Speaker 1: So if you walked into almost any tech company historically,
Speaker 1: there was basically a war going on, a literal epic
Speaker 1: turf war. Oh.
Speaker 2: Absolutely, a total battleground.
Speaker 1: Right, because on one side you had the software developers
Speaker 1: and their entire job, I mean, their whole performance metric
Speaker 1: was to just move fast, break things, and push continuous updates.
Speaker 2: Yeah, just ship it exactly.
Speaker 1: And then on the other side you had the operations team,
Speaker 1: the knops folks. Their job was stability. They were essentially
Speaker 1: the gatekeepers, and.
Speaker 2: They were the ones holding the pager.
Speaker 1: Right, So because they held that pager, their default answer
Speaker 1: to you almost any new developer idea was just an
Speaker 1: emphatic no.
Speaker 2: Well, I mean it really was a necessary survival tactic
Speaker 2: for them. Think about the stakes for an OPS engineer
Speaker 2: back then.
Speaker 1: Yeah, it was high stakes.
Speaker 2: Huge. If a developer wanted to try out, say a
Speaker 2: brand new, highly experimental library, and that code caused a
Speaker 2: memory leak, oh man, the dreaded memory leaks, right, and
Speaker 2: it brings down the entire server at two am. It
Speaker 2: wasn't the developer getting out of bed, it was the
Speaker 2: ops team waking up to all those alarms. So they
Speaker 2: had to be defensive. The infrastructure just simply couldn't handle
Speaker 2: that level of chaos.
Speaker 1: And that tension is exactly what we're unpacking today. We're
Speaker 1: doing a deep dive decoding the reality of running Docker
Speaker 1: when real customers and you know, real money are on.
Speaker 2: The line, which changes everything.
Speaker 1: It really does. We're pulling from this fascinating book called
Speaker 1: Docker in Production Lessons from the Trenches. And for you listening,
Speaker 1: this isn't your standard like Hello World tutorial, No, not
Speaker 1: at all. We're talking about the duct tape, the midnight alerts,
Speaker 1: the really gritty reality of what happens when you actually
Speaker 1: put this technology into production. Because Docker didn't just introduce
Speaker 1: a neat technical.
Speaker 2: Trick, No, it fundamentally shifted the culture.
Speaker 1: It essentially forced a peace treaty in that epic dev
Speaker 1: versus OPS war. Yeah, it completely changed the ops response
Speaker 1: from no to yes as long as it runs in Docker.
Speaker 2: Yeah. And that shift happened because Docker completely read to
Speaker 2: find the blast radius of bad code.
Speaker 1: The blast radius.
Speaker 2: I like that, right, Let's look at the before picture.
Speaker 2: In a traditional virtual machine setup, you might have say,
Speaker 2: five different applications running on one operating.
Speaker 1: System, right, all sharing the same space.
Speaker 2: Exactly, So if one of those apps has a catastrophic failure,
Speaker 2: it could crash the whole virtual machine. It takes the
Speaker 2: other four instant applications down with it, which is a nightmare,
Speaker 2: total nightmare. But with Docker, that bad code is tracked.
Speaker 2: It is entirely isolated inside its specific container. So if
Speaker 2: it crashes, only that container dies.
Speaker 1: Wow.
Speaker 2: The host server and all the other containers sharing that
Speaker 2: server they just keep humming along completely unaware.
Speaker 1: Okay, let's unpack this because getting to the point where
Speaker 1: ops actually trust that isolation, that requires a massive mental
Speaker 1: shift in how we even view our servers in the
Speaker 1: first place.
Speaker 2: Oh massive, It's a whole new paradigm.
Speaker 1: Yeah. And the book uses this brilliant analogy from a
Speaker 1: company called peer Space, detailing what they call the cattle
Speaker 1: versus pets philosophy.
Speaker 2: It is arguably the most critical mental model for modern infrastructure. Honestly,
Speaker 2: I mean, if you've ever managed servers, you know.
Speaker 1: What a pet is, right, you get attached.
Speaker 2: You really do think about your stateful services, like your
Speaker 2: massive relational databases or your search engines like elastic search.
Speaker 2: Those are pets. You give them specific names, like you
Speaker 2: know database primary era one.
