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Every Exit is an Entry Somewhere

A little over nine years ago, RedMonk made its first analyst hire. As an aside, if that number makes you feel old, well, you’re not alone. Anyway, our choice for the first non-founder analyst was a then little known BMC software developer based out of Austin, who was perhaps best recognized for his rather irreverent technology blog, Drunk and Retired. At the time, there was some consternation in the industry about the idea of hiring a developer to be an analyst. We fielded a lot of questions about the selection, but the quality of Cote’s work pretty quickly put those to rest.

It is probably in part due to Cote’s success – both with RedMonk and in his subsequent career at Dell, The 451 Group and now Pivotal – that we didn’t get nearly as many questions when we hired his replacement out of the Mayo Clinic. Superficially it might sound odd to hire as a technology industry analyst a Research Fellow doing drug discovery, but we’ve always been believers that we can teach someone to be an industry analyst – we’ve been in the business for thirty years collectively, after all. What we can’t do as easily is teach the skills necessary to be a good analyst: being creatively inquisitive, being able to communicate effectively or having an understanding and ability to grasp the macro trends shaping our industry.

When we find those, then, wherever they might be and whatever the background, we’re interested.

And find those we did in Donnie Berkholz. In spite of – or was it perhaps because of? – his non-traditional industry background, Donnie hit the ground running with us. With his background in statistical and quantitative analysis, he quickly made a name for himself exploring statistical trends, making predictions and that most important of RedMonk analyst duties: buying developers beers. He’s done nothing but prove us right in our initial belief that he could do this work at a high level, which is why we’re sad to be saying goodbye.

But like his predecessor, the time has come for Donnie to graduate from RedMonk. He’ll have more to tell you about his future plans shortly I’m sure, but suffice it to say you will still be seeing him around. His last day with us will be next Friday, as he wraps up a few projects with us. In the meantime, on behalf of all of us at RedMonk: we wish you all future success, Donnie, thank you for all of your efforts in helping RedMonk keep advancing the ball downfield. We’re happy to have played a small part in helping you transition into this industry.

As with any departure, the obvious next question is: what does this mean for RedMonk?

In the short term, more travel – they’re only making more conferences, and there are only so many of us. And we’re no more looking forward to filling Donnie’s shoes than we were to filling Cote’s. But over the longer term, our mission remains the same: we’re the analyst firm that is here for – and because of – developers. We will continue to fight the good fight on behalf of that constituency, even as market awareness of their importance adds more and more allies to our ranks. As a species we have a tendency to take progress for granted, but if you stop and think it really is amazing how different the reception our developer-centric message is today versus even four or five years ago.

Who will we hire? The best fit we can find. Like the Oakland A’s, we’ll think creatively about the opening and we’re already in the process of talking to some interesting candidates. That said, we’re open to all interested parties. And given our trajectory, we might even be adding more than one new analyst, but we’ll take it one step at a time.

Fair warning to all applicants: we will be very picky. You need to be able to communicate effectively, write well and be committed to rational discourse. You should have a reasonable online presence and a passion for developers and the tools they use. Other things we’ll look for include programming skills, economics and statistics training and experience with rich media. Previous experience as an analyst is a bonus, but absolutely not required. Interested? Send a CV and anything else you believe we should consider to hiring @

You will have big shoes to fill, whoever you are. The analysts that have come before you have done some incredible work, and we expect nothing less from you.

Why work here? The most obvious reason is that RedMonk remains, in my obviously biased opinion, an amazing place to work. There aren’t many too many jobs available that allow you to influence the strategic direction and decision making process of some of the biggest and most important technology companies in the world – as well as their disruptors, that give you a pulpit to produce public research for some of the best and brightest developers on the planet. Fewer jobs still let you work on things that are important, things that improve the day to day lives of developers, and by extension, the users they service. Tim O’Reilly says to “work on stuff that matters“; we think we do, almost every day. And as you might guess from conferences like the Monktoberfest, we try and have fun doing it.

Add in the flexibility that working for a small firm offers, from the ability to define your own research agenda to good hardware to variable vacation time to the option of working from home, and it’s a damn good gig. If any of that sounds interesting to you, drop us a line.

Last, to our clients and customers: if any of you have questions about this news, feel free to contact myself (sogrady @ or James (jgovernor @ if you like, or Juliane (juliane @ as always. We’re happy to answer anything we can.

So we wish you well, Donnie, and look forward to seeing who will step up in your place.

Categories: RedMonk Miscellaneous.

Open Source and the Rise of as-a-Service Businesses

I Want You To Open Source!

As discussed in my recap of the 2014 predictions from this space, it has been interesting to see Oracle’s SEC filings reflect the structural changes to both its business and the industry as a whole.

In 2012 and prior, Oracle reported on:

  • New software licenses
  • Software license updates and product support

By 2013, that became (additions in bold):

  • New software licenses and cloud software subscriptions
  • Software license updates and product support

And for the last fiscal year, Oracle expanded that into:

  • New software licenses
  • Cloud software-as-a-service and platform-as-a-service
  • Cloud infrastructure-as-a-service
  • Software license updates and product support

In just a few lines of a Consolidated Statement of Operations is writ the recent history of the software industry. Even companies that have efficiently extracted billions of dollars in profit from traditional, perpetual license software businesses are increasingly looking to cloud and service-enabled lines of business for future growth.

The numbers make it easy to see why. From 2012 to 2014, Oracle’s new software license revenue was down 0.37%. Over the same time period, its IaaS, PaaS and SaaS offerings combined reported growth of 75% – and if you exclude IaaS, which is a nascent business for the company – the growth rate jumps to 146%.

If we can accept for the sake of argument that this is not a unique adjustment of Oracle’s, but a pattern replicating itself across a wide range of businesses and industries, there are many questions to be answered about what the impacts will be to the industry around it. Of all of these questions, however, none is perhaps as important as the one I have discussed with members of the Eclipse and Linux Foundations over the past few weeks: what does the shift towards as-a-service businesses mean for open source? Is it good or bad for open source software in general?

The problem is that this question is difficult to answer precisely, because evidence can be found to support opposing arguments.

The Good News

On the positive front, the creation of services businesses has indirectly and directly led to an enormous portfolio of open source software. With the introduction and subsequent commercialization of the internet, a new class of problems demanded a new class of solutions. Prior to the internet, the types of scale-out architectures that are now standard within large service providers were relatively uncommon outside of specialized areas like HPC. The prevailing design assumption at the time, as Joe Gregorio observed, was N = 1, not the N > 1 fundamental assumption on which today’s platforms are built.

To satisfy the immediate demand for an enormous volume of new software written to solve new classes of problems, fundamental shifts were required. Most obviously, an unusually high percentage of this software was not written for purposes of sale. Unlike prior eras in which industry players lacking technical competencies effectively outsourced the job of software creation to third party commercial software organizations, companies like Amazon, Facebook and Google looked around and quickly determined that help was not coming from that direction – and even if it did, the economics of traditional software licensing would be a non-starter in scale-out environments.

Which is how this scale imperative led to a seismic shift in the way that software was designed and written. The decision by many of the original organizations to make these assets freely available as open source software consequently led to similarly titanic shifts in how software was distributed, marketed and sold.

The sheer number of companies not in the business of selling software who are releasing their creations as open source has dramatically inflated both the number and quality of available open source solutions. It has also put enormous competitive pressure on software vendors to either compete against open with a closed alternative or make their software similarly available.

The net, then, is that the rise of service-based businesses has directly and indirectly led to the creation of a lot of new open source software, which is positive for the industry – from a functional if not commercial standpoint – and customers alike. And having disrupted first the enterprise software industry and then compute, open source is now turning its eyes towards previously immune sectors like networking and storage.

The Bad News

One of the most important advantages open source enjoyed and continues to enjoy over proprietary alternatives is availability. As developers began to assert control over technical selection and direction in increasing numbers, even in situations where a proprietary alternative is technically superior, the sheer accessibility of open source software gave it an enormous market advantage. Choosing between adequate option A that could be downloaded instantly and theoretically superior option B gated by a salesperson was not in fact a choice. Thus Linux became the most widely adopted operating system on the cloud and MySQL the most popular relational database on the planet.

What is widely under-recognized, however, is the fact that from a convenience standpoint, open source does not enjoy the same advantages over its services counterparts that it did over proprietary competitors. Open source is typically less convenient than service-based alternatives, in fact. If it’s easier to download and spin up an open source database than talk to a salesperson, it’s even easier to download nothing at all and make setup, operation and backup of that database someone else’s problem.

If convenience is an increasingly important factor in technology adoption, then, and all of the available evidence suggests that it is, open source’s relative disadvantage in this area is a potential problem.

Particularly when you consider the motivations of vendors, who have not forgotten one of the primary lessons of the proprietary software market: locking in customers is good for business. As Shapiro and Varian put it 1999, “the profits you can earn from a customer – on a going-forward, present-value basis – exactly equal the total switching costs” (emphasis theirs). Put another way, then, the more it costs to switch, the more profits it is possible in theory to extract.

Among services companies, meanwhile, we see radically different attitudes towards the value of software, and thereby attitudes towards the act of open sourcing internal software. Facebook, at one end of the spectrum, is radically open, contributing everything from infrastructure software (e.g. Cassandra, HHVM, PrestoDB) to datacenter designs to the public knowledge pool. Amazon and Microsoft, however, are not major contributors of net new projects or packages to upstream projects.

The Net

There is little debate that to this point, the rise of service-based businesses has been a boon for open source. And for many of the pioneers of these scale-out businesses, open source is a competitive weapon in their biggest challenge: talent recruitment and retention. Developers evaluating open positions today are often faced with a choice: develop in a black box, where your work will touch thousands of other developers daily, but for which you’ll receive no external credit. Alternately, they can work on interesting problems and be given some latitude for sharing their work outside the firewall, improving their visibility and marketability moving forward.

To some extent, the lack of interest in the AGPL is a testament to the foundational role of open source in building out service-based businesses. If collaborative development and upstream contributions to key infrastructure projects was a major issue, the AGPL would probably be employed more frequently as a solution. Instead, it is an infrequently used license whose protections are widely regarded as unnecessary.

