You Are Not Google (2017) | Hacker News Reader

You Are Not Google (2017)

blog.bradfieldcs.com

1,623 pointsgerbilly572 comments

The big issue I think we miss when people say "why are you using Dynamo, just use SQL" or "why are you using hadoop, a bash shell would be faster" or "why are you using containers and kubernetes, just host a raspberry pi in your closet":

The former examples are all managed! That's amazing for scaling teams.

(SQL can be managed with, say, RDS. Sure. But it's not the same level of managed as Dynamo (or Firebase or something like that). It still requires maintenance and tuning and upkeep. Maybe that's fine for you (remember: the point of this article was to tell you to THINK, not to just ignore any big tech products that come out). But don't discount the advantage of true serverless.)

My goal is to be totally unable to SSH into everything that powers my app. I'm not saying that I want a stack where I don't have to. I'm saying that I literally cannot, even if I wanted to real bad. That's why serverless is the future; not because of the massive scale it enables, but because fuck maintenance, fuck operations, fuck worrying about buffer overflow bugs in OpenSSL, I'll pay Amazon $N/month to do that for me, all that matters is the product.

For anyone who's not used these "managed" services before, I want to add that it's still a fuck ton of work. The work shifts from "keeping X server running" to "how do I begin to configure and tune this service". You will run into performance issues, config gotchas, voodoo tuning, and maintenance concerns with any of AWS's managed databases or k8s.

> I'll pay Amazon $N/month to do that for me

Until you pay Amazon $N/month to provide the service, and then another $M/month to a human to manage it for you.

Exactly. There's no silver bullet, only trade offs.

In this case you're only shifting the complexity from "maintaining" to "orchestrating". "Maintaining" means you build (in a semi-automated way) once and most of your work is spent keeping the services running. In the latter, you spend most of your time building the "orchestration" and little time maintaining.

If your product is still small, it makes sense to keep most of your infrastructure in "maintaining" since the number of services is small. As the product grows (and your company starts hiring ops people), you can slowly migrate to "orchestrating".

"For anyone who's not used these "managed" services before, I want to add that it's still a fuck ton of work. The work shifts from "keeping X server running" to "how do I begin to configure and tune this service"."

I have noticed that too. With some managed services you are trading a set of generally understood problems with a lot of quirky behavior of the service that's very hard to debug.

The general fact of reality is that if you are building anything technical, then knowing and managing the details, whatever the details are, will get you a lot more bang for your buck. Reality isn't just a garden variety one-size fits all kind of thing, so creating something usually isn't either. If you just want a blog like everyone else's, then that comes packaged, but if you want something special, you will always have to put in the expertise.

What really bugs me about this is that it reminds me of the dotcom days. Microsoft ASP stacks made it easier to find less experienced developers to quickly develope sites, no one having an awareness of optimisation and instead would throw hardware at it. Large clusters of DEC Alpha servers would handle the same amount of traffic as a single FreeBSD on a PC, but this cost difference wasn't a problem when investor cash flowed easily and shifting Microsoft and Cisco products fed into the revenues of the system integrators which was fine since revenue growth mattered more than profits.

I've seen this with many AWS deployments, which on a pure hardware cost is 3-5 times more, but with the way it makes it 'easy' to scale instead of optimise instead costs 10-20 times more. When the investor cash starts drying up and the focus is going to be on competing for profits in a market that is much smaller in general, many organisations are going to find themselves locked into AWS and for them it's going to feel like IE6 and asp.net all over again.

Bang on. Realistically the value of Amazon's managed side is in the early stages. At latter stages with people, it's significantly lower cost to tune real resources, and you get added performance benefits.

We make a decent business out of doing just this, at scale for clients today.

If your "managed services" are a ton of work, then they're not really managed.

I built a system selling and fulfilling 15k tshirts/day on Google App Engine using (what is now called) the Cloud Datastore. The programming limitations are annoying (it's useless for analytics) but it was rock solid reliable, autoscaled, and completely automated. Nobody wore a pager, and sometimes the entire team would go camping. You simply cannot get that kind of stress-free life out of a traditional RDBMS stack.

Yeah, I think it's a trade off. Certain services can be a no-brainer, but others will cause pain if your particular use case doesn't align precisely with the service's strengths and limitations.

DynamoDB vs RDS is a perfect example. Most of that boils down to the typical document store and lack of transactions challenges. God forbid you start with DynamoDB and then discover you really need a transnational unit of work, or you got your indexes wrong the first time around. If you didn't need the benefits of DynamoDB in the first place, you will be wishing you just went with a traditional RDBMS vs RDS to start with.

Lambda can be another mixed bag. It can be a PITA to troubleshoot, and their are a lot of gotchas like execution time limits, storage limits, cold start latency, and so on. But once you invested all the time getting everything setup, wired up...

In for a penny, in for a pound.

When you buy a service from a big company, and it doesn't work, you get to debug the service.

>You will run into performance issues, config gotchas, voodoo tuning, and maintenance concerns with any of AWS's managed databases or k8s.

The default config for a LAMP stack will easily handle 100 requests per second. 10 if you app isn't optimized.

Run apt upgrade once a month and enable automatic security updates on ubuntu.

That is neither hard nor "vodoo".

I've used managed services and I don't see the point until you hit massive scale, at which point you can afford to hire your engineers to do it.

That doesn't sound like a shift of work. It sounds like work I already would have done - performance tuning doesn't go away by bringing things in house.

Now that person I pay to operate the service can focus on tuning, not backups and other work that's been automated away.

Sounds like a massive win to me.

Then you pay $O/month for AWS Enterprise Support (who are actually quite good and helpful) to help augment your $M/month employees and $N/month direct spend.

To be fair, this is only a valid shift for folks moving. If you are creating something, you have both "how do I configure" and "how do I keep it moving?"

That is, the shift to managed services does largely remove a large portion, and just changes another.

Yes, but it's still pretty much a straight cost offset. If you hold your own metal, you have to do all of that and still administer the database. Sure, there could be a little overlap in storage design, nut most of the managed systems have typical operational concerns at a button click: backup, restore, HA... Unless your fleet is huge and your workload is special, you're going to win with managed services.

And a lot of the time keeping X running is simpler than configuring and tuning this service.

He specifically mentioned Dynamo. There is nothing to configure except indexes, and read and write capacity units.

That's no joke. I have a decent software background and it was far from trivial to get going with aws services. Their documentation doesn't always quite tel you everything you need to know and half thhe time there are conflicting docs both saying to do something that's wrong. Still has been less work than a production server at my last engineering job but then again that project had a lot of issues related to age and shitty code bases. Hard to say which would have been less work honestly.

> but because fuck maintenance, fuck operations, fuck worrying about buffer overflow bugs in OpenSSL, I'll pay Amazon $N/month to do that for me, all that matters is the product.

That's nice in theory, but my experience with paying big companies for promises like that is I still end up having to debug their broken software; and it's a lot easier to do that if I can ssh into the box. I've got patches into OpenSSL that fixed DHE negotiation from certain versions of windows (especially windows mobile), that I really don't think any one's support team would have been able to fix for me / my users [1], unless I had some really detailed explanation -- at that point, I may as well be running it myself.

[1] And as proof that nobody would fix it, I offer that nobody fixed this, even though there were public posts about it for a year before my patches went in; so some people had noticed it was broken.

This.

I've been informally tracking time spent on systems my team manages and managed tools we use. (I manage infra where I work.)

There is very little difference.

And workarounds and troubleshooting spent on someone else's system mean we only learn about some proprietary system. That's bad for institutional knowledge and flexibility, and for individual careers.

Our needs don't mesh well with typical cloud offerings, so we don't use them for much. When we have, there has yet to be a cost savings - thus far they've always cost us more than we pay owning everything.

I mean, I personally like not dealing with hardware or having to spend time in data centers. But I can't justify it from a cost or a service perspective.

absolutely. I am _right_now_ fighting ELB because we see traffic going to it but not coming out of it. If it were MY load balancer, I would just log in and tcpdump.

Your experience with companies or your specific experience with AWS?

I work on a team where our products are all in Fargate containers. I understand the appeal of serverless -- you never need to exec into the container, but half the time when we're debugging an issue in prod that we can't reproduce locally, we'll say, "wouldn't this be easier if we could just exec into the container and find out exactly what's going on?"

Not being able to ssh into a container sounds like a missing feature of that particular container solution? I would expect that I can ssh into a docker container hosted in a kubernetes cluster. Hmm, pretty sure I must've done this dozens of times.

Removing SSH should be the goal though. If you follow the old Visible Ops book you also "Electrify the Fence" and introduce accountability, etc. If your goal is to see what a process is doing introduce tracing. If you need to add a "println()" then push that out as a change because the environment is changing from your altering of it. Because the tool doesn't exist yet that you need to SSH into a box doesn't mean it shouldn't - you have to instrument the tooling to prevent you from needing this adhoc ability. Admittedly it scares me still but ideally the end game is to never need to or have the ability to do so through a tool which has all the things you are looking for without allowing a human to be too human and miss a semi-colon.

> My goal is to be totally unable to SSH into everything that powers my app

That sounds like a nightmare to me, and I'm not even a server guy, I'm a backend guy only managing my personal projects.

> I'll pay Amazon $N/month to do that for me, all that matters is the product.

I don't want to pay Amazon anything, I want to pay Linode/Digital Ocean 5 or 10 or 20 dollars per month and I can do the rest myself pretty well. My personal projects will never ever going to reach Google's scale and seeing that they don't bring me anything (they're not intended to) I'm not that eager to pay an order a magnitude more to a company like Amazon or Google in order to host those projects.

it's astounding how much a $20 linode can handle (as a web host) with a proper software stack and performance-minded coding.

but companies throw money, hardware and vendor lock-in at problems way before commiting to sound engineering practices.

i have a friend who works at an online sub-prime lender, says their ruby backend does hundreds of queries per request and takes seconds to load. but they have money to burn so they just throw hardware at it. they spend mid-five figures per month on their infrastructure and third party devops stuff).

meanwhile, we run a 20k/day pageview ecommerce site on a $40/mo linode at 25% peak load. it's bonkers how much waste there is.

i think offloading this stuff to "someone else" and micro-services just makes you care even less about how big a pile of shit your code is.

Cloud providers have generous free tiers, so many personal projects pay $0. However, I'm in the same boat. I'd rather pay $20/month to DO and risk my site going down in a DDoS attack than having a REST endpoint basically tied to my credit card and hoping Amazon blocks all the malicious traffic.

As someone tasked with protecting infrastructure, devs not being able to SSH into production is a godsend.

Enjoy the many (many, many) connection reset by peer, TCP idle timeouts, and 500 Internal Server Errors on Dynamo's end.

Between that and the dogshit documentation, it's truly thrilling to be paying them a ridiculous amount of money for the privilege of having a managed service go undebuggably AWOL on the regular, instead of being able to resolve the issues locally.

Yeah and if you’re on gcp you also get dogshit support whose main strategy is to sandbag you with endless (and useless) logs collection until you give up and move on or take too long so they can close the ticket.

I can see my code handling all the exceptions correspond to the points you raised with exponential backoff. Which part of Dynamo's documentation is dogshit?

To me this comment is not a response to the article.

The article isn’t asking why use Dynamo vs SQL in the context of the traditional NoSQL vs SQL way

It’s why Dynamo vs QLDB.

Or why Cloud Firestone vs Cloud Spanner.

Or Firehose + some lambda functions vs EMR

It’s wholly separate of the managed angle and focuses on the actual complexity of the products involved and the trade offs therein.

In the surface the two sound similar, but there’s much more nuance to this article's point (take the example of Dynamo, the author isn’t opposed to it for being NoSQL, they’re opposed to it for low level technical design choices that are a poor fit for the problem their client had)

Yeah, "not my problem" sounds like a winning strategy, until you realize that it also means "nothing I can do about it" when things inevitably go wrong.

Not to mention that you're the lowest priority when something does go wrong.

But these services do break, and when they do break, often times you don't even get a useful error or log file to grep. Sometimes I just really wish I could SSH into a box to figure out what the hell is going on.

You're gaining an expertise, but instead of it being with the underlying technology, it's with the management services for that technology. I think the premise of your argument is correct that understanding these services is simpler than the technology itself, but I seem to inevitably find myself in some edge use case of the service that is poorly documented or does not work as described, and trolling stack overflow or AWS developer forums is the only solution other than trial and (no) error.

Let me tell you: it's not fun when you're entrenched into a proprietary service that is now outdated and kept only for legacy reasons, and the version you are on doesn't allow you to do things like update your programming language to a more recent version. Now you get to move to another managed service and lose all that expert knowledge you had with the old one.

> My goal is to be totally unable to SSH into everything that powers my app.

If the 0day in your familiar pastures dwindles, despair not! Rather, bestir yourself to where programmers are led astray from the sacred Assembly, neither understanding what their programming languages compile to, nor asking to see how their data is stored or transmitted in the true bits of the wire. For those who follow their computation through the layers shall gain 0day and pwn, and those who say “we trust in our APIs, in our proofs, and in our memory models and need not burden ourselves with confusing engineering detail that has no value anyhow” shall surely provide an abundance of 0day and pwnage sufficient for all of us.

Thus preacheth Pastor M. Laphroaig.

If you go server less because fuck accountability, shouldn't the extension be jobless because someone else will provide the service you did?

In that way, cloud/serverless migration is really the whole worldwide IT workforce progressively willingly migrating IT (design, development, operations & applications) out of companies, to just a few big providers.

Just like energy.

And who gets to decide destinies in the world? Those who control, have and provide energy.

What a time to be alive.

End of XIX century was the rush to oil-based energy to control the world. End of XX was the rush to data-based capture and control.

Which Dynamo are you talking about? The Dynamo paper, or Dynamo, the AWS managed database product? Dynamo-the-product makes sense for all sorts of random tasks. Full-on implementations of Dynamo-the-paper (for instance: Cassandra) fall into the bucket of "understand the problem first" that the author is talking about.

Security and privacy yet again traded for convenience. We need strict data privacy laws and then I would have a little more trust in large centralized entities that would otherwise have every incentive to abuse people's data.

Such centralized systems also become significant points of failure. What happens to your small business if one of the many services you rely on disappears or changes their API?

"All that matters is the product" sounds very much like "all that matters is the bottom line" and we've seen the travesties that occur when profit is put above all else.

It's not clear that having your own datacenter is more secure than using AWS/GCP/Azure services. In both cases there are good monitoring solutions, however I'd say that cloud-based solutions have easier immediate access to most things because they just integrate with the provider's API whereas on prem you're installing agents and whatnot.

Also having granular IAM for services and data is very helpful for security. You have a single source of truth for all your devs, and extremely granular permissions that can be changed in a central location. Contrast to building out all of that auditing and automation on your own. Granted, IAM permissions give us tons of headaches on the regular, but on balance I still think it's better when done well.

If you're concerned about AWS/GCP/Azure looking at your private business's data, I think 1.) that's explicitly not allowed in the contracts you sign 2.) many services are HIPAA compliant, hence again by law they can't 3.) They'd for sure suffer massively both legally and through loss of business if they ever did that.

It is important to understand the following:

Depending on the problem, choose the right tools. For example so far everything I've seen about React and GraphQL tells me that maybe GQL isn't necessarily the best solution for a 5-person team, but a 100 person team may have a hard time living without it.

Kubernates / Docker is significant work. And when we had 4 developers making that effort was wasteful and we literally didn't have the time. Now at 20 we're much more staffed and the things Kubernates / Docker solves is useful.

Meanwhile we have a single PostgreSQL server. No nosql caching layer. We're at a point where we aren't sure how far we gotta go before we hit limits of a PSQL server, but at least 3-4 years before we hit it.

Point is look at the tools. See where these tools win it for you. Don't just blindly pick because it can scale super well. Pick it because it gives you things you need and a simplicity you need for the current and near-term context, and have a long-term strategy how to move away from it when it is no longer useful.

> My goal is to be totally unable to SSH into everything that powers my app.

In reality that means that when something doesn't work as expected, instead of logging in and solving the problem, you'll be spending your time writing tickets and fighting through the tiers of support and their templated replies, waiting for them to eventually escalate your issue to some actual engineer. Wish you a lot of luck with that, I prefer being able to fix my own stuff (or at least give it a try first).

I'm not sure I follow, how does managed services help with choosing the right database technology for the problem set?

He doesn't care about that. JUST THE PRODUCT. That's how amazing his fantasies are.

Yeah, I can't quite square what the OP is saying with how to deal with something like Amazon Dynamo's lack of large scale transactions. (Even basic ACID was only introduced late last year, if I read correctly.)

A SQL database, bash, and a Raspberry Pi in your closet all are managed too. They're built on top of software that's been battle-tested over decades to keep itself running. MySQL/Postgres/whatever will manage running multiple queries for you and keeping the data consistent as well as Dynamo will. bash will manage running multiple processes for you as well as Hadoop will. A Raspberry Pi with cron will manage starting a service for you every time it powers on as well as Lambda will. The reasons to use Dynamo/Hadoop/Lambda are when you've got different problems from the ones that MySQL/bash/cron solve.

If you don't believe that this counts as "managed," then for all your words at the end, you believe at the end of the day that making a solution more complex so that failures are expected and humans are required is superior to doing a simple and all-in-code thing. For all that you claim to not like operations, you think that a system requiring operational work makes it better. You are living the philosophy of the sysadmins who run everything out of .bash_history and Perl in their homedir and think "infrastructure as code" is a fad.

With this definition unless you write your own database system, everything is managed. That makes it pretty useless to qualify anything as managed.

The Raspberry Pi is managed by you, as opposed to managed by a different company / different people so you can focus on your business problem.

Here I am still using SQLite (had to add an index yesterday!) Maybe one day I will grow up :(

SQLite is good quality software, and very heavily tested. There's nothing wrong with using it so long as you don't outright misuse it, and they are clear about when you shouldn't be using it.

How do you access remotely, from your workstation, to the SQLite database on your server, while it is still serving requests?

... Fuck debuggablity. Fuck performance. Fuck understanding what is actually going on....

(Sure, managed solutions can satisfy all of those. But not without understanding a few orders of magnitude more complexity than a simpler solution, and you'll never get there if your attitude is "I'll pay Amazon to do that for me." Amazon doesn't care whether you succeed.)

> Amazon doesn't care whether you succeed.

Wait, what? Why wouldn't they? Successful companies will spend money with them; defunct companies can't.

> My goal is to be totally unable to SSH into everything that powers my app. I'm not saying that I want a stack where I don't have to. I'm saying that I literally cannot, even if I wanted to real bad.

So you want technology that prevents you from doing something you might want to do, real badly?

If the main reason for your switch from sql to dynamo is that it is managed why not use aws aurora, aurora serverless, or any other more managed sql solution?

Now when many sql databases support json I don’t quite understand why you would use a documentdb for anything else then for actual unstructured data, or key value pairs that needs specific performance.

I used mongodb for a while now I’m 100% back in sql. Document dbs really increased my love for sql. But I’m also long from being a nosql expert and I never have to deal with data above a TB.

Serverless is not a panacea, actually most of the times is true the opposite. You will spend much more time dealing with configuration, deployments, managing cold starts, orchestrating everything. Sure, for some use cases is perfect, but I can tell you almost certainly that the future won’t be on AWS lambda &co.

Its moving complexity from the application layer to the deployment layer.

To your point, when I often see the "shell script" solution it's often missing basic things you'd want like version control or any kind of bread crumb trail (I find myself sshing in and "grep -r" trying to find things). That works if you're cobbling something together, but doesn't scale beyond 1 person.

It's tough to get the correct level because you can ansible > docker > install an rpm or just change the file in place. Both have their place and "hacky" solutions can work just fine for many years.

> That's why serverless is the future;

Future of what exactly?

The future of the pit they will dump money into.

The future of making nouns out of adjectives, apparently.

Future of server ?

Before we make a technology choice, we should be clear what those choices are. SQL is a query language and DynamoDB is a database. "NoSQL" technologies can be fronted with SQL interfaces. Relational databases can vary a lot too in their ACID compliance ("I" being the component with maximum variance).

The choice of technology should first be based on the problem and not whether it is serverless. Choosing DynamoDB when your application demands strict serializability would be totally wrong. I hope more people think about the semantic specifications of their applications (especially under concurrency).

Well, if all you want is managed, per request hosting of SQL, use Aurora serverless! It's a game changer. As for Dynamo vs SQL -- you really cannot substitute one for the other. They're different tools for different jobs. Dynamo is extremely low latency and great for that purpose, while SQL is great for being a store of record, and if you use it as long as you can get away with it for as many things as possible, it's usually not a bad idea.