Speaker 1: Right, they have a caller and a tag.
Speaker 2: Exactly because they hold precious, irreplaceable data. They require constant,
Speaker 2: meticulous upkeep. And if a pet gets sick, you.
Speaker 1: Call ve, you log in, you diagnose it.
Speaker 2: Yes, you carefully nurse that specific machine back to health.
Speaker 2: You cannot just delete your primary customer database and pretend
Speaker 2: it didn't happen.
Speaker 1: Yeah, that would be bad. But the other side of
Speaker 1: the infrastructure is entirely different. You have your stateless services,
Speaker 1: your web servers, the APR routers, the code that just
Speaker 1: processes requests without actually storing anything permanently on its own
Speaker 1: local hard.
Speaker 2: Drive, right, and those are your cattle. They are completely nameless.
Speaker 2: They don't store anything unique at all. So if they break,
Speaker 2: if one of them starts acting up or get sick,
Speaker 2: you absolutely do not nurse it back to health. You
Speaker 2: don't even investigate it. Wow, just brutal it is. You
Speaker 2: pull it out of rotation, destroy it, and immediately replace
Speaker 2: it with an identical, fresh clone. And peer space just
Speaker 2: embrace this entirely. They operate over twenty different micro services,
Speaker 2: and almost all of them are treated as disposable cattle.
Speaker 1: And the way they manage these disposable containers is shockingly
Speaker 1: bare bones. I mean they just use static environment variables,
Speaker 1: basically simple text strings telling the container like, hey, the
Speaker 1: database is over here. Yeah, it's incredibly simple, and they
Speaker 1: let standard Linux tools like systems manage them. And for
Speaker 1: anyone less familiar, systemed is just the built in Linux
Speaker 1: service manager that runs in the background to ensure programs
Speaker 1: start up and stay running.
Speaker 2: Right, it's just basic Linux plumbing.
Speaker 1: But hold on, if they are literally typing in environment
Speaker 1: variables and using basic Linux background commands to manage these cattle,
Speaker 1: doesn't that defeat the whole purpose? Well, that sounds incredibly tedious,
Speaker 1: like a massive recipe for human error. If someone has
Speaker 1: to restart these things manually all the time.
Speaker 2: Oh absolutely, If a human being were doing it by hand,
Speaker 2: it would be a complete disaster.
Speaker 1: Okay, good, I thought I was missing something.
Speaker 2: No. No. The key takeaway from pure Space's approach is
Speaker 2: that this simple static configuration is designed to be the
Speaker 2: foundation for automation. Ah automation. Right, You don't have an
Speaker 2: engineer typing those variables. You have a continuous integration and
Speaker 2: continuous delivery system a CICD pipeline doing all the typing
Speaker 2: for you.
Speaker 1: Okay, that makes sense, But to get to.
Speaker 2: A point where a machine can deploy these identical cattle
Speaker 2: incredibly fast and automatically, you first need to understand their DNA.
Speaker 2: You have to look under the hood at how Docker
Speaker 2: images are actually constructed.
Speaker 1: Which brings us to copy on write or COW. This
Speaker 1: is where the efficiency really shines, right. It's the secret sauce,
Speaker 1: because if your team was using traditional virtual machines and
Speaker 1: you needed, say ten web servers, you'd have ten massive
Speaker 1: duplicate copies of an entire operating system just eating up.
Speaker 2: Your hard drives, which is incredibly.
Speaker 1: Wasteful exactly, but Docker uses a layering system. Think of
Speaker 1: copy on write, like those old transparent acetate sheets teachers
Speaker 1: used to use on overhead projectors in school.
Speaker 2: I love this analogy. The overhead projector is a fantastic
Speaker 2: way to visualize the mechanics here.
Speaker 1: Right, So the base operating system, let's say it's a tiny,
Speaker 1: stripped down version of Linux. Is that bottom clear plastic sheet. Now,
Speaker 1: if you want to add a mustache to a face
Speaker 1: drawn on that bottom sheet, you don't take an eraser
Speaker 1: to the base layer.
Speaker 2: Right, You don't touch the base.
Speaker 1: You just draw your mustache on a brand new totally
Speaker 1: clear sheet of plastic and you drop it directly on
Speaker 1: top of the first one. When the projector light shines
Speaker 1: up through both sheets, you see the face with the mustache.
