架构师是一个技术领导者的角色,这就是最大的区别。

Read more...

Categories: Architecture

UC Berkeley paper unveils what business leaders should learn from the Agile WikiSpeed case.

Xebia Blog - Wed, 02/04/2015 - 23:26

WIKISPEED_my_next_car_gets_100mpg_1024Last fall, I was approached by Tom van Norden from the UC Berkeley School of Information. A team of professor Morten T. Hansen, famous from his bestseller with Jim CollinsGreat by Choice”, was investigating the magic around Joe Justice’ WikiSpeed. His agile team outperformed companies like Tesla during the XPrize a couple of years ago. Berkeley did research on the Agile and Lean practices being applied by the WikiSpeed team and it’s current status.

Joe and I work closely together how we can apply powerful IT-practices like Scrum and TDD (Test Driven Development) in a non-IT environment.  In parallel with WikiSpeed, together with Xebia coaches Jeroen Molenaar and Jasper Sonnevelt we're currently coaching the hydrogen and solar raceteams of Delft University, Forze and Nuna. In our coaching we use many of WikiSpeeds XM-practices, especially Scrum. The Berkeley paper confirmed and unveiled a number of lessons I would like to share in this blogpost.

Unification

An inspiring recognizable goal gives the team extreme focus, spirit and energy. It actually provides them with an identity.  When the goal fades or is not unique and inspiring anymore, the focus, spirit and energy goes down significantly.  The Forze team was also struggling with this.  The 2014-team handed over the P6 Hydrogen Race Car to the 2015- team in september 2014.  This car was still buggy and last but not least, with a goal which was not realistic and committed by the 2015-team.  Not endless bug fixing on the previous car to complete a goal from last years team.  Their new goal is building a new car, competing at a spectacular race in 2016 with conventional race cars. Therefor, we recently advised the team to start a parallel track and preparations how we should develop this new P7-car the agile way. As a result, the spirit of the team went up significantly and also provided them with extra energy to fix the bugs in the P6 car.

slider_Forze_VI_Valkenburg

Nimble Networks

Like every team, the presence of an inspiring leader is important.  They're  like the glue keeping the team together and keeping them motivated. For example, when Joe was travelling, the attendance at agile ceremonies and the Wikispeed teams velocity went down.   The same phenomena I’ve observed coaching The Forze team.  Team members are recruited on their technical skills, not for their leadership qualities. As a result, we had a great bunch of technicians but no leadership in the team. TU-Delft students are are currently not educated to recognize develop natural and/or acquired leadership skills. As a result, the 2015-team is vulnerable. For example, there is only one member with electrotechnical knowledge rightnow.  A skill which is crucial for engineering a hydrogen car.   The recommendation of Berkeley is very useful here. The team should recruit new members with strong personalities combined with leadership qualities and develop a more distributed network which makes the team less vulnerable.

Finally, Berkeley stated the WikiSpeed solution is an exceptional example how disciplined collaboration could do magic but can not be blindly copied to another existing business to do the same magic there.  Agile methods should be tailored to fit it’s goals. I cannot more agree on this.  An agile process should be agile within itself, fit for purpose with a very clear goal.

Download the full paper here.

 

Matt Cutts: 10 Lessons Learned from the Early Days of Google


I mainly know of Matt Cutts, long time Google employee (since 2000) and currently head of Google's Webspam team, from his appearances on TwiT with Leo Laporte. On TwiT Matt always comes off as smart, thoughtful, and a really nice guy. This you might expect.

What I didn’t expect is in this talk he gave, Lessons learned from the early days of Google, is that Matt also turns out to be quite funny and a good story teller. The stories he’s telling are about Matt’s early days at Google. He puts a very human face on Google. When you think everything Google does is a calculation made by some behind the scenes AI, Matt reminds us that it’s humans making these decisions and they generally just do the best they can.

The primary theme of the talk is innovation and problem solving through creativity. When you are caught between a rock and a hard place you need to get creative. Question your assumptions. Maybe there’s a creative way to solve your problem?

