Growing New Leaders: A Modest Proposal

I've often thought that the startup industry has a leadership problem. It has a leadership problem thanks to its virtues: small companies, agile processes, minimum viable products. The dirty, tiring work of people leadership is often at odds with the hustle of startup life. And yet we as an industry need to make space for that work, because we're starving for leaders and hurting our companies due to this lack of leadership.

Part of the problem is the myth of the natural leader. We'll promote from within, just take the smartest engineer that is able to communicate adequately and make them team lead. But there is very little that is natural about team management. Management is more than just being articulate in meetings and giving a good presentation now and again. It's learning how to communicate with stakeholders in their language. It's figuring out how to resolve conflicts between personalities on your team, even when one of the personalities is your own. It's the detail work of project planning for two people over the next month and twenty people over the next year. Heck, people go to business school and spend a good chunk of time studying these issues. We often disparage the MBA, but we do very little to replicate that skill channel in our own industry.

How does one end up with these abilities? Well, some read books and try to muddle their way through, with varying degrees of success. An ambitious person might reach out to external parties for help, people they know have been there and can provide advice. But both of these channels pale in comparison to learning leadership in an apprenticeship model. I know because I myself have tried books and advisers, but in the end I've learned most of my leadership skills thanks to hours of one-on-one time with current and former bosses, walking through presentations and plans, discussing personnel issues, perfecting pitches. I'm still learning and growing these skills, having moved from mentoring one or two people, to managing a small team, to working as a director of engineering and technical architect for a growing company. And now it is time for me to grow my own leaders. I've written before about the importance of training your replacement, I wish that as an industry we would all take this step seriously.

Here's my proposal. Let's stop promoting people to the role of senior engineer who have never had the experience of hands-on direction of people and projects. If someone has five to ten years of experience writing software in teams, and they have never had the responsibility of mentoring junior developers in a meaningful way, they are lacking something in their skill set. Let's give them the tools they need to be successful as leaders, taking time from the hustle to teach planning, managing, communicating across teams. This experience might just be a short stopover from individual contributor position to more senior individual contributor position, but it's a necessary part of that career path. If we could get a standard across our industry that everyone that was called senior developer had this experience in mentoring and management, we would have something to work on. It would be very unlikely for a company of 20 people to have no one with prior leadership experience. We could successfully grow our leaders from within knowing that we have a small core of people that has already learned the fundamentals of management.

This training isn't free. It will cost us time that we might think should be spent towards the hustle of delivering. But we know that software is humans, and we know that the payoff for delivering is bigger teams, bigger companies, and more responsibilities to manage. If the title of senior engineer recognizes that you can deliver, it should also recognize that you can handle what happens when you do.

The Siren Songs of Hack Day Projects

I love hack days. I think spending a day every now and then to just work on whatever inspires you is one of the best trends of the last five years. Whether you use it to try out a new idea, fix a problem that's been bothering you for months, or play with a new tool, it's a great use of engineering time.

However, we can't forget the seductive illusion of the hack. When you have one day to make something, and you decide to make something new, you make it however you can. You don't write tests (unless perhaps you are the most enlightened TDDer). You don't worry about edge cases. You don't plan for scale. You're playing! You're having fun! You're creating something beautiful and impressive that can bring new frontiers to your business!

What's the downside? A good hack day produces ideas and projects that you want to put into production. But when the time comes to actually take those projects and make them production-worthy it's often quite a letdown. At Rent the Runway we've seen this happen twice over our two hack days. On the first, the whole team worked on a project to rebuild "Rent the Runway" on a completely new platform. The story goes that they got 80% of the way through in one 24 hour binge. 80%! This incredible feat actually lead us to say that we would move the entire site off of our existing platform within the next 3-4 months. Heck, if we could get 80% of the site rewritten by the whole team working for a day, surely we could get the site rewritten completely in a few months.

I'm a bit embarrassed to admit that we all bought into the hype of the 80% completion for longer than we should've. In reality, the 80% completion was more like "80% of basic functionality completed", which is still very impressive but ignores the 50% of required functionality that goes well beyond basic. A 3-4 month rewrite timeline might be realistic if you could take the entire engineering team, build no new features, and eventually ship something similar but not quite the same. Of course, none of these things are ever true.

The most recent hack day had some of our engineers build a very cool project utilizing our reviews data. So cool, in fact, that our product team latched on to it and decided to make it into a full-fledged major launch. Success! And now... we're just finishing a couple of intense weeks of project planning for what is quickly turning into a man-year's worth of an engineering project. It will be cool when it launches, but I don't think any of us expected this cool thing that took a day to hack to turn into 4 engineers working for three full months just to get a polished v1.

The hack day siren song can also cause you to fall in love with your hack so much you don't see the cost to making it production-ready. I've known of engineers that went off on their own and neglected their other duties for projects that would never see the light of production due to their complexity and questionable business value. It's hard to complain when people are working on something they're passionate about, but it's important for a team to feel like they are all working toward the same goal, and that can be difficult when some members are spending most of their time tinkering with side projects.

I'm jaded and boring, so my most recent hack day project took some images that were stored in the database and put them on the CDN. It took me longer than anticipated, but I wrote tests and made it work in both production and staging and released it that day. (My expected 2 hour hack still took me closer to 6 hours end to end.) It also enabled other hack day projects, which was really the point. At the end of the day, I'm glad most of the engineers I work with use their hack days to shoot for the moon, even if the final results always take longer to achieve than we hope and maybe spill over past that one day. After all, what's the point in fast-loading images if no one ever views them?

On Yaks and Hacks

A Yak Story

Today I released a nice little feature into production that tests promoting a customer's "hearted" items to the top of their search results. I decided that it would be a good test after seeing some analytics data on users of this particular feature, and sold it to the head of analytics and myself as a quick feature to spin up, half a day's dev work for me. We already had the data, I just needed to play with the ordering of the products to display.

Of course, it didn't turn out to be quite that simple. When I opened the code and prepared to add the new feature, I discovered a few things. First, my clean checkout of the code base wouldn't even compile. Apparently in the time I had been away from it, the other devs had renamed a maven dependency, rtr_infra_common, to rtr_services_common, only to discover that they needed both. Instead of starting the new rtr_infra_common from, say, version 2, they started it back at version 1.0. My local maven was very, very confused. 

Having figured that out, I finally had a clean, compiling workspace, so time to code. I have all the data I need to do this right here, right? Except, this "score" field that claims to be in the DTO for our products is not in fact being serialized by the providing service. Oh and in my absence the package that holds that DTO has also gotten refactored into its own project. And rtr_services_common depends on that. And rtr_infra_common depends on THAT. And I depend on  rtr_infra_common. So just to get the new data, I have to update the product catalog service, the DTO, and the whole chain of dependent projects.

As an aside, I think maven is kind of nice, but dependency management systems have caused the biggest yaks I've ever shaved, and those couple of hours of annoyance are a small drop in the bucket. 

So, got my data. But now I find another problem. The framework I'm working in was designed to take all of this data and apply experiments to it before returning it to the front end. But we hadn't actually built out a notion of the "user" beyond a userId for logging and bucketing purposes. There was nowhere to put the personalized data I needed to use to enhance the results. I could just save the list of items I needed and add an additional argument to the experiment processor with that data, but I knew that we would want more and different signals in the near future. So I took another couple of hours, refactored all of the code to properly model a user, changed and enhanced all the tests, and finally, finished the job... Except it didn't work. I had forgotten to update the clone method in our DTO to copy the new data I had added. My colleague stepped in for that last bit of belly shaving, as I had now blown my estimate by a good day and my other work was piling up. After going through the irritation of changing 4 projects for 1 line of code, he understood my earlier cursing, and we had our feature finally ready.

