Shipping Software Should Not Be Scary

On twitter this week, @srhtcn noted that “Many incidents happen during or right after release” and asked for advice on ways to fix this.

And he’s right!  Rolling out new software is the proximate cause for the overwhelming majority of incidents, at companies of all sizes.  Upgrading software is both a necessity and a minor insanity, considering how often it breaks things.

Image result for deploy production memeI’m not going to recap the history of continuous integration and delivery, suffice it to say that we now know that smaller and more frequent changes are much safer than larger and less frequent changes.

But it’s still risky.  And most issues are still caused by humans and our pesky need for “improvements”.  So what can be done?

It’s not ok for software releases to be scary and hazardous

First of all: If releasing is risky for you, you need to fix that.  Make this a priority.  Track your failures, practice post mortems, evaluate your on call practices andImage result for test in production meme culture.  Know if you’re getting better or worse.  This is a project that will take weeks if not months until you can be confident in the results.

You have to fix it though, because these things are self-reinforcing.  If shipping changes is scary and fraught, people will do it less and it will get even MORE scary and treacherous.

Likewise, if you turn it into a non-cortisol inducing event and set expectations, engineers will ship their code more often in smaller diffs and therefore break the world less.

Fixing deploys isn’t about eliminating errors, it’s about making your pipeline resilient to errors.  It’s fundamentally about detecting common failures and recovering from them, without requiring human intervention.

Value your tools more

As an short term patch, you should run deploys in the mornings or whenever everyone is around and fresh.  Then take a hard look at your deploy pipeline.

In too many organizations, deploy code is a technical backwater, an accumulation of crufty scripts and glue code, forked gems and interns’ earnest attempts to hack up Capistrano.  It usually gives off a strong whiff of “sloppily evolved from many 2 am patches with no code review”.Image result for test in production meme

This is insane.  Deploy software is the most important software you have.  Treat it that way: recruit an owner, allocate real time for development and testing, bake in metrics and track them over time.

If it doesn’t have an owner, it will never improve.  And you will need to invest in frequent improvements even after you’re over this first hump.

  • Signal high organizational value by putting one of your best engineers on it.
  • Recruit help from the design side of the house as well.  The “right” thing to do must be the fastest, easiest thing to do, with friendly prompts and good docs.  No “shortcuts” for people to reach for at the worst possible time.  You need user research and design here.  Image result for deploy production meme
  • Track how often deploys fail and why.  Managers should pay close attention to this metric, just like the one for people getting interrupted or woken up, and allocate time to fixing this early whenever it sags.  Before it gets bad.
  • Allocate real time for development, testing, and training — don’t expect the work to get shoved into people’s “spare time” or post mortem cleanup time.  Make sure other managers understand the impact of this work and are on board.  Make this one of your KPIs.Image result for deploy production meme

In other words, make deploy tools a first class citizen of your technical toolset.  Make the work prestigious and valued — even aspirational.  If you do performance reviews, recognize the impact there.

(Btw, “how we hardened our deploys” is total Velocity-bait (&& other practitioner conferences) as well as being great for recruiting and general visibility in blog post form.  People love these stories; there definitely aren’t enough of them.)

Turn software engineers into software owners

The canonical CI/CD advice starts with “ship early, ship often, ship smaller change sets”.  That’s great advice: you should definitely do those things.  But they are covered plenty elsewhere.  What’s software ownership?

Software ownership is the natural end state of DevOps.  Software engineers, operations engineers, platform engineers, mobile engineers — everyone who writes code should be own the full lifecycle of their software.

Software owners are people who:

  1. Write codeImage result for deploy production meme
  2. Can deploy and roll back their own code
  3. Are able to debug their own issues in prod (via instrumentation, not ssh)

If you’re lacking any one of those three ingredients, you don’t have ownership.

Why ownership?  Because software ownership makes for better engineers, better software, and a better experience for customers.  It shortens feedback loops and means the person debugging is usually the person with the most context on what has recently changed.

Some engineers might balk at this, but you’ll be doing them a favor.  We are all distributed systems engineers now, and distributed systems require a much higher level of operational literacy.  May as well start today.

Fail fast, fix fast

This is about shifting your mindset from one of brittleness and a tight grip, to one of flexibility where failures are no big deal because they happen all the time, don’t impact users, and give everyone lots of practice at detecting and recovering from them.

Here are a few of the best practices you should adopt with this practice.