The talk is short and well worth watching. There are lots of those fun little details that only someone with experience and perspective can give. And there’s lots of wisdom here too. Here’s my gloss on Matt’s talk:

1. Sometimes creativity makes a big difference.
Categories: Architecture

Microservices versus the common SOA implementation

Xebia Blog - Wed, 02/04/2015 - 15:02

When I was first reading about MSA architectures (MSA) I had a hard time figuring out what was so different from a Service Oriented Architecture (SOA). Main reason for this is that the SOA paradigm leaves quite a bit of room for interpretation and various people have different interpretations. When Martin Fowler wrote about MSA almost a year ago he also mentioned that some people see it as “SOA done right”, he himself considers MSA a subset of SOA. So, what are the differences, if any?
At first glance there seem to be more than a few similarities. Both talk about services as the cornerstone for the architecture, services need to loosely coupled, technology agnostic and there’s probably a few more. What sets the Microservices architectural style apart is that it’s more clear on where the focus needs to be when designing your services. The name suggests that the services need to very small and probably very fine grained but in reality no size restrictions apply. Ironically, it’s the size (and focus) of services that commonly cripple SOA implementations.
The main characteristics of MSA are: autonomy, high cohesion and low coupling. Out of these characteristics autonomy is the defining one. Here autonomy not only means that it can run or function independently (that is what low coupling is about), it means that a service should be able to provide business value on its own. This principle ties together the focus on low coupling and high cohesion. Lower coupling allows the service to operate independently and high cohesion increases it’s ability to add value on its own.
What you often see in SOA implementations is the focus on reuse. This means that a certain function should only exist in one place or a single service should handle a certain function. Where things go sour is in how these “functions” are determined. This is where cohesion comes into play. Cohesion is the degree in which functionality in services belongs together. The highest cohesion is functional cohesion where the services contribute to a well-defined task or business capability. MSA strives towards functional (high) cohesion. The cohesion found in common SOA implementations is logical cohesion (one step up from the worst kind, coincidental cohesion). With logical cohesion the services are grouped around similar tasks, like the one service for one function mentioned above. This approach leads to finer grained services that focus on atomic tasks. Take for instance a “data service” that handles all communication with a database. It accepts messages in a common format and then uses that information to execute the desired action on the database. Benefit is that applications that use this service don’t need to worry about the tech behind said service and only need to provide messages in a common format. If the database is shared by multiple services they all use this service when communicating with the database. Now imagine what would happen if this service goes down (intentionally or not). Or what the impact is when this service needs to be modified. And this is assuming that this service is well designed and built so it can handle all the different requests thrown at it.
With MSA the goal should be high cohesion, this means grouping things around a business capability (so it can add business value). It also provides all aspects of that capability end-to-end, from data storage to user interface functions. So instead of having a “data service” you create a “customer service” or “shipping service”.
Another aspect that is relevant here is that with MSA one should strive for low coupling (or better yet, no coupling). Low coupling boils down to the dependency between services. With no coupling the services can function completely independent of each other. If two services have no coupling downtime of one of the two will have zero impact on the other. The higher the coupling, the higher the impact.
With SOA implementations based on logical cohesion the coupling tends to be high. Because services depend on other services to function. High coupling increases the impact of changes.
For MSA the goal is no coupling. But lowering the coupling does not mean they can’t or shouldn’t interact. Services can still interact with other services but they don’t depend on them. Another distinction to take into consideration is that these are more technical dependencies, not functional ones. Take for instance an order service and a shipping service. Both can operate independently. The shipping service will process all the orders it has received, regardless of the availability of an order service. If the order service is down it simply means no new orders will be created for the shipping service. So when the shipping service is done handling its last known order it stops. Vice versa, if the shipping service is down the order service will still be able to process orders. They just won’t be processed by the shipping service. When the shipping service comes back up it will process the backlog created by the order service.
How much the Microservices architectural style differs from SOA depends on who you talk to. If nothing else, MSA offers a clearer and better defined approach on setting up an architecture built around services. The key differentiator being the focus on autonomously adding value. There is a lot more to say on the subject but hopefully this gives some insight into how MSA differentiates itself from what you typically see with SOA implementations.