A Hack Story

On Wednesday, I get a report that there's a snag in a release that is supposed to go out this week. We're taking some work that used to be done manually by our email marketing staff and automating it. The developer on the project has hit a bug. Apparently, our email service provider does not support UTF-8 encoding, and of course, being a fashion company, we have designer names with the occasional accent that we need to throw around. He's been struggling with this, trying to figure out why escaping isn't working, and finally discovering that only Latin-1 will suffice. Before he sits down to write this re-encoding of everything, we ask our email marketers how they did this in the past. "Oh, we just change them to plain text characters". How many characters are we talking about? "Just an é and a ë". Would it be easier to just convert them to plain text than to Latin-1? Yes. And so we did.

Yaks vs Hacks

I don't like hacks, but they can be expedient. Instead of spending time going back and forth with our ESP we will get this manual email automated and out the door, and worry about our text encoding problems later. Shaving this yak buys us nothing at this point. 

On the other hand, I thought my new feature would be a quick hack that could give us a bunch of interesting data, and it turned into a minor yak (or perhaps just a shaggy water buffalo) that took a bit too long but was not quite a full-blown project. I couldn't hack the dependency problems, and hacking the code restructuring would have resulted in threading an arbitrary parameter through a bunch of unrelated code just to avoid thinking about the right solution. The upside of the yak shaving was more data, a better code base for making such changes in the future, and a recognition of a serious dependency structure problem that we need to address. So today I celebrate Yak Shaving Day. I hope it only comes once a year!

Moneyball

The first thing they always do is quiz you. When big tech companies look for employee prospects, the first test is always basic knowledge. The Googles of the world have a checklist. "Coverage Areas" is what they call the talents they're checking for in a candidate. The big three are Coding, Algorithms and Analysis, Systems Design. Maybe they will also care about Communication skills, and Cultural Fit. They put you through the paces. Can she code? Does she know her Big-O analysis, her data structures? Can she design a distributed system to mimic Twitter? Tick, tick, tick. Pass enough of the checkboxes, and they'll make an offer. Nice salary, great benefits, the chance to work on big problems with these intimidatingly smart folks. When it comes to the deep pockets and well-oiled recruiting machine of a big tech company, it can be hard for a startup to compete for talent. 

I was thinking about this a lot as I watched Moneyball last night. It got me thinking. I wish there was a sabermetrics for developers. After all, what is a startup but a cash-strapped team trying to win against the Yankees? This is an imperfect analogy, but let me break it down.

First a step back for those of you that haven't watched the movie and don't give a flip about baseball. Sabermetrics is basically a system that tries to predict, through past performance, how well a player will do in the future on certain metrics. In contrast to tests of basic skills (running, throwing, fielding, hitting, power) that talent scouts look for, sabermetrics focuses on the statistics of a player's actual game performance. Instead of going broke on a couple of big-name players, as many teams would, or gambling on hyped up fresh "potential", the Oakland As used this system to draft cheap players that together ended up forming a competitive team against the A-list roster of the New York Yankees.

The thing about baseball that makes it a useful analogy for many startups is that baseball is a team sport where the size of the team is fixed and the potential of the team in aggregate matters. The same is true for a tech team. We see so many stories about how you should only hire "A" players, that they attract other A players, and they out-perform other developers 10-1 (or 50-1 or 100-1 depending on how grandiose you're feeling). But how do you find these A players? The Derek Jeters are probably settled, and cost more than you can afford. Google and its kin have deep enough pockets to hire anyone that ticks off all the "Coverage Areas", know that they will get lucky and find some natural As in that bunch, train up some more, and the rest will be good enough to take on the mountains of other work needed at such a huge company.

The thing that Google intentionally neglects, and the thing you absolutely can't afford to ignore, is past performance in the game. What is the tech equivalent of on-base percentage? Product shipped. This is why everyone drools over open source developers as hiring potential. You can see their work, you can know that they've shipped. Teasing this out of others is a tough process. We don't have visibility into the work that most people have done. Writing code for you proves that they have the tools to deliver, and is an essential part of the interview process, but it's not enough to show that they actually have delivered. All we can do is ask them, as a detailed part of their interview, what they have done. 

I realize that relying on reports of past experience as part of the hiring process is fraught. I would never suggest you neglect technical vetting in favor of experience. It's easy to get burned by someone whose personality you like and who sounds smart, only to find out they don't have the basic skills to succeed. And even with the best interviewers in the world, it's always possible that you will still hire someone that chokes at the plate. In the case where you find yourself with a player that isn't shipping, you have to take to heart the other thing that you can do more easily than Google: get rid of underperformers. It sucks, and no one enjoys letting people go, but a startup cannot afford to keep people that aren't delivering.

Will this result in your team being full of A-list all stars? Maybe. Or you may find yourself with a team where few people are exceptional standouts, but everyone delivers. When everyone in your roster can reliably get on base, you're winning. You're shipping. And that's what the game is about.

Code Reviews, Code Stories

Code reviews are a tool that incites a lot of strong emotions from developers. Those who love them write tweets like 

Code review is hard - both reviewing and being reviewed make people cranky. But I know of no better way to make great software. -- @yminsky

This tweet, even in its adoration, still captures some of what people hate about code reviews. They make people cranky, to put it mildly. I personally don't like code reviews very much. In my experience, they are just as likely used to bully, score points, or waste time on pedantic style notes as they are to produce great software. In fact, I think code reviews are absolutely necessary only in three kinds of situations:

1) You don't have good automated testing practices in place to catch most errors at or before checkin.
2) You work on a big project with a very geographically distant team of developers (most open source).
3) You work in a company of many developers and huge code bases where strict style conformity is very important.

In my company, we have one team that falls into slot number 1, and they do code reviews for all checkins. The rest of the team does ad-hoc code reviews and occasional code walkthroughs, and we rely on the heavy use of testing to ensure quality. In general I feel that this enables a good mix of code correctness and checkin velocity.

The one thing this misses, though, is the required opportunity for developers to learn from each other. I like pair programming, and encourage my developers to do it when they are first starting projects or figuring out tricky bugs. But a lot of the time my developers are working in parallel on different code bases, and I felt that we were missing that learning moment that code reviews and pairing can provide. So I started asking people in interviews how they encouraged their team to work together and learn from each other, and I got a great suggestion: encourage the developers to show off work that they're proud of to other team members. Thus the Power Hour was born.

"Power Hour" is an hour that we do every Friday morning with my team and our sister team of warehouse developers. In that hour every developer pairs up with someone else and they take turns sharing something they did that week. Show off something you're proud of, ask for help with a problem you're stuck on, or get feedback on the way something was or is being designed. The power and choice of what to show lies in the hands of the person showing it, which takes away the punitive nature of code reviews; all they are required to do is to share some code. Show off, get help/feedback, and then switch. It's not about code review, that line-by-line analysis and search for errors big and small. It's about code stories, whether they are war stories, success stories, or stories still being written.

There are a lot of features of code review that this Power Hour doesn't satisfy. It's not a quiet opportunity to evaluate someone else's code without them looking over your shoulder. It's driven by the person that wrote the code and they are encouraged to highlight the good parts and maybe even completely bypass the ugly parts. It's not possible for the other person to fully judge the quality of what they're seeing, because judging isn't the point at all. Power Hour is a learning and sharing opportunity, and it is explicitly not an occasion for judging.