Issue is, this works only for very simple systems. With complexity comes maintenance and operations. You also cannot build anything innovative in case of heavy load systems because you cannot embbed your custom highly performant solutions (cloud provider simply does not support them ootb).

Serverless sounds great for "some" kind of systems but in other cases its a complete missfire. Our job is to know which tool to use.

N gets to be a big number my friend. I love aws but I can’t get over the number of startups I’ve seen with $100k MRR and $35k MRC allocated to AWS.

>"why are you using Dynamo, just use SQL"

On the other hand, I find it frustrating how many developers either don't know basic SQL or try to get cute and use the flat side of an axe to smack down nails instead of just using the well-known, industry-standard beauty of a hammer that SQL.

If everyone did this, in the limiting case, no one has the ability to investigate and repair actual bugs until the point where the application can only crash every 10 ms and does nothing but get continually restarted.

>SQL can be managed with, say, RDS. Sure. But it's not the same level of managed as Dynamo (or Firebase or something like that).

It's managed at every shared hosting in the world.

Not at the same level, hardly anywhere. Replication, auto-scaling, failover and point-in-time backups are typically not managed for you. AWS and GCP have decent solutions now, but they're not nearly as hands-off in terms of management.

What specific managed services do you use, if I may inquire?

>> My goal is to be totally unable to SSH

Great goal. I guess you never had any use case to login to a production server to investigate a business critical issue.

I have had to SSH into prod boxes many times to debug emergent issues. Now I'm using all serverless, and I get a happy fuzzy feeling whenever I think about how I never ever have to do that again.

It's more likely that parent has had to do that a lot of times, or perhaps has managed people who have. That would be a sign of organizational dysfunction, and fixing that is a good goal to have.

I recommend heroku and heroku postgres

Using Dynamo is the worst thing we've done for productivity and costs in our product. It introduces onboarding overhead, unnecessary implementation time overhead, costs a lot, and is always a bottleneck in every operation. Using DAX requires paying for VPC which already costs more than a server that could power many small to mid-sized products. Resizing table throughputs requires lots of manual work to keep it cost effective, because the default settings are designed to nickel and dime you to death. If we just used a simple SQL or MongoDB server attached as a container we'd save literally months of development time and thousands of dollars per month.

This sounds beyond painful. We have similar problems at work with expensive system that I believe we could reimplement with traditional technology, but we don't.

If you don't care about cost and don't care about someone else having all of your data, that is probably a fine route.

I see that HN now steps into the gutter of reddit-like language and can damn all professional decorum.

> As of 2016, Stack Exchange served 200 million requests per day, backed by just four SQL servers: a primary for Stack Overflow, a primary for everything else, and two replicas.

This was the most enlightening piece of the article for me. Their alexa rank today is 48 (globally, 38 in U.S.), so whatever your site is, you are probably not dealing with as heavy a load as them. What techniques do you have to employ to serve this many requests from a single database server?

https://stackexchange.com/performance

https://nickcraver.com/blog/2016/02/17/stack-overflow-the-ar...

Lots of caching (redis, CloudFlare) and trying not to use the database unless absolutely necessary, I would expect.

That's the beauty of serving mostly read-only data - you can just cache the most frequented pages. I can't think of a time that I clicked on a link for Stack Exchange that wasn't served via a search engine. I've never purposefully clicked a link from within Stack Exchange, and I've never posted any answers. I can thank those who do for enabling my success in my career, but I'm willing to bet that most Stack Exchange users are just like me.

Also, caching at the correct granularity. I've seen too many systems that cache on a fine grained level and performance drops because because there are so many cache lookups, even with an in memory cache.

They have details here: https://nickcraver.com/blog/2016/02/17/stack-overflow-the-ar...

Lots of caching, lots of RAM, SSD storage, and a low-level ORM for SQL.

well the "orm" is more like a database driver that can serialize to models than an orm.

but consider their stack there are better "starting" alternatives (they also used them before dapper), like Linq2Sql, EF Core, etc...

Also compared to other languages Linq and EF Core are way better than most alternatives (besides that some things are just not working, like lateral joins)

Caching and a whole lot of it. Most of their pages could be served by a CDN.

How much do you think stack overflow is reading vs writing? How often do you even click a link from a stack overflow page once there?

Upvotes are clicked a lot but I guess they are just an api call and can be tuned to be high performance.

Well it's easier than you think (if you know what you are doing). I am serving around 90-100 mil requests per day from one 4-core xeon machine with most of the CPU idle. With around 2500 queries/s to redis and ~2k queries per second to postgres. App is written mostly in Go. A lot of those requests are updates.

Where did you look for tuning Postgres? I am interested.

I would love to know as well.

Well you have to remember that outside of cloud unknown blac kbox vcpus, generic networking setups and extremely underpowered storage options exist a different world where you can have a box with 200+ physical cores few TBS of RAM and extremely high performance storage

Well, regardless of caching and special ORM, doing 11K peak sql qps on 15% cpu load, is remarkable. They did a great job optimizing the database and access.

They probably also save a lot on ops team - running a single sql server with a failover is well-understood, error-resistant and covered in vendor's guides/best practices.

Except that Stack Exchange is a very static site so caching and read ratio is probably very high, I guess 99% of the people just type a question on Google land on SE and that's it.

Simple: don't hit the database with every request. SO is mostly hosting static text content that updates infrequently and can be very heavily cached. You don't need to read the database to generate every pageview, quite the opposite.

While the 2 servers for StackOverflow is true, if i remember correctly those were two very, very, very beefy machines. Im talking 64 cores and 256GB RAM beefy bare metal, not your average random 4core/4gb VPS.

and they're using that very un-hip microsoft .net

Which goes to show that the answer to "what technology should I use to start my startup in [current year]" is always "the one you know best" since Joel was a former Microsoft employee before starting Stack Overflow and his other projects.

C# is great. It's a high level programming language and it's also super-fast handling ~7.000.000 requests per second on a single server according to the following benchmark:

https://www.ageofascent.com/2019/02/04/asp-net-core-saturati...

C#/.NET can be super fun and productive. Use what you know.

C# was great in 2008, then it got kind of enterprisey and closed - not it's ridiculous and getting steam into all avenues of software engineering. It's arguably the most powerful language in the world.

in the past two years .NET has become (normally/probably) the best performing GC runtime out there. With some nice features as well, and a better cross platform story.

hipness has increased

It's the outcome of good architecture, application code, and the fact that single row lookups are already just as fast as a cache, especially in SQL Server running on highend hardware.

By the way, 200M is less than 2500 requests/second which is not much at all. In terms of actual throughput, they are not that big.

> By the way, 200M is less than 2500 requests/second which is easily doable.

They are probably not spready uniformly throughout the day...

They're also not all full page loads either. Their stats show only 66M of those 200M are pageviews.

It is difficult to make a conclusion. We can have a simple architecture but we spend 200% effort on tuning every part of the system.

Good example for which I’d highlight a particular thing to note: The kinds of systems that minimize failure probability at a large scale are often not the kinds of systems that minimize failure at a small scale.

At a large scale (e.g. hundreds or especially millions of hardware nodes) the most common faults will be due to individual nodes / services / whatever failing, so you want a complex fault tolerant system to deal with those faults.

At a small scale (e.g. stuff that can fit on one or several servers) the most common faults are from the system itself, not from individual nodes. Here, using a complex system will drive up the likelihood of failures, especially when you don’t have a large team to manage the system.

Our longest outage was our cloud provider (one of the big guns) turning off our entire account for 10 hours due to suspicious activity.

If im not mistaken, Azure went down recently in one region for a wooping 24 hours.

That's pretty scary for anyone having at least three nines SLA

Grandparent speaks about a different scenario. In a similar vein: imagine your credit card blocks AWS payments, you don't notice, and then AWS payment reminders land in your spam folder. Boom, services out.

At the small scale, the most likely failures are that your product isn’t something users want to use or pay for.

That is, tech performance rarely killed a startup, but programmers not understanding product kills them all the time.

I would really love to read more about this, with some examples and antipatterns for small systems

What this article, and the comments on it at the moment, are missing is that developers choose many of these technologies because they are sexy and will help the developer get their next job.

Which of the following two developers has the better chance of breaking six figures next year:

"Used Hadoop, MapReduce, and GCP for fraud detection on..."

...or...

"Used MySQL and some straightforward statistics for fraud detection on..."

This is a big part of why all these things exist in places where they shouldn't. As a dev that always goes for the simplest solution first and has yet to break a hundred k at 40 ... I'm spending my evenings now trying to figure out how to deploy the latest technologies where they're totally not needed.

Maybe I’m just swimming against the tide, but I work at a “big N” company and I am more impressed by “saved X dollars”, “made process Y faster” or “built feature Z” than I am with a specific set of technologies.

I’ve interviewed a lot of incredibly bright people that didn’t know any technologies more modern than C++.

+1. It feels like most people making comments such as the OP are people actually not working in BigN companies and just making assumptions.

I've done a lot of interviews at BigCo and definitely not looking much at the technology.

Part of the problem is that many software developers work in organisations where that kind of information - dollars saved or made, time saved, steps reduced - isn't shared. In a siloed org, the development team writes code to satisfy bug reports or feature requests, which come from a business analyst or product owner, who is the sole conduit to "the business". Why these things are needed, their relative priorities, or their ultimate impact, are not regarded as important concerns for the development team.

tldr: software developers often can't measure the impact of their code, so they fall back on describing the technologies they use, which drives a counter-productive desire to employ "sexy" technologies.

That's surely because C++ is the language To Rule Them All!

I jest partly, because every time I write something in C# or PHP (even with the half-baked "strong" typing they are introducing), I constantly curse and think how easy it'd be in C++

I do a lot of interviewing at a big company. It's easy to detect when people are cramming technologies on their resume, and it's a moderate negative signal. They aren't thinking of the customer, or of the problem; they're thinking about the tech. That gets in the way of good design and is a red flag, in my book. I'm not at all alone in this, so I'm not sure it's as clear as you make it out to be.

Same here, but I can tell you from experience with group interviews that while we're definitely not alone, we're probably in the minority.

There is a term for this: resumé driven development. I experienced it too first hand and it is maddening. Every problem becomes a microsevice kubernetes problem.

The competent software teams and companies don't hire by keyword though, they look at actual output and results delivered.

