05 Sep

To Be Continuous: Category Creation

In this episode, Paul and Edith are joined by Martin Casado, General Partner at Andreessen Horowitz. The group discusses the deeply complicated and difficult process of category creation, with a special focus on technology infrastructure products.  This is episode #24 in the To Be Continuous podcast series all about continuous delivery and software development.

Continue reading “To Be Continuous: Category Creation” »

31 Aug

Secrets of Netflix’s Engineering Culture

Netflix is not just known for the cultural phenomena of “Netflix and chill”, but for its legendary engineering team that releases hundreds of times a day in a data-driven culture. Netflix is the undisputed winner in the video wars, having driven Blockbuster into the “return” bin of history. Netflix won by iterating quickly and innovating with numerous micro-deployments. Could what worked for Netflix work for you?

Netflix had a virtuous cycle of product innovation. Every change made in the product is with the goal of getting new users to become subscribers. Netflix has a constant flow of new users every month, so they always have new users to test on. Also, they have a vast store of past data to optimize on. Did someone who liked “Princess Bride” also like “Monty Python and the Holy Grail”? When is the right time to prompt for a subscription? Interesting tests that Netflix can run include whether TV ads drive Netflix signups, or whether requiring Facebook to create an account drives enough social activity to counteract the drop in subscriptions from people who don’t have Facebook. If a change increased new user subscriptions, it went into the product. If it didn’t increase new user subscriptions, it didn’t make it in – hypothesis driven development.

However, what if you’re not Netflix? What if you’re a steady SaaS business with 1,000 business customers, on boarding 30 new customers a month? This is a healthy business, doubling in size annually. However what if you wanted to test whether you get more subscriptions with a one step or two step process to add a credit card. With a sample set of 30 a month & 90% current success rate, it will take you three months to determine success. Not everything can be tested at small scale. Tomasz Tunguz talks more about the perils of testing early here.

The other “gotcha” to watch out for with Netflix style development is obsessive focus on one metric can degrade other metrics. For example, focusing on optimizing new user signup might mean degrading experience for old users. Let’s say that 10,000 customers could be served with “good” speed, or 2,000 with “superfast” speed and 8,000 with “not good speed”. Or 1,000 with lightning fast and 9,000 with terrible speed. You might make the 1,000 new customers very happy, but piss off the 9,000 existing customers and have them quit. A good counterweight is to always have a contra-metric to keep an eye on. It’s okay if it dips slightly if the main metric rises. However, if the other metric tanks, re-consider whether the overall gains are worth it.

So what lessons can you take from Netflix to help your own business?

One, have a clear idea of why you’re making changes, even if it’s not something that you can a/b test. Is it to increase stability in your system? Make it quicker for someone to onboard? Know what your success criteria are, even if there’s not a statistically significant “winner”.

Two, break down projects into easily quantifiable chunks of value. Velocity can be as important (if not more important) than always being right. For example if you try 20 small changes, and half are right, you’ll end up 50% better. If you try one big change, and it’s not accretive, you’ll end up with a zero percent gain. Or, as Adrian Cockcroft, Netflix Architect says “If you’re doing quarterly releases and your competitor is doing daily releases you will fall so far behind”.

Three, don’t underestimate the importance of your own domain expertise. If you’re constantly testing ideas, even without having enough data, you’re quicker to get into the right path. Let your competitors copy your past mistakes, while you move forward. As Kris Gale, co-founder and CTO of Clover Health said, “You will always make better decisions with more information, and you will always have more information in the future.” But the way to get more information is to iterate.

LAUNCHDARKLY HELPS YOU BUILD BETTER SOFTWARE FASTER BY HELPING MANAGE FEATURE FLAGS AT SCALE. START YOUR FREE TRIAL NOW.

 

30 Aug

To Be Continuous: The Process of Category Creation

In this episode, Paul and Edith are joined by Martin Casado, General Partner at Andreessen Horowitz. The group discusses the deeply complicated and difficult process of category creation, with a special focus on technology infrastructure products.  This is episode #24 in the To Be Continuous podcast series all about continuous delivery and software development.

Continue reading “To Be Continuous: The Process of Category Creation” »

14 Jul

To Be Continuous: Scott Raney of Redpoint Ventures on Why Continuous Delivery Matters

In this episode, Edith and Paul are joined by Scott Raney, Partner at Redpoint Ventures. Edith, and Paul hear from Scott why continuous delivery matters in modern software development. This is episode #21 in the To Be Continuous podcast series all about continuous delivery and software development.

Continue reading “To Be Continuous: Scott Raney of Redpoint Ventures on Why Continuous Delivery Matters” »

26 Jun

Staging Servers are Dead! Long Live a Staging Server

Earlier this year, I wrote about why staging servers should die – that they actually increase risk and time and decrease quality. I’ve been very pleased at the thoughtful comments and feedback I’ve gotten about why effective continuous delivery and DevOps means no staging server. The one that made me the happiest was “Dreams exist to become reality. Here is one I’d like to achieve at work.”

Here I’ll address the feedback I received, and what’s standing in the way of achieving this dream.

 

First, there was a large agreement that “waterfall deployment” with a staging server added time and risk to the launch process. Rapportive founder Sam Stokes agreed, saying “Staging environments are an evolutionary dead end. They get out of sync, slow you down, just get neglected.” https://twitter.com/samstokes/status/690689667306369024 Christan Deger, Autoscout Software Architect  said “staging environment, even in a completely automated cloud setup, requires constant effort and produces costs.” Dave Nolan gave a talk at Pipeline London https://vimeo.com/162633462 on #nostaging.