All of that being said, however, it is equally true that the future for open source in a services world is ambiguous. There are substantial incentives for vendors to drift towards non-open software, and the current trend towards permissive licensing, if anything, could accelerate this. By placing few if any restrictions on usage of open source software, permissive licenses present no barrier to any form of usage of open source software in proprietary contexts. It is true, however, that for services-based businesses, even copyleft licenses such as the GPL pose little threat because the distribution trigger is not tripped.

Taken as a whole, then, open source advocates would be wise to be appreciative of how far service businesses have gotten them to date, while being wary and watchful of their intentions moving forward.

Disclosure: Amazon, Microsoft and Oracle are RedMonk clients. Facebook is not.

Categories: Cloud, Hardware-as-a-Service, Open Source, Software-as-a-Service.

What’s in Store for 2015: A Few Predictions

If it seems odd to be posting predictions for the forthcoming year almost three months in, that’s because it is. In my defense, however, the 2015 iteration of this exercise comes little more than ten days late by last year’s standards. Which were, it must be said, very late themselves. Delayed or not, however, predictions are always a useful exercise if only, as Bryan Cantrill says, because they may tell us as much about the present as the future.

Before we continue, a brief introduction to how these predictions are formed, and the weight you may assign to them. The forecast here is based, variously, on both quantitative and qualitative assessments of products, projects and markets. For the sake of handicapping, the predictions are delivered in groups by probability; beginning with the most likely, concluding with the most volatile.

From the initial run in 2010, here is how my predictions have scored annually:

  • 2010: 67%
  • 2011: 82%
  • 2012: 70%
  • 2013: 55%
  • 2014: 50%

You may note the steep downward trajectory in the success rate over the past four years. While rightly considered a reflection of my abilities as a forecaster, it is worth noting that the aggressiveness of the predictions was increased in the year 2013. This has led to possibly more interesting but provably less reliable predictions since; you may factor the adjustment in as you will.


  • Amazon is Going to Become More Dominant Thanks to “Honeypots”
    Very few today would argue that Amazon is anything other than the dominant player in the public cloud space. In spite of its substantial first mover advantage, the company has continued to execute at the frantic pace of a late market entrant. This has maintained or even extended the company’s lead, even as some of the largest technology companies in the world have realized their original mistake and scramble to throw resources at the market.

    In 2015, Amazon will become even more dominant thanks to its ability to land customers on what I term “honeypot” services – services that are exceedingly easy to consume, and thus attractive – and cross/upsell them to more difficult-to-replicate or proprietary AWS products. Which are, notably, higher margin. Examples of so-called “honeypot” services are basic compute (EC2) and storage (S3) services. As consumption of these services increases, which it is across a large number of customers and wide range of industries, the friction towards other AWS services such as Kinesis, Redshift and so on decreases and consumption goes up. Much as was the case with Microsoft’s Windows platform, the inertia to leave AWS will become excessive.

    The logical question to ask about escalating consumption of services that are difficult or impossible to replicate outside of AWS, of course, is lock-in.

    The answer to what impact this will have on consumption can be found from examining the history of the software industry. If the experience of the past several decades, from IBM to Microsoft to VMware, tells us anything, it’s first that when asked directly, customers will deny any willingness to lock themselves into a single provider. It also demonstrates, however, that for the right value – be that cost, or more typically convenience or some combination of the two – they are almost universally willing to lock themselves into a single provider. Statements to the contrary notwithstanding. Convenience kills.

  • Kubernetes, Mesos, Spark et al are the New NoSQL
    Not functionally, of course. But the chaos of the early NoSQL market is remarkably similar to the evolution of what we’re seeing from projects like Mesos or Spark. First, there has been a rapid introduction of a variety of products which require a new conceptual understanding of infrastructure to appreciate. Second, while there may be areas of overlap between projects, in general they are quite distinct from one another. Third, the market’s understanding of what these projects are for and how they are to be used is poor to quite poor.

    In the early days of NoSQL, for example, we used to regularly see queries to our content that were some variation of “hadoop vs mongodb vs redis.” While these projects all are similar in that they pertain to data, that is about all they have in common. This was not obvious to the market for some time, however, as generations accustomed to relational databases being the canonical means of persisting data struggled to adapt to a world of document and graph databases or MapReduce engines and key-value databases. In other words, the market took very dissimilar products and aggregated them all under the single category NoSQL, in spite of the fact that the majority of the products in said category were not comparable.

    This is currently what we see when customers are evaluating projects like Kubernetes, Mesos and Spark: the initial investigation is less functional capability or performance than basic education. Not through any failing on the part of the individual projects, of course. It just takes time for markets to catch up. For 2015, then, expect these and similar projects to achieve higher levels of visibility, but remain poorly understood outside the technical elite.

    If the term NoSQL was reintroduced in 2009 then, per Wikipedia, and may have achieved mainstream status in 2014, it may be 2020 before the orchestraters, schedulers and fabrics are household names.


  • Docker will See Minimal Impact from Rocket
    Following the announcement of CoreOS’s container runtime project, Rocket, we began to field a lot of questions about what this meant for Docker. Initially, of course, the answer was simply that it was too soon to say. As we’ve said many times, Docker is one of the fastest growing – in the visibility sense – projects we have ever seen. Along with Node.js and a few others, it piqued developer interest in a way and at a rate that is exceedingly rare. But past popularity, while strongly correlated with future popularity, is not a guarantee.

    In the time since, we’ve had a lot of conversations about Docker and Rocket, and the anecdotal evidence strongly suggested that the negative impact, if any, to Docker’s trajectory would be medium to long term. Most of the conversations we have had with people in and around the Docker ecosystem suggest that while they share some of CoreOS’s concerns (and have some not commonly cited), the project’s momentum was such that they were committed for the foreseeable future.

    It’s still early, and the results are incomplete, but the quantitative data from my colleague above seems to support this conclusion. At least as measured by project activity, Docker’s trendline looks unimpacted by the announcement of Rocket. I expect this to continue in 2015. Note that this doesn’t mean that Rocket is without prospects: multiple third parties have told us they are open to the idea of supporting alternative container architectures. But in 2015, at least, Docker’s ascent should continue, if not necessarily at the same exponential rate.

  • Google Will Hedge its Bets with Java and Android, But Not Play the Swift Card
    Languages and runtimes evolve, of course, and eventually Google may shift Android towards another language. Certainly the fledgling support for Go on the platform introduced in 1.4 was interesting, both because of the prospect of an alternative runtime option and because of the growth of Go itself.

    That being said, change is unlikely to be coming in 2015 if it arrives at all. For one, Go is a runtime largely focused on infrastructure for the time being. For another, Google has no real impetus to change at the moment. Undoubtedly the company is hedging its bets internally pending the outcome of “Oracle America, Inc. v. Google, Inc.,” which has seen the Supreme Court ask for the federal government’s opinions. And certainly the meteoric growth of Swift has to be comforting should the company need to make a clean break with Java.

    But the bet here is, SCOTUS or no, we won’t see a major change on the Android platform in 2015.

  • Services Will Be the New Open Core
    I’ve written extensively (for example) on how companies are continuing to shift away from the tried and true perpetual license model, and for those interested in this topic, I have an O’Reilly title on it due any day now entitled “The Software Paradox.” The question is what comes next?

    Looking at the industry today, it’s clear that at least with respect to infrastructure software, it is difficult to compete without some component of your solution – and typically, a core that is viable as a standalone product – being open source. As Cloudera co-founder Mike Olson puts it, “You can no longer win with a closed-source platform.” Which sounds like a win for open source, and indeed to some extent is.

    It is equally true, however, that building an open source company is inherently more challenging than building one around proprietary software. This has led to the creation of a variety of complicated monetization mechanisms, which attempt to recreate proprietary margins while maintaining the spirit of the underlying open source project. Of these, open core has emerged as the most common. In this model, a company maintains an open source “core” while layering on premium, proprietary components.

    While this model works reasonably well, it does create friction within the open source community, as the commercial organization is inevitably presented with complicated decisions to make about what to open source and what to withhold as proprietary software. Coupled with the fact that for some subset of users, the open source components may be “good enough” and it’s clear that while open core is a reasonable adaptation to the challenges of selling software today, it’s far from perfect.

    Which is why we are and will continue to see companies turn to service-based revenue models as an alternative. When you’re selling a service instead of merely a product, many of the questions endemic to open core go away. And even in models where 100% of the source code is made available, selling services remains a simpler exercise because selling services is not just selling source code: it’s selling the ability to run, manage and maintain that code.

    In the late 1990’s when the services model was first proposed by companies then referred to as “Application Service Providers,” the idea was laughable. Why rent when you could buy?

    Whether it’s software or cars today, however, customers are increasingly turning towards on-demand, rental models. It’s partially an economic decision, as amortizing capex spend over a longer period of time in return for manageable premiums is often desirable. But more importantly, it’s about outsourcing everything from risk to construction, support and maintenance to third parties.

    Consider, for example, Oracle. In the space of three years the company has gone from reporting on “New software licenses” in its SEC filings to “New software licenses,” “Cloud software-as-a-service and platform-as-a-service,” and “Cloud infrastructure-as-a-service.” There will be exceptions, as there always are, but the trajectory of this industry is clear and it’s towards services. We’ll leave the implications of this shift for open source as a topic for another day.


  • One Consumer IoT Provider is going to be Penetrated with Far Reaching Consequences
    If it weren’t for the fact that there’s only ten months remaining in the year and the “far reaching” qualifier, this would be better categorized as “Likely” or even “Safe.” But really, this prediction is less about timing and more about inevitability. Given the near daily breaches of organizations regarded as technically capable, the longer the time horizon the closer the probability of a major IoT intrusion gets to 1. The attack surface of the consumer IoT market is expanding dramatically from thermostats to smoke detectors to alarms to light switches to door locks to security systems. Eventually one of these providers is going to be successfully attacked, and bad things will follow. The prediction here is that that happens this year, much as I hope otherwise.

  • AWS Lambda Will be Cloned by Competitive Providers
    With so many services already available and more being launched by the day, it’s difficult for any single service to stand out. Redshift is hailed as the fastest growing in the history of the BU, EC2 and S3 continue to provide the base core to build on, and accelerating strikes into application territory via WorkDocs and Workmail make it easy for newly launched AWS offerings to get lost. Particularly when they don’t fit pre-existing categories.