This has turned out to be a smashing success. It's amazing how much you can teach and learn in a concentrated hour of working with another person. Last week I showed off a new Redis-based caching implementation I wrote, and got to see all the code and a demo of a new Shiro authentication and entitlement system. I caught a few things I still needed to clean up in the process of explaining my work, and so did my partner. If you, like me, shun formal code reviews, I encourage you to try out a weekly turn at code stories. It's a great way to build up teamwork, encourage excellence, and produce great software, without the crankiness.

There Is No Number

Is it time to quit your longtime job? It's pretty easy to tell. Have you gone out and interviewed? Gotten job offers? Seriously considered them? Then you should quit. It's that simple. Not quitting now is delaying the inevitable and robbing yourself of the opportunity to grow.

I've seen this time and again, and gone through it myself. It's hard to leave a longtime job, especially when you are treated holistically well. Great perks, good pay, a big network, lots of friends. You may hate how things are done but you know how to get them done. It's scary to think about leaving that comfort zone. And when you bring up the possibility of leaving, either speculatively or actively trying to resign, it's likely that your boss will promise you up and down that they will move mountains for you. A new project! More responsibility! Leadership opportunities! Your pick of teams! Maybe even the biggest enticement out there: more money.

Take a lesson from Peggy Olsen. For those Mad Men fans out there, you know that Don will never change. What made him a great boss for her, a mentor, a leader, was also keeping her boxed in. He wasn't changing, and she needed to grow. Your Don could be your actual manager, but more likely it's the company culture and dynamics that are beyond the ability for one person to change. Be real: will more money make you excited to get out of bed in the morning? Will it make you feel like your contributions are truly important? Will it make you feel like you're actually growing and learning new things?



Sometimes, there is no number.

If that isn't enough to get you moving, here's one more the thing about leaving a long-time job, especially one at a big company: if you are leaving on good terms, there's a very good chance you can go back if things don't work out. A friend of mine recently did just that. After leaving her job at a larger tech company due to malaise with the work and a bad fit with her team, she went to a very small startup to do mobile development in a related genre. When she realized that the startup was just not managed well enough to succeed she reached out to her old employer and was hired back, happier than ever having both learned some new tricks and gotten into a new team.

Throw back your drink, and do the deed. It will probably feel like breaking up with a long-term partner, and it may be one of the most stressful things you've ever done, but it's also a good bet that you will end up better off in the long run. If nothing else, when the deed is done, I bet you will leave with a smile.

Corporate culture: A lesson in unintended consequences

It's been a little more than six months since I left finance to join Rent the Runway. Upon hearing that I moved from a big investment bank to a small startup, many people comment on how different it must be. And it is, but in ways than you might not expect.

The day-to-day aspects of the job are not that different. On the technical front, I'm designing SOA-structured systems, thinking about building a messaging-based data bus, worrying about how to model business workflow into software and making sure we can process everything we need to process in time. I have more freedom to choose the technologies I want to use but I'm also more constrained by limited resources to manage the complexity of additional systems. The giant internal infrastructural support organization I had for hardware and level one support is replaced by a giant cloud vendor that does pretty much the same thing but with crappier hardware and faster turnaround. The people-related work is mostly unchanged. I still use the skills I learned for building consensus across teams, coaching people in their careers, and managing up, down, and laterally. I run plenty of meetings, make plenty of schedules, and continue my struggle balancing management and organization with architecture and coding. My schedule starts and ends slightly later in the day, but even my hours are not much different.

Unlike some that have left finance, I don't hold any major grudges against it. I learned a lot in my six plus years working there, about technology, leadership, and working with people. So given that the work is similar, why leave the better paycheck and general stability for the uncertainty of startup life? I'm much happier now than I was in my last years in finance, and the reason for that is not technical, but cultural. When I started in finance, the culture was surprisingly startup-like. It was more formal, but each team was dedicated to the business they served and we were able to work fairly autonomously in support of that business. Over time, though, the culture changed to become more centralized and thus bureaucratic. Instead of picking the right thing for your business area, you had to get approval from people that had nothing but a vested interest in seeing their pet technology promoted in order to get themselves promoted. That of course led to politically-driven technology which is an absolute nightmare for a person that cares deeply about building great systems.

Ironically, I believe this happened at least partly because the company was concerned with building a formal technical career path in order to promote and retain technical talent. At first, this resulted in senior technical people getting much-deserved recognition and it was great. I wanted nothing more than to reach that highest level myself. But soon the highest-level title ("technology fellow") had been awarded to the backlog of people that truly deserved it, and it became something that was expected to be awarded to more people every year; each group needed to have one, and it was a cheap way to recognize and retain people in years when salaries were stagnant since it didn't necessarily confer any increase in compensation. This growing group of people, many qualified by deep work in one small area, were now expected to help guide the technology decisions of the whole company. And so a bureaucracy was born. Decisions that would have been made via influence within a group and business area started to be dictated by committees far away from that group. The fellows started, unintentionally, to squash creativity of those below them on that same technical track they were supposed to be growing.

As bad as that was, in my opinion the biggest problem with this role was that it was not equivalent to the same level title for managers, "Managing Director". Because the role of fellow became more of an honorary recognition than an actual promotion, it came to pass that the savviest fellows would also get themselves promoted to Managing Director (MD), and thus become first among equals. No longer did the mere tech fellow have the strongest voice in the technical decision making; everyone started striving to become more than just a fellow, and in order to get that MD promotion they needed the backing of the other tech fellows that were also Managing Directors. As we all know, the best technologists are not necessarily the best politicians and decisions started being guided more and more by those better at politics than technology. In this way, an effort to promote and retain technologists ended up building a technical culture of decision making driven by politics and bureaucracy.

At the end of the day, finance is an industry that cares first about money (duh), and secondly about power, and that is true for every successful person that works there in any capacity. Technical qualifications will only ever be rewarded as they make or save money. If you don't find finance fascinating, or care about money above all other things, this cultural friction may grind you down. I've discovered the truth in the wisdom that you enjoy your job more when you're working on a product that you use and enjoy. Rent the Runway has a different goal than my old investment bank; yes, we want to make money, but we want to make it by delivering a great experience to our customer, an experience that I participate in and really love. And that is a goal that I know I can build a great technical culture around.

War Stories: Guava, Ehcache, Garbage Collection

We're in the process of moving all of our major business logic out of our clunky Drupal frontend to Java backend services, and we took another big step down that road this week by moving all of the logic for filtering of our product grids to our new integration service. This release was the culmination of months of work and planning that started at the beginning of the year, and it gets us over a major functionality hump. The results are looking good, we've saved almost 3s average page load time for this feature. Yes, that's right, three seconds per page load.

As you may guess from the title of this post, the release was not entirely smooth for our infrastructure team. The functionality got out successfully, but two hours after we released we started noticing slowness on the pages, and a quick audit showed frequent full GCs on the services. Some rogue caching was being exercised much more than we had seen during load testing. After some scrambling, we resized the machines and restarted the VMs with more memory. Fortunately the cache would only get so big, and we could quickly throw more memory onto the machines (thank the cloud!). Crisis averted, we set to fixing the caching so that we wouldn't hit slow FGCs.

The fix seemed fairly straightforward; take the cache, which was originally caching parameters mapped to objects, and instead just cache the object primary ids. So the project lead coded up the fix, and we pushed it out. 