I have this silent debate with my engineers from time to time when one of them gets an itch they feel the need to scratch with an industrial strength back-scratcher. I usually go "lawyer mode" and ask them question after question to justify their choices. They either forget their itch or realize that rubbing themselves against a wall will fix it. I understand their desire to put en vogue frameworks on their resume, but I can't have someone's flight of fancy fucking up our tech stack.

I actually think it's incredibly insulting when people assume that engineers are choosing frameworks just for their resume. Most in fact are looking to these tools e.g. React, Spark, Kafka etc because so many other engineers are using them with success. And so they think they will equally have success.

But then they didn't have the context as to why those tools were chosen and so often they aren't suited. But I've never met anyone in the last 20+ years with thousands of engineers who was doing it for their resume. In fact the best thing for your resume is for the project to be a success anyway.

I've followed people managing tech teams and I think, like anyone else, engineers spend more time doing things they like and are interested in and slow-walk things they aren't. Trying to align those incentives with the bigger project or the company is what managers do.

Looking at new tech is way more interesting than doing the same thing you've been doing for 10 years. Its good to have some mechanism in place to vet that since I know I don't trust myself to always make a good choice (and in my opinion I have excellent taste).

A perverse incentive I've noticed at almost every company I've worked for is that the guy who spends every day slugging it out in a bash terminal to keep a ten year old service running won't ever get promoted. However, but the guy who tries to rewrite it in Apache Flamboozle and AWS Fuzzworks will get promoted long before his house of cards tumbles down.

The end result is that not only is the routine stuff boring but it's also career-limiting.

> I actually think it's incredibly insulting when people assume that engineers are choosing frameworks just for their resume

I don't find it insulting at all. Reality is that many, if not most, recruiters are looking for candidates that have experience in whatever technologies are hot at the time. Expertise in a sought after technology can be worth tens of thousands of dollars extra in annual compensation. It's natural that some people will try to use that technology to further their career if possible. And I think to a degree, a lot of people base their job decisions on what they'll get to work on. 'Is it a stack that is in demand and growing?' 'Is it a stack that might be difficult to learn but pays really well?'

Now the sensible thing to do is to not shoehorn in some tech if it doesn't make sense for a given application. And beyond that, the a big reason people are tempted to chase the shiny new object is because the IT recruiting process is broken.

I just see it as people trying to do the best they can for their career, usually there's no malice involved.

> I actually think it's incredibly insulting when people assume that engineers are choosing frameworks just for their resume.

Maybe, or maybe it's just realistic in a world where common advice is not to stay at any company for more than 2 years.

My experience is engineers get heavily judged for not having the latest bro tech on their resume.

Mostly younger people are choosing to jump around jobs.

The common advice and wisdom is actually not to do this.

It's not just for their resumes, it's for the pleasure and challenge of learning something new, the feeling of confidence and with-it-ness when you get to say "yeah, we use that, I set it up" when people are talking about the latest and greatest thing, the fear of missing something transformative and getting left behind. It all boils down to nurturing a person's confidence that they are good at what they do and their ability to sound that way to other people, so "resume" for short.

And even if it is just for the resume, it's smart and understandable. Consider the difference between, "Eh, I evaluated some NoSQL options here and there, but it seemed safer to stick with Postgresql since we didn't have a compelling reason to experiment. Postgresql works fine at the scale of our product and we're very familiar with it," versus, "Yeah, we used Mongo for a project and it was a shit show, switched to DynamoDB which has been solid. The product is still 80% on Postgresql, but we use Cassandra for analytics, and of course we run InfluxDB for Grafana, which we're going to replace with Prometheus for better scalability." These could be two people facing exactly the same serious of engineering choices, with the first guy making the better decision every single time, but the second guy sounds like the kind of curious and hardworking person you'd want to hire, while the first guy sounds maybe... stuck? Counting the days to retirement? Maybe boring SQL guy has saved his company a ton of engineering work that they were then able to invest in product work instead of engineering, but when he and NoSQL dilettante guy are both interviewing at a new company that wants people who are "curious" and "passionate" about "dedicated to learning and growing their skills" and "ready to meet new challenges head-on" he has to be worried about sounding like a dud.

But I've never met anyone in the last 20+ years with thousands of engineers who was doing it for their resume.

It's not something you can distinguish from being eager to learn and overenthusiastic about new tech, which we all are to some extent.

Unfortunately, people can and do indulge in exactly this behaviour, and there does need to be some pushback against it. Individual self-interest must be counterbalanced by what is in the interest of the business when the two are not aligned.

I have seen this happen before, and when the interests and ambitions of an individual are counterproductive to the direct needs of the company, that can be deeply problematic.

I doubt it's usually for the resume, per se. But I do think peer pressure is a very strong force, as are aspirations. Most of us would rather be able to tell our friends, "We're using this huge and complicated but massively scalable Kafka cluster", than, "Yeah, our message queue is a database table. No, it isn't web scale. No, we don't anticipate ever growing large enough for that to become a problem."

Also, we like playing with new toys. Solving the same old problems the same old way we were doing it 20 years ago just isn't sexy.

Oh come on, it is not just developers but non technical higher ups also read some articles and now I have to come up with something that has "Micro-services, Docker, Blockchain, AI" in name to get budget for simple web api. Hell it does not even matter if we actually do it that way, just have to fill in buzzword bingo correctly and you win budget money.

> I actually think it's incredibly insulting when people assume that engineers are choosing frameworks just for their resume.

Uh? That's just the norm. I've met very, very few people who actually think about introducing a shiny new technology as a last resort. Usually it's the complete opposite, they start with the shiny new thing they've read about and build systems and even features around it. I've often heard the CV mentioned explicitly as a reason, but there is also a lack of imagination- they just can't think of ways to adapt the current system to their requirements.

Ah, as for the project to be a success... "I've started as the lead developer in a team of two, and after X years the project has grown and I was leading a team of 20"- this is a common success story. Nobody cares about the fact that the team of 20 was needed because the tech choices made were so poor you needed to explode the size of the team.

>Most in fact are looking to these tools e.g. React, Spark, Kafka etc because so many other engineers are using them with success.

This is not a real criteria for choosing anything. You can say the exact same thing about ASP.NET WebForms, MySQL, MSMQ or pretty much anything that was popular at any point in time. If you're only looking for "success" stories, you're not doing due diligence as an engineer. Who defines "success" anyway? The same person who chose React, Spark and Kafka in the first place and whose salary is directly proportional to how hyped-up those technologies currently are?

"ask them question after question to justify their choices"

Asking questions is not assuming. I think you're letting yourself be triggered by a flippant remark at the end of an otherwise solid comment.

Honestly this is why I have a homelab and side projects. If there's an interesting tech that I think may be applicable(or it's just interesting) I'll spin it up at home and give it a run.

Keeps me sharp and gives me an opportunity to explore tech that isn't in my day-to-day wheelhouse.

Bad news is pretty much anybody smart, and talented wants to work on new, exciting and bleeding edge stuff.

If you are still running on Tomcat, all the best getting the market standard genius talent to work for you.

You would also like to learn about what happens to workplaces where smart people don't work or even like to work at.

I used to agree with this, but then I started to look for a new job without knowing en-vogue frameworks. Now I make sure I include some fashionable tech, its important for people's careers.

or maybe you discover that it's an industrial size itch and the chosen backscratcher is needed.

Almost never.

Often there's an aspiration to reach Google-level operation. Invariably some director/VP insists on building for that future. Then it turns out sales can't break into the market and adoption is low.

Now you have a neglected minimum viable product because you're scaling that skeleton instead of adding features your existing customers want. Or you're delivering those features at a slow pace because you're integrating them into two versions of the software: the working one and the castle-in-the-sky one. Then there are all sorts of other things that throw a monkey wrench into your barely moving gears: new regulations, competitors nipping at your heels, pivots, demands from management for "transparency and visibility" into why it's taking you forever to deliver their desired magnum opus.

Products that reach Google scale probably get there without even noticing it because they're correctly iterating for the marginal growth.

I'm having flashbacks to when, at a startup in 2000, our CIO insisted we needed an EMC Storage Solution. "We're going to be _huge_!" (Over my written objections, mind you.) I spent precious time and huge amounts of money to build out the colo cages to fit his folly and then the company promptly went belly up. Good times.

yes and no. there is a time for building and a time for learning. I actually encourage people to think how they would build something “google scale”. it’s not meant to be what pays the bills - but it can have dramatic effects on motivation, productivity and making the thing that pays the bills better. you see, once really smart people are free to dream they understand the cage they’re in better. timebox it and share what you’ve learned.

You should optimize for the more likely scenario.

there is a time for optimization and a time for learning.

It's frankly bizarre to have a knee-jerk reaction that new technology is worse. New technology is probably better, given that it was created by people who had more context about both problems and solutions than was available when older technology was created. When I hear people talking about doing the same thing for 10 years I wonder what problem they are working on that could be so complex that in 10 years they have not managed lights-out automation for it. My suspicion is that attitudes like yours are not only wrong but also massively destructive of value.

Google's search engine is more than 10 years old, yet Google is still hiring engineers. Why is that? Should they not have managed lights-out automation for it?

More importantly, the world changes and software has to change with it. An accounting system will need to be fitted to changing regulatory frameworks in the future. That knowledge is the basis of good application design. Schemaamnesia databases with highly-scalable vendor-locked PaaS peripherals that are constantly changing also require constant developer attention, but do not rise to that challenge.

I completely disagree. All new technology goes through a pruning phase for the first few years where people realize whether it's worthwhile or not. It's like natural selection, only the fittest survive.

A technology that's been around for decades and is still considered a viable candidate will be much more reliable, documented, mature, bugfree... than some new library that is untested hasn't been worn smooth over time.

Could not agree more with this. Whether it is data warehousing, a maze of micro-services, or machine learning for your basic crud app you should look into whether you truly actually need it and whether it helps solve a real problem you have. A basic stack with Rails/Django and Postgres can get you quite far. This is often as much as most companies/startups ever need.

Also personally love the callout to Joe who while being a professor is consistently practical on when approaches do or don't make sense and at what scale they do.

Not sure what reality people are living in.

But a basic stack can only be used for basic systems and for basic problems. And there just isn't many of these going around anymore as they've all been solved. Or more commonly nobody is interested (including from the business) in delivering something basic. They want to innovate for their users.