    For my money, however, Lambda was the most interesting service introduced by AWS last year. On the surface, it seems simplistic: little more than Node daemons, in fact. But as the industry moves towards services in general and microservices specifically, Lambda offers key advantages over competing models and will begin to push developer expectations. First, there’s no VM or instance overhead: Node snippets are hosted effectively in stasis, waiting on a particular trigger. Just as interest in lighter weight instances is driving containers, so too does it serve as incentive for Lambda adoption. Second, pricing is based on requests served – not running time. While the on-demand nature of the public cloud offered a truly pay-per-use model relative to traditional server deployments, Lambda pushes this model even further by redefining usage from merely running to actually executing. Third, Lambda not only enables but compels a services-based approach by lowering the friction and increasing the incentives towards the servicification of architectures.

    Which is why I expect Lambda to be heavily influential if not cloned outright, and quickly.


  • Slack’s Next Valuation Will be Closer to WhatsApp than $1B
    In late October of last year, Slack took $120M in new financing that valued the company at $1.12B. A few eyebrows were raised at that number: as Business Insider put it: “when it was given a $1.1 billion valuation last October, just 8 months after launching for the general public, there were some question marks surrounding its real value.”

    The prediction here, however, is that that number is going to seem hilariously light. The basic numbers are good. The company is adding $1M in ARR every 11 days, and has grown its user base by 33X in the past twelve months. Those users have sent 1.7B messages in that span.

    But impressive as those numbers might be, how does that get Slack anywhere close to WhatsApp? The same month that Slack was valued at a billion dollars, WhatsApp hit 600 million users – or 1200 times what Slack is reporting today. How would they be even roughly comparable, then?

    In part, because Slack’s users are likely to be more valuable than WhatsApp’s. The latter is free for users for the first year, then priced at $0.99 per annum following. Slack meanwhile, offers a free level of service with limitations on message retention, service integrations and so on, with paid pricing starting at $6.67 – per month. Premium packages go up to $12.50 per month, with enterprise services priced at $49-99 coming. This structure should allow Slack to hit a substantially higher ARPU than WhatsApp – which it will need to because it’s so far behind in subscriber count.

    The real reason that Slack is undervalued, however, is because its versatility is currently being overlooked. Slack is currently viewed by parties external as a business messaging tool, not a consumer one. See, for example, this graphic from the Wall Street Journal.

    Source: The Wall Street Journal

    The omission of Slack here is understandable, because Slack itself has made no effort to brand itself or price itself as a consumer-friendly messaging tool. From the marketing to the revenue model, Slack is pitched as a product for teams.

    The interesting thing, however, is that what makes it useful for teams also makes it useful for social groups. A group of my friends, for example, has turned Slack into a replacement not just for email or texting but WhatsApp. While a number of us started using WhatsApp while in Europe last year, Slack’s better for two reasons. First, it’s mobile capable, but not mobile only. True, WhatsApp has added, but that’s Chrome only. And frankly, for a messaging app that you use heavily, a browser tab doesn’t offer the experience that a native app does – and Slack’s native apps are excellent. For Slack, the desktop is a first class citizen. For WhatsApp and the like, it’s an afterthought.

    Second, the integrations model makes Slack far more than just a messaging platform. My particular group of friends has added a travel channel where our Foursquare and TripIt notifications are piped in, an Untappd channel where our various checkins are recorded, a blog channel which notifies us of posts by group members and so on. We also have our own Hubot linked to our Slack instance, so we can get anything from raccoon gifs to Simpsons quotes on demand.

    Slack has been so successful with this group of friends, in fact, that I created another room for our local Portland chapter of Computers Anonymous. While it’s early, it seems as if the tool will have legs in terms of helping keep an otherwise highly distributed group of technologists in touch.

    Anecdotal examples do not a valuation make, of course. And there’s no guarantee that Slack will do anything to advance this usage, or even continue to permit it. But it does speak to the company’s intrinsic ability to function well as a message platform outside of the target quote unquote business team model. What might a company with the ability to sell to both WhatsApp users and enterprises both be worth? My prediction for 2015 is a lot more than $1.12B.


  • The Apple Watch Will be the New Newton
    If this were any other company besides Apple, this prediction would likely be in the “Safe” category. “Likely,” at worst. Smartwatches as currently conceived seem like solutions in search of a problem. Is anyone that desperate to see who’s calling them that they need a notification on their wrist? Who makes phone calls anymore anyway? As for social media and email notifications, aren’t we already dangerously interrupt driven enough? And then there’s the battery life. For the various Android flavors, it varies from poor to abysmal. Apple, for its part, hasn’t talked much about the battery life on their forthcoming device, which probably isn’t a great sign.

    But it’s Apple, and counting them out is dangerous business indeed. How many couldn’t see the point of combining a computer with a phone? Or the appeal of a tablet (in 2010, not 1993)? Hence the elevation of this prediction to the spectacular category.

    It could very well be that Apple will find a real hook with the Watch and sell them hand over fist, but I’m predicting modest uptake – for Apple, anyway – in 2015. They’ll be popular in niches. The Apple faithful will find uses for it and ways to excuse the expected shortcomings of a first edition model. If John Gruber’s speculation on pricing is correct, meanwhile, Apple will sell a number of the gold plated Edition model to overly status conscious rich elites. But if recent rumors about battery life are even close to correct, I find it difficult to believe that the average iPhone user will be willing to charge his iPhone once a day and her watch twice in that span.

    While betting against Apple has been a fool’s game for the better part of a decade, then, call me the fool. I’m betting the under on the Apple Watch. Smartwatches will get here eventually, I’m sure, but I don’t think the technology is quite here yet. Much as it wasn’t with the Newton once upon a time.

Categories: Business Models, Cloud, Collaboration, Containers, Databases, Hardware-as-a-Service, Microservices, Mobile, Platform-as-a-Service, Programming Languages, Software-as-a-Service.

Revisiting the 2014 Predictions

With the calendar now reading January, it’s time to look ahead to 2015 and set down some predictions for the forthcoming year. As is the case every year, this is a two part exercise. First, reviewing and grading the predictions from the prior year, and second, the predictions for this one. The results articulated by first hopefully allow the reader to properly weight the contents of the second – with one important caveat that I’ll get to.

This year will be the fifth year I have set down annual predictions. For the curious, here is how I have fared in years past.

Before we get to the review of 2014’s forecast, one important note regarding the caveat mentioned above. Prior to 2013, the predictions in this space focused on relatively straightforward expectations with well understood variables. After some ferocious taunting constructive feedback from Bryan Cantrill, however, emphasis shifted towards trying to better anticipate the totally unexpected than the reverse.

You can see from the score how that worked out. Nevertheless, we press on. Without further delay, here are the 2014 predictions in review.


38% or Less of Oracle’s Software Revenue Will Come from New Licenses

As discussed in November of 2013 and July of 2012, while Oracle has consistently demonstrated an ability to grow its software related revenues the percentage of same derived from the sale of new licenses has been in decline for over a decade. Down to 38% in 2013 from 71% in 2000, there are no obvious indications that 2014 will buck this trend.

Because of some changes in reporting, it’s a bit tricky to answer this one simply. When Oracle reported its financial results in 2012, their consolidated statement of operations included just two categories in software revenue: “New software licenses” and “Software license updates and product support.” The simple, classic perpetual license software business, in other words. A year later, however, Oracle was still reporting in two categories, but “New software licenses” had become “New software licenses and cloud software subscriptions.”

While this made it impossible to compare 2012 to 2013 on an Apples to Apples basis, the basic premise held: theoretically reporting new software licenses-only in 2012, the percentage of overall software revenue that represented was 37.93%. The number in 2013, with “cloud software subscriptions” now folded in? 37.58%. With or without cloud revenue included, then, a distinct minority – and declining percentage – of the overall Oracle revenue was derived from the sale of new licenses.

For the fiscal year 2014, however, Oracle finally abandoned the two category reporting structure and broke cloud revenue out into not just one but two entirely new categories. In its 2014 10-K, Oracle provides revenue numbers for the following:

  • New software licenses
  • Cloud software-as-a-service and platform-as-a-service
  • Cloud infrastructure-as-a-service
  • Software license updates and product support

Which begs the question: if we’re trying to determine what percentage of Oracle’s software revenue derives from the sale of new software licenses, do we include one or both cloud categories, or evaluate software on a stand alone basis?

If the latter, the 2015 prediction is easily satisfied. If we exclude cloud revenue, only 32.25% of Oracle’s non-hardware revenue was extracted from new software licenses – a drop of 5.68% since the last time Oracle only reported on that category. But given that 2013 conflated software and cloud revenue and was used as the basis for the 2015 prediction here, it seems only fair to use that as the basis for judgement.

So what percentage of overall revenue did new cloud (IaaS, PaaS and SaaS included) and software licenses generate for the company in 2014? 37.65%.

Which means we’ll count this prediction as a hit.

The Biggest Problem w/ IoT in 2014 Won’t Be Security But Compatibility

Part of the promise of IoT devices is that they can talk to each other, and operate more efficiently and intelligently by collaborating. And there are instances already where this is the case: the Nest Protect smoke alarm, for example, can shut off a furnace in case of fire through the Nest thermostat. But the salient detail in that example is the fact that both devices come from the same manufacturer. Thus far, most of the IoT devices being shipped are designed as individual silos of information. So much so, in fact, that an entirely new class of hardware – hubs – has been created to try and centrally manage and control the various devices, which have not been designed to work together. But while hubs can smooth out the rough edges of IoT adoption, they are more band-aid than solution.

And because this may benefit market leaders like Nest – customers have a choice between buying other home automation devices that can’t talk to their Nest infrastructure or waiting for Nest to produce ones that do – the market will be subject to inertial effects. Efforts like the AllSeen Alliance are a step in the right direction, but in 2014 would-be IoT customers will be substantially challenged and held back by device to device incompatibility.

If the high profile penetrations of JP Morgan, Sony et al had been IoT related, this prediction would have been more problematic. But while there were notable IoT related security incidents like those described in this December report in which the blast furnace in a German factory was remotely manipulated, in 2014 the bigger issue seems to have been compatibility.

Perhaps in recognition of this limiting factor, manufacturers have indicated that 2015 is going to see progress in this area. Early in January, for example, Nest announced at CES that it would partnering with over a dozen new third party vendors, from August to LG to Philips to Whirlpool. 2014 also saw the company acquire the manufacturer of a potentially complementary device, the Dropcam. This interoperation will be crucial to expanding the market as a whole, because connected devices unable to interoperate with each other are of far more limited utility.

I’ll count this as a hit.

Windows 7 Will Be Microsoft’s Most Formidable Competitor

The good news for Microsoft is that Windows 7 adoption is strong, with more than twice the share of Windows XP, the next most popular operating system according to Statcounter. The bad news for Microsoft is that Windows 7 adoption is strong.

With even Microsoft advocates characterizing Windows 8 as a “mess,” Microsoft has some difficult choices to make moving forward. Even setting aside the fact that mobile platforms are actively eroding the PC’s relevance, what can or should Microsoft tell its developers? Embrace the design guidelines of Windows 8, which the market has actively selected against? Or stick with Windows 7, which is widely adopted but not representative of the direction that Microsoft wants to head? In short, then, the biggest problem Microsoft will face in evangelizing Windows 8 is Windows 7.

The good news for Microsoft is that Windows 7 declined slightly, from 50.32% in January to 49.14% in December. The bad news is that Windows 8.1 (11.77%) is still behind Windows XP (11.93%) in share. Back on the bright side, that was up from Windows 8’s 7.57% in January and the next closest non-Microsoft competitor was Mac OS at 7.83%.

Still, it seems pretty clear that Windows 7 is Microsoft’s most formidable competitor – we’ll see how Windows 10 does against it. Hit.

The Low End Premium Server Business is Toast

Simply consider what’s happened over the last 12 months. IBM spun off its x86 server business to Lenovo, at a substantial discount from the original asking price if reports are correct. Dell was forced to go private. And HP, according to reports, is about to begin charging customers for firmware updates. Whether the wasteland that is the commodity server business is more the result of defections to the public cloud or big growth from ODMs is ultimately irrelevant: the fact is that the general purpose low end server market is doomed. This prediction would seem to logically dictate decommitments to low end server lines from other businesses besides IBM, but the bet here is that emotions win out and neither Dell nor HP is willing to cut that particular cord – and Lenovo is obviously committed.

It’s difficult to measure this precisely because players like Dell remain private and shipment volumes from ODM suppliers are opaque, but there are several things we know. In spite of growth in PCs, HP’s revenue was down 4% (1% net) in 2014. And while CEO Meg Whitman expects x86 servers to play a part in a 2015 rebound, there are no signs that that was the case in 2014. Cisco, meanwhile, which eclipsed HP for sales of x86 blade servers in Q1, grew its datacenter business (which includes servers) in 2014 at a 27.3% clip compared to the year prior, but that was down from 59.8% growth 2012-2013 and the 2014 revenue represents only 7.3% of Cisco’s total for the year.

Amazon, on the other hand, is growing by virtually any metric, and rapidly in terms of users, consumption metrics and its portfolio of available services. Nor is Amazon the only growth area in public cloud: DigitalOcean has become the fourth largest web host in the world in less than two years, according to Netcraft.

Whether you base it on Amazon’s one million plus customers, then, or the uncertain fortunes of the x86 alternatives, it’s clear that traditional x86 businesses remain in real trouble. Hit.


2014 Will See One or More OpenStack Entities Acquired

Belatedly recognizing that the cloud represents a clear and present danger to their businesses, incumbent systems providers will increasingly double down on OpenStack as their response. Most already have some commitment to the platform, but increasing pressure from public cloud providers (primarily Amazon) as well as proprietary alternatives (primarily VMware) will force more substantial responses, the most logical manifestation of which is M&A activity. Vendors with specialized OpenStack expertise will be in demand as providers attempt to “out-cloud” one another on the basis of claimed expertise.

There are a few acquisitions here that are not OpenStack entities but certainly influenced by same – HP/Eucalyptus and Red Hat/Inktank come to mind – but it’s not necessary to include these to make this prediction come true. Just in the last year we’ve seen EMC acquire Cloudscaling, Cisco pick up Metacloud and Red Hat bring on eNovance. That leaves a variety of players still on the board, from Blue Box to Mirantis to Piston, and it will be interesting to see whether further consolidation lies ahead. But in the meantime, this prediction can safely be scored as a hit.

The Line Between Venture Capitalist and Consultant Will Continue to Blur

We’ve already seen this to some extent, with Hilary Mason’s departure to Accel and Adrian Cockcroft’s move to Battery Ventures. This will continue in large part because it can represent a win for both parties. VC shops, increasingly in search of a means of differentiation, will seek to provide it with high visibility talent on staff and available in a quasi-consultative capacity. And for the talent, it’s an opportunity to play the field to a certain extent, applying their abilities to a wider range of businesses rather than strictly focusing on one. Like EIR roles, they may not be long term, permanent positions: the most likely outcome, in fact, is for talent to eventually find a home at a portfolio company, much as Marten Mickos once did at Eucalyptus from Benchmark. But in the short term, these marriages are potentially a boon to both parties and we’ll see VCs emerge as a first tier destination for high quality talent.

The year 2014 did see some defections to the VC ranks, but certainly nothing that could be construed as a legitimate trend for the year. This is a miss.

Netflix’s Cloud Assets Will Be Packaged and Create an Ecosystem Like Hadoop Before Them

My colleague has been arguing for the packaging of Netflix’s cloud assets since November of 2012, and to some extent this is already occurring – we spoke to a French ISV in the wake of Amazon reInvent that is doing just this. But the packaging effort will accelerate in 2014, as would-be cloud consumers increasingly realize that there is more to operating in the cloud than basic compute/network/storage functionality. From Asgard to Chaos Monkey, vendors are increasingly going to package, resell and support the Netflix stack much as communities have sprung up around Cassandra, Hadoop and other projects developed by companies not in the business of selling software. To give myself a small out here, however, I don’t expect much from the ecosystem space in 2014 – that will only come over time.

In spite of some pilot efforts here and there including services work, there was little “acceleration” of the packaging of Netflix’s cloud assets. This is a miss.


Disruption Finally Comes to Storage and Networking in 2014

While it’s infrequently discussed, networking and storage have proven to be largely immune from the aggressive commoditization that has consumed first major software businesses and then low end server hardware. They have not been totally immune, of course, but by and large both networking and storage have been relatively insulated against the corrosive impact of open source software – in spite of the best efforts of some upstart competitors.

This will begin to change in 2014. In November, for example, Facebook’s VP of hardware design disclosed that they were very close to developing open source top-of-rack switches. That open source would eventually come for both the largely proprietary networking and storage providers was always inevitable; the question was timing. We are beginning to finally seen signs that one or both will be disrupted in the current year, whether its through collective efforts like the Open Compute Project or simply clever repackaging of existing technologies – an outcome that seems more likely in storage than networking.

As discussed previously, strictly speaking, disruption had already come for storage at the time that this was originally written. As for networking, the disclosure that some of the largest potential networking customers – Amazon and Facebook, among others – are now designing and manufacturing their own networking gear instead of purchasing it from traditional suppliers was disruptive enough. The fact that it’s that Facebook’s custom network designs, at least, are likely to be released to the public should be that much more concerning to traditional networking suppliers.

With the caveat then that the storage timing, at least, was off, this is a hit.


The Most Exciting Infrastructure Technology of 2014 Will Not Be Produced by a Company That Sells Technology

More and more today the most interesting new technologies are being developed not by companies that make money from software – one reason that traditional definitions of “technology company” are unhelpful – but from those that make money with software. Think Facebook, Google, Netflix or Twitter. It’s not that technology vendors are incapable of innovating: there are any number of materially interesting products that have been developed for purposes of sale.

The difficulty, as I should know by now, with predictions like these, is that they’re dependent on arbitrary and subjective definitions – in this case what’s the most “exciting” project of 2014. While there are many potential candidates, however, for us at RedMonk, Docker was one of our most discussed infrastructure projects over the past calendar year. By a variety of metrics, it’s the one of the most quickly growing projects we have ever seen. The Google Trends graph above corroborates this, albeit in an understated manner.

As a result, it seems fair to argue that Docker is a good candidate for the most exciting infrastructure technology of 2014. And unfortunately for my prediction, it is in fact produced by a company that sells software. So this is a miss.

Google Will Buy Nest Google Will Move Towards Being a Hardware Company

In the wake of Google’s acquisition of Nest, which I cannot claim with a straight face that I would have predicted, this prediction probably would have been better positioned in the Safe or Likely categories, as it seemed to indicate a clear validation of this assertion. But then they went and sold Motorola to Lenovo, effectively de-committing from the handset business.

So while I don’t expect hardware to show up in the balance sheet in a meaningful way in 2014, it seems probable that by the end of the year we’ll be more inclined to think of Google as a hardware company than we do today.

In spite of the launch of the Nexus Player, the acquisition of Nest, the continued success of the Chromecast, beating Apple to market with Android-powered smartwatches and a new pair of Nexus phone and tablet devices – not to mention the self-driving cars – it can’t realistically be claimed that people think of Google as a hardware company today. Certainly the company has more involvement in physical hardware than it ever has, but by and large the company’s perception is shaped by its services: Search, AdSense/Words, Gmail, GCE etc. That might have shifted somewhat if the Nest brand had been folded into Google’s and the company had released additional device types, but that’s merely speculation.

The fact is that Google is not materially more of a hardware company today than it was when these predictions were made. Ergo, this is a miss.


Google Will Acquire IFTTT

Acquisitions are always difficult to predict, because of the number of variables involved. But let’s say, for the sake of argument, that you a) buy the prediction that a major problem with the IoT is compatibility and b) that you believe Google’s becoming more of a hardware company broadly and IoT company over time: what’s the logical next step if you’re Google? Maybe you contemplate the acquisition of a Belkin or similar, but more likely you (correctly) decide the company has quite enough to digest at the moment in the way of hardware acquisitions. But what about IFTTT?