Here's the fix. Notice anything wrong? I didn't. We're big fans of Guava and use List transformers all over the place in our code base. So we load test that again, and it looks ok for what our load tests are minimally worth, so we push it onto one of our prod boxes and give it a spin.

At first, it seemed just fine. It hummed along, seeming to take less memory, but slowly but surely the heap grew and grew, and garbage collected more and more. We took it out of the load balancer, forced a full GC, and it still had over 600m of active heap memory. What was going on?

I finally took a heap dump and put the damned thing into MAT. Squinting at it sideways showed me that the memory was being held by Ehcache. No big surprise, we knew we were caching things. But why, then, was it so big? After digging into the references via one of the worst user interfaces known to man, I finally got to the bottom of an element, and saw something strange. Instead of the cache element containing a string key and a list of strings as the value, it contained some other object. And inside that object was another list, and a reference to something called "function", that pointed to our base class. 

As it turns out, Lists.transform is a lazy function. Instead of applying the transformer to the list immediately and returning the results, you get back an object that acts like a list but only applies the transform on the objects as you retrieve them the first time. Which is great for saving a bit of time up front, but absolutely terrible if you're caching the result to save yourself memory. Now, to be fair, Guava tells you that this is lazy in the javadoc:

But not until you get to the third part of the doc, and we are even lazier than Guava in our evaluation. So, instead of caching the list as it is returned from Lists.transform, we call Lists.newArrayList on the result and cache that. Finally, problem solved.

The best part of this exercise was teaching other developers and our ops folks about the JVM monitoring tools I've mentioned before; without jstat -gc and jmap I would have been hard-pressed to diagnose and fix this problem as quickly as I did. Now at least one other member of my team understands some of the fundamentals of the garbage collector, and we've learned a hard lesson about Guava and caching that we won't soon forget.

Hammers and Nails: Managing Complexity

I've been thinking a lot about complexity lately. As a systems developer at heart, I love complexity. I love complex tools that have enormous power when wielded properly. In my last position I designed systems to provide infrastructure software services to thousands of developers and systems running around the globe, and I enjoyed the process of finding the best tools for that job no matter how esoteric. I prided myself in looking beyond good enough, and was rewarded for building things that would last for years, even if they took years to build.

Now that I work at a small startup, I have begun to view complexity in a very different way. I'm rebuilding critical business infrastructure with at most a couple of developers per project and an ops team of two for all of our systems. In this scenario, while we have the freedom to choose whatever stack we want to use, for every component we choose we have to weigh complexity and power against the cost of developer time and operational overhead.

Take Play as an example of a framework whose simplicity was a major selling point. While we ended up scrapping it (and I would advise against using the 1.X branch in production), I do not think that trying it in the first place was a bad idea given what we knew at the time. Using Play, all of our developers were able to get projects created, working against the data stores, and tested in very little time. I still have developers ask me why they can't just start up new projects in Play instead of having to to use one of the other Java frameworks that we've moved forward with, even though the new frameworks have relatively little more complexity. I love Dropwizard to death, and find it to be a good replacement for Play, but it doesn't support JPA out of the box yet, it requires just a bit more thought to get a new project up and running, and even this minimal additional complexity is enough to slow everyone down a noticeable amount on new projects. Every bit of thought we have to put out there is mental overhead that takes away from delivery velocity.

Another painful example of unexpected complexity happened this past week. We are moving our stack towards a service-oriented (SOA) model, and as part of that move we have load balancers set up in front of our various services. These load balancers are provided by our cloud hardware vendor, and have a hard limit of 30s per request. Any request that runs longer than 30s will be killed by the load balancer and send an unspecified 500 to our storefront. We have a best practice that our staging environment should be run in the same way as our production environment, but when it comes to setting up load balancers we often forget about this policy, and we released a new major service migration that called some very heavyweight db queries now behind a layer of load balancers. So, we do the release, and immediately test the most intensive requests that could be run. Some of these requests fail, with "unexpected" 500s. Surprise surprise, we forgot about the load balancer, and not only that, but everyone that was doing the release forgot about the sniping and was mystified by the behavior. The load balancer is just a tiny bit of added complexity, but it was enough to scuttle a release and waste hours of development time.

All this thinking about complexity has come to a head lately as I have been pondering our current storage platforms and the choices I have made in what to use. I have chosen, in this case, to minimize upfront complexity, and I've chosen to go with MongoDB. Moreover, I believe that, of all the NoSQL stores that I have personal experience with out there (HBase, Cassandra, MongoDB), MongoDB is the mostly likely to be long-term, widely successful. Why? It's not the most powerful solution, it's probably not the most performant solution, and it currently has some quirks that have caused many people grief. But the CTO of 10Gen, Eliot Horowitz, is laser-focused on creating a system that is easy for developers to use and reason about. His philosophy is that developer time is the biggest fixed cost in most organizations, and I think from small startups to big companies that is generally true. Why do most companies use SQL-based RDBMS systems? They often have some serious limitations and challenges around scalability, and yet they are the first thing most people turn to when looking for a data storage solution. But you can pick almost any developer up off the street and they will be able to find tools to be productive in a SQL-based environment, you can hire an ops person to maintain it, and you can find answers to all your questions easily without having to fully understand the implications of the CAP theorem. And so it goes with Mongo.

You can manage your developer dollar spend in lots of ways. If you are a big company, you can afford to hire a small, dedicated team to manage certain elements of complexity. You can simply try to hire only the absolute best developers, pay them top dollar, and expect them to learn whatever you throw at them. And you can do your best to architect systems that balance complexity and power tradeoffs with developer complexity overhead. The last is not easy to do. Simplicity often comes with a hidden price tag (as we found with Play 1.X), and it's hard to know when you're buying in to hype or speeding towards a brick wall. Of course, not every problem is a nail. But, as a startup, you probably can't outspend your problems, so before you buy the perfect tool to solve your next issue, make sure you can't at least stun it for a while with a good whack.

Process Debt and Team Scalability

Most of us in tech spend time thinking about technical debt. Whether it's a short-term loan or a massive mortgage, we've all seen the benefits and costs of managing and planning a code base with technical debt, and we are generally familiar with ways to identify, measure and eliminate this debt.

What about process debt? Process debt is a lot like technical debt, in that it can be a hack or a lack. The lack of process as debt is pretty easy to see. For example, never bothering to create an automated continuous build for your project. That's a fine piece of process to avoid when you have only one or two developers working on a code base, but as your team grows, your lack of process will start to take its toll in broken code and wasted developer time.

There are also plenty of ugly process hacks. Take for example the problem of a team of varying skill levels, and a code base that is increasingly polluted by poorly formatted, totally unreadable code. When faced with this problem you may be tempted to institute a rule where every line of code must be reviewed by a teammate. Later you discover that the two people with the worst style are reviewing each other, and things still aren't improving enough. So you declare that everyone must have their code reviewed by one of the senior developers that you have appointed. Now you have better style, but at the cost of everyone's productivity, especially your senior developers.

Adding code reviews was the easiest path to take. You just hacked in some process instead of paying up front to think about what the best process would be. But process, once added, is hard to remove. This is especially true for process intended to alleviate risk. Maybe you realize after a few months that the folks with questionable style have learned the ropes, and you remove the rule. But what if you added code reviews not to fix a style problem, but to act as a risk mitigant? I've done this myself in the case of a code base with very little automated testing and a string of bad releases. I'm now paying for my technical debt (a lack of automated testing) by taking out a loan on process.

An easy way to identify process debt is to ask yourself the following question: Will this solution still work when I have twice as many developers? What about ten times as many? A hundred? Process debt, like technical debt, makes scaling very difficult. If every decision requires several meetings and a sign-off, if every git pull is a roll of the dice for your continued productivity, or if you have to hire three project managers for every five developers just to keep track of the task list, you're drowning yourself in process debt.

Developers are wary of process because we too often experience process debt of the hack sort. I believe that both a lack of process and an excess of process can cause problems, but if we begin to look at process debt with the same awareness and calculations that we apply to technical debt, we can refactor our playbook the way we refactor our code and end up with something functional, simple, and maybe even elegant.

Budgeting for Error

What's your uptime SLA over the last month, six months, year? Do you know off the top of your head? Is your kneejerk response, "as close to 100% as possible"? Consider this: by not knowing your true current SLA, you not only turn a blind eye to a critical success metrics for your systems, you also remove the ability to budget within the margins of that metric.

There are about 8765 hours in a year. How many of those hours do you believe your code absolutely needs to be up to keep your business successful? 99% of the time buys you almost 88 hours of downtime over the course of a year. 99.9% of the time still buys you almost 9 hours. Even 99.99% gives you about 52 minutes a year that your systems can be down. Think of what you can do with these minutes. Note that the much-vaunted "5 9s" reliability (99.999%) breaks down to 8 minutes over the course of a year, which is great if you're Google or the phone company, but probably not a smart goal for your average startup.

Let's say you know that your deployment process is rock-solid without outage and you will never need planned hardware downtime due to the way you've architected your systems. But you also know that you have some risky features that you want to push now, before you announce a critical partnership that should result in a big membership bump. If you're sure that the bump won't cause downtime, you might choose to push the features and risk some downtime in smoothing out rough edges on the code so that you have a really compelling site for those new members.

On the other hand, if you know that you're pretty solid under your current load but a 50% increase in usage has the potential for some degree of system failure, your error budget might not accommodate both the risky new features and the membership growth. And if your business pushes you to do both the risky new features and the growth risk? Make sure they know that your SLA may suffer as a consequence. When you know your goal SLA, and you know something is likely to reduce or violate it, that's a strong signal that you should think carefully about the risks of the project. This can also be a useful negotiation tool when being pushed to implement a feature you don't think is ready for prime time. When they say we need to release this new feature today, which means at least two hours of downtime that pushes you out of SLA, it becomes their job to get authorization from the CTO instead of your job to convince them why it is a bad idea.

I will admit that I do not currently have an uptime SLA for my services. Up until recently, it never occurred to me that there would be any value to trying to pin down a number and measure to it. As a result, while liveness and stability is always a consideration, I haven't taken the time to think through the rest of the year when it comes to hardware upgrades, new features, or deployment risk as measured by likely downtime impact. I'm missing out on a key success metric for my infrastructure.

Once I've managed to nail down a course-grained uptime SLA for my systems, the next phase of this work is to nail down a more fine-grained response time SLA. Of 100 requests, what is the 95th percentile response time from my infrastructure services? This is much trickier than a simple uptime SLA due to the interaction of multiple systems each with their own SLAs. For now though, I need to focus on the big picture.

Intuition, Effort, and Debugging Distributed Systems

I recently watched this great talk by Coda Hale, "The Programming Ape". It's heavily influenced by Thinking Fast and Slow, a book about cognitive processes and biases. One of the major points of the book, and the talk, is that we have two types of thinking: intuitive thinking, which is fast, easy, creative, and sloppy, and attention-based thinking, which is harder, but more accurate.

One of the great points that Coda makes in his talk is that most of the ways we do things in software development are very attention-heavy. At the most basic level, writing correct code requires a level of sustained attention that none of us possesses 100% of the time, which is why testing (particularly automated testing) is such an essential part of quality software development. Attention doesn't stop when you get the code into production, you still have the problem of monitoring, which often comes in the form of inscrutable charts or messages that take a lot of thought to parse. Automation helps here, but as anyone that has ever silenced a Nagios alert like a too-early alarm clock knows, the current state of automation has limits when it hits up against our attention.

By far the most attention-straining thing I do on a regular basis is debugging distributed systems. Debugging anything is a very attention-heavy process; even if you have good intuition about where the problem may lie, you still have to read the code, possibly step through it in a debugger or read through a log output and try to find the error. Debugging errors in the interaction between distributed systems is several times more difficult. A debugger is often of no help, at least not initially, because you have to get a series of events to happen in a particular way to trigger the bug. Identifying that series of events in most cases requires staring hard at a series of log files and/or system state dumps, and trying to piece together the ordering based on timestamps that may slightly differ between systems. I consider myself to be a very good debugger and it still took me a solid 4 hours of deep concentration, searching through and replaying transaction logs before I was able to crack through this particular bug. I would never hold the ZooKeeper code base up as a paragon of debugability, but what can we do to make this easier?

When you're writing a distributed system, think hard about what you log. This may be impossible to always get right, but so often the only way you have to find that bug is log files from around the time it happened. If you're going to reconstruct a series of events, you need evidence that those events happened, and you need to know when they happened. Should you rely on the clocks of the machines to line up enough to put the time series together, and should you fail the system if the clocks are too far apart? Since it's a distributed system, is there a way for all of the members of the system to agree on a clock that you could use for logging? As for the events themselves, it is important to be able to easily identify them, their particular behavior, and the state they are associated with (the session that made this request, for example).

One of the problems with ZooKeeper logs, for example, is that they don't do a great job of highlighting important events and state changes. Look at this, does it make your eyes glaze over immediately?


Events are hidden towards the ends of lines, in the middle of output (type:setData, type:create). Important identifiers are held in long hex strings like 0x773516a5076a0000, and it's hard to remember which server/connection they are associated with. To debug problems I have to rely on pen/paper or notepad records of what session id goes with what machine and what the actual series of events was on each of the quorum peers. Very little is scannable and it makes debugging errors a very tedious and attention-heavy process.

Ideally, we want to partially automate debugging. To do this, the logs have to be written in a form that an automated system could parse and reason about. Perhaps we should log everything as JSON. There's a tradeoff though, now a human debugger probably needs another tool to parse the log files at all. This might not be a bad thing. Insisting on basic text for logging leaves out the huge potential win of formatting that can draw the eye to important information in ways other than just text.

Are there tools out there now to aid in distributed debugging? A quick google search shows several scholarly papers and not much else, but I would guess that given the ever-increasing growth of distributed systems we'll see some real products in this area soon. In the meantime, we're stuck with our eyes and our attention, so we might as well think ahead about how we can work with our intuitive systems instead of against them.

Scaling in the Small

Last week I wrote about scaling and some examples of recent scaling successes. That post has been in my mind this week as I've been thinking about what I need to be doing here at Rent the Runway to enable our systems to scale.

Unlike the developers at Instagram or Draw Something, I'm not coming to this problem from a fresh code base with a very small, focused team. Instead, I'm taking a system that has been in production for almost two and a half years and trying to correct a series of short-sighted decisions made over that time, all while supporting a thriving business with more feature requests and ideas than a team of a hundred devs could handle. The problem is in some ways smaller than your typical scaling story: I don't have to handle anywhere near the volume of a popular app. But I can't afford to make changes that would bring the business to a halt for hours, lose orders, or block our warehouse. So we replace the jet engine one bolt at a time, all while flying over the Atlantic Ocean.