Make operability a high-value skill set.  Never promote someone to “senior engineer” if they can’t deploy and debug their Image result for test in production memeown code.

Software engineers don’t have to become operational experts.  They do need to know the bare basics of instrumentation, deploy/revert, and debugging.

Everyone who puts software in production needs to understand and feel responsible for the full lifecycle of their code, not just how it works in their IDE.

Baking: it’s not just for cookies

Shipping something to production is a process of incrementally gaining confidence, not a switch you can flip.

You can’t trust code until it’s been in prod a while, Image result for deploy production memeuntil you’ve seen it perform under a wide range of load and concurrency scenarios, in lots of partial failure modes.  Only over time can you develop confidence in it not being terrible.

Nothing is production except production.  Don’t rely on never failing; expect failure, embrace failure.  Practice failure!  Build guard rails around your production systems to help you find and fix problems quickly.

The changes you need to make your pipeline more resilient are roughly the same changes you need to safely test in production.  These are a few of your guard rails.

  • Use feature flags to switch new code paths on and offImage result for test in production meme
  • Build canaries for your deploy process, so you can promote releases gracefully and automatically to larger subsets of your traffic as you gain confidence in them
  • Create cohorts.  Deploy to internal users first, then any free tier, etc in order of ascending importance.  Don’t jump from 10% to 25% to 50% and then 100% — some changes are related to saturating backend resources, and the 50%-100% jump will kill you.
  • Have robots check the health of your software as it rolls out to decide whether to promote the canary.  Over time the robot checks will mature and eventually catch a ton of problems and regressions for you.

The quality of code is not knowable before it hits production.  You may able to spot some problems, but you can never guarantee a lack of then.  It takes time to bake a new release and gain incremental confidence in new code.

In summary.

  1. Get someone to own the deploy software
  2. Value the work
  3. Create a culture of software ownership
  4. LOOK at what you’ve done after you do it
  5. Be suspicious of new versions until they prove themselves

Image result for deploy production meme

Two blog posts in one weekend!  That’s definitely never happened before.  Thanks to Baron for asking me to draft this up following the weekend’s twitter thread: https://twitter.com/mipsytipsy/status/1030340072741064704.

 

 

Shipping Software Should Not Be Scary

The Accidental DBA

This morning there was yet another comment thread on hacker news about Yet Another outage involving MongoDB and data loss, this time by some company called “CleverTap”.

Recap

To summarize: the CleverTap engineering team noticed that the WiredTiger storage engine was faster than MMAPv1 for MongoDB.  They decided to … “upgrade the following weekend” (that sentence alone made my eyes bulge).

According to the blog post, they upgraded from 2.6 to 3.0, while simultaneously changing storage engines from MMAPv1 to WiredTiger, while leaving zero secondaries snapshot nodes with data on MMAPv1.  All over the course of 3 days.

(They are also running sharded mongo, with a mere 300 ops/sec on each primary, which RAISES A LOT OF QUESTIONS but I already feel like I’m beating up on these kids so I won’t pursue that.)

Questions …

(But seriously what the *hell* can you be doing to have such a low request rate, that you
rainbow-umbrella-hineed to shard at an infinitesimal volume?  Why did you specify it in req/min instead of req/sec?  What is the breakdown of reads/writes?  What is the lock percentage?  What is the avg object size??  Are these like multi-MB documents????  Why did you pause all incoming traffic and process it after the upgrade?  If the primary can’t take the extra load, why not rs.syncFrom() a secondary?   If that doesn’t work, don’t you have other, bigger problems??)

Most bafflingly of all: why wait only a few minutes after electing a new WiredTiger primary for the first time ever, and then immediately DELETE your only known-good copies of the data on MMAPv1 and re-sync over them with WiredTiger?

Accidental DBAs

Okay.  So here’s the thing: you are clearly a team of accidental DBAs.  You are operations and software engineers who have found yourselves in charge of the data.

It’s cool.  I am too!  It’s a really neat and fun place to be in.  DBAs and network admins are kind of the last remaining priesthoods in our industry. umbrella-rainbow_cm-f

There’s a lot of powerful and fun stuff to be done for generalists who pick up specialty knowledge in one of those areas, or specialists (like my neteng friend Leslie) who start bringing their skills back to the generalist side and merging the two.

(Oh Right, We Wrote A Book About This!!!)