By more closely marrying the service to their collaboration tools, Google could a) differentiate same, b) begin acclimating consumers to IoT-style interconnectivity, and c) begin generating even more data about consumer habits to feed their existing (and primary) revenue stream, advertising.

Not much argument here, as IFTTT was not acquired by anyone, Google included. The logic behind the prediction remains sound, but there’s no way to count this as anything other than a miss.

The Final Tally

To wrap things up, how did the above predictions score? The short answer is not well. Out of the ten predictions for the year, five were correct. Which means, unfortunately, that five were not, good for a dismal 50% average. In the now five years of this exercise, 50% is the lowest score ever, and the lowest since last year, which saw the debut of the new more aggressive format – which is obviously not a coincidence.

In my defense, however, the misses were primarily drawn from the least certain predictions; all of the “Safe” predictions, for example, were hits. In terms of scoring, then, the context is important. The failure rate of predictions is highly correlated to their difficulty. It’s simpler, obviously, to predict acquisitions in a given category than to predict a specific acquirer/acquiree match.

All of that said, the forthcoming predictions for 2015 will remain aggressive in nature, even if that means 2016 will see a similarly contrite and humble predictions wrap up.

Categories: Cloud, Hardware, IoT, Network, Storage.

The RedMonk Programming Language Rankings: January 2015

With two quarters having passed since our last snapshot, it’s time to update our programming language rankings. Since Drew Conway and John Myles White originally performed this analysis late in 2010, we have been regularly comparing the relative performance of programming languages on GitHub and Stack Overflow. The idea is not to offer a statistically valid representation of current usage, but rather to correlate language discussion (Stack Overflow) and usage (GitHub) in an effort to extract insights into potential future adoption trends.

In general, the process has changed little over the years. With the exception of GitHub’s decision to no longer provide language rankings on its Explore page – they are now calculated from the GitHub archive – the rankings are performed in the same manner, meaning that we can compare rankings from run to run, and year to year, with confidence.

This is brought up because one result in particular, described below, is very unusual. But in the meantime, it’s worth noting that the steady decline in correlation between rankings on GitHub and Stack Overlow observed over the last several iterations of this exercise has been arrested, at least for one quarter. After dropping from its historical .78 – .8 correlation to .74 during the Q314 rankings, the correlation between the two properties is back up to .76. It will be interesting to observe whether this is a temporary reprieve, or if the lack of correlation itself was the anomaly.

For the time being, however, the focus will remain on the current rankings. Before we continue, please keep in mind the usual caveats.

  • To be included in this analysis, a language must be observable within both GitHub and Stack Overflow.
  • No claims are made here that these rankings are representative of general usage more broadly. They are nothing more or less than an examination of the correlation between two populations we believe to be predictive of future use, hence their value.
  • There are many potential communities that could be surveyed for this analysis. GitHub and Stack Overflow are used here first because of their size and second because of their public exposure of the data necessary for the analysis. We encourage, however, interested parties to perform their own analyses using other sources.
  • All numerical rankings should be taken with a grain of salt. We rank by numbers here strictly for the sake of interest. In general, the numerical ranking is substantially less relevant than the language’s tier or grouping. In many cases, one spot on the list is not distinguishable from the next. The separation between language tiers on the plot, however, is generally representative of substantial differences in relative popularity.
  • GitHub language rankings are based on raw lines of code, which means that repositories written in a given language that include a greater number amount of code in a second language (e.g. JavaScript) will be read as the latter rather than the former.
  • In addition, the further down the rankings one goes, the less data available to rank languages by. Beyond the top tiers of languages, depending on the snapshot, the amount of data to assess is minute, and the actual placement of languages becomes less reliable the further down the list one proceeds.

(click to embiggen the chart)

Besides the above plot, which can be difficult to parse even at full size, we offer the following numerical rankings. As will be observed, this run produced several ties which are reflected below (they are listed out here alphabetically rather than consolidated as ties because the latter approach led to misunderstandings).

1 JavaScript
2 Java
4 Python
5 C#
5 C++
5 Ruby
9 C
10 Objective-C
11 Perl
11 Shell
13 R
14 Scala
15 Haskell
16 Matlab
17 Go
17 Visual Basic
19 Clojure
19 Groovy

By the narrowest of margins, JavaScript edged Java for the top spot in the rankings, but as always, the difference between the two is so marginal as to be insignificant. The most important takeaway is that the language frequently written off for dead and the language sometimes touted as the future have shown sustained growth and traction and remain, according to this measure, the most popular offerings.

Outside of that change, the Top 10 was effectively static. C++ and Ruby jumped each one spot to split fifth place with C#, but that minimal distinction reflects the lack of movement of the rest of the “Tier 1,” or top grouping of languages. PHP has not shown the ability to unseat either Java or JavaScript, but it has remained unassailable for its part in the third position. After a brief drop in Q1 of 2014, Python has been stable in the fourth spot, and the rest of the Top 10 looks much as it has for several quarters.

Further down in the rankings, however, there are several trends worth noting – one in particular.

  • R: Advocates of the language have been pleased by four consecutive gains in these rankings, but this quarter’s snapshot showed R instead holding steady at 13. This was predictable, however, given that the languages remaining ahead of it – from Java and JavaScript at the top of the rankings to Shell and Perl just ahead – are more general purpose and thus likely to be more widely used. Even if R’s grow does stall at 13, however, it will remain the most popular statistical language by this measure, and this in spite of substantial competition from general purpose alternatives like Python.

  • Go: In our last rankings, it was predicted based on its trajectory that Go would become a Top 20 language within six to twelve months. Six months following that, Go can consider that mission accomplished. In this iteration of the rankings, Go leapfrogs Visual Basic, Clojure and Groovy – and displaces Coffeescript entirely – to take number 17 on the list. Again, we caution against placing too much weight on the actual numerical position, because the differences between one spot and another can be slight, but there’s no arguing with the trendline behind Go. While the language has its critics, its growth prospects appear secure. And should the Android support in 1.4 mature, Go’s path to becoming a Top 10 if not Top 5 language would be clear.

  • Julia/Rust: Long two of the notable languages to watch, Julia and Rust’s growth has typically been in lockstep, though not for any particular functional reason. This time around, however, Rust outpaced Julia, jumping eight spots to 50 against Julia’s more steady progression from 57 to 56. It’s not clear what’s responsible for the differential growth, or more specifically if it’s problems with Julia, progress from Rust (with a DTrace probe, even), or both. But while both remain languages of interest, this ranking suggests that Rust might be poised to outpace its counterpart.

  • Coffeescript: As mentioned above, Coffeescript dropped out of the Top 20 languages for the first time in almost two years, and may have peaked. From its high ranking of 17 in Q3 of 2013, in the three runs since, it has clocked in at 18, 18 and now 21. The “little language that compiles into JavaScript” positioned itself as a compromise between JavaScript’s ubiquity and syntactical eccentricities, but support for it appears to be slowly eroding. How it performs in the third quarter rankings should provide more insight into whether this is a temporary dip or more permanent decline.

  • Swift: Last, there is the curious case of Swift. During our last rankings, Swift was listed as the language to watch – an obvious choice given its status as the Apple-anointed successor to the #10 language on our list, Objective-C. Being officially sanctioned as the future standard for iOS applications everywhere was obviously going to lead to growth. As was said during the Q3 rankings which marked its debut, “Swift is a language that is going to be a lot more popular, and very soon.” Even so, the growth that Swift experienced is essentially unprecedented in the history of these rankings. When we see dramatic growth from a language it typically has jumped somewhere between 5 and 10 spots, and the closer the language gets to the Top 20 or within it, the more difficult growth is to come by. And yet Swift has gone from our 68th ranked language during Q3 to number 22 this quarter, a jump of 46 spots. From its position far down on the board, Swift now finds itself one spot behind Coffeescript and just ahead of Lua. As the plot suggests, Swift’s growth is more obvious on StackOverflow than GitHub, where the most active Swift repositories are either educational or infrastructure in nature, but even so the growth has been remarkable. Given this dramatic ascension, it seems reasonable to expect that the Q3 rankings this year will see Swift as a Top 20 language.

The Net

Swift’s meteoric growth notwithstanding, the high level takeaway from these rankings is stability. The inertia of the Top 10 remains substantial, and what change there is in the back half of the Top 20 or just outside of it – from Go to Swift – is both predictable and expected. The picture these rankings paint is of an environment thoroughly driven by developers; rather than seeing a heavy concentration around one or two languages as has been an aspiration in the past, we’re seeing a heavy distribution amongst a larger number of top tier languages followed by a long tail of more specialized usage. With the exceptions mentioned above, then, there is little reason to expect dramatic change moving forward.

Update: The above language plot chart was based on an incorrect Stack Overflow tag for Common Lisp and thereby failed to incorporate existing activity on that site. This has been corrected.

Categories: Programming Languages.

DVCS and Git Usage in 2014

To many in the technology industry, the dominance of Decentralized Version Control Systems (DVCS) generally and Git specifically is taken as a given. Whether it’s consumed as a product (e.g. GitHub Enterprise/Stash), service (Bitbucket, GitHub) or base project, Git is the de facto winner in the DVCS category, a category which has taken considerable share from its centralized alternatives over the past few years. With macro trends fueling further adoption, it’s natural to expect that the ascent of Git would continue unimpeded.

One datapoint which has proven useful for assessing the relative performance of version control systems is Open Hub (formerly Ohloh)’s repository data. Built to index public repositories, it gives us insight into the respective usage at least within its broad dataset. In 2010 when we first examined its data, Open Hub was crawling some 238,000 projects, and Git managed just 11% of them. For this year’s snapshot, that number has swelled to over 674,000 – or close to 3X as many. And Git’s playing a much more significant role today than it did then.

Before we get into the findings, more details on the source and issues.


The data in this chart was taken from snapshots of the Open Hub data exposed here.