Apache Spark

Xebia Blog - Tue, 02/03/2015 - 21:22

Spark is the new kid on the block when it comes to big data processing. Hadoop is also an open-source cluster computing framework, but when compared to the community contribution, Spark is much more popular. How come? What is so special and innovative about Spark? Is it that Spark makes big data processing easy and much more accessible to the developer? Or is it because the performance is outstanding, especially compared to Hadoop?

This article gives an introduction to the advantages of current systems and compares these two big data systems in depth in order to explain the power of Spark.

Parallel computing

First we have to understand the differences between Hadoop and the conventional approach of parallel computing before we can compare the differences between Hadoop and Spark.

Distributed computing

In the case of parallel computing, all tasks have access to shared data to exchange information and perform their calculations. With distributed computing, each task has its own data. Information is exchanged by passing data between the tasks.

One of the main concepts of distributed computing is data locality, which reduces network traffic. Because of data locality, data is processed faster and more efficiently. There is no separate storage network or processing network.

Apache Hadoop delivers an ecosystem for distributed computing. One of the biggest advantages of this approach is that it is easily scalable, and one can build a cluster with commodity hardware. Hadoop is designed in the way that it can handle server hardware failures.

Stack

To understand the main differences between Spark and Hadoop we have to look at their stacks. Both stacks consist of several layers.

Stacks of Spark and Hadoop

The storage layer is responsible for a distributed file system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster. Spark uses the Hadoop layer. That means one can use HDFS (the file system of Hadoop) or other storage systems supported by the Hadoop API. The following storage systems are supported by Hadoop: your local file system, Amazon S3, Cassandra, Hive, and HBase.

The computing layer is the programming model for large scale data processing. Hadoop and Spark differ significantly in this area. Hadoop uses a disk-based solution provided by a map/reduce model. A disk-based solution persists its temporary data on disk. Spark uses a memory-based solution with its Spark Core. Therefore, Spark is much faster. The differences in their computing models will be discussed in the next chapter.

Cluster managers are a bit different from the other components. They are responsible for managing computing resources and using them for the scheduling of users' applications. Hadoop uses its own cluster manager (YARN). Spark can run over a variety of cluster managers, including YARN, Apache Mesos, and a simple cluster manager called the Standalone Scheduler.

A concept unique to Spark is its high-level packages. They provide lots of functionalities that aren't available in Hadoop. One can see this layer also as a sort of abstraction layer, whereby code becomes much easier to understand and produce. These packages are

  • Spark SQL is Spark’s package for working with structured data. It allows querying data via SQL.
  • Spark Streaming enables processing live streams of data, for example, log files or a twitter feed.
  • MLlib is a package for machine learning functionality. A practical example of machine learning is spam filtering.
  • GraphX is a library that provides an API for manipulating graphs (like social networks) and performing graph-parallel computations.

The Spark Core is also written in Scala and supports Scala natively, which is a far better language than Java for implementing the kinds of transformations it supports. This results in less code which is therefore more intuitive.

Screen Shot 2015-01-24 at 16.41.37

(Source: infoq)

Computational model

The main difference between Hadoop and Spark is the computational model. A computational model is the algorithm and the set of allowable operations to process the data.

Hadoop uses the map/reduce. A map/reduce involves several steps.

Hadoop computational model: Map/Reduce

  • This data will be processed and indexed on a key/value base. This processing is done by the map task.
  • Then the data will be shuffled and sorted among the nodes, based on the keys. So that each node contains all values for a particular key.
  • The reduce task will do computations for all the values of the keys (for instance count the total values of a key) and write these to disk.

With the Hadoop computational model, there are only two functions available: map and reduce.
Note that doing distributed computing with Hadoop results, most of the time, in several iterations of map/reduce.  For each iteration, all data is persisted on disk. That is the reason it is called disk-based computing.