My friend Laine and I are writing a book for people on the data side, called “Database Reliability Engineering“, which is aimed at generalist engineers who want to learn how to deal with data responsibly and effectively.

(Actually that’s a good point, I am supposed to be pitching this book! — which is really screen-shot-2016-10-01-at-7-00-15-pmmostly Laine with a smidgen of me but it’s going to be super awesome.  Consider this your sales pitch.)

So first, as an accidental DBA, you should obviously buy this book  :).  Second: stateful services require a different mindset[*].  It’s cool that you are running your own databases!  But reading post mortems like this where the conclusion is “MongoDB sucks” makes me fucking grind my teeth.

Stop treating your databases like stateless services.

There are lots of ways that MongoDB (and every other database on the planet) really sucks.  Mongo set themselves up for special rage by overpromising too much early on, and seeming tone deaf to criticism from real database engineers.

But *I* can criticize Mongo all day long.  You children on hacker news who have never run it don’t get to. 😛  If you don’t know what the fuck you’re talking about, if you’re cargo culting other people’s years-old complaints, just shut up already.

Managing stateful services like databases means that you need to be more paranoid than you did with stateless services.  With stateless services the best practices are to to roll early, roll fast, roll often, roll back.  When you’re dealing with state, you need to be careful.

With stateful services you can’t play it fast and loose like that.  You’re going to have data loss, corruption, unpredictable results, catastrophic failures that you can’t simply roll back from.  Data loss can be ruinous to your company.  (This can also be true for stateless services that sit close to your data and mutate it a lot.)

But that’s what makes it fun.  🙂

Be paranoid.

When we were moving from MMAPv1 to RocksDB at Parse, we ran hybrid replica sets for 6-9 months.  We were paranoid.  It was justified!  We spent half a year capturing production workloads and replaying them, electing Rocks primaries and rolling back, and even then keeping snapshlightningots and secondaries of both storage engines for *months*.

This isn’t because MongoDB sucks.  It’s the nature of the game, it’s the difference between stateful and stateless services.

Do you know that there was a total query engine rewrite in 2.6?  We spent months flushing out tons of crazy bugs.  Do you know about the index intersection changes?  We helped chase down bugs in those too.  (You’re welcome.)

You can’t just go “dudes it’s faster” and jump off a cliff.  This shit is basic.  Test real production workloads. Have a rollback plan.  (Not for *10 days* … try a month or two.)

Lessons

If CleverTap had run their plan past anyone experienced with data, they would have called out all of those completely predictable failures, and advised them to change it:

  1. Make one change at a time.  Do a major version upgrade separately from the storage engine upgrade.
  2. Delay between each change.  Two weeks is absolutely minimal, any thing less is careless.  Let them bake.
  3. Storage engine changes are scary.  It takes years to gain confidence in a new way of laying bits down on disk.  (Whenever people bitch and moan about mongo, I remind Rainbow-Umbrella-Z-5_5them that I’ve still lost WAY more data to MyISAM, InnoDB, and MySQL overall than Mongo.
  4. You can run lots and lots of replicas (up to 7 votes per replica set, even more nodes) per each replica set in Mongo.  This is a killer feature.  Why didn’t you use it?
  5. Keep backups around for months in the new storage engine *and* the old storage engine, just in case.  Have two hidden snapshot nodes.  The only cost is in dollars, which is fucking cheap compared to data or engineering time.

If you are a new accidental DBA, you have to make a point of learning things.  Go to conferences.  Read books.  Buy bottles of whiskey for your data friends and pick their brains.  Remember that they know things you do not.  Don’t blame the vendors when you fucked up.

Network engineering is the same way, but mistakes tend to be a lot less … permanent.  You drop some packets..  like grains of sand. ^_^

Remember that you’re in charge of keeping people’s data safe and secure.  You have much to learn.  Learn it.

And get off my fucking lawn.  ❤

Some slides from a couple of relevant talks I’ve given on the subject:

 

[*] P.S.:  “Stop treating your stateful services like stateless services” … this is a fact, but it’s not the aspiration.  DB folks should all be leaning in to the model of learning to treat our stateful services like stateless services, with the same casual disregard for individual nodes.  This is hard, and it’s going to take some time, but it’s clearly where the world is heading and it’s definitely a good thing.  🙂  The learning goes both ways!