Objections & Responses

  • Open Hub data cannot be considered representative of the wider distribution of version control systems“: This is true, and no claims are made here otherwise. While it necessarily omits enterprise adoption, however, it is believed here that Open Hub’s dataset is more likely to be predictive moving forward than a wider sample.
  • Many of the projects Open Hub surveys are dormant“: This is probably true. But even granting a sizable number of dormant projects, it’s expected that these will be offset by a sizable influx of new projects.
  • Open Hub’s sampling has evolved over the years, and now includes repositories and forges it did not previously“: Also true. It also, by definition, includes new projects over time. When we first examined the data, Open Hub surveyed less than 300,000 projects. Today it’s over 600,000. This is a natural evolution of the survey population, one that’s inclusive of evolving developer behaviors.

With those caveats in mind, let’s start with the big picture. The following chart depicts the total share of repositories attributable to centralized (CVS/Subversion) and distributed (Bazaar/Git/Mercurial) systems.

Even over a brief three year period (we lack data for 2011, and have thus omitted 2010 for continuity’s sake) it’s clear that DVCS systems have made substantial inroads. DVCS may not be quite as dominant as is commonly assumed, but it’s close to managing one in two projects in the world. When considering the inertial effects operating against DVCS, this traction is impressive. In spite of the fact that it can be difficult even for excellent developers to shift their mental model from centralized to decentralized, that version control systems are not typically the priority of other infrastructure elements, that the risks associated with moving from one system to another are non-trivial, DVCS has clearly established itself as a popular, mainstream option. Close observation of the above chart, however, reveals a slight hiccup in adoption numbers which we’ll explore in more detail shortly.

In the meantime, let’s isolate the specific changes per project between our 2014 snapshot and the 2010 equivalent. How has their relative share changed?

As might be predicted, comparing 2010 to 2014, Git is the clear winner. The project with the idiosyncratic syntax made substantial gains (25.92%) partially at the expense of Subversion (-12.02%) but more CVS (-16.64%). Just as clearly, Git is the flag bearer for DVCS more broadly, as other decentralized version control systems in Bazaar and Mercurial showed only modest improvement over that span – 1.33% and 1.41% respectively. The takeaways, then, from this span are first that DVCS is a legitimate first class citizen and second that Git is the most popular option in that category.

What about the past year, however? Has Git continued on its growth trajectory?

The short answer is no. With this chart, it’s very important to note the scale of the Y axis: the changes reflected here are comparatively minimal, which is to be expected over the brief span of one year. That being said, it’s interesting to observe that Subversion shows a minor bounce (1.28%), while Git (-1.17%) took a correspondingly minor step back. Bazaar and CVS were down negligible amounts over the same span, while Mercurial was ever so slightly up.

Neither quantitative nor qualitative evidence supports the idea that Git adoption is stalled, nor that Subversion is poised for a major comeback. Wider market product trends, if anything, contradict the above, and suggest that the most likely explanation for the delta in Open Hub’s numbers is the addition of major new centrally managed codebases to Open Hub’s index.

It does serve as a reminder, however, that as much as the industry takes it for granted that Git is the de facto standard for version control systems, a sizable volume of projects have yet to migrate to a decentralized system of any kind. The implications for this are many. For service providers who are Git-centric, it may be worth considering creating bridges for users on other systems or even offering assistance in VCS migrations. For DVCS providers, the above may be superficially discouraging, but in reality indicates that the market opportunity is even wider than commonly assumed. And for users, it means that those still on centralized systems should consider migrating to decentralized alternatives, but by no means are condemned to the laggard category.

While it is thus assumed here, however, that the step back for Git is an artifact, it will be interesting to watch the growth of the platform over the next year. One year’s lack of growth is easily dismissed as an anomaly; a second year would be more indicative of a pattern. It will be interesting to see what the 2015 snapshot tells us.

Disclosure: Black Duck, the parent company of Open Hub, has been a RedMonk customer but is not currently.

Categories: Version Control.

The Scale Imperative

The Computing Scale Co

Once upon a time, the larger the workload, the larger the machine you would use to service it. Companies from IBM to Sun supplied enormous hardware packages to customers with similarly outsized workloads. IBM, in fact, still generates substantial revenue from its mainframe hardware business. One of the under-appreciated aspects of Sun’s demise, on the other hand, was that it had nothing to do with a failure of its open source strategy; the company’s fate was sealed instead by the collapse in sales of its E10K line, due in part to the financial crisis. For vendors and customers alike, mainframe-class hardware was the epitome of computational power.

With the rise of the internet, however, this model proved less than scalable. Companies founded in the late 1990’s like Google, whose mission was to index the entire internet, looked at the numbers and correctly concluded that the economics of that mission on a scale-up model were untenable. With scale-up an effective dead end, the remaining option was to scale-out. Instead of big machines, scale-out players would build software that turned lots of small machines into bigger machines, e pluribus unum writ in hardware. By harnessing the collective power of large numbers of low cost, comparatively low power commodity boxes the scale-out pioneers could scale to workloads of previously unimagined size.

This model was so successful, in fact, that over time it came to displace scale-up as the default. Today, the overwhelming majority of companies scaling their compute requirements are following in Amazon, Facebook and Google’s footprints and choosing to scale-out. Whether they’re assembling their own low cost commodity infrastructure or out-sourcing that task to public cloud suppliers, infrastructure today is distributed by default.

For all of the benefits of this approach, however, the power afforded by scale-out did not come without a cost. The power of distributed systems mandates fundamental changes in the way that infrastructure is designed, built and leveraged.

Sharing the Collective Burden of Software

The most basic illustration of the cost of scale-out is the software designed to run on it. As Joe Gregorio articulated seven years ago:

The problem with current data storage systems, with rare exception, is that they are all “one box native” applications, i.e. from a world where N = 1. From Berkeley DB to MySQL, they were all designed initially to sit on one box. Even after several years of dealing with MegaData you still see painful stories like what the YouTube guys went through as they scaled up. All of this stems from an N = 1 mentality.

Anything designed prior to the distributed system default, then, had to be retrofit – if possible – to not just run across multiple machines instead of a single node, but to run well and take advantage of their collective resources. In many cases, it proved simpler to simply start from scratch. The Google Filesystem and HDFS papers that resulted in Hadoop are one example of this; at its core, the first iterations of the project were designed to deconstruct a given task into multiple component tasks to be more easily executed by an array of machines.

From the macro-perspective, besides the inherent computer science challenges of (re)writing software for distributed, scale-out systems – which is exceptionally difficult – the economics were problematic. With so many businesses moving to this model in a relatively short span of time, a great deal of software needed to get written quickly.

Because no single player could bear the entire financial burden, it became necessary to amortize the costs across an industry. Most of the infrastructure we take for granted today, then was developed as open source. Linux became an increasingly popular operating system choice as both host and guest; the project, according to Ohloh, is the product of over 5500 person-years in development. To put that number into context, if you could somehow find and hire 1,000 people high quality kernel engineers, and they worked 40 hours a week with two weeks vacation, it would take you 24 years to match that effort. Even Hadoop, a project that hasn’t had its 10 year anniversary yet, has seen 430 person-years committed. The even younger OpenStack, a very precocious four years old, has seen an industry conglomerate collectively contribute 594 years of effort to get the project to where it is today.

Any one of these projects could be singularly created by a given entity; indeed, this is common, in fact. Just in the database space, whether it’s Amazon with DynamoDB, Facebook with Cassandra or Google with BigQuery, each scale-out player has the ability to generate its own software. But this is only possible because they are able to build upon the available and growing foundation of open source projects, where the collective burden of software is shared. Without these pooled investments and resources, each player would have to either build or purchase at a premium everything from the bare metal up.

Scale-out, in other words, requires open source to survive.

Relentless Economies of Scale

In stark contrast to the difficulty of writing software for distributed systems, microeconomic principles love them. The economies of scale that larger players can bring to bear on the markets they target are, quite frankly, daunting. Their variable costs decrease due to their ability to purchase in larger quantities; their fixed costs are amortized over a higher volume customer base; their relative efficiency can increase as scale drives automation and improved processes; their ability to attract and retain talent increases in proportion to the difficulty of the technical challenges imposed; and so on.

If it’s difficult to quantify these advantages in precise terms, but we can at least attempt to measure the scale at which various parties are investing. Specifically, we can examine their reported plant, property and equipment investments.

If one accepts the hypothesis that economies of scale will play a significant role in determining who is competitive and who is not, this chart suggests that the number of competitive players in the cloud market will not be large. Consider that Facebook, for all of its heft and resources, is a distant fourth in terms of its infrastructure investments. This remains true, importantly, even if their spend was adjusted upwards to offset the reported savings from their Open Compute program.

Much as in the consumer electronics world, then, where Apple and Samsung are able to leverage substantial economies of scale in their mobile device production – an enormous factor in Apple’s ability to extract outsized and unmatched margins – so too is the market for scale-out likely to be dominated by the players that can realize the benefits of their scale most efficiently.

The Return of Vertical Integration

Pre-internet, the economics of designing your own hardware were less than compelling. In the absence of a global worldwide network, not to mention less connected populations, even the largest companies were content to outsource the majority of their technology business, and particularly hardware, to specialized suppliers. Scale, however, challenges those economics on a fundamental level, and forced those at the bleeding edge to rethink traditional infrastructure design, questioning all prior assumptions.

It’s long been known, for example, that Google eschewed purchasing hardware from traditional suppliers like Dell, HP or IBM in favor of its own designs manufactured by original device manufacturers (ODMs); Stephen Shankland had an in depth look at one of their internal designs in 2009. Even then, the implications of scale are apparent; it seems odd, for example, to embed batteries in the server design, but at scale, the design is “much cheaper than huge centralized UPS,” according to Ben Jai. But servers were only the beginning.

As it turns out, networking at scale is an even greater challenge than compute. On November 14th, Facebook provided details on its next generation data center network. According to the company:

The amount of traffic from Facebook to Internet – we call it “machine to user” traffic – is large and ever increasing, as more people connect and as we create new products and services. However, this type of traffic is only the tip of the iceberg. What happens inside the Facebook data centers – “machine to machine” traffic – is several orders of magnitude larger than what goes out to the Internet…

We are constantly optimizing internal application efficiency, but nonetheless the rate of our machine-to-machine traffic growth remains exponential, and the volume has been doubling at an interval of less than a year.

As of October 2013, Facebook was reporting 1.19B active monthly users. Since that time, then, machine to machine east/west networking traffic has more than doubled. Which makes it easy to understand how the company might feel compelled to reconsider traditional networking approaches, even if it means starting effectively from scratch.