I'd argue the complete opposite: every company and their mother is online these days, and nearly always they're a) doing something that's been done before and b) are nowhere near the amount of throughput that would require some kind of overcomplicated setup.

No, your company isn't special, and "innovating for your users" is probably the worst thing you could do, compared to delivering a good product using established practices that are tried and tested to deliver results.

I think I'm on a different page here. If a business wants to sell an innovative product, that doesn't mean they have to have an innovative database for purchase orders.

Three worries for me:

1. I know the risk of only being able to peer through the fence at the distant piece of software that is running your business, unable to gain any insight while your production application is limping badly and customers are running away like water. On top of that, thrashing it out with Mr Clippy is better than average support. If my business depends on it I want experts at hand who have the tools and the access they need to do the job they do.

2. The insane pace of serverless is entirely fad driven and lacks quality engineering which is required for critical pieces of software. The tools are universally poor quality, unstable, unreliable, poorly documented and built on gaining mindshare, making IPO and selling conference seats. Best practice never materialise as the rate of change does not allow an ecosystem to settle and work the bugs out. The friction is absolutely terrible but no one speaks of this in fear of their cloud project being labelled a failure. Every person I have spoken to for months is hiding little pockets of pain under a banner of success. Some people clearly will never deliver and burn mountains of cash hiding this.

3. Once you enter an ecosystem, you are at the mercy of the ecosystem entirely, be that a service provider or a tool. Portability is always valuable. It has cost, scalability, redundancy and risk benefits far beyond the short term gain of a single vendor decision. I'm currently laughing at an AWS quote for a SQL Server instance with half the grunt, no redundancy, no insight possible for only 2x the capex and opex combined of dedicated hardware including staff. But can't move to Azure because everything is tied into SQS.

I can never be behind anything but IaaS myself. This is contrarian especially in this arena but I will put my 25 years of experience on the line every time and say that it is the right thing to do. IaaS is choice, flexibility, allows you to gain deep insights and protects you from serfdom, fad technology and pick and choose mature products rather than what the vendor sees fit.

This is just another rehash of buying a mainframe. It's just bigger and you pay hourly to write COBOL.

1. You have to pay for those but the thinking goes that if you are at the complexity and scale of those problems you reach out to a TAM to sort them out for you (ie. $$$).

2. Give it time; this will evolve and it is still in its infancy. Like all things it is buggy in the beginning but as adoption hockey-sticks so too will the stability, documentation, etc.

3. Portability is traded for breadth of services and depth is gained through vendor lock-in and the one-size-fits-all package. Of your concerns I would say this one will be around for a long time or at least until a "conversion kit" is built to shoehorn all of your stuff into another provider allowing you to jump ship or test the waters elsewhere.

I am a veteran like you (20 years) and while you choose IaaS because you like the control most want to punt their problem to someone else and to pay for that.

If our goal is to get from LAX to JFK we could fly our own plane, charting our own course, looking up weather, doing engine checks, refueling, dealing with air traffic control. and we'll get there and be in full control the whole way. However most will pay to be shuttled in to a commercial airplane. Then of course there are some that are willing to pay more to have their own personal pilot get them there in a chartered aircraft where they are afforded a more tailored experience.

There are some really great pilots you can hire out there to do it all for you (or if you are in fact one yourself) but if the goal is to simply go from point A to point B in as little time as possible we don't have time to find, train, and rely on a single pilot or ourself to get there. We will take the hit and pay others to get us there.

That is what all this serverless non-sense is about if you ask me. The tradeoffs of simplicity and handing the busywork off to someone else is more enticing than the control we have in the process. Also, isn't it nice to be able to say, when you arrive late, that it was the airlines fault? :)

Yup, experiencing that at work quite a bit. People are currently arguing about big data solutions for a data set of 300Gb or so, growth in the area of 100ish Gb / year. I'm still arguing that this could be an in-memory dataset for postgres, and we wouldn't need that with a good schema.

Or other people are wondering if we could replace all of our relational databases with kafka. While complaining about inconsistent data sets. Well let's talk about advantages of a good relational schema first.

Maybe I'm turning into a grumpy old admin/DBA... but mariadb or postgres used well just solve so many problems.

>> Maybe I'm turning into a grumpy old admin/DBA

No you're not; you're saving the company tens or hundreds of thousands of dollars over the coming years.

> Or other people are wondering if we could replace all of our relational databases with kafka.

Unless you're using your DB as a data transport, dear God why. And if you are, dear God why. I work with Kafka and it's very good for a certain subset of problems. Replacing RDBMSes is not one of them.

I once asked if Postgres would have any negative performance impact from holding 1.8m records and once the laughing stopped and they realized I was serious I got a good lesson on how robust Postgres can be.

I once worked for a company that maxed out a 32 bit autoincrement primary key on a mysql table. Relational databases can hold a lot more than people give them credit for.

Yep. I worked at a company that had to migrate to 64-bit IDs. This was with MySQL 5.0 on servers with HDs, not SSD. Some of those "alter table" commands took all night!

A colleague describes this problem as "running out of numbers".

Oh man, I hope they saw that one coming ahead of time.

I managed complex datasets of a few TB with standard Unix tools just fine. If you are lucky, you have a sustained read and processing speed of 200M/s (or more with, if you are put in some work), which allows you to sift through a TB in a good hour.

Sometimes I am wondering, how people will rationalize the layer bloat in 5-10 years, when hardware went through a few more upgrades.

This all boils down to "make cost benefit decisions".

The problem is that maybe cost benefit decisions are really hard. To understand the cost of map reduce, for example, I probably have to use it at least once. High cost just to understand something. And I have to understand the alternatives, so I'm trying those out too.

You know what's faster? Signaling. When large companies say "this approach works for us" it's really cheap and easy to say "Well that's probably good enough then".

Do you lose out on efficiencies by not fully understanding the problem? Duh. But probably not as much as understanding an entire domain for every technical decision you make.

The steps outlined, particularly 1,2,3,4, are really expensive for every single technical solution. Reading multiple technical papers for a db is probably more costly than just acquiring customers, hitting scaling limits for your uninformed choice, and moving to a new db based on a somewhat more in depth approach or hiring a consultant.

Let's assume that if you guessed a technical solution, the cost of 'guessing' is 0, and the cost of picking the wrong solution is 'low'. Should I waste time doing anything other than guessing? If the cost of 'do what google does' is 0, or near 0, and better average chance of working, why not pick it?

Unless the stakes are high, putting that kind of investment into every technical decision just seems needless. And they arguably have to be very high to offset the cost, and I would state that determining those costs upfront itself may be difficult and error prone.

For every mis-guess listed in that article, how many new customers did they acquire due to making the right 'guess' based on signaling from other companies? The companies clearly hadn't gone under from those bad decisions, so would it really have been the right call to have invested in such deep thinking about the problem early on?

Not sure I believe what I wrote entirely, just thinking out loud.

> Let's assume that if you guessed a technical solution, the cost of 'guessing' is 0, and the cost of picking the wrong solution is 'low'. Should I waste time doing anything other than guessing? If the cost of 'do what google does' is 0, or near 0, and better average chance of working, why not pick it?

That makes more sense if you s/cost/risk/. The answer to why you shouldn't just do whatever Google is doing is because trying to copycat the leader is the one thing that will guarantee your _failure_ in the market place.

Same with AWS. AWS Lambda was built to solve existing problems. Everybody will use it to solve the same problems it's designed for, but only a tiny number of people will actually succeed in the marketplace because it just becomes a numbers game. If you want to win you have to play a different game--look for problems where AWS Lambda is a poor fit.

If you're doing the same thing as everybody else you're by definition just reinventing the wheel. Why bother?

I mean cost in terms of 'time to make a decision', without considering 'cost of making the wrong decision', which is closer to risk.

> The answer to why you shouldn't just do whatever Google is doing is because trying to copycat the leader is the one thing that will guarantee your _failure_ in the market place.

The article seems to demonstrate the opposite - decisions following the leader, and paying for it later, but surviving in the interim.

> If you're doing the same thing as everybody else you're by definition just reinventing the wheel. Why bother?

I don't really agree. When it comes to something like a DB or architecture, that's probably not where you need to be inventing new solutions - products are rarely sold based on their technical implementation, and more about the results they drive.

If you were building a new Google search, and you used all of the same tech, that's probably a bad idea. But if I'm building a product for a completely isolated domain, who cares if it's solved using the same tools that Google solves search?

Regarding lambda, there's likely tons of room for competitors even within a domain to implement off of the same technology - again, it's just a tool, and not likely to be the differentiator.

This. Some of the biggest mistakes I've made have been too much "thinking". The article makes good points, but seems to argue all these newfangled systems have a very high cost if adopted in the wrong situation. I would argue the cost is probably not that high especially in a managed cloud and many decisions can be tweaked later.

In other words, if a Postgres based solution takes 1 month to build, and a CloudSpanner/Aurora/pick-cloud-managed-sql-DB solution takes 1 month to build, just pick one. Most of the time, if you make a mistake, you'll find out when you have more customers/funding/engineers/knowledge of what actually matters.

Yes, I think it's really easy to look at a project, after it has scaled, and go "wow they made such bad technical decisions! Why didn't they learn more about this?" and ignore that the system is running, they have customers, a business, and they're solving the problem when it has become important to do so.

I've seen lots of time wasted overthinking solutions only to find out that the requirements were changed a month before release, or customers would use the system completely differently than expect.

At some level this is one of the most "no shit, Sherlock" pieces I've ever read. On the other hand you see it happening absolutely everywhere so it very much needed to be said.

I don't know why it is but people seem much more interested in the tech than in the value they can create. Which is odd. Why? Because it's the second part where you get to exercise the most creativity and do the most interesting work. How do you think LinkedIn arrived at Kafka, for example?

What'a possibly more baffling is it's not just the devs who buy into the technology hype: often it's the so-called business types, and those in management and leadership positions, advocating and egging them on.

> How do you think LinkedIn arrived at Kafka, for example?

Well, Franz Kafka wrote about incomprehensible, oppressive bureaucracy as a way of life, and LinkedIn did a decent job of porting that to Web 2.0.

>I don't know why it is but people seem much more interested in the tech than in the value they can create.

Well, for some engineers who don't have much say in product development, the amount of value they can create is bounded by the business specification/deliverables they have to produce (and maybe equity or lack thereof plays a role here). However, if the architecture is considered their responsibility, then there's a lot of fun in using _shiny new toys_ even if it's overkill and will incur unnecessary technical debt. It's fun to overengineer things.