Spark uses RDD, also called Resilient Distributed Datasets. Working and processing data with RDD is much easier:

Spark computational model: RDD

  • Reading input data and thereby creating an RDD.
  • Transforming data to new RDDs (by each iteration and in memory). Each transformation of data results in a new RDD. For transforming RDD's there are lots of functions one can use, like map, flatMap, filter, distinct, sample, union, intersection, subtract, etc. With map/reduce you only have the map-function. (..)
  • Calling operations to compute a result (output data). Again there are lots of actions available, like collect, count, take, top, reduce, fold, etc instead of only reduce with the map/reduce.

Performance Hadoop vs Spark

Behind the scenes, Spark does a lot, like distribute the data across your cluster and parallelizing the operations. Note that doing distributed computing is memory-based computing. Data between transformations are not saved to disk. That's why Spark is so much faster.

Conclusion

All in all Spark is the next step in the area of big data processing, and has several advantages compared to Hadoop. The innovation of Spark lies in its computational model. The biggest advantages of Spark against Hadoop are

  • Its in-memory computing capabilities that deliver speed
  • Packages like streaming and machine-learning
  • Ease of development - one can program natively in Scala

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Categories: Architecture

Marco Arment Uses Go Instead of PHP and Saves Money by Cutting the Number of Servers in Half

On the excellent Accidental Tech Podcast there's a running conversation about Marco Arment's (Tumblr, Instapaper) switch to Go, from a much loved PHP, to implement feed crawling for Overcast, his popular podcasting app for the iPhone.

In Episode 101 (at about 1:10) Marco said he halved the number of servers used for crawling feeds by switching to Go. The total savings was a few hundred dollars a month in server costs.

Why? Feed crawling requires lots of parallel networking requests and PHP is bad at that sort of thing, while Go is good at it. 

Amazingly, Marco wrote an article on how much Overcast earned in 2014. It earned $164,000 after Apple's 30%, but before other expenses. At this revenue level the savings, while not huge in absolute terms given the traffic of some other products Marco has worked on, was a good return on programming effort. 

How much effort? It took about two months to rewrite and debug the feed crawlers. In addition, lots of supporting infrastructure that tied into the crawling system had to be created, like the logging infrastructure, the infrastructure that says when a feed was last crawled, monitoring delays, knowing if there's queue congestion, and forcing a feed to be crawled immediately.

So while the development costs were high up front, as Overcast grows the savings will also grow over time as efficient code on fast servers can absorb more load without spinning up more servers.

Lots of good lessons here, especially for the lone developer:

Categories: Architecture

How to make the sprint review meeting worth your while

Xebia Blog - Mon, 02/02/2015 - 16:44

My work allows me to meet a lot of different people, who actively pursue Scrum. Some of them question the value of doing a sprint review meeting at the end of every sprint. Stakeholders presumably do not “use” nor “see” their work directly, or the iterated product is not yet releasable.

Looks like this Scrum ritual is not suited for all. If you are a person questioning the value of a demo, then focus on your stakeholders and start to demo the delta instead of just your product. Here is a 3-step plan to make your sprint reviews worth your while.

Step 1: Provide context

Stakeholders are not interested in individual user stories. They want to see releasable working software they can use and work with. That is were the value delta is for them; in practical services and products they can use to make their lives a little bit better than before.
So the thing you should do in the sprint review is to explain the completed stories in the context of the iteration and how this iteration will add value to the upcoming product-release. Providing the context will make it easier for stakeholders to give your team the feedback it is looking for.

Step 2: Demo the delta

Search for the abstraction level on which the user is impacted by the story you have completed, even if this means combining stories to demo as a whole. This is especially useful if you are working in component-based teams.

It will enable you to explicitly illustrate the added value from a stakeholder perspective after the change. It’s not just about adding an input screen or expansion of variables. It’s about being able to do something different as a stakeholder.