Our scaling problems arise mostly from the decision, in the initial website implementation, to create our site in Drupal and have most of the business logic live in PHP code that resides in that Drupal server. This was a bad choice. Drupal is not an e-commerce platform, and using it as such is a nightmare. The business logic, entangled with the view logic, is not easy to test, and every change to that code requires a re-release of the whole site. Drupal forced certain database modeling decisions that are almost impossible to reason about, and the end result of all of this is a rat king of PHP, SQL, and CSS/JS. Even if we could scale our site by throwing money at database servers, we can't scale our feature velocity in such a system.

So the first scaling problem I've been tackling in my time here has not been the ability to handle more users on the site. It's been the ability for our developers to actually deliver more features in a timely manner without nightmarish releases. This effort was started by my boss before I joined, and eight months later, we're really starting to see the fruits of our labor.

Our first attempt was to pull heavy logic behind our reservation system out into a Java service. In the process, we learned exactly how daunting the task ahead of us would be. Have you ever been tempted to say, this won't be too hard to rewrite, only to eat your words? We ate our words several times over in this project. From the migration and reconciliation of old data, to the performance of the new logic, to the integration with our warehouse code, we got hammered over and over. This was a system that had to be replaced all at once, with no opportunity for iterative releases. The new Java service, while not perfect, was fairly straight-forward to write, test, and debug. But the PHP code that had to change to use this service was untested, and we made the fatally stupid mistake of letting it remain so throughout the development process. The project ended up taking several months and almost the entire team to complete.

Learning our lesson from that, we've moved to some pieces of the system that we can replace iteratively: storing more of our product metadata inside of MongoDb and providing a service layer to serve new data while slowly moving existing functionality onto that system. This has been much easier than the first project, launching three new features over three weeks all while improving site performance and sparking a creative revival among the developers. When you have fast and easy access to your data, and you can release whenever you feel good about your testing, you turn your feature scaling from being throttled by your platform to being throttled by your own creativity and developer hours.

So today, I gave our developers a talk about the long-term (9-18 month) goals for our system design. It's straightforward: continue to move business logic out of Drupal, write services that are horizontally scalable, use some judicious vertical scaling where needed. Cache read-only data as much as possible, think about sharding points, plan for more users who stay longer and interact more heavily. And as we think about user growth in addition to feature growth, don't forget to add performance testing to our bag of tricks. It's great to be here, and to recognize the scaling problems we have already conquered to get to this point.

Scaling: It's Not What It Used To Be

We've seen a lot of interesting stories lately about companies scaling massively from just a few users to millions in a very short period of time. Contrary to expectation, some of them weren't even initally designed for scaling. Take, for example, Instagram (a fabulous slide deck, btw). Their first strategy for scaling was vertical: buy a bigger database server. That didn't last too long, and they found themselves doing horizontal scaling (via sharding) after a few months. But instead of writing their own custom database solution, or using one of the many NoSQL options out there, they stuck with Postgres and via a clever virtual sharding strategy combined with Redis and Memcached, they managed to scale.

Then there's Draw Something. While the engineers designed it to scale, they had no idea that it would hit hundreds of drawings per second in its first few weeks. In contrast to Instagram, they ended up rewriting their backend data store in Membase during their growth spurt, but that combined with sharding and adding hardware (both vertically via more RAM and SSDs, and horizontally via more machines) got them through their growth period.

10 years ago, scaling to millions of users would have probably taken a large, smart team with months of advance planning to achieve. As a point of reference, Livejournal had about 1.5 million active users per month in 2005, a very popular site at the time, and memcached was created to handle its load. I'm sure that the developers at Draw Something, Instagram and other popular tech start ups are very talented, but it's more than talent that is allow this kind of unplanned growth to happen. The common points between these two teams are not what I was expecting when I sat down to write this post. So what are they?

1. Redis. I was at a NoSQL meetup last night when someone asked "if you could put a million dollars behind one of the solutions presented here tonight, which one would you choose?" And the answer that one of the participants gave was "None of the above. I would choose Redis. Everyone uses one of these products and Redis."
2. Nginx. Your ops team probably already loves it. It's simple, it scales fabulously, and you don't have to be a programmer to understand how to run it.
3. HAProxy. Because if you're going to have hundreds or thousands of servers, you'd better have good load balancing.
4. Memcached. Redis can act as a cache but using a real caching product for such a purpose is probably a better call.
And finally:
5. Cloud hardware. Imagine trying to grow out to millions of users if you had to buy, install, and admin every piece of hardware you would need to do such a thing.

Redis, Nginx, HAProxy, Memcached. All free open-source software. AWS and other cloud vendors let any of us grab machines to run our software at the touch of a button. The barriers to entry have gone way down, and we've all discovered that scaling, while hard, isn't nearly as much of a monster as we made it out to be. It's truly an exciting time to be a software engineer.

Debug Your Career: Ask for Advice

One of the most powerful relationship-building tools I have found in my career is the simple act of asking for advice. It's also something that I don't see a lot of people take advantage of, especially in tech. In some cases, it's an ego thing. What if they think I'm weak for not knowing, for not figuring this out on my own? Sometimes, it's a desire not to be a bother to the other person. Sometimes it just doesn't occur to you to seek outside help. But when you don't ever ask others for real advice (more than simply, "how does this code work anyway?", or, "what was that cool hack you cooked up the other day?") you leave a lot of potential on the table.

It shows you respect the other person's opinion (and thus, the other person)
We all want to believe that our opinion is important, and when you are actually asked for your opinion it is a token of respect for you as a person. You've heard that people like those that like them back? People tend to respect those that respect them back. After all, you have such good judgement in respecting them!
This really comes to play when you ask people that work for you or are junior to you for their advice, especially when you follow it or use it to open a dialogue on a bigger topic. As a technical leader this will usually come in the form of advice about technology, and let's be honest: none of us has the time to be a true expert in all of the technologies we use in our job. Why would you hoard the decision making on all technical matters? Your employees probably come to you with suggestions and opinions already, but taking care to specifically ask them for their advice is an important way to ensure they know that you respect them and the knowledge they posses.

It makes them a partner in your success
Think about a time when you've helped someone solve a particularly hard bug they were facing. A bug that was meaty and fun and made you think and also made you feel awesome and smart for being involved in solving it. Afterwards, did you feel some ownership stake in the success of that project? This is the feeling you give people when you ask them for their advice, especially when you take it and turn it into success.
Now think about a time when you've hit a bug in someone else's code and they have ignored your attempts to help them debug it, or worse, totally ignored your patches and pull requests. You probably feel a bit more animosity towards the project, and if it fails, perhaps it deserves to fail.
This set of feelings is analogous to proactively going to your manager and asking for advice on how to grown in an area, versus having to be told by your manager that you are failing. In the first scenario, you make your manager a partner in your success, and they feel pride and sense of ownership when you succeed. In the second case, you are at best an area for improvement and a worst a burden or even a write-off. 

It forces you to think about the problem you really have
You shouldn't ask someone for advice without knowing what you're looking for. Just as you're not likely to get much help debugging if all you know is that the program crashes, simply asking someone "Hey, any advice?" will probably get you little better than a cliche like... "ask for advice". Really valuable advice doesn't come from emailing Kevin Systrom and asking how you can get a billion dollars by putting your Hadoop cluster in a Cray-1. It's delivered when you tell your boss you're struggling with the challenge of managing a team and writing code at the same time, and how does he manage to keep an eye on everyone without micromanaging or spending every waking hour watching GitHub and Jira?


Of course, this comes with some stipulations. The act loses its power if you're just doing it for the sake of doing it. You have to be willing to at least consider taking the advice you are given, and you don't want to waste people's time. But if you take the time to truly think about areas where you'd like to improve, and go to people honestly asking for advice on how to do so, you may find that an extra set of eyeballs makes the bugs in your career shallow.