Not saying it's a good thing or even professional behaviour, but I understand it.

I want a word for things that are simultaneously "no shit, Sherlock" and yet also profound things that we forget very easily. There's a lot of them. For engineering, I think "always consider costs/benefits" is probably #1. It sounds so stupid simple, and yet I feel like it's the exception when it gets deployed successfully.

"common sense"

"Whoa!!!!!!"

If everyone just focused on business value and ignored the technology aspects then nothing would've been invented.

There would be no Internet, Web, VR, ML, AI etc and we would all be writing assembler or using punch cards.

> If everyone just focused on business value and ignored the technology aspects then nothing would've been invented. > There would be no Internet, Web, VR, ML, AI etc and we would all be writing assembler or using punch cards.

The Internet, Web, ML, and AI at least were all originally invented using research funding, not business funding. The Internet (specifically the TCP/IP suite) was funded by DARPA, the research arm of the US Department of Defense. So you're right that some people need to focus on something other than business value, but usually that is someone paid to do research, not paid to implement a specific solution that needs to be widely deployed within 1-3 years.

But if you're picking a product to use for a particular task, you aren't doing that kind of research. You are instead doing engineering. That is, you are determining how to solve a particular problem using the knowledge and resources (including reusable code) at hand. So you need to think to determine what collection of resources will actually do the job well, at reasonable cost and time, etc. Different problem.

Did you actually read my comment? Right in the middle of it I made the following remark: "How do you think LinkedIn arrived at Kafka, for example?" That's just one example cited in TFA of something invented as a result of focussing on value.

Last time I checked, necessity was the mother of invention and not the other way around.

I use managed services a lot. And fortunately my data size is big enough for it to actually make sense to be using these big data tools.

The issue is the quirks. Every managed service has at least a dozen quirks that you're not going to know about when you visit the flashy sales page on the cloud provider's website. And for the vast majority of users, they're not going to have access to the source code to understand how these quirks work on the backend. So you end up in a situation where yes, it does take way less time to get 95% of the functionality done, but getting that last 5% can still take a considerable amount of work.

As an example, I am using Azure Event Hubs lately. It is supposed to provide something like a simple consumer api like Kafka does, but with consumer group load balancing across partitions. Awesome, there is a system that automatically handles leasing across partitions in a way that abstracts this all away from the client! Except, well actually the load balancing is accomplished via "stealing leases" (meaning, they are not true leases) so if you use the api you are meant to use, you will get double reads - potentially very many if you want to commit reads after doing more-than-light processing which can take time. So you need to use the much more poorly documented, barebones low level api and probably still end up writing a bunch of logic to dedupe.

Except, you use this kind of tool to begin with because you want to set up a distributed consumer group to read from a stream... so now you have a non-trivial engineering problem figuring out a way to get a distributed system to manage deduping in a light-weight way across hundreds of processes and machines...

Are there any good "introduction to AWS" books or book series that actually mention those problems and how to work around them? All that I've seen just parrot the sales pitches, and it would be excellent to know about such problems beforehand.

Sounds like RabbitMQ would be a good fit.

Several good managed offerings exist if you do not want to run it yourself.

This is great. It reminds of that Adam Drakes’ 2014 article “Command-line Tools can be 235x Faster than your Hadoop Cluster” [1] was the wake up call I needed. 1. https://adamdrake.com/command-line-tools-can-be-235x-faster-...

Reminds me of my first job processing radar data on a Masscomp mini-computer. The full processing was a bunch of simple c programs all unix piped together into a processing chain. Simple and elegant.

We appeared to have a persistent bug though, processing left running overnight always seemed to crash. So after a couple of nights I stayed in the lab to try and see what the cause was. At 7:30pm the lab door opened and in walked the cleaner who reached down and unplugged the computer so that he could plug in the vacuum cleaner. Problem solved.

Did that really happen to you or are you recounting a common tale?

https://www.reddit.com/r/talesfromtechsupport/comments/5yrs1...

That article totally misrepresents the normal use case for a Hadoop cluster though. Hadoop clusters are meant for when you have multiple petabytes of data and this network bandwidth becomes the bottleneck for doing batch processing jobs.

Let me know when your command line tools run multiple large scale processing jobs on petabyte datasets.

His article was him constructing a straw man about why people use Hadoop and attacking that.

I've worked with these "straw men" you say he constructed, they are absolutely out there. There was a time in 2014 when Hadoop/MapReduce was the hammer and every problem out there looked like a nail.

How many people have used Hadoop for a project? How many Petabyte+ datasets do you think are out there?

Unless you truly believe the answer to those 2 questions are the same, I think you can see why that article had to be written.

I feel it is in a similar spirit as the OP here. In both articles the important take away for me is focusing on an approach and tools that match the problems. It sounds like you are making a similar point.

I also appreciate quality of the writing and step by step journey.

When I first read the article, I found it timely as I felt bombarded by IaaS & PaaS offerings with “web scale” solutions for problems I couldn’t relate to, basic examples, and few case studies.

The only reason to use Hadoop is if you need a Shuffle phase, i.e. the intermediate data between Map and Reduce is too big to fit on one machine. If you have big input but small append-only output, use a work-queue (SQS or MySQL/PostGres will let you set this up in minutes), dump to files, and merge them with gzcat | uniq | gzip > output.txt.gz. If you have big input but a small volume of update-based processing (< ~1000 or so outputs/sec), have your workers update an RDBMS. If you have small input but big output, build up output file chunks and progressively write to S3 and delete them. If both your input & output fit on disk, use UNIX pipelines and command-line tools. If they fit in RAM, just load them with Pandas or equivalent and manipulate them in your favorite interactive programming language.

This is really a very good article, and I've seen this same behavior manifest on numerous client projects.

BUT. I have also grown a bit skeptical of the "Just use a RDBMS!" mantra. The same advice applies. Think about your use case. Even if your data isn't exoscale, a relational DB might not be the best choice.

My current project has data on the scale of hundreds of billions of rows. Nothing crazy, easily handled by a good Postgres box (by the numbers.) And for certain access patterns, it would be.

Unfortunately, it turns out that a lot of the analytics queries our business users need end up being joins and aggregations involving full or nearly-full table scans. PG is not particularly well optimized for this; it gets CPU and disk-access bound. Queries take tens of minutes, causing timeout errors in our BA tools.

Loading the data into a Presto (Facebook product) cluster instead dropped query times for the same aggregation into the tens of seconds range. Sure, our data doesn't even really count as "big" and Presto is probably overkill if you're only looking at size, but it is optimized and built for highly partitioned parallel aggregations.

I would object that your experience is actually a good example how to choose a larger scale solution. Start with some RDBMS, and switch once it stops working.

I think that's the better way of saying "just use a RDBMS" - "If you're not sure, use an RDBMS to delay a decision until you have more experience and knowledge".

Exactly same case here - we're using Athena (packaged presto from AWS) and have a Postgres instance w/ identical data in a different data model. Presto is mindblowingly good at the low latency queries and scales well to anything nontrivial. It seems opening files for reading is the biggest source of delay in presto! Postgres is not handling the scale well, but it was a nice experiment.

Your Athena data set are immutable S3 blobs of data however, while Postgres reads data that could be modified while it's reading them, concurrently.

Postgres has to deal with a transaction modifying 100,000 rows in a table, needing that transaction to then see its changes but then aborting, leaving the data exactly as it was before. Athena/Presto don't have to worry about any transactions, table files bloated by changes that need vacuuming, etc. etc.

So I don't think it's fair to claim PG does not scale, when you are comparing a transactional and analytical database.

There are multiple RDBMS data warehouse systems based on PostgreSQL that scale pretty well. I work for a company that develops one -- Greenplum. There are others like Citus.

Programmers often make the fallacy of assuming that only the asymptotic case should be considered (just look at Big-O notation). But when it comes to specialized tools and frameworks, unlike sorting algorithms, many use-cases never break past the scale where the asymptote is the primary thing driving the curve.

With that in mind, it's my experience that every tool or framework has a different window of scale in which it's ideal, which has both lower and upper bounds, and one simply needs to find one's own project along that axis when choosing a technology. Hadoop may be the best solution as N approaches infinity - and we as programmers love thinking in terms of infinity - but it may not start being the best solution until 10x your actual range for N.

The thing is, at least from my experience, the engineers working in startups know the solution they're implementing is overkill. Its just that everything ends up getting built assuming the startup is going to take off and become the next huge unicorn.

A lot of the time design meetings in a startup revolve around "Is this scalable?" or "What happens when we have 10,000 users or 1 million users?".

I think the problem isn't choosing overkill technologies, the problem is trying to solve a problem that doesn't exist yet and probably never will.

There's also sometimes a lack of discipline (or at least, I lacked discipline when I worked in startups) when it comes to focusing on the impactful stuff, not the fun stuff.

That's just bad engineering practice unless you can articulate why there isn't a transition path to more scalable tech when you actually need it. Usually in practice there is, and certainly for most companies. You can probably point to counterexamples, but to reuse the OP's term "you aren't them, either"

Unfortunately I've definitely seen cases where scalability wasn't taken into account and now it's impossible to bolt-on after the fact.

My rule of thumb is to assume something significant will have to change in the architecture when you get 10x bigger. At any given moment, you shouldn’t have made that change yet, but you should know what it will be and be ready to make that transition. But don’t gum up the current system with what you’ll have to do at 100x bigger.

I have participated in lots of startups. I have 3 successful companies myself. My background is cs. And what I can tell is that every single service like aws or GCS is absolutely insanely priced. Not affordable unless you have a new Netflix. Even though it looks like you are saved from a lot of maintainance you still have to deal with limitations you sometimes don't have a solution for.i have seen companies switching to aws going from 400 dollars a month to about 5-10k per month. Silly amounts for big organisations, but for startups it does make a lot of difference

We're still in a boom period, where many start-ups/VCs are operating with deep pocket books and instructions to scale fast at any cost. During the next bust, lean will come back in fashion.

It's an interesting question, because Fed rates are going to be held permanently low due to the US Government's debt / interest costs (and the next ~$18 trillion that will be stapled on over the coming ~15 years). Those Japan-style permanently low rates will always press upwards on risk capital markets like venture capital. It spurs capital to seek higher returns versus the weak yields everywhere else (whether treasuries or other). Start-ups will become more valuable persistently due to this effect, at least for the next 10-15 years prior to the end fallout once the US Government is formally drowning in debt interest cost (which is when the really bad consequences kick in, like full blown aggressive currency debasement to chop the debt down and stealth default; everyone will be taking real haircuts then, anyone with assets in dollars anyway).