Speaker 1: That is exactly how a Docker layer works.
Speaker 2: And the storage savings are just astronomical. If you have
Speaker 2: twenty different containers all running that same base Linux operating system,
Speaker 2: Docker only stores that bottom transparent sheet once on the
Speaker 2: hard drive. Wow, just once, exactly. Every single container shares
Speaker 2: that exact same foundation, and they only add their own
Speaker 2: incredibly lightweight mustache layers on top. It saves time, it
Speaker 2: saves bandwidth, and it obviously saves disk space. Right. What's
Speaker 2: fascinating here is, however, there is a very dangerous sized
Speaker 2: trap hidden within these transparent layers.
Speaker 1: The permanent marker trap, because you can't just erase things
Speaker 1: once they're drawn.
Speaker 2: Exactly. That's the danger of immutability. Images never shrink, never never.
Speaker 2: Imagine on layer three your container needs to download a
Speaker 2: massive five hundred megabyte compressed archive just to compile some
Speaker 2: software from source.
Speaker 1: Okay, pretty common.
Speaker 2: Then on layer four, the compiling is done, so you
Speaker 2: tell Docker to delete that temporary five hundred megabyte archive
Speaker 2: from the top down. Looking through the sheets, the file
Speaker 2: appears to be gone.
Speaker 1: Because as the top sheet covers it up.
Speaker 2: Right, But because layers cannot be altered once they are created,
Speaker 2: that massive file is still permanently glued to layer three.
Speaker 2: It is completely invisible to the application, but it will
Speaker 2: be downloaded, transferred, and stored by every single server that
Speaker 2: ever uses your image.
Speaker 1: Oh man, that is brutal. So if the sheets are immutable,
Speaker 1: you can't just delete it later. You would have to
Speaker 1: somehow download the archive, extract it, compile a software, and
Speaker 1: clean up all the garbage, all before that transparent sheet dries. Right,
Speaker 1: you'd have to chain it all together into one single step.
Speaker 2: You just describe the exact solution from the trenches. You
Speaker 2: fix this by stringing your commands together using operators in
Speaker 2: your docro file. Oh instead of using three separate ru
Speaker 2: in commands you know, one to download, one to install,
Speaker 2: one to clean up, you combine them into a single
Speaker 2: long r ruin command. The file is downloaded, the software
Speaker 2: is built, and the garbage is deleted, all during the
Speaker 2: creation of a single transparent sheet.
Speaker 1: That was so smart, and the sources emphasize that this
Speaker 1: all star with choosing a tiny bottom sheet to begin with,
Speaker 1: you wouldn't use a massive, bloated desktop operating system as.
Speaker 2: Your base, No, that would defeat the purpose.
Speaker 1: You'd use something purpose built for containers like Alpine Linux
Speaker 1: or BusyBox, which are just phenomenally small. I mean, BusyBox
Speaker 1: is literally two point five megabytes.
Speaker 2: It's crazy.
Speaker 1: It's wild to think you can run a production web
Speaker 1: server on a foundation the size of a low quality
Speaker 1: MP three from nineteen ninety nine.
Speaker 2: The mineralism is really what makes the whole system viable.
Speaker 2: And once your team has mastered building these tiny, highly
Speaker 2: optimized images, you face the next operational hurdle orchestration. Exactly,
Speaker 2: how do you actually take the old version of your
Speaker 2: application down and put the new version up without dropping
Speaker 2: a single user's connection?
Speaker 1: Ah, the holy grail of zero downtime deployment. The book
Speaker 1: highlights an amazing real world set up from a company
Speaker 1: called relate Iq to achieve this, and honestly, given how
Speaker 1: complex this sounds, you'd think they're running some massive, enterprise
Speaker 1: grade cluster. Manager thinks, oh yeah, but their orchestration is
Speaker 1: shockingly simple. It's literally a bashscript.
Speaker 2: A simple basscript running on a kron job every five.
Speaker 1: Minutes wait, just a kron job.
Speaker 2: Yep, and a cron job is really just a basic
Speaker 2: time based task scheduler built into Linux. Every five minutes,
Speaker 2: it wakes up and asks is there a new version
Speaker 2: of our web server available?