Parallelism and the Limits of Languages

While we make heavy use of the core of Clojure, we don't use its concurrency primitives (atoms, refs, STM, etc.) because a function like pmap doesn't have enough fine grained control for our needs. We opt instead to build our own concurrency abstractions in Clojure on top of the outstanding java.util.concurrent package.

Aria Haghighi, Prismatic 

 Once upon a time, in the days when RAM was not quite as cheap as it is now, I worked on a team that had a giant in-memory cache of data that it used to do various financial calculations. This data had, with the growth of the financial markets it represented, grown to be too big to fit into the memory of a standard server without having GC absolutely wreck the process. Having exhausted all minor efforts to tune the data, and not wanting to buy an actual supercomputer, the team turned to Azul, who at that time sold boxes with enough memory to fit our process and a fairly nifty no-pause GC. Great! Except there was one catch: instead of having the super fast CPUs we were used to, it had hundreds of tiny little slow cores (with rather crappy fpus). And on these hundreds of tiny little cpus our code was very, very slow. So we set out to parallelize.

When you're forced into parallelism at that level of detail, you quickly realize how difficult it is to have a generic way of doing it. The fact that languages like scala and clojure will allow you to just say "apply this logic over the collection in parallel" is very cool, and a great way for developers to dip their toes in parallel processing of their collection data. But it breaks down quickly when you are talking about vastly different types of data running in vastly different types of computation. If you need to eke out every ounce of performance, you want every computation to be tuned to the right batch size and the right number of threads, and the size of the thread pool for task X may depend on whether it tends to run at the same time as task Y or task Z. Don't forget that task Z, if requested, must be prioritized above all else. The size of the batches themselves may depend on the type of object inside them, and how that affects the cache lines of the processor you're running on. Oh, and of course, you're not just tuning this for prod, it has to at least run to completion on your integration boxes and they are of course slightly different processors. And don't forget, your developers aren't writing code on Azul boxes, they're writing code on desktops with 2 cores each; threading even the test data to a pool of 50 doesn't make any sense, but they still need to run things in parallel or they'll never see the stupid threading bugs they added when they forgot to use concurrent data structures.

I've always thought that the next truly big leap in VMs and languages will be those that can do smart things with threading without making developers sweat too much. If we're really entering a world of more and more data over more but slower cores, we need a language and a VM that revolutionize our management of threads the way Java and the JVM have revolutionized our notion of memory management. The functional languages out there help, but you can't forget the underlying VM magic that will really make this possible. Look to the example of memory management: it's not Java itself that enables you to write code without worrying about deleting references only when they are truly not being used, but rather the VM that watches all objects and knows how to find the live ones and delete those that are no longer referenced, all without the developer needing to indicate a thing. For us to conquer the thread problem, we need a VM to observe the behavior of the system beyond just the number of cores and help us to appropriately occupy CPUs over collections of varying size and composition. Languages can force developers to think about applying logic over their data (and thus help with parallelism as a mindset), but they will never solve the problem of automated parallelism without the VM to tune it in the background.

There will always be people that need to tune their code down to exactly what instructions run on what core, just as there are still people that need to do their own memory management, but it shouldn't be a requirement for getting good mileage out of those cores. I can't wait for the day when calling submit feels as archaic as calling malloc.

Yes, and...

I have a terrible habit. When confronted with a new idea, any idea, my knee-jerk instinct is to find its flaws. Or, to put it more succinctly, I'm a "no, wait!" kind of person. You want to add a new feature into my library? No, wait! That won't maintain API backwards-compatibility! No, wait! No one but you really needs that, why should we put it in a library used by everyone? No, wait! The work that will take us to review overwhelms it's worth as a new feature.

There is a well-trod school of thought that says to maintain creative collaboration, anytime you want to say "no, wait!", "no, but" or even just "but", you should instead start the conversation with "Yes, and". Want to add a new feature into my library? Yes, and we will have to think about how to add it without breaking API backwards-compatibility. Yes, and we should see what other community members think about the usefulness of the feature. Yes, and we'll have to evaluate the work that it will take to correctly implement and review.

"Yes, and" is a great baby step on the path to thought leadership and productive collaboration. But let's face it, it's easy to get a bit snarky with our yeses. Yes, and six months later, we'll have another release. Yes, and you can be on hand to fix the bugs that come in. Yes, and why don't we rewrite the whole thing in Scala while we're at it? (Watch Peggy struggle with this on Mad Men[16:50], when Megan shows her work)

So how do you really take "yes, and" to heart? My boss, who is a natural at this sort of thing, taught me one of his favorite tactics. It's not just saying yes. When a new idea is presented, find at least one positive thing to say about it, and praise that quality first.

 "That is an interesting idea. People using our service to do complex state maintenance could really use this feature, it would save them having to write a lot of code themselves."

The goal of this exercise is not only to keep the flow of positive collaboration going. It is also to force yourself to acknowledge the problem that the idea is trying to solve. You may not think the problem is worth solving, but this is something that your collaborator wants to explore, perhaps because it is causing them pain, perhaps because they have a different set of priorities than you, perhaps because they're simply trying to contribute to the discussion.

I will admit that despite knowing this is the right thing to do, I am absolutely terrible at following this advice in person. My knee-jerk nerd comes out and she is very into scoring points by pointing out flaws. Among the tech community, I'm hardly unique. Read the comments on almost any Hacker News thread if you don't believe me. And yet, the easiest place to start with this is online. You have time to think before you hit send on that email, submit on that comment, post on that tweet. Why not take the time to acknowledge the idea fully before pointing out its downsides? Just because you didn't think of it doesn't mean you lose by making it better.

Java console monitoring basics: The "j" series

Think fast: It's 10pm, you have a production java application on a box you can only ssh into, and it's in distress. This is the third time this month it's happened. You didn't write the code, and the joker who did didn't bother to put in any metrics for you to grab. What do you do? After cursing, but before giving up, restarting the process, and promising to debug it in the morning, you might want to go a round with the Java command-line monitoring tools.

Perhaps you are already familiar with these tools, but I've found that despite their incredible usefulness many seasoned Java developers have never heard of this tool stack. They hide away in the jdk bin directory, but they can be your best friend when you are stuck with nothing but a console and a prayer.

First we have the lowly jps. It does what you would expect: shows you java processes running as that user (or all users, if you're root).

Moving up the stack, we have jinfo. Show me the vm version, jars, and all the flags this process is running with, or the value of a particular flag. You may have this information elsewhere, but it's nice to have a shortcut.

More useful is jstack. Yeah, I'm sure you know how to kill -QUIT but this is a nice way to teach the newbie how to get a stack trace without the risk that they'll accidentally kill the process. If you should be so lucky as to have an obvious deadlock, jstack will kindly point that out to you so you can go fix it. Stack traces are the bread and butter of figuring out what's wrong with a process. Take several, see how they change, or don't, and you'll come closer to finding your problem code.