Think about the possibilities given the new additions to the system. Maybe people can be more effective or more efficient saving time, money and energy. If possible try to explicitly show the effect of the delta on the stakeholder, for instance by measuring key variables in the before and after situation. Seeing the explicit effect of the changes will get your stakeholders fired up to provide your team with additional ideas and feedback for your next release.

Step 3: Ask for feedback

The goal of the sprint review is to generate feedback. Often, this won’t happen automatically and means you have to explicitly ask for it. Doing it wrong is to do the review without interruption and to conclude by asking: “any questions?” This will certainly not evoke people to participate in a group discussion as posing questions is, unfortunately, still seen by many as a sign of weakness.

To counter this group dynamic, you should be the one asking the questions to your audience. Example stakeholder focused questions to generate feedback might be; “what are your thoughts on improving this feature further?” Or “How would this fit in your day-to-day routine?”

By doing so, you will lower the barrier for others to start asking questions themselves. Another tip to turn around the question dynamic is to specifically target your question to a single stakeholder, getting the group out of the conversation.

Up until now these steps helped me a lot in improving my sprint review meetings. These 3 simple steps will let your key-stakeholders be the subject of your review meeting, maximizing the chance that they will provide you with valuable feedback to improve your product.

I'm speaking at the O'Reilly Software Architecture Conference

Coding the Architecture - Simon Brown - Mon, 02/02/2015 - 13:19

I'm thrilled to say that I'll be speaking at the inaugural O'Reilly Software Architecture Conference in Boston during March. The title of my session is Software architecture vs code and I'll be speaking about the conflict between software architecture and code. This is a 90-minute session, so I look forward to also discussing how can we solve this issue. Here's the abstract...

Software architecture and coding are often seen as mutually exclusive disciplines, despite us referring to higher level abstractions when we talk about our software. You've probably heard others on your team talking about components, services and layers rather than objects when they're having discussions. Take a look at the codebase though. Can you clearly see these abstractions or does the code reflect some other structure? If so, why is there no clear mapping between the architecture and the code? Why do those architecture diagrams that you have on the wall say one thing whereas your code says another? In fact, why is it so hard to automatically generate a decent architecture diagram from an existing codebase? Join us to explore this topic further.

Software Architecture Conference 2015

You can register with code FRIEND20 for a discount. See you there!

Categories: Architecture

The Great Leadership Quotes Collection Revamped

A while back I put together a comprehensive collection of leadership quotes.   It’s a combination of the wisdom of the ages + modern sages.   It was time for a revamp.  Here it is:

The Great Leadership Quotes Collection

It's a serious collection of leadership quotes and includes lessons from the likes of John Maxwell, Jim Rohn, Lao Tzu, Ralph Waldo Emerson, and more.

Leadership is Influence

John Maxwell said it best when he defined leadership as influence.  Tom Peters added a powerful twist to leadership when he said that leadership is not about creating followers—it’s about creating more leaders.

I like to think of leadership in terms of incremental spheres of influence starting with personal or self-leadership, followed by team leadership, followed by organizational leadership, etc.   Effectively, you can expand your sphere of influence, but none of it really works, if you can’t lead yourself first.

Leadership is Multi-Faceted (Just Like You)

I also like to think about the various aspects of leadership, such as Courage, Challenges, Character, Communication, Connection, Conviction, Credibility, Encouragement, Failure, Fear, Heart, Influence, Inspiration, Learning, Self-Leadership, Servant-Leadership, Teamwork, and Vision.  As such, I’ve used these categories to help put the leadership quotes into a meaningful collection with simple themes.

I’ve also included special sections on What is Leadership, Leadership Defined, and Leading by Example. 

Sometimes the Source is More Interesting than the Punch line

While I haven’t counted the leadership quotes, there are a lot.   But they are well-organized and easy to scan.   You’ll notice how the names of famous people that said the leadership quote will pop out at you.  I bolded the names for extra impact and to help you quickly jump to interesting people, to see what they have to say about the art and science of leadership.

I bet you can find at least three leadership quotes that you can use on a daily basis to think a little better, feel a little better, or do a little better.