Speaker 1: And if there is, it pulls down that tiny new image.
Speaker 1: But how does it swap the traffic without users seeing
Speaker 1: an error page? I mean, they don't want.
Speaker 2: A four or four error right, So they use a
Speaker 2: proxy called hippasche backed by a reddist database. Think of
Speaker 2: that hipache proxy as a train switch operator.
Speaker 1: Okay, I'm picturing it.
Speaker 2: It sits at the front of the station directing all
Speaker 2: the incoming user traffic. When that five minute kron job
Speaker 2: pulls down the new container image, it spins it up
Speaker 2: on the server alongside the.
Speaker 1: Old one, so they're both running at the same time.
Speaker 2: Yes, but crucially, the proxy doesn't send any user traffic
Speaker 2: to the new container immediately.
Speaker 1: Ah. It waits for a health check exactly. The script
Speaker 1: actually tests the new container to make sure it's fully
Speaker 1: booted up and breathing before putting.
Speaker 2: It on the front lines. Once the hell check passes,
Speaker 2: the bash script updates the reddest database. It pash monitors
Speaker 2: that database instantly sees the new destination and immediately flips
Speaker 2: the train tracks just like that. From that millisecond forward,
Speaker 2: all new user traffic flows seamlessly into the new container, and.
Speaker 1: They leave the old container running right next to it
Speaker 1: for a little while, which is brilliant. Just in case
Speaker 1: the new code has a weird bug that wasn't caught
Speaker 1: in testing, they can flip the switch back instantly.
Speaker 2: The rollback takes seconds instead of minutes.
Speaker 1: That's huge.
Speaker 2: It is in a traditional virtual machine environment, spinning up
Speaker 2: a second copy of your server, ensuring it's stable and
Speaker 2: manually switching dns or load balancers could take ten minutes
Speaker 2: or more.
Speaker 1: Which feels like an eternity during an outage.
Speaker 2: With Docker, because the containers are so lightweight, the infrastructure
Speaker 2: can pivot in milliseconds.
Speaker 1: Here's where it gets really interesting though. Related Iq didn't
Speaker 1: just use this architecture for zero downtime production deploys. They
Speaker 1: use this exact same concept to create parallel universes for
Speaker 1: their developers using awsban Stock.
Speaker 2: Yes, by completely decoupling their web tier from their database tier,
Speaker 2: they essentially created a playground. A playground.
Speaker 1: I like that.
Speaker 2: It's always one of the biggest bottlenecks in software development.
Speaker 2: The staging environment q oh.
Speaker 1: Right, waiting for your turn to test your code. So
Speaker 1: imagine you're a UI designer on their team. You just
Speaker 1: changed the CSS to make the checkout buttons blue instead
Speaker 1: of red. You commit your code to a specific branch, Right,
Speaker 1: the CICD system sees that automatically builds a brand new
Speaker 1: Docker image just for your specific branch and spins up
Speaker 1: a dedicated web container.
Speaker 2: And because that web tier is entirely stateless, it's cattle right. Yeah,
Speaker 2: it requires no special setup. It just boots up and
Speaker 2: points itself at a staging database.
Speaker 1: But the best part is with a quick change of
Speaker 1: a single environment variable, that designer can point their isolated
Speaker 1: test container at the actual production database.
Speaker 2: Which is incredibly powerful.
Speaker 1: Right, they can see exactly how their new blue buttons
Speaker 1: look and function with real customer data flowing through the app.
Speaker 1: But they aren't risking a single real user seeing their
Speaker 1: test environment if it completely breaks.
Speaker 2: It creates an incredibly tight feedback loop. I mean, a
Speaker 2: designer or a product manager can sketch an idea, write
Speaker 2: the code, and see it functioning in total isolation against
Speaker 2: real data in under fifteen minutes.
Speaker 1: That sounds like an absolute dream for a developer. But
Speaker 1: and I have to ask, if it's really this easy
Speaker 1: and perfectly isolated, why not just let every developer push
Speaker 1: their containers straight to production whenever they want? Oh boy,
Speaker 1: why do we still need OPS folks acting as gatekeepers?
