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Gcp vnc server on linux instance video streaming performance

gcp vnc server on linux instance video streaming performance

Specialties: Development, developer workflow, devops, linux, Debian, CI, benchmarking, profiling, bug fixing, performance, scalability, ops planning, capacity. Any client can connect to any server because they negotiate to determine They have a mirror video driver built into their system that helps speed up. Google supports virtual display devices on Linux instances and on Windows instances that use any xbased Windows images v or later. ULTRAVNC O QUE Вы можете прийти к нам.

It spikes, though. So I should probably actually do it. But B, it runs on Heroku. So it's self. It's kind of weird, Heroku logs going to Heroku, then going to you guys. So, yeah, super cool. So once the data's in BigQuery, what are you actually doing with it there? So we have a bunch of standard queries that we run to find some issues. So we either run them directly in your dashboard, or we use something called Redash, which, I think, you actually recommended to me a while ago.

But Redash, for those of you that don't know, is an open source dash boarding system. It's super cool for that. So we use that. We have a bunch of queries that run regularly to find certain things that we're looking for, do stats. And it's super awesome because it's cheap to run on you guys and it's also fast. Do you actually have a bunch of different tables? Is it just one humongous table with millions of rows? This is the new mistake, which I need to fix, which I was reading the docs about recently.

When I first started, I didn't realize that we should do named, date version tables. It turns out-- at least in the past-- that was impossible to fix without just making a new set of tables, which I didn't really want to do. So I think I read the docs correctly, saying that I can actually do that now without taking offline, which looks kind of cool. So, anyway, I haven't gotten around to it, because the cost is actually low enough that I don't care yet.

Is that correct? There is some discount if the data doesn't get added to after a period of time. Anyway, most of our interest I'm actually not sure. But Google it yourself. MARK: One cent per gigabyte per month. And it's also awesome. We learned BigQuery from him. MARK: That's why we did the podcast. And it's working, which is kind of cool. RUSSELL: So, normally we get-- we don't really run queries over the full data set unless there's some security thing that we're trying to investigate.

So almost all our queries-- I can't remember what it's called. But anyway, almost all our queries are done by that. So depending on what you're doing, they're normally either a day or a week's worth of stuff But only on rare circumstances do we query all of the data ever.

So it's normally if we have something that somebody's, like, oh, this thing happened two months ago. Can you help us? So I'm, like, yeah, maybe we can. It turns out. Yeah, we don't normally query the whole data set. So things are pretty quick, normally under a minute.

We queried earlier to find out how many words were in the table today. And that was the longest query I've ever seen, which was just over a minute. And that's because it counted every single thing ever, which is kind of still really cool. MARK: So you were talking about your visualizations and you were saying you were using it to find certain issues and things. Can you talk a bit more about what sort of issues that you're looking for, or how are you using that in the MARK: Yeah. So we're also using another awesome product to do this, which seems to actually be the better way of doing it.

Short version, we're looking at trends in memory usage or loads. Can we spot any trends that are outliers and then dig into why this happened. So we're actually using a service called honeycomb. But I can super recommend those guys. Short version, we're trying to find-- this bunch of requests was slow. Why was that? And then, for instance, we were investigating the other day and found one of our Dyynos-- which I guess Roku parlance for a machine running a process-- was using way more swap than all the other machines for no apparent reason.

So digging into that, even finding that, was only possible using BigQuery and Honeycomb. MARK: So most of your data really is about machine analytics and seeing what your actual things are doing? RUSSELL: