When we announced that RethinkDB is shutting down, I promised to write a post-mortem. I took some time to process the experience, and I can now write about it clearly.
In the HN discussion thread people proposed many
reasons for why RethinkDB failed, from inexplicable perversity of
human nature and clever machinations of MongoDB’s marketing people, to
failure to build an experienced go-to-market team, to lack of numeric
type support beyond 64-bit
float. I aggregated the comments into a
list of proposed failure reasons here.
Some of these reasons have a ring of truth to them, but they’re symptoms rather than causes. For example, saying that we failed to monetize is tautological. It doesn’t illuminate the reasons for why we failed.
In hindsight, two things went wrong – we picked a terrible market and optimized the product for the wrong metrics of goodness. Each mistake likely cut RethinkDB’s valuation by one to two orders of magnitude. So if we got either of these right, RethinkDB would have been the size of MongoDB, and if we got both of them right, we eventually could have been the size of Red Hat.
Our thinking went something like this. New companies aren’t getting built on top of Oracle, so there is a window of opportunity to build a new infrastructure company. The database market is huge. If we build a product that captures some of that market, we’ll end up building a very successful company.
Unfortunately you’re not in the market you think you’re in – you’re in the market your users think you’re in. And our users clearly thought of us as an open-source developer tools company, because that’s what we really were. Which turned out to be very unfortunate, because the open-source developer tools market is one of the worst markets one could possibly end up in. Thousands of people used RethinkDB, often in business contexts, but most were willing to pay less for the lifetime of usage than the price of a single Starbucks coffee (which is to say, they weren’t willing to pay anything at all).
This wasn’t because the product was so good people didn’t need to pay for support, or because developers don’t control budgets, or because of failure of capitalism. The answer is basic microeconomics. Developers love building developer tools, often for free. So while there is massive demand, the supply vastly outstrips it. This drives the number of alternatives up, and the prices down to zero.
To see how this plays out for other companies consider MongoDB (valued at roughly $1.6B with ~700 employees), and Docker (valued at roughly $1B with ~300 employees). Both companies completely dominate in their respective markets. Two very rough rules of thumb for private growth stage technology companies is that valuations are a 10x multiple of annual revenue, and that revenue per employee is around $200K/year. Which means that MongoDB’s annual revenue is around $140-$160M, and Docker’s annual revenue is around $60-$100M.
That looks pretty good, until you look at dominant B2B technology companies in markets that aren’t developer tools. Companies like SalesForce, or Palantir, or Box (which faces stiff competition). All of a sudden MongoDB and Docker start looking tiny.
And these are massive successes. If relatively established companies with existing partnerships, distribution infrastructure, and access to large accounts are having trouble growing, what does it mean for a startup in its germination stage?
For us, it meant an intractable customer acquisition funnel. If a startup in a fertile B2B market has to process a hundred leads to get to ten opportunities to get to a single sale, for a developer tools startup that number goes up 10x. You have access to plenty of high quality prospects – lots of people are downloading your product and engaging with you, but you have to burn through a ridiculous number of leads to converge to a single sale.
This has disastrous domino effects. It demoralizes the team, and makes it very challenging to attract investment and hire top talent. In turn, that constrains your resources so you can’t make sufficient investment in product and distribution. Startups live and die by momentum, and early distribution challenges almost always doom you to eventual death.
Wrong metrics of goodness
Ok, so the market is bad, but other developer tools companies are still selling a lot of product. Why not RethinkDB?
While we couldn’t do anything about the dynamics of the market (other than building something else), the product decisions were entirely within our control. We wanted to build an elegant, robust, and beautiful product, so we optimized for the following metrics:
- Correctness. We made very strict guarantees, and fulfilled them religiously.
- Simplicity of the interface. We took on most of the complexity in the implementation, so application developers wouldn’t have to.
- Consistency. We made everything from the query language, to the client drivers, to cluster configuration, to documentation, to the marketing copy on the front page as consistent as possible.