Speaker 2: Because OPS still owns the underlying platform and they own
Speaker 2: the security of the host. Let's say the developer is
Speaker 2: rushing to me to deadline, they find a tutorial online
Speaker 2: and base their new container on an outdated Python image
Speaker 2: from three years ago.
Speaker 1: Oh where this is going?
Speaker 2: Yeah, if you let them push that directly to production,
Speaker 2: they've just introduced a container full of unpatched, highly exploitable
Speaker 2: security vulnerabilities into your network. That's terrifying. OPS completely loses
Speaker 2: control over what dependencies are running on their servers.
Speaker 1: Which leads us straight into the reality check of security
Speaker 1: in a containerized world. If you're giving developers the power
Speaker 1: to package their own environments, you need serious guardrails on
Speaker 1: the server side.
Speaker 2: Absolutely. The foundational concept that everyone needs to grasp here
Speaker 2: is that, unlike a virtual machine, containers share the host's kernel.
Speaker 1: Right, the kernel being the absolute core brain of the
Speaker 1: operating system, the part that actually talks to the physical
Speaker 1: hardware exactly.
Speaker 2: A virtual machine brings its own completely isolated kernel. It's
Speaker 2: a fortress. But a docker container is ultimately just a
Speaker 2: heavily restricted process running on the host server's brain. So
Speaker 2: if it's breached, if a hacker manages to compromise your
Speaker 2: application and they find a vulnerability in the shared Linux
Speaker 2: kernel itself, they can break out of the container entirely.
Speaker 2: Oh wow, they take over the entire host machine and
Speaker 2: by extension, every other container running on that metal.
Speaker 1: That is a massive threat. The sources actually mention a
Speaker 1: paper from Google about their internal container system BORG and
Speaker 1: it's very telling. Even Google, I mean, the pioneers of
Speaker 1: this technology. They use full heavy virtual machines when they
Speaker 1: are running untrusted external code.
Speaker 2: Yes that detail is crucial.
Speaker 1: They save their raw containers exclusively for their own highly trusted,
Speaker 1: internally written applications.
Speaker 2: When the engineers at Google don't trust a container boundary
Speaker 2: to hold back a dedicated attacker that tells you everything
Speaker 2: you need to know. The isolation is strong, but it
Speaker 2: is not impenetrable.
Speaker 1: To mitigate this, the book lists a bunch of security
Speaker 1: don'ts to lock these containers down in production One major
Speaker 1: rule is removing sued binaries from your images.
Speaker 2: Ah.
Speaker 1: Yes, SOUTH essentially stands for set owner User ID. It's
Speaker 1: a special permission in Linux that allows a normal user
Speaker 1: to execute a file with full root privileges temporarily, things
Speaker 1: like the ping command or the mount command.
Speaker 2: Use this right, which sounds harmless, but it's.
Speaker 1: Not, and the logic is bulletproof. Why on earth does
Speaker 1: a basic web server container need the ability to ping
Speaker 1: other servers on the network or mount physical hard drives.
Speaker 1: It doesn't.
Speaker 2: No, it really doesn't. It is the principle of least
Speaker 2: privilege applied to the extreme. You ruthlessly strip away any
Speaker 2: capability the application doesn't strictly need to function, vastly reducing
Speaker 2: the attack surface.
Speaker 1: They also talk about kernel security frameworks like a parmer
Speaker 1: or SELinux, though the author's joke that the most common
Speaker 1: Google search for cylinicx is how to disable Sylinics.
Speaker 2: Yeah, that's a very common joke in the ops.
Speaker 1: Community because it's so hard to use, because.
Speaker 2: Those frameworks are notoriously complex to configure, but they provide
Speaker 2: mandatory access control at the kernel level. If you can't
Speaker 2: implement Celinux, you should at least be utilizing Linux capabilities
Speaker 2: right capabilities.
Speaker 1: Instead of giving it container full root access, which is
Speaker 1: like giving a stranger the master skeleton key to your
Speaker 1: entire office building, you grant them one very specific capability,
Speaker 1: exactly like natadmin. They get the key to the networking closet,
Speaker 1: but they absolutely cannot open any file system doors.
Speaker 2: Which brings us to the most emphatic, non negotiable rule
Speaker 2: in the entire book. It is written in all caps
Speaker 2: for a reason.