I think it would take a very severe recession, in the 2009-2010 style, to hammer venture capital down considerably. Instead I expect Japan stagnation to continue to envelop the US economy, and for the exact same debt-laden reason. Ever slower growth, low traditional inflation (higher real inflation from currency debasement QE), debt taking up an ever larger share of capital available for investment, stagnant productivity, stagnant wages. With the enormous amount of wealth in the US, trillions of dollars will always be looking at the venture capital market in a given decade. ~2012-2035 will probably be the best years for venture capital in US tech history, and broadly the best context for start-ups, that we'll ever see. It's early in the loose money sloshing period from perma low rates, but not yet late such that you're eating an always-on QE real-value debasement hammer constantly (which sucks down your real value creation, as you run uphill against the Fed while it tries to debase government debt to keep the US Govt solvent).

In principle I agree with the author, but some of the patterns these big companies have introduced are valuable for companies with significantly lower amounts of traffic. For instance, in the article they reference Kafka. In one of our products, we use Kinesis, which has similar semantics, for data that is no more than 25k records per day. However, we find it useful because it enables us to have multiple consumers that operate independently, plus using Kinesis Firehose to automatically archive those records off to S3. We just use a single shard, which is more than enough throughput for us. We don't have any plans to scale to hundreds of shards, but find what it provides to be very useful in separating what each process does, and makes the code much simpler. And if we ever did need to scale, it wouldn't be much work to do so.

There was no section on "You are not Netflix", so I guess microservices are OK.

Just thought of a naming convention, how many types of microservices do you run:

  10? deciservices, 100? centiservices, 1000? milliservices.

From here in enterprise-land, the advantage of microservices isn't architecture, it's resource contention in development. Team A is much less likely to interfere with the work of Team B. Performance etc claims are nonsense, but keeping teams from breaking each other is real.

Assuming they aren't randomly changing unversioned APIs, of course...

Other direction, deca-, hecto-, and kiloservices.

https://en.wikipedia.org/wiki/Metric_prefix

I think OP's point was that if your company provides a service, and runs 10 containers to do it, then each container provides 1/10th of the total functionality of the service and is therefore a "deciservice". The joke being that you then can't say you use "microservices" until you've got 10^6 of them.

UNPHAT - these are great, but I'd add one more - ask yourself if you're an engineer before making a technology choice.

The number of projects I've had where the team has had one arm tied behind their back because they "have" to use Hadoop or NiFi or Lambdas or something else a manager is determined is the thing everyone's using is just lunacy. They're all tools which have their place, but you really have to know them to know when to use them. And as importantly when not to.

I mostly do consulting gigs where projects are 6 weeks to 6 months long and it's been years since I've seen that not hamper a team.

While I largely agree with the thesis of this article, I actually really like the entire "scalable microservice" way of designing things.

It's overkill for anything I need to do, but my home server is six 8-core ODroid devices, orchestrated with Docker Swarm, with most of my services on there being load-balanced, and glued together with Kafka if they need to talk to each other.

Do I need my internal video streaming server to be able to scale horizontally? Of course I don't, there's only ever three people max watching things from there at any point in time. However, I find that, overall, my brain thinks more-or-less in terms of these microservices, and it doesn't really hurt much to do it that way.

If I find it pleasant enough to do, why the hell not make it hyper-scalable and able to reach Google levels?

EDIT: Just a note, I know that Docker Swarm probably isn't quite capable of Google-size. Still, moving to Kubernetes or something wouldn't be terribly hard (the reason I didn't use it was because a few years ago I had some issues with Kubernetes on ARM and Swarm worked outta the box).

There are Swarm clusters in production with tens of thousands of hosts. There's nothing in Swarm that makes that impossible.

Fair enough; I didn’t know that Swarm was used for any large projects because apparently I don’t know how to read or use search engines; I stand corrected!

I guess that backs up my original point even more then; my stupid video streaming server might actually be able to scale to Google size some day :)

I can assure the author that those of us solving problems for small companies (in terms of employee count, not revenue or assets under management) do not jump on the typical bandwagons. We tend to be very critical and pragmatic.

I'm not even sure it is engineers at bigger companies who choose to jump on these bandwagons. I suspect it is often wannabe-technical managers who read that MegacorpX is using tech Y that sells the idea to upper management, along with some unfounded beneficial promises, that causes some of the trends we see.

Do you know how many engineers work at Google and Amazon? A lot. Chances are that many readers of this article actually are Google and Amazon.

My point is: these generalizations in either direction are not helpful. Many people are operating at Google scale, and many people aren't. Be aware of that when you read about potential technologies.

If you're Google, you know.

So if you're the audience of this article, you're not Google.

I'd dare to say that many groups/products at Google aren't even operating at Google scale. I'd also dare to say that some Google products operating at Google scale can be a bad thing (i.e. unprofitable). The latter statement, or both statements, might be supported by the number of products they kill off.

I have worked in many industries e.g. telecommunications, insurance, finance where our challenges are just as big and sophisticated as Google.

If you're doing large scale ML, having to respond to requests in very low millisecond numbers or having to crunch through significant amounts of data (often more than Google produces) then you need to use similar approaches to them.

Even doing ML at all can be quite demanding.

I find the the amount of traffic and comments articles with this message generate quite amusing, given the article can be boiled down to: don't use tools like Cassandra, Kafka, etc. until you've thought through whether they are the right tool for your use case. That last part is often forgotten -- these tools may not always be the right tool for the job, but SOMETIMES they are, regardless of your scale.

Well, duh.

The corollary is: if you're using one of these tools, and you HAVE thought through your use case and reasons, don't get defensive about it. If challenged by those with lesser knowledge after reading an article like this, calmly and rationally explain your reasoning. And like any technology choice, be prepared for someone else to offer another option you weren't aware of, with better reasons.

Again, duh.

I’m not sure why people frame the use of certain technologies as orthogonal to non Google-scale use cases.

For example, one might like using MapReduce or Kafka Streams for their programming paradigms, not for the redundancy or scale they provide.

Another example, Kubernetes, which makes it trivial to run containers and attach storage to them.

I guess the idea is that for all the people using MapReduce, most would be better served by a different paradigm than MapReduce. Because in most cases, there's a better paradigm than MapReduce.

> Because in most cases, there's a better paradigm than MapReduce.

You'd think so, right? But what happens to me all the time is I try to use "small-scale" tools to handle some small problem, and then I find myself writing glue code that's already implemented in, say, Kafka Streams. So I might as well just run one Kafka broker locally and write a Kafka Streams application. Or the same with the Django ORM, which I reach for all the time just because I don't like writing database access code when I can write up some models and be done with it. Every time I reach for "small" tools I end up writing tons of code that's not actually solving my immediate problem or question.

We like to think that we’re hyper-rational

Don't we just. Which makes us vulnerable to every shiny gewgaw and every personal and cultural bias out there.

Great article, and one that I think a lot of people here could learn from. Everyone gets all excited about fancy architectures and algorithms because they're fun and cool, but pragmatically, you can buy a LOT of server for the same price as a couple of engineers messing around for a month.

I find it funny with all these cloud providers giving huge free credits for startups (AWS giving away 15k for e.g.) like they could utilize all the amount. Reality? 10 visits per day.

Hype driven development is the real curse of the last decade. Not just big data, but also smaller techs like mongodb, or the countless javascript frameworks... choosing the right stack for the job could be a consulting job in itself, even for simple web development.

"The thing is there’s like 5 companies in the world that run jobs that big. For everybody else… you’re doing all this I/O for fault tolerance that you didn’t really need."

That's not necessarily entirely true. In the early 2000s, I worked for Cadence Design Systems, who at the time needed to build and test the plethora of tools they'd built or acquired on a large variety of systems. I worked on "GridMatrix", sort of like make but using a large, heterogeneous cluster of machines, built on top of the Condor and LFS batch scheduling systems---the same sort of thing underlying MapReduce.

On the other hand, I get where the author's going: cargo-cult design is rampant in enterprise software development. But it's not just cargo-culting Amazon or Google; it also involves fashion and resume padding.

And then, there's "eNumerate multiple candidate solutions. Don’t just start prodding at your favorite!"

Bwahahahahah. <- Unamused laughter.

Our first and only response, as an engineering discipline (if you want to call it that) is to pick the first idea that comes to mind and beat it to death with a stick.

How many times can we run this same exact type of article? If it didn't change anything the first time it ran, what are people expecting to happen now?

Maybe Hadoop isn't necessary for everyone who needs it. They'll learn. And if they don't, more job security for you.

Otoh there are some crazy advantages of running certain "large scale" softwares. Not everyone needs the "scale" Kubernetes offers, but managing tasks declaratively as containers that get scheduled on boxes in a hermetic fashion? Or maybe continuous delivery - not everyone needs CD, but it certainly offers many advantages to those who do it.

Most people could probably live off of shell scripts running under Cron on a box with CGI scripts written in Perl without any issues, that does Not imply there are no advantages to new technology because not everything is about scale and fault tolerance.

Most people simply don't realize just how fast PostgreSQL is on a modern machine (and, by extension, just how bloody fast a modern machine is).

My old laptop could do something like 150K transactions per second in PostgreSQL. You can scale really far before your database becomes the issue.

I don't realise how bloody fast a modern machine is because the software I use day to day takes seconds to do basic operations like move the cursor, or scroll a window, or load a list of ten items.

Hudson giveth and Nashua taketh away.

Or, for the youngsters: Portland giveth and Seattle taketh away.

People are told to do less with more, and machines are purchased to handle future growth.

Some things never change.

How many of these clever VC say to themselfs "None of our companies will be the next FAANG/MAGA"..

Oh wait. They dont say that. They fund, and hope and pray!

You should be telling "you are not Google" to people who try to build the next social network or any app that is basically an enormous chicken-and-egg problem.

Most programmers try to do that. And they keep trying. They're wasting their lives.

A lot of these decisions can be made with a bit of "back of the envelope calculations"; playing with orders of magnitudes based on latency times:

http://highscalability.com/blog/2011/1/26/google-pro-tip-use...

https://people.eecs.berkeley.edu/~rcs/research/interactive_l...