If these trade-offs seem familiar, they’re straight from the worse is better essay. It turned out that correctness, simplicity of the interface, and consistency are the wrong metrics of goodness for most users. The majority of users wanted these three trade-offs instead:
- Timely arrival. They wanted the product to actually exist when they needed it, not three years later.
- Palpable speed. People wanted RethinkDB to be fast on workloads they actually tried, rather than “real world” workloads we suggested. For example, they’d write quick scripts to measure how long it takes to insert ten thousand documents without ever reading them back. MongoDB mastered these workloads brilliantly, while we fought the losing battle of educating the market.
- A use case. We set out to build a good database system, but users wanted a good way to do X (e.g. a good way to store JSON documents from hapi, a good way to store and analyze logs, a good way to create reports, etc.)
It’s not that we didn’t try to ship quickly, make RethinkDB fast, and build the ecosystem around it to make doing useful work easy. We did. But correct, simple, and consistent software takes a very long time to build. That put us three years behind the market.
By the time we felt RethinkDB satisfied our design goals and we were confident enough to recommend it to be used in production, almost everyone was asking “how is RethinkDB different from MongoDB?” We worked hard to explain why correctness, simplicity, and consistency are important, but ultimately these weren’t the metrics of goodness that mattered to most users.
To be honest, it hurt. It hurt a lot. It was unfathomable to us why people would choose a system that barely does the thing it’s supposed to do (store data), has a big kernel lock, throws away errors at random, implements single node features that stop working when you shard, has a barely working sharding system despite it being one of the core features of the product, provides essentially no correctness guarantees, and exposes a hodge-podge of interfaces that have no discernible consistency or unity of vision.
Every time MongoDB shipped a new release and people congratulated them on making improvements, I felt pangs of resentment. They’d announce they fixed the BKL, but really they’d get the granularity level down from a database to a collection. They’d add more operations, but instead of a composable interface that fits with the rest of the system, they’d simply bolt on one-off commands. They’d make sharding improvements, but it was obvious they were unwilling or unable to make even rudimentary data consistency guarantees.
But over time I learned to appreciate the wisdom of the crowds. MongoDB turned regular developers into heroes when people needed it, not years after the fact. It made data storage fast, and let people ship products quickly. And over time, MongoDB grew up. One by one, they fixed the issues with the architecture, and now it is an excellent product. It may not be as beautiful as we would have wanted, but it does the job, and it does it well.
When it became clear in mid-2014 that we couldn’t compete, we worked hard to differentiate from MongoDB. We found a very elegant way to add realtime push, hoping to enable developers to build a generation of apps they couldn’t build before. But that wasn’t enough. Suddenly we found ourselves competing with Meteor and Firebase, companies that were dedicated to solving the realtime problem for years before we even thought of it. Again we were three years behind the market, and again we found ourselves unable to compete.
What about the cloud?
A few people suggested that we should have built a cloud offering. We actually did have one in the works, so it’s an interesting topic I’d like to cover.
The obvious problem with a small database company building a cloud service is that it pattern matches to a common startup failure mode – splitting focus. Building, shipping, and operating reliable multi-tenant cloud services is hard. It requires non-trivial expertise and resources, so if you go down that path you find yourself running two startups at once. But we were facing an existential threat and were rapidly running out of options, so we gave it a shot anyway. Let’s suppose for the moment we could have pulled it off.
Our reasoning went like this. A database cloud offering could mean one of three things: managed hosting, database as a service (DBaaS), or value-added platform as a service (PaaS). Let’s do a quick back of the napkin market analysis using a $200K/employee in annual revenue rule of thumb we used above:
|Company||Compose.io, mLab||FaunaDB||Parse, Firebase, Meteor|
|Revenue||< $10M||< $10M||< $10M|
So these markets are small, even smaller than the database market itself. But could one of them be a better bet than others?
Managed hosting is essentially running the database for people on AWS so they don’t have to. The alternative to using these services is setting up the database on AWS yourself. That’s a pain, but it isn’t actually that hard. So there is a very hard cap on how much managed database hosting services can charge. Considering that Compose.io and mLab are offering MongoDB which has one to two orders of magnitude more users than RethinkDB, we reasoned that offering managed hosting wouldn’t make a dent.