 

rainbow-cloud-droplet

The Accidental DBA

DevOps vs SRE: delayed coverage of the dumbest war

Last week was the West Coast Velocity conference.  I had a terrific time — I think it’s the
best Velocity I’ve been to yet.  I also slipped in quite late, the evening before last, to catch Gareth’s session on DevOps vs SRE.

had to catch it, because Gareth Rushgrove (of DevOps Weekly glory) was taunting @lusis and me about it on the Internet.

rainbowdropletAnd it was worth it!   Holy crap, this was such a fun barnburner of a talk, with Gareth schizophrenically arguing both for and against the key premise of the talk, which was about “Google Infrastructure for Everyone Else (GIFEE)” and whether SRE is a) the highest, noblest goal that we should all aspire towards, or b) mostly irrelevant to anyone outside the Google confines.

Which Gareth won?  Check out the slides and judge for yourself.  🙃

 

At some point in his talk, though, Gareth tossed out something like “Charity probably already has a blog post on this drafted up somewhere.”  And I suddenly remembered “Fuck!  I DO!”  it’s been sitting in my Drafts for months god dammit.

So this is actually a thing I dashed off back in April, after CraftConf.  Somebody asked me for my opinion on the internet — always a dangerous proposition — and I went off on a bit of a rant about the differences and similarities between DevOps and SRE, as philosophies and practices.
colorful-rainbow

Time passed and I forgot about it, and then decided it was too stale.  I mean who really wants to read a rehash of someone’s tweetstorm from two months ago?

Well Gareth, apparently.

Anyway: enjoy.


SRE vs DevOps: TWO PHILOSOPHIES ENTER, BOTH ARE PHENOMENALLY SUCCESSFUL AND MUTUALLY DUBIOUS OF ONE ANOTHER


 

So in case you haven’t noticed, Google recently published a book about Site Reliability Engineering: How Google Runs Production Systems.  It contains some really terrific wisdom on how to scale both systems and orgs.  It contains chapters written by dear friends of mine.  It’s a great book, and you should buy it and read it!

Rainbow-Umbrella-Z-5_5It also has some really fucking obnoxious blurbs.  Things like about how “ONLY GOOGLE COULD HAVE DONE THIS”, and an whiff of snobbery throughout the book as though they actually believe this (which is far worse if true).

You can’t really blame the poor blurb’ers, but you can certainly look askance at a massive systems engineering org when it seems as though they’ve never heard of DevOps, or considered how it relates to SRE practices, and may even be completely unaware of what the rest of the industry has been up to for the past 10-plus years.  It’s just a little weird.

So here, for the record, is what I said about it.

 

Google is a great company with lots of terrific engineers, but you can only say they are THE

sre
The Google SRE Bible

BEST at what they do if you’re defining what they do tautologically, i.e. “they are the best at making Google run.”  Etsyans are THE BEST at running Etsy, Chefs are THE BEST at building Chef, because … that’s what they do with their lives.

Context is everything here.  People who are THE BEST at Googling often flail and flame out in early startups, and vice versa.  People who are THE BEST at early-stage startup engineering are rarely as happy or impactful at large, lumbering, more bureaucratic companies like Google.  People who can operate equally well and be equally happy at startups and behemoths are fairly rare.

And large companies tend to get snobby and forget this.  They stop hiring for uniquerainbow-swirl strengths and start hiring for lack of weaknesses or “Excellence in Whiteboard Coding Techniques,” and congratulate themselves alot about being The Best.  This becomes harmful when it translates into to less innovation, abysmal diversity numbers, and a slow but inexorable drift into dinosaurdom.

Everybody thinks their problems are hard, but to a seasoned engineer, most startup problems are not technically all that hard.  They’re tedious, and they are infinite, but anyone can figure this shit out.  The hard stuff is the rest of it: feverish pace, the need to reevaluate and reprioritize and reorient constantly, the total responsibility, the terror and uncertainty of trying to find product/market fit and perform ten jobs at once and personally deliver to your promises to your customers.

rainbow-cloud-dropletAt a large company, most of the hardest problems are bureaucratic.  You have to come to terms with being a very tiny cog in a very large wheel where the org has a huge vested interest in literally making you as replicable and replaceable as possible.  The pace is excruciatingly slow if you’re used to a startup.  The autonony is … well, did I mention the politics?  If you want autonomy, you have to master the politics.