Earlier that week at its re:Invent conference, meanwhile, Amazon went even further, offering an unprecedented peek behind the curtain. According to James Hamilton, Amazon’s Chief Architect, there are very few remaining aspects to AWS which are not designed internally. The company has obviously dramatically grown the software capabilities of its platform over time: on top of basic storage and compute, Amazon has integrated an enormous variety of previously distinct services: relational databases, a Map Reduce engine, data warehousing and analytical capabilities, DNS and routing, CDN, a key value store, a streaming platform – and most recently ALM tooling, a container service and a real-time service platform.

But the tendency of software platforms to absorb popular features is not atypical. What is much less common is the depth to which Amazon has embraced hardware design.

  • Amazon now builds their own networking gear running their own protocol. The company claims their gear is lower cost, faster and that the cycle time for bugs is reduced from months to weekly.
  • Amazon’s server and storage designs are custom to the vendor; the storage servers, for example, are optimized for density and pack in 864 disks at a weight of almost 2400 pounds.
  • Intel is now working directly with Amazon to produce custom chip designs, capable of bursting to much higher clock speeds temporarily.
  • To ensure adequate power for its datacenters, Amazon has progressed beyond simple negotiated agreements with power suppliers to building out custom substations, driven by custom switchgear the company itself designed.

Compute, networking, storage, power: where does this internal innovation path end? In Hamilton’s words, there is no category of hardware that is off-limits for the company. But the relentless in-sourcing is not driven by religious objections – such considerations are strictly functions of cost.

In economic terms, of course, this is an approximation of backward vertical integration. Amazon may not own the manufacturers themselves as in traditional vertical integration, but manufacturing is an afterthought next to the original design. By creating their own infrastructure from scratch, they avoid paying an innovation tax to third party manufacturers, can build strictly to their specifications and need only account for their own needs – not the requirements of every other potential vendor customer. The result is hardware that is, in theory at least, more performant, better suited to AWS requirements and lower cost.

While Amazon or Facebook have provided us with the most specifics, then, it’s safe to assume that vertical integration is a pattern that is already widespread amongst larger players and will only become more so.

The Net

For those without hardware or platform ambitions, the current technical direction is promising. With economies of scale growing ever larger and gradual reduction of third party suppliers continuing, cloud platform providers would appear to have margin yet to trim. And at least to date, competition on cloud platforms (IaaS, at least) has been sufficient to keep vendors from pocketing the difference, with industry pricing still on a downward trajectory. Cloud’s pricing advantage historically was the ability to pay less upfront and more over the longer term, but with base prices down close to 100% over a two year period, the longer term premium attached to cloud may gradually decline to the point of irrelevance.

On the software front, an enormous portfolio of high quality, highly valuable software that would have been financially out of the reach of small and even mid-sized firms even a few years ago is available today at no cost. Virtually any category of infrastructure software today – from the virtualization layer to the OS to the runtime to the database to the cloud middleware equivalents – has high quality, open source options available. And for those willing to pay a premium to outsource the operational responsibilities of building, deploying and maintaining this open source infrastructure, any number of third party platform providers would be more than happy to take those dollars.

For startups and other non-platform players, then, the combination of hardware costs amortized by scale and software costs distributed across a multitude of third parties means that effort can be directed towards business problems rather than basic, operational infrastructure.

The cloud platform players, meanwhile, symbiotically benefit from these transactions, in that each startup, government or business that chooses their platform means both additional revenue and a gain in scale that directly, if incrementally, drives down their costs (economies of scale) and indirectly increases their incentive and ability to reduce their own costs via vertical integration. The virtuous cycle of more customers leading to more scale leading to lower costs leading to lower prices leading to more customers is difficult to disrupt. This is in part why companies like Amazon or Salesforce are more than willing to trade profits for growth; scale may not be a zero sum game, but growth today will be easier to purchase than growth tomorrow – yet another reason to fear Amazon.

The most troubling implications of scale, meanwhile, are for traditional hardware suppliers (compute/storage/networking) and would-be cloud platform service providers. The former, obviously are substantially challenged by the ongoing insourcing of hardware design. Compute may have been first, with Dell being forced to go private, HP struggling with its x86 business and IBM being forced to exit the commodity server business entirely. But it certainly won’t be the last. Networking and storage players alike are or should be preparing for the same disruption server manufacturers have experienced. The problem is not that cloud providers will absorb all or even the majority of the networking and storage addressable markets; the problem is that it will absorb enough to negatively impact the scale traditional suppliers can operate at.

Those that would compete with Amazon, Google, Microsoft et al, meanwhile, or even HP or IBM’s offerings in the space, will find themselves faced with increasingly higher costs relative to larger competition, whether it’s from premiums paid to various hardware suppliers, lower relative purchasing power or both. Which implies several things. First, that such businesses must differentiate themselves quickly and clearly, offering something larger, more cost-competitive players are either unable or unwilling to. Second, that their addressable market as a result of this specialization will be a fraction of the overall opportunity. And third, that the pool of competitors for base level cloud platform services will be relatively small.

What the long term future holds should these predictions hold up and the market come to be dominated by a few larger players is less clear, because as ever in this industry, their disruptors are probably already making plans in a garage somewhere.

Disclosure: Amazon, Dell, HP, IBM and Microsoft are RedMonk clients. Facebook and Google are not.

Categories: Cloud.

The Implications of IaaS Pricing Patterns and Trends

With Amazon’s re:Invent conference a week behind us and any potential price cuts or responses presumably implemented by this point, it’s time to revisit the question of infrastructure as a service pricing. Given what’s at stake in the cloud market, competition amongst providers continues to be fierce, driving costs for customers ever lower in what some observers have negatively characterized as a race to the bottom.

While the downward pricing pressure is welcome, however, it can be difficult to properly assess how competitive individual providers are with one another, all the more so because their non-standardized packaging makes it effectively impossible to compare service to service on an equal footing.

To this end we offer the following deconstruction of IaaS cloud pricing models. As a reminder, this analysis is intended not as a literal expression of cost per service; this is not, in other words, an attempt to estimate the actual component costs for compute, disk, and memory per provider. Such numbers would be speculative and unreliable, relying as they would on non-public information, but also of limited utility for users. Instead, this analysis compares base hourly instance costs against the individual service offerings. What this attempts to highlight is how providers may be differentiating from each other – deliberately or otherwise – by offering more memory per dollar spent, as one example. In other words, it’s an attempt to answer the question: for a given hourly cost, who’s offering the most compute, disk or memory?

As with previous iterations, a link to the aggregated dataset is provided below, both for fact checking and to enable others to perform their own analyses, expand the scope of surveyed providers or both.

Before we continue, a few notes.


  • No special pricing programs (beta, etc)
  • Linux operating system, no OS premium
  • Charts are based on price per hour costs (i.e. no reserved instances)
  • Standard packages only considered (i.e. no high memory, etc)
  • Where not otherwise specified, the number of virtual cores is assumed to equal to available compute units

Objections & Responses

  • This isn’t an apples to apples comparison“: This is true. The providers do not make that possible.
  • These are list prices – many customers don’t pay list prices“: This is also true. Many customers do, however. But in general, take this for what it’s worth as an evaluation of posted list prices.
  • This does not take bandwidth and other costs into account“: Correct, this analysis is server only – no bandwidth or storage costs are included. Those will be examined in a future update.
  • This survey doesn’t include [provider X]“: The link to the dataset is below. You are encouraged to fork it.

Other Notes

  • HP’s 4XL (60 cores) and 8XL (103 cores) instances were omitted from this survey intentionally for being twice as large and better than three times as large, respectively, as the next largest instances. While we can’t compare apples to apples, those instances were considered outliers in this sample. Feel free to add them back and re-run using the dataset below.
  • While we’ve had numerous requests to add providers, and will undoubtedly add some in future, the original dataset – with the above exception – has been maintained for the sake of comparison.

How to Read the Charts

  • There was some confusion last time concerning the charts and how they should be read. The simplest explanation is that the steeper the slope, the better the pricing from a user perspective. The more quickly cores, disk and memory are added relative to cost, the less a user has to pay for a given asset.

With that, here is the chart depicting the cost of disk space relative to the price per hour.

(click to embiggen)

This chart is notable primarily for two trends: first, the aggressive top line Amazon result and second, the Joyent outperformance. The latter is an understandable pricing decision: given Joyent’s recent market focus on data related workloads and tooling, e.g. the recently open sourced Manta, Joyent’s discounting of storage costs is logical. Amazon’s divergent pattern here can be understood as two separate product lines. The upper points represent traditional disk based storage (m1), which Amazon prices aggressively relative to the market, while the bottom line represents its m3 or SSD based product line, which is more costly – although still less pricy than alternative packages from IBM and Microsoft. Google does not list storage in its base pricing and is thus omitted here.

The above notwithstanding, a look at the storage costs on a per provider basis would indicate that for many if not most providers, storage is not a primary focus, at least from a differentiation standpoint.

(click to embiggen)

As has historically been the case, the correlation between providers in the context of memory per dollar is high. Google and Digital Ocean are most aggressive with their memory pricing, offering slightly more memory per dollar spent than Amazon. Joyent follows closely after Amazon, and then comes Microsoft, HP and IBM in varying order.

Interestingly, when asked at the Google Cloud Live Platform event whether the company had deliberately turned the dial in favor of cheaper memory pricing for their offerings as a means of differentiation and developer recruitment, the answer was no. According to Google, any specific or distinct improvements on a per category basis – memory, compute, etc – are arbitrary, as the company seeks to lower the overall cost of their offering based on improved efficiencies, economies of scale and so on rather than deliberately targeting areas developers might prioritize in their own application development process.

Whatever their origin, however, developers looking to maximize their memory footprint per dollar spent may be interested in the above as a guide towards separating services from one another.

(click to embiggen)

In terms of computing units per dollar, Google has made progress since the last iteration of this analysis, where it was a bottom third performer. Today, the company enjoys a narrow lead over Amazon, followed closely by HP and Digital Ocean. IBM, Joyent and Microsoft, meanwhile, round out the offerings here.