My general test for most new technologies is that the desire to use it has to have arisen out of the pain of not using it. If I'm looking at a framework for any reason other than having shouted "there has to be a better way!" then I'm not in a position to evaluate its merits, and even if it is the right choice I'm not in a position to understand why, and will thus be prone to using it in a stupid way.

It might seem expensive to do things the "wrong" way first every time, but I really do think it's necessary.

I don't think a lot of people actually think in terms of "they are Google", but more so in terms of "we hope to one day be Google" so they build (more complex than needed) systems with that ambition of scale and everything. Almost as if they are over planning for the future. Not to say that is either right or wrong, but just something worth noting, in my opinion. I don't think anyone actually over engineers something thinking they ARE Google when they are clearly not.

> just, think for yourself. Is it the best solution to your problem? What is your problem exactly, and what are other ways you could solve it?

This is the crux of the issue, psychologically. When making an important decision in the face of uncertainty, having someone else with a lot of clout (or a group of people whose collective wisdom you trust) simply tell what you do feels amazing. That feeling is hard to ignore in favor of constructing your own cost-benefit analysis.

I used to consult for a public service department.Their internal infrastructure was incredibly complex with hot fail-overs/redundant/scale-out hardware (running homegrown custom LoB applications for less than 200 employees). This paradoxically lead to huge downtime as no-one was smart enough to configure this mess, and deploying a new service which should have been trivial always ran into worst case scenarios.

I'm reminded of JWZ and his very practical advice on Backups:

https://www.jwz.org/doc/backups.html

In particular Addendum B:

RAID is a waste of your goddamned time and money. Is your personal computer a high-availability server with hot-swappable drives? No? Then you don't need RAID. RAID is not a backup solution. Even if you use RAID, you still need backups.

With modern filesystems, however, RAID-like implementations do have their purpose in ensuring data integrity, as is the case with btrfs and ZFS.

Side note: the linked URL redirects to a questionable image when navigated from HN. Interesting choice by the site owner.

That doesn't mean his point is not spot on.

I have more than one friend that has overengineered an industrial solution for basic computing needs. (and when things break, complexity goes exponential)

as to URL redirect: I don't see the image/redirect you refer to, even clicking on the link here on hacker news. Can you elaborate? I take the links I recommend seriously.

I think this article is an instance of a more general observation: most programmers should spend more time understand the needs of the business and users, rather than learning new technologies. The examples given were large companies solving their business problems rather than developing technology for its own sake.

Or to put it another way, form follows function.

In my case, a multi-tenant application, i split the application into multiple services, one service per module.

It's not about scalability or about anything technical. It's about domain understanding and data isolation.

There's NO problem here to understand. It's just one way to manage our data. Or another way speaking, design for failure.

>It's just one way to manage our data

Sure, but there are tradeoffs to any approach. More services == more devops == more complexity in fault tolerance, communication, data storage/consistency, etc.

I'm not saying it's a bad model _for you_, obviously I have no idea, but the point of the article is that some tend to jump toward complexity when they shouldn't.

It's not "more services == more devops". It's like this: Only manage the nessessary data.

If i need to change one part of data, i don't migrate the whole data. I just need to change that only changed part.

Please don't introduce new acronyms like UNPHAT when we have something like YAGNI that works just fine (and probably there might be something, a rule of thumb, before Fowler, or Ron Jeffries, or whoever, coined YAGNI).

Secondly, extreme YAGNI and extreme future-proofing are two ends of one of the considerations on how to approach writing a software. Neither extremes is good. In reality, the best location on that spectrum would depend on the situation at hand.

There is no silver bullet. And I'm sorry but I didn't learn anything new from this blog post, something that is already not part of the software engineering vast body of knowledge.

edit: I know UNPHAT is not an "exact synonym" of YAGNI. But what you're describing is a problem solving approach. That's probably even less new. Please look at Polya 'How to Solve it' if interested.

Note that this was from 2017.

This, for Docker. Everybody is using it these days, and I understand there are valid use cases. But it's way overused and very often does not justify the added complexity. Keep it simple.

This is true but it's also true that small/medium businesses are riding the same hype train and they'll gladly pay you to take them along for the ride so in the end, meh-money.

This is one reason Snowflake is killing it. The real market for "big data" is data warehousing, and users want a data warehouse that's in the cloud and portable among vendors.

Only in aws,azure portable.

We did an entire show on this with Ozan Onay back in 2017 ~> https://changelog.com/podcast/260

You can read through the transcript too ~> https://changelog.com/podcast/260#transcript

Great article - I'm kind of at a crossroads myself. I'm very productive with Rails and can scale it probably well beyond anything I'd ever need and can move away the big chunks to microservices later.

But there's also Elixir which can do away with most of these concerns.

So what do I do? Go with Rails to ship stuff? Or struggle a little bit more with Elixir to get that much more legroom?

I wrote an open response to this. Looking back I was a bit off-topic and I could write it better today but what the hell..... https://www.honestrepair.net/index.php/2017/06/08/re-you-are...

Assuming low level techs finally get realistic about the complexity they sign up for... If an executive says "we will use only use X technology for all X tasks", they probably picked the Google thing, and you have to deal with it, or they'll hire someone else who will.

Architects and engineers alike love increasing the complexity of systems just to use buzzword products.

This is a good article. I'd say it draws heavily on the same themes as "Hard Facts, Dangerous Half Truths and Total Non-Sense" by Sutton and Pfeiffer. Drinking Wild Turkey every morning isn't going to turn you into the Southwest Airlines CEO lol

This article has been written many times by many authors (even using the same cargo cult airplane image) and yet is no less true than the first time it was written.

That said, clamoring toward things like React has yielded a net positive result. So cults can be a double-edged sword.

A place I worked was building a video dating site, we spent thousands upon thousands ensuring that the site would 'scale' to millions of concurrent users.

I think we maxxed at somewhere around 60 concurrent before I left. They gave up on the idea a few months later.

Great advice, and to double click on it: google wasn't google either when they started and part of their value to investors was their bare-bones hardware infrastructure. That part of google is (philosophically) worth emulating.

Is this really a thing though? I would be surprised if I find someone that's able to deploy a spark cluster for a production application and at the same time doesn't know that is not a good solution for their problem.

Really interesting article and good points. Although, I always had the opposite problem: people wanting to use technology that is too basic without caring about the implications at scale. "As long as it's free".

Funny how a blogger reminds us to get in touch with reality once in a while, and then every commenter on here bends over backwards pretending that they actually are Google.

Oh, I had to spend an hour "fixing" my incorrectly autofilled fields for Chrome to please stop playing games with my users data.

I'm acutely aware I'm not Google.

Usually I implement things for future workloads. Sometimes I like to imagine 1 billion people could use each component, then it is truly ready.

What is meant by "“solve” the problem mostly within the problem domain, not the solution domain."?

To use a restaurant analogy, if you are a chef, and you are opening a new restaurant, you should focus more on the problem of devising the best recipes and experience for your customers, rather than on whether your kitchen has a high-end cooking stove.

You should do a show with this script. Educational and fun. Well done.

Resume Driven Development

You Are Not Google (2017)

You Are (Still) Not Google

How many people on this thread actually are google?

> I understand that Kafka is still useful for lower throughput workloads, but 10 orders of magnitude lower.

Should we stop using MySQL too? That handles more data than I have.

I think a good analogy might be using MySQL for storing a single key/value. You could do it, but why? Instead you could just use an environment variable or simple file.

i work for google and now i'm confused

Been saying this for years. People build these ridiculous contraptions: Spark/Hadoop, key value stores, NoSQL, microservices, distributed filesystems all over the place, fault tolerance and so on. Then you ask "how much data and traffic do you have?" Nearly always you get an answer where a single machine and properly designed software would be more than enough.

I figure, in a way we should be thankful for this cluelesness. If people had any clue whatsoever, IT employment would shrink by a factor of at least 3, and salaries would drop pretty massively as well.

One common mistake I see often is in step 1, understand the problem. Assuming the problem is not understanding the problem.

I'm reminded of an issue my old boss had at his house. His power bill was raising at an alarming rate, he assumed it was the central hvac, spent a few hundred getting it tuned up, no effect. Spent a bunch of weekends replacing weather stripping, adding sealant, etc. minor effect.

I came in with an app that lets you estimate current power usage by looking at the power meter[1], and killed circit breakers one by one while measuring current usage.

The cause turned out to be a malfunctioning swamp pump he forgot the house even had.

He assumed he knew the problem, by focusing on the common cause of such problems, he assumed wrong, and because of this, every solution was addressing a different problem than the one he was trying to solve.

Another example is the story[2] of the old as fuck ibm mainframe that was powering a website that would take on the order of 10s of seconds to return data. Everybody assumed all of the delays was to be blamed by the "legacy hardware". Every solution pitched involved migrating off of it, a daunting task no manager wanted to green light. Finally they brought a consultant in to figure out what was going on. Turned out it returned data in 6ms or less, the cause of the lag was the java app that would read the output from the mainframe to transform and send to the browser.

They could have solved the problem years ago if they had just actually tried to understand the problem.

[1] (power meters have a thing that happens every n watts, along with a decal that tells you what n is, so you can determine current power usage by measuring time between these n watts, there is a handy dandy app https://play.google.com/store/apps/details?id=com.sam.instan...)

[2] 7074 says Hello World

Not entirely related but one problem solving issue I see often is people not testing and refusing to test their assumptions. Often while trying to help someone with a problem I ask if they tried something and they say

"No that cant be the issue because it shouldn't be affecting it"

Yes under ideal situations this wouldn't be an issue but if the system is not working as expected then how can you be so sure that this bit is working as expected.

We are a 50 person team here and the cool kids have created over a 1500 apis that are served over 6000 container instances. The “system” only serves < 1 mm users. We’re definitely not cargo-culting:)

One gripe I have with this thinking- Until you are, and you have failure.

Things that don't scale or be AA Quality will show up.

Go viral and fail to service. 15 minutes of fame squandered on an avoidable tech mishap.

One time a top comment on a thread was about a misspelling.

Whatttttt

This entire article kind of assumes that you're stuck with your current workload and will never scale, which is awfully pessimistic. Should we all just take the attitude that, oh well, we'll never be massive so why bother trying to reach any sort of throughput?

If you make that assumption you will be right most of the time.

Deal with problems when you have them.

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