My personal all-time favorite is jstat. More specifically, jstat -gc 3s. That is, jstat of the garbage collector printing new results every 3 seconds. Back in the days when I wrangled gigantic VMs, this tool was invaluable for spot-checking garbage collection. The output is admittedly hideous. For example:

 S0C    S1C    S0U    S1U      EC       EU        OC         OU       PC     PU    YGC     YGCT    FGC    FGCT     GCT
37568.0 41984.0 6592.0  0.0   74816.0  38651.6   70272.0    39290.5   63616.0 55956.0     38    1.608   4      1.441    3.049

Yes, that unformatted barf is what you will see on a screen console output for jstat -gc. But that barf tells you a lot. First things first, the most useful stuff is at the very end. See that "4" followed by "1.441"? That shows the stop-the-world GC collections, and the total time they have taken. If your application is running particularly slow, or is frequently unresponsive, the FGC count will quite possibly be high, and the time related to it will be very high. Remember, your app is essentially dead when FGC is running, so a high number of FGCs is a bad sign.

This also shows you the various used and total sizes of the generations. I'm not going to go into details of Java Garbage Collection but it is useful to be able to see them broken out and growing or shrinking. One pattern of GC to watch out for is the case when you don't have enough survivor/eden space to handle all of the transient data you need to do a big chunk of work, and not enough old capacity to take it all, but just enough freeable state to keep slowly moving forward with your computation. The result will be neverending full-gcs while your process moves slightly forward, does a full GC, moves forward again, does a full GC, and moves forward again. This looks in jstat like an ever-increasing series of FGC where each one finishes but only causes a small amount of space to be freed in OU, while EU and the survivor spaces are constantly near-full. The details of the actual GC behavior may change with versions of the JVM (and the flags that you are using), but the ability to monitor the behavior easily in real-time is always useful.

Finally, for the tools I usually use, there's jmap. jmap -heap is a nice way to get a pretty-print of the heap info. jmap -histo and jmap -dump are heavier commands that you might want to hold off on doing until you're ready to restart your process anyway, because sometimes running them will result in bad results for the process. If you're producing a ton of garbage and you don't know why, jmap can show you where the memory is going. jmap -dump will product a file that you can push into something like MAT for analysis.

None of these tools are an answer for proper monitoring of your JVM, but they're great for a quick and dirty debug before you restart a troubled process, and something to make sure your whole team has in their toolbox.

One Feature in One Month or Ten in Six?

The best story I can tell to illustrate the difference between project planning and technical design at a big company vs a small startup happened today. I was talking to our head of analytics about a new project we are speccing out. This project will inevitably be the beachhead of a huge set of new features and systems that we'll want to write over the coming months. He sees this as an opportunity to start building something that will be able to accomplish all of those features in one multi-faceted system. And he's right, this is definitely a feature that we could easily implement in such a system.

However, it's also a feature that we can implement fairly cheaply using building blocks we already have. The cost difference in engineering this feature is pretty stark; my estimation is that to implement it in what we have takes roughly a month, most of which would be spent getting the presentation layer to look good. To implement it in the larger space would probably take closer to 6. If we implemented the larger system, we would get a lot more than just this feature. We would probably get 10 features out of it, and the ability to really start powering several different things.

I'm still going with the one month solution.

On the surface, this seems like a pretty foolish trade. I know that I'm probably going to build the bigger system sooner or later, I know that I would probably get more value for my time building it now, and not building it now means writing some code that will eventually be thrown away. If I were doing this at a big company, I would always choose to do the 6 month project. In fact, I would probably be busy thinking of all the other things we could do with another 6 months, and building up a plan and design that would emphasize the power and complexity of my solution. I'd be aiming for a system that people could use and grow for years to come.

But I don't work at a big company any more. There's no certainty that this larger feature set will actually move the needle on our business. We need to be able to see it in production and learn from the results before we commit resources to engineering a long-term solution. Priorities can change a lot in the span of a month, especially in a data-driven customer-facing business such as ours.

And ironically, I buy myself engineering time by going for the quick 1-month win. That bit about the majority of the time requiring front-end work is the trick. I can slap RESTful endpoints out to serve up and consume this data fairly quickly. While my front-end folks are busy making things pretty, we have the chance to catch our breath and evaluate, start laying groundwork, and eventually swap out the services without the front-end team ever having to know that things have changed. So while it seems like I'm doing one feature in one month, I'm really doing ten features in six and a half months. They may not be exactly the same ten features I would have if I started tomorrow, but that's half the point: By getting one feature out fast I give my business something concrete to evaluate, and I make it more likely that the ten features I eventually implement are the right ten for the job.

Why I'm Moving Away from the Play Framework

I've been using the Play framework since I started at RtR 3 months ago. Last week, I made the decision that no new services will be written in Play from that point forward. It started out as a great little framework that was pretty quick to learn and easy to use, but it's turned into something that I would not recommend anyone use for serious production applications. What happened?

First, I lost faith in the developers.

One of the first things that annoyed me about Play was the inability to run a single test from within a play test class inside your IDE. I suppose the thinking was that you will always run the play test app, or something, but I prefer to leave my IDE as little as possible when I'm working, and running an entire test class worked fine. So, being the good little open source programmer that I am, instead of bitching I rolled up my sleeves and fixed the bug. It was a pretty trivial fix. I even wrote a test case. Then I put in a pull request and waited.
After submitting the pull request, I commented on the pull request, commented on the ticket, and finally sent an email to the mailing list. And the response I got was basically that the team is too busy working on the next generation of the product to absorb fixes for the older generation. Having worked on open source projects myself, I understand what it's like to have limited bandwidth to look at changes. But if the project team's bandwidth is so limited that they can't even afford to look at small fixes like this, it seems like the project is basically abandoned. At that point I lost faith that I could rely on the community to support the 1.X branch of this product. Not necessarily a dealbreaker, but definitely a bad sign. 

Then, I lost faith in the platform.

We started to hit some serious bugs in the platform during a big push on a complex service. First, our developers that used Mac and Windows hit a bug similar to this, where they just simply couldn't get the app to work no matter what they did. It worked fine in linux, but even a clean checkout would fail to run for them. It was inexplicable, irritating, and we lost a couple of days of development work trying to get around it (rolling back checkins, pulling out modules, poring through stack traces). By this point, I had lost faith in the community, so I didn't see the point in going to them for help. Fortunately, we did finally get around it (it seemed to be a bug in the CRUD module), but we were all really frustrated and annoyed with the framwork after that experience.

Finally, I lost faith in my own ability to debug the framework.

The issues above were enough for me to want to move off of Play for new projects. The thing that caused me to move off of Play for projects that are already in development (but not in production) was this: At some point, we had written a migration job in Play for a major data migration. We discovered the strangest thing would happen. The job would run across several job threads, and at some point, one of the threads would hang. But it would not hang in a way that I have ever seen a JVM thread hang. The thread was in RUNNABLE state, and it was in a HashMap method (either get or put) and it was just sitting there. Not doing anything. No locks, no database or other IO, plenty of memory, plenty of resources, just sitting in that HashMap.get method, hanging out.
Now, maybe you've seen that before (and if you have, please leave a comment!). But I have seen a lot of JVM issues in my day, and this is a new one. There was no reason for this thread to be hung. And yet it was. I can debug just about anything you can throw at me in Java, but I had absolutely nowhere to start looking to debug this issue, except a vague suspicion that it was related to the way the framework was rewriting the classes under the covers. That is a dealbreaker, ladies and gents. I could've probably debugged why the module was causing the app to crap out for my developers, if given enough time. But I cannot say with any certainty that I could debug whatever the hell was causing that thread to hang.

If I felt the developers supporting play were committed to building a real community of support around the 1.X version, I might have stayed with it longer. It's a giant pain to find something else that is easy, lightweight, supports JPA and doesn't force me to write XML. But I can't use a product that I know has issues even I can't debug, and a team I don't trust to maintain the product to the standards my team needs to confidently use it in production.