It is interesting to note the wider distribution within computing units versus memory, as one example. Where there is comparatively minimal separation between providers with regard to memory per dollar, there are relatively substantive deltas between providers in terms of computing power per package. It isn’t clear that this has any material impact on selection or buying preferences at present, but for compute intensive workloads in particular it is at least worth investigating.

IaaS Price History and Implications

Besides taking apart the base infrastructure pricing on a component basis, one common area of inquiry is how provider prices have changed over time. It is enormously difficult to capture changes across services on a comparative basis over time, for many of the reasons mentioned above.

That being said, as many have inquired on the subject, below is a rough depiction of the pricing trends on a provider by provider basis. In addition to the caveats at the top of this piece, it is necessary to note that the below chart attempts to track only services that have been offered from the initial snapshot moving forward so as to be as consistent as possible. Larger instances recently introduced are not included, therefore, and other recent additions such as Amazon’s m3 SSD-backed package are likewise omitted.

Just as importantly, services cannot be reasonably compared to one another here because their available packages and the attached pricing vary widely; some services included more performant, higher cost offerings initially, and others did not. Comparing the average prices of one to another, therefore, is a futile exercise.

The point of the following chart is instead to try and understand price changes on a per provider basis over time. Nothing more, and nothing less.

(click to embiggen)

Unsurprisingly, the overall trajectory for nearly all providers is down. And the exception – Microsoft – appears to spike only because its base offerings today are far more robust than their historical equivalents. The average price drop for the base level services included in this survey from the initial 2012 snapshot to today was 95%: what might have cost $0.35 – $0.70 an hour in 2012 is more likely to cost $0.10 – $0.30 today. Which raises many qustions, the most common of which is to what degree the above general trend is sustainable: is this a race to a bottom, or are we nearing a pricing floor?

While we are far from having a definitive answer on the subject, early signs point to the latter. In the week preceding Amazon’s re:Invent, Google announced across the board price cuts to varying services, on top of an October 10% price cut. A week later, the fact that Amazon did not feel compelled to respond was the subject of much conversation.

One interpretation of this lack of urgency is that it’s simply a function of Amazon’s dominant role in the market. And to be sure, Amazon is in its own class from an adoption standpoint. The company’s frantic pace of releases, however – 280 in 2013, on pace for 500 this year – suggests a longer term play. The above charts describe pricing trends in one of the most basic elements of cloud infrastructure: compute. They suggest that at present, Amazon is content to be competitive – but is not intent on being the lowest cost supplier.

By keeping pricing low enough to prevent it from being a real impediment to adoption, while growing its service portfolio at a rapid pace, Amazon is able to get customers in the door with minimal friction and upsell them on services that are both much less price sensitive than base infrastructure as well as being stickier. In other words, instead of a race to the bottom, the points of price differentiation articulated by the above charts may be less relevant over time, as costs approach true commodity levels – a de facto floor – and customer attention begins to turn to time savings (higher end services) over capital savings (low prices) as a means of cost reduction.

If this hypothesis is correct, Amazon’s price per category should fall back towards the middle ground over time. If Amazon keeps pace, however, it may very well be a race to the bottom. Either way, it should show up in the charts here.

Disclosure: Amazon, HP, IBM, Microsoft and Rackspace are RedMonk customers. Digital Ocean, Google and Joyent are not.

Link: Here is a link to the dataset used in the above analysis.

Categories: Cloud.

What are the Most Popular Open Source Licenses Today?

For a variety of reasons, not least of which is that fewer people seem to care anymore, it’s been some time since we looked at the popularity of open source licenses. Once one of the more common inquiries we fielded, questions about the relative merits or distribution of licenses have faded as we see both consolidation around choices and increased understanding of the practical implications of various licensing styles. Given the recent affinity for permissive licensing, however, amongst major open source projects such as Cloud Foundry, Docker, Hadoop, Node.js or OpenStack, it’s worth revisiting the question of license choices.

Before we get into the question of how licensing choices have changed, it’s necessary to establish a baseline number for distribution today. While it cannot be considered definitive, Black Duck’s visibility into a wide variety of open source repositories and forges serves as a useful sample size. Based on the Black Duck data, then, the following chart depicts the distribution of usage amongst the ten most popular open source licenses.

(click to embiggen)

Moving left to right, from less popular licenses to the most popular, it is easy to determine the overall winner. As has historically been the case, the free software, copyleft GPLv2 is the most popular license choice according to Black Duck. Besides high profile projects such as Linux or MySQL, the GPL has been the overwhemingly most selected license for years. The last time we examined the Black Duck data in 2012, in fact, the GPL was more popular than the MIT, Artistic, BSD, Apache, MPL and EPL put together.

Popular as the GPL remains, however, it no longer enjoys that kind of advantage. If we group both versions (2 and 3) of the GPL together, the GPL is in use within 37% of the Black Duck surveyed projects. The three primary permissive license choices (Apache/BSD/MIT), on the other hand, collectively are employed by 42%. They represent, in fact, three of the five most popular licenses in use today.

License selection has clearly changed, then, but by how much? For comparison’s sake, here’s a chart of the percent change in license usage from this month’s snapshot of Black Duck’s data versus one from 2009.

(click to embiggen)

As we can see, the biggest loser in terms of share was the GPLv2 and, to a lesser extent, the LGPLv2.1. The decline in usage of the GPLv2 can to some degree be attributed to copyleft license fans choosing instead the updated GPLv3; that license, released in 2007, gained about 6% share from 2009 to 2014. But with usage of version 2 down by about 24%, the update is clearly not the only reason for decreased usage of the GPL.

Instead, the largest single contributing factor to the decline of the GPL’s dominance – it’s worth reiterating, however, that it remains the most popular surveyed license – is the rise of permissive licenses. The two biggest gainers on the above chart, the Apache and MIT licenses, were collectively up 27%. With the BSD license up 1%, the three most popular permissive licenses are collectively up nearly 30% in the aggregate.

While this shift will surprise some, and suggests that much like the high profile of projects like Linux and MySQL led to wider adoption of reciprocal or copyleft-style licenses, Hadoop and others are leaving a sea of permissively licensed projects in their wake.

But the truth is that a correction of some sort was likely inevitable. The heavily skewed distribution towards copyleft licenses was always somewhat unnatural, and therefore less than sustainable over time. What will be interesting to observe moving forward is whether these trends continue, or whether further corrections are in store. Currently, license preferences seem to be accumulating at either ends of the licensing spectrum (reciprocal or permissive); the middle ground in file-based licenses such as the LGPL/MPL remain a relatively distant third category in popularity. Will MPL-licensed projects like the recently opened Manta or SmartDataCenter change that, or are they outliers?

Whatever the outcome, it’s clear we should expect greater diversity amongst licensing choices than we’ve seen in the past. The days of having a single dominant license are, for all practical purposes, over.

Disclosure: Black Duck, the source of this data, has been a RedMonk client but is not currently.

Categories: licensing, Open Source.

Model vs Execution

One of the things that we forget today about SaaS is that we tried it before, and it failed. Coined sometime in 1999 if Google is to be believed, the term “Application Service Provider” (ASP) was applied to companies that delivered software and services over a network connection – what we today commonly call SaaS. By and large this market failed to gain significant traction. Accounts differ as to how and when a) SaaS was coined (IT Toolbox claims it was coined in 2005 by John Koenig) and b) replaced ASP as the term of choice but the fact that ASP could be replaced at all is an indication of its lack of success. While various web based businesses from that period are not only still with us, but in Amazon and Google among the largest in the world, those attempting to sell software via the web rather than deploying it on premise generally did not survive.

A decade plus later, however, and not only has the SaaS model itself survived, but it is increasingly the default approach. The point here isn’t to examine the mechanics of the SaaS business, however; we’ve done that previously (see here or here, for example). The point of bringing up SaaS here, rather, is to serve as a reminder that there’s a difference between model and execution.

Too often in this industry, we look upon a market failure as a permanent indictment of potential. If it didn’t work once, it will never work.

The list of technologies that have been dismissed because they initially failed or seemed unimpressive is long: virtualization was widely regarded as a toy, it’s now an enterprise standard. Smart people once looked at containers and said “neato, why would you want to do that?” Two plus years after Amazon’s creation of the cloud market, then Microsoft CTO Ray Ozzie admitted that cloud “isn’t being taken seriously right now by anybody except Amazon.” In the wake of the anemic adoption – particularly relative to Amazon’s IaaS alternative – of the first iterations of PaaS market pioneers and Google App Engine, many venture capitalists decided that PaaS was a model with no future. DVCS tools like Git were initially scorned and reviled by developers because they were different on a fundamental level.

In each case, it’s important to separate the model from the execution. Too often, failures of the latter are perceived as a fatal flaw in the former. In the case of PaaS, for example, it’s become obvious that the lack of developer adoption was driven by the initial constraints of the first platforms; not having to worry about scaling was an attractive feature, but not worth the sacrifice of having to develop an application in a proprietary language against a proprietary backend that ensured the application could never be easily ported. Half a dozen years later, PaaS platforms are now not only commonly multi-runtime but open source, and growth is strong.

SaaS, meanwhile, would prove to be an excellent model over time, but initially had to contend with headwinds consisting of inconsistent and asymmetrically available broadband, far more functionally limited browser technologies and a user population both averse to risk and brought up on the literal opposite model. In retrospect, it’s no surprise that the ASP market failed; indeed, it’s almost more surprising that the SaaS market followed so quickly on its heels.

In both cases, the initial failures were not attributable to the models. There is in fact demand for PaaS and SaaS, it was simply that the vendors did not (PaaS) or could not (SaaS) execute properly at the time.

Given the rate and pace of change in the technology industry, it is both necessary and inevitable that new technology and approaches are viewed skeptically. As with most innovation, in the technology world or outside of it, failure is the norm. But critical views notwithstanding, it’s important to try and understand the wider context when evaluating the relative merits of competing models. It may well be that the model itself is simply unworkable. But in an industry where what is old is new again, daily, it is far more likely that a current lack of success is due to a failure of or inability to (due to market factors) execute.

In which case, you may want to give that “failed” market a second look. Opportunity may lie within.

Categories: Business Models.