Leadership is Everyone’s Job

For those of you that think that leadership is something that other people do, or something that gets done to you, or that leadership is a position, I’ll share the words of John Maxwell on this topic:

“A great leader’s courage to fulfill his vision comes from passion, not position.” —  John Maxwell

In fact, if you’ve never seen it before or need a quick reminder that everyone is a leader, this is a great video that makes the point hit home:

Everyone is a Leader

It’s one of those cool, simple, cartoon videos that shows how leadership is everyone’s job and that without that philosophy, people, systems, organizations, etc. all fail.

The world moves too fast and things change too much to wait for somebody at the top to tell you what to do.   The closer you are to where the action is, the more context you have, and the more insight you can use to make better decisions and get better results.

Leadership is a body of principles, patterns, and practices that you can use to empower yourself, and others, with skill.

Just like a Jedi, your force gets stronger the more you use it.

If You Want to Grow Your Leadership, Then Give Your Power Away

But always remember the surprise about leadership – the more you give your power away, the more power that comes back to you.

It’s not Karma.  It’s caring.  And it’s contagious.

(As Brian Tracy would say, the three C’s of leadership are Consideration,Caring,and Courtesy.)

Well, maybe it is like Karma in that what goes around, comes around, and leadership amplifies when you share it with people and help everyone become all that they are capable of.

Stand strong when tested, and lead yourself from the inside out.

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Categories: Architecture, Programming

Happiness Quotes Revamped

I've completely overhauled my collection of happiness quotes.  It was time for a revamp.  Hear what Charlie Brown, Dale Carnegie, Aristotle, Confucius, and more have to say about the art and skill of happiness.

Here is the new collection of happiness quotes fresh from the press:

Happiness Quotes (Sources of Insight)

It should be a lot easier to read and a lot easier to use.   And it’s exhaustive, so you’ll most likely find at lead a few quotes you’ve never heard before.   While I have some general quotes on happiness, I also organized my happiness quotes collection into key themes:  What is Happiness, Skilled Happiness, and the Pursuit of Happiness.

Why Happiness Quotes?

Well, life happens.  And life has it’s ups and downs, whether you’re an executive, an IT leader, a Program Manager, a developer, or you name it.

As the world changes faster around us, our thoughts shape our work and life.

When people aren’t happy, they don’t work well.  You can argue it’s the job, but a study in the Journal of Occupational and Organizational Psychology showed that it’s the other way around:

Happiness leads to job satisfaction

They found that the causal relationship between subjective-well being to job satisfaction was stronger than the causal relationship from job satisfaction to subjective well-being.

It helps explain why one person’s treasure is another’s trash.

The HOW of Happiness

If you arm yourself with a great set of happiness quotes, you’ll find ways think better.  Some of the quotes are actually profound wisdom distilled into a sentence.   Some are just entertaining.

Sometimes it just helps to validate your thinking.  For example, are you worried that every time you start to feel happy that something will go wrong?   You are not alone.  Charlie Brown feels the same way:

"I think I’m afraid to be happy, because whenever I get too happy, something bad always happens."Charlie Brown

I think you’ll find a great deal of wisdom within the quotes and they will help you find your own personal HOW of happiness.  While happiness can be a team sport, it’s really an individual skill that you learn to master over a lifetime.   While you may not end up as a shiny, happy Pollyanna, you can find ways to raise your frustration tolerance, enjoy more of your moments, and bounce back faster when life throws you the proverbial curve ball.

The Secret of Happiness

While some might say that the secret of happiness is finding your one thing, I think it’s more than that.  As a mentor at Microsoft, I coach a lot of individuals on personal effectiveness, and a big part of it, is figuring out how to do what makes you come alive.

But there’s actually more to the story.

The ultimate way to find your happiness is to treat happiness like a verb, right here and right now, and find ways to spend more time in your values, while in the service of others or some greater good.   And, as Zig Ziglar would say, “Enjoy the price of success.”

That’s fulfillment.

And, that my friends is deep happiness.

Dig deeper.

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