 

Everyone.  Operational excellence is everyone’s job.  Dude, if you have a candidate come in and they’re a jerk to your office manager or your cleaning person, don’t fucking hire that person because having jerks on  your team is an operational risk (not to mention, you know, like moral issues and stuff).

But the more engineering-focused your role is, the more direct your impact will be on operational outcomes.

As a software engineer, developing strong ops chops makes you powerful.  It makes you better at debugging and instrumentation, building resiliency and observability into your own systems and interdependent systems, and building systems that other people can come along and understand and maintain long after you’re gone.rainbow-shade

As an operations engineer, those skills are already your bread and butter.  You can increase your power in other ways, like by leveling up at software engineering skills like test coverage and automation, or DBA stuff like query optimization and storage engine internals, or by helping the other teams around you level up on their skills (communication and persuasion are chronically underrecognized as core operations engineering skills).

This doesn’t mean that everyone can or should be able to do everything.  (I can’t even SAYrainbow-dot-ball the words “full stack engineer” without rolling my eyes.)  Generalists are awesome!  But past a certain inflection point, specialization is the only way an org can scale.

It’s the only way you make room for those engineering archetypes who only want to dive deep, or who really really love refactoring, or who will save the world then disappear for weeks.  Those engineers can be incredibly valuable as part of a team … but they are most valuable in a large org where you have enough generalists to keep the oars rowing along in the meantime.

So, back to Google.  They’ve done, ahem, rather  well for themselves.  Made shitbuckets of money, pushed the boundaries of tech, service hardly ever goes down.  They have operational demands that most of us never have seen and never will, and their engineers are definitely to be applauded for doing a lot of hard technical and cultural labor to get there.

So why did this SRE book ruffle a few feathers?

Mostly because it comes off a little tone deaf in places.  I’m not personally pissed off by
the google SRE book, actually, just a little bemused at how legitimately unaware they seem to be about … anything else that the industry has been doing over the past 10 years, in terms of cultural transformation, turning sysadmins into better engineers, sharing on-call rotations, developing processes around empathy and cross-functionality, engineering best practices, etc.

devops
DevOps for the rest of us

If you try and just apply Google SRE principles to your own org according to their prescriptive model, you’re gonna be in for a really, really bad time.

However, it happens that Jen Davis and Katherine Daniels just published a book called Effective DevOps, which covers a lot of the same ground with a much more varied and inclusive approach.  And one of the things they return to over and over again is the power of context, and how one-size-fits-all solutions simply don’t exist, just like unisex OSFA t-shirts are a dirty fucking lie.

Google insularity is … a thing.  On the one hand it’s great that they’re opening up a bit!rainbow-umbrella-clipart-1 On the other hand it’s a little bit like when somebody barges onto a mailing list and starts spouting without skimming any of the archives.  And don’t even get me started on what happens when you hire long, longterm ex-Googlers back into to the real world.

So, so many of us have had this experience of hiring ex-Googlers who automatically assume that the way Google does a thing is CORRECT, not just contextually appropriate.   Not just right for Google, but right for everyone, always.  Which is just obviously untrue.  But the reassimilation process can be quite long and exhausting when the Kool-Aid is so strong.

Because yeah, this is a conversation and a transformation that the industry has been having for a long time now.  Compared with the SRE manifesto, the DevOps philosophy is much more crowd-sourced, more flexible, and adaptable to organizations of all stages of developments, with all different requirements and key business differentiators, because it’s benefited from loud, mouthy contributors who aren’t all working in the same bubble all along.

And it’s like Google isn’t even aware this was happening, which is weird.

Orrrrrr, maybe I’m just a wee bit annoyed that I’ve been drawn into this position rainbow-dot-ballof having to defend “DevOps”, after many excellent years spent being grumpy about the word and the 10000010101 ways it is used and abused.

(Tell me again about your “DevOps Engineering Team”, I dare you.)

P.S. I highly encourage you to go read the epic hours-long rant by @matthiasr that kicked off the whole thing.  some of which I definitely endorse and some of which not, but I think we could go drink whiskey and yell about this for a week or two easy breezy  ❤

Anyway what the fuck do I know, I’ve never worked in the Google lair, so maybe I am just under-equipped to grasp the true glory, majesty and superiority of their achievements over us all.

Or maybe they should go read Katherine and Jen’s book and interact with the “UnGoogled” once in a while.  ☺️

colorful-rainbow

 

DevOps vs SRE: delayed coverage of the dumbest war