Speaker 1: Right, do not run SSH in your containers.
Speaker 2: Yes, it completely violates the philosophy of immutable infrastructure.
Speaker 1: It really does, and for anyone listening, SSH is how
Speaker 1: you remotely log into a service command line to look
Speaker 1: around or fix things. Ssaging into a Docker container to
Speaker 1: troubleshoot is like popping the hood of your car while
Speaker 1: flying down the highway at seventy miles an hour to
Speaker 1: check the oil.
Speaker 2: That is exactly what it's like.
Speaker 1: You shouldn't be under the hood, you should just be
Speaker 1: looking at the dashboard telemetry.
Speaker 2: But this raises an important question. If you forbid engineers
Speaker 2: from popping the hood and a container crashes in production,
Speaker 2: how do you actually diagnose what went wrong? Right?
Speaker 1: You still need to know what happened telemetry and log mapping.
Speaker 1: You never store the application logs inside the container itself.
Speaker 2: No, never.
Speaker 1: You can figure Docker to map a volume, so the
Speaker 1: container is actually writing its logs directly to the host
Speaker 1: machine's hard drive. Then the host machine scoops up those
Speaker 1: logs and shicks them off to a central dashboard. Like
Speaker 1: Splunk or some logic.
Speaker 2: The container remains entirely disposable. If it crashes and burns,
Speaker 2: the crucial diagnostic logs survive safely on the host. It
Speaker 2: is brilliant an engineer can analyze what happened from their
Speaker 2: central dashboard without ever needing to SSH into a fragile,
Speaker 2: dying container.
Speaker 1: So what does this all mean? If we follow this
Speaker 1: logic to its conclusion. If the container is this perfectly
Speaker 1: sealed black box where we only extract logs from the outside,
Speaker 1: it completely changes how we think about the inside.
Speaker 2: It really does.
Speaker 1: What happens to all those configuration tools we used to
Speaker 1: rely on for a decade. Tools like chef, Puppet and
Speaker 1: antsible were the undisputed kings of configuration management. If the
Speaker 1: Docker image handles all the application dependencies, what do those
Speaker 1: tools even do now?
Speaker 2: Well, they suffer a major demotion. Really Yeah, the entropy,
Speaker 2: you know, the chaos of changing dependencies, library updates, and
Speaker 2: application code has been successfully contained inside the Docker image.
Speaker 1: So chef and Puppet are practically out of a job.
Speaker 2: I wouldn't say they are unemployed, but their scope is
Speaker 2: vastly reduced. Instead of intricately configuring every delicate detail the
Speaker 2: application environment, they are now mostly relegated to simply preparing
Speaker 2: the bare metal host machine. Oh I see, they install
Speaker 2: the base operating system, they install the Docker demon and
Speaker 2: they configure the logging agents. They prepare the soil, but
Speaker 2: Docker plants the.
Speaker 1: Seeds, which brings us to the actual soil, the metal itself.
Speaker 1: Where does all this data physically live? The sources get
Speaker 1: into the gritty reality of storage drivers, specifically comparing two
Speaker 1: major options, AUFS and device.
Speaker 2: Mapper, and this is exactly where academic theory meets hard
Speaker 2: operational reality. Yes, AUFS is incredibly fast, it's the default
Speaker 2: in many setups, and it handles that transparent copy on
Speaker 2: right layering we talked about very efficiently. The problem is
Speaker 2: it has a massive political issue.
Speaker 1: It's not in the mainline Linux kernel exactly.
Speaker 2: It was never officially merged into the core Linux codebase,
Speaker 2: which means, depending on which version of Linux your company uses,
Speaker 2: you might have to heavily hack your system to make
Speaker 2: AUFS work, or it might not be supported by your
Speaker 2: vendor at all.
Speaker 1: So Ops teams naturally turn to the alternative device mapper
Speaker 1: because it's native, it's built right into Linux. But the
Speaker 1: book warns of a massive.
Speaker 2: Trap here, a huge trap, a trap that has caused
Speaker 2: countless midnight outages. Yeah. By default, Device mapper utilizes sparse
Speaker 2: files also known as loopback devices, for its data and
Speaker 2: metadata storage.
Speaker 1: Okay, we need to define this because it sounds harmless.