 

Here are the questions and concerns about the practicality and validity of the “#noStaging” Dream.

 

“Don’t we need to test major infrastructure changes in Staging?”

I’m not advocating pushing untested code willy nilly into production. I am advocating for through and complete automation, continuous integration and feature flagging, with the goal to get as quickly as possible to production. The reason to kill staging servers was said very well by Joseph Rustcio, Librato CTO & co-founder  “You think of everything in advance of how a user will use a feature, and you still miss half of them.” The purpose of DevOps is to move as fast as possible from code on a developers box to customer.

 

One great case study is Librato. Librato uses feature flags to wrap features, deploying their code and then ramping volume up and down, controlling risk. The allure of a staging server is that all systems can be thoroughly tested there, “guaranteeing” a painless final deployment. However, the moment where you push code to the real world is the scariest, because the real world NEVER matches staging. By using feature flagging, Librato could de-risk a new infrastructure project. They used “Branch-by-abstraction” to ramp on and off a new infrastructure. The old way would have been to push the entire new infrastructure at a slow time like 3 am, then when something went wrong, scramble to fix, precisely when memories and tempers are short due to sleep deprivation and key people might be asleep. With feature flags, Librato could ramp up volume in the real world, with real data. Ruscio says of their rollout with feature flags, “We never got paged at night for an issue with the new system, as we were only introducing it during the day when we were available. So all issues could be addressed while we were available. With feature flags, we could even dial back risk during lunch hour.”

 

“But we need a staging server for contractual reasons”

Having a staging server for contractual reasons is arguing for an artificial artifact. As Dave Farley, co-author of Continuous Delivery” says “it’s not done until it’s delivered to users”. Lamont Lucas, FastRobot co-founder, says “media and advertising companies want final approval before a feature goes live, generally from non-technical approvers. Flagging all users coming from a gateway lets you fulfill the contractual obligation (of showing a feature to them for approval) while preventing you from having an entire legacy/pointless staging setup.”

 

“What about performance testing?”

Sean Byrnes, CEO/Founder of Outlier and the Founder of Flurry, often had to “test that new features work in a production-like environment and don’t compromise the integrity of our systems”, as Flurry had millions of users worldwide.  However, Byrnes  continued, “You don’t have to load test EVERY feature to failure”.

The failure of most features it’s lack of load as there’s no customer interest. However for feature where you do really need to know the failure rate, an alternate server other than production can be spun up ephemerally for that reason. Just don’t call it “the staging server” – it’s a production-like load system spun up for a specific reason – to test load – and then shut down after it’s purpose is complete.

 

“Feature flags are only good for shallow UE changes, not for Microservices”.

Actually, micro services make using staging servers even more painful as many versions of different microservices might be running across staging and production. Deger says,  “ In a microservices architecture with lot of independent deployments going on, using a single staging environment would inadvertently test integrations with services in different versions, than what is currently in production. This gives a false sense of security. So we decided to not have a staging environment at all and find different ways to release features with confidence while only integrating in production.

We are constantly learning new techniques to deploy into production without a staging environment. There are many different ways of integrations that we need to care of, but overall it makes us faster and the testing and release experience is better.””

 

“Harbaugh is biased”

I advise that instead of a confusing, time consuming, expensive, redundant and ultimately unsuccessful staging server; development teams should use feature flags to move features to production and do their feature validation as quickly as possible on production. I’m not unbiased – I’m CEO of a feature flagging management platform, LaunchDarkly. However, I founded LaunchDarkly the same reason I publish articles, podcast , and give talks on DevOps: because I care deeply about the power of feature flagging to create better quality software and reduce risk. You don’t need LaunchDarkly to feature flag – it’s easy to get started with a simple config file or open source library. Deger, is the contributor to an open source framework for feature flagging and huge proponent of no staging servers.

 

“Let’s talk when you want to do serious software development”.

Just as waterfall development once seemed the only way to guarantee success, so did waterfall deployment until recently. The most innovative and fast moving companies like Netflix, Google, and Amazon have found that they can move faster if they’re more agile. And serious? I think of software development as fun, as exciting, as liberating, transforming – with software people are connected worldwide, interacting in incredible ways, and their lives are better. I hope software never becomes too serious for me.

02 Jun

Powering Continuous Delivery With Feature Flags

Continuous Delivery LaunchDarkly

Separating feature rollout from code deployment to mitigate risk in continuous delivery

We are in the era of continuous delivery, where we are expected to quickly deliver software that is stable and performant.  We see development teams embracing a suite of continuous integration/delivery tools to automate their testing and QA, all while deploying at an accelerated cadence.

However, no matter how hard we try to mitigate the risk of software delivery, almost all end-user software releases are strictly coupled with some form of code deployment. This means that companies must rely on testing and QA to identify all issues before a release hits production. It also means that companies primarily rely on version control systems or scraped together config files to control feature releases and mitigate risk.  For instance, many homegrown feature release systems rely on hard coded values read from config files.  These systems can work with a handful of configuration values, but accrue massive technical debt at scale and may require a full redeploy for any updates.

Once a release is in production, it is basically out in the wild.  Without proper controls, rolling back to previous versions becomes a code deployment exercise, requiring engineering expertise and increasing the potential for downtime. Continue reading “Powering Continuous Delivery With Feature Flags” »