Database as a service is a more complex version of managed hosting – DBaaS offerings abstract node management from the user entirely. You simply run your queries and the system handles them. You don’t know anything about how many nodes are run under the hood. This business is very challenging – partly because DBaaS companies have to compete with the giants (e.g. DynamoDB and DocumentDB), and partly because customers are very resistant to completely hand off data management to a startup when there are so many other substitutes and alternatives (do you know anyone who uses a DBaaS offering from a startup?) So a DBaaS offering was out.
The last option was to build a value-added platform as a service. We thought this was a promising direction because here we had a massive technical advantage. Firebase and Meteor had to build application-level realtime logic on top of MongoDB, which fundamentally limits the realtime querying capabilities and performance at scale. On the other hand, we controlled the stack all the way down, so we could offer significant advantages Firebase and Meteor couldn’t build.
So we built Horizon and started working on Horizon Cloud – a way for users to deploy and scale RethinkDB/Horizon apps. The challenges of building three large projects (RethinkDB, Horizon, and Horizon Cloud) with a very small team eventually caught up with us, and we never managed to ship the cloud offering before we ran out of money. Kudos to the engineering team, though. They came very, very close.
There is one more level of root cause analysis that we can do. Why did we pick a bad market and optimize the product for the wrong metrics?
When I was a little kid I wanted to build my own radio. I made a box out of plywood, threw some metal junk inside, and connected the box to a power cord. I had books on electronics at home, but didn’t think I needed them – I had unwavering faith that I could do it on my own. Eventually I did build a working receiver, but it took me years to finally realize I needed to learn basic electronics.
Early RethinkDB was quite a bit like that. We had no intuition for products or markets, so we’d go through the motions of building a company without actually understanding what we were doing. What’s more, we had enormous optimism bias. Just like physicians know that gifts from pharmaceutical companies have biasing effects for other physicians but believe they are immune from the effect, we believed we were immune from the laws of economics and the math of running a business. The math, of course, eventually caught up with us.
Could we have done anything to avoid these mistakes? Not any more than I could have built a working radio as a little kid. We were unconsciously incompetent, and it took years for that incompetence to become conscious.
A few people pointed out that we would have done better if we had built an experienced go-to-market team. That’s 100% true, but the timing of our personal development didn’t line up with the needs of the company. Initially we didn’t know we needed go-to-market expertise, so we didn’t seek to include it on the founding team. By the time we built up a mental model that maps well to reality, we found ourselves short on cash, in a difficult market filled with capable competitors, and a product that’s three years behind. By then, the best go-to-market team in the world couldn’t have saved us.
Many people have very strong feelings about the developer tools market. Engineers love building developer tools, so they badly want developer tools companies to thrive.
I am hesitant to dismiss the market entirely – partly because I don’t want to generalize from a single experience, partly because I don’t like saying “it cannot be done”, and partly because there are quite a few exceptions. GitHub, MongoDB, and Docker have built formidable companies. GitLab and Unity seem to be doing well.
If you do set out to build a developer tools company, tread carefully. The market is filled with good alternatives. User expectations are high and prices are low. Think deeply about the value you’re offering to the customer. Remember – wanting the world to be a certain way doesn’t make it so.
In 2009, we were pitching the early idea for RethinkDB (we had no software yet) to an audience of investors at the YCombinator demo day. We ended the pitch with a slide of three key points to remember. “If you only remember three things about RethinkDB,” we said, “remember these.” It worked. People didn’t remember anything else about the pitch, but they did remember the three points at the end.
I’ll now leave you with three key points to remember. If you remember anything about this post, remember these:
- Pick a large market but build for specific users.
- Learn to recognize the talents you’re missing, then work like hell to get them on your team.
- Read The Economist religiously. It will make you better faster.
 Don’t read into these numbers too closely. I’m ball-parking it, but it should give you a general idea of the cost of these mistakes.
 Incidentally, recognizing good business people without having strong business intuition is about as hard as recognizing good engineers without having a strong intuition for engineering.