Speaker 1: A sparse file is essentially a trick. It's a file
Speaker 1: that pretends to be huge, say one hundred gigabytes to
Speaker 1: the application, but it only actually allocates physical space on
Speaker 1: your real hard drive at the exact moment the application
Speaker 1: tries to write new data to it.
Speaker 2: It's a form of thin provisioning that relies on the
Speaker 2: existing file system, and it is catastrophically slow. Catastrophically yes,
Speaker 2: every single time your container needs to write a new
Speaker 2: piece of data, the entire system has to pause. It
Speaker 2: has to ask the physical hard drive for a new
Speaker 2: block of storage. Way for it to be allocated and
Speaker 2: only then write the data.
Speaker 1: Wow, that sounds inefficient.
Speaker 2: In a high traffic production environment, that constant pausing to
Speaker 2: allocate new blocks completely destroys disk performance. It will bring
Speaker 2: a busy database to its absolute knees.
Speaker 1: So the crucial lesson from the trenches here is about
Speaker 1: true thin provisioning. If you are forced to use device mapper,
Speaker 1: you absolutely must configure it to use real, dedicated physical
Speaker 1: block devices. Yes, always so handing it a raw, unformatted
Speaker 1: chunk of your hard drive directly so it manages the
Speaker 1: space natively instead of using those painfully slow default loop
Speaker 1: back files.
Speaker 2: Exactly. Never trust the default settings and production without completely
Speaker 2: understanding the underlying mechanisms.
Speaker 1: We have covered some serious ground today. I mean, we
Speaker 1: started with the epic dev versus ops turf for and
Speaker 1: saw how Docker's cattle versus pet's mentality acted as the
Speaker 1: ultimate peace.
Speaker 2: Treaty, a very necessary treaty.
Speaker 1: We looked at how to build incredibly efficient copy on
Speaker 1: right layers, chaining those r in commands before the transparent
Speaker 1: sheet drives. We explored zero downtime routing with simple proxies,
Speaker 1: the reality of locking down shared kernel security by dropping
Speaker 1: root capabilities and banning SSH and finally navigating the performance
Speaker 1: traps of storage drivers.
Speaker 2: It is a fundamentally different paradigm for infrastructure. It forces
Speaker 2: engineering teams to build systems that are entirely disposable, heavily isolated,
Speaker 2: and ruthlessly automated.
Speaker 1: But I want to leave you with a final sort
Speaker 1: of mind expanding thought. While reading the author bios in
Speaker 1: the text, specifically looking at Justin Kormack's work, there is
Speaker 1: a massive hint the next evolution of all this.
Speaker 2: Oh this is good.
Speaker 1: We've spent this entire deep dive talking about how containers
Speaker 1: abstract the application away from the operating system.
Speaker 2: Right, they make this specific operating system irrelevant to the
Speaker 2: software developer.
Speaker 1: But what happens when we remove the operating system entirely?
Speaker 2: Wait? What?
Speaker 1: There is an emerging concept in computer science called unkernels.
Speaker 1: Imagine if your application code and the absolute bare minimum
Speaker 1: operating system components. I'm talking just the specific networking and
Speaker 1: hardware drivers. It needs nothing else. We're compiled together into
Speaker 1: a single, hyper fast, highly secured, disposable entity.
Speaker 2: That is wild.
Speaker 1: No extra baggage, no SSH, no PIN commands, no unused
Speaker 1: USPE drivers, just your code and exactly what it needs
Speaker 1: to run directly on the bare metal server. If Docker
Speaker 1: made the traditional OS irrelevant to developers, well, unkernels might
Speaker 1: make the traditional OS irrelevant entirely.
Speaker 2: That is a fascinating trajectory. It takes the isolation, the security,
Speaker 2: and the minimalism of containers to as absolute logical excas.
Speaker 1: Something for you to ponder on your own as you
Speaker 1: look at your team's infrastructure this week. Thank you so
Speaker 1: much for joining us on this deep dive. Hopefully, the
Speaker 1: next time you sit down with your debonops teams, you're
Speaker 1: building peace treaties instead of throwing servers at each other.
Speaker 1: We'll be back to explore the cutting edge of tech
Speaker 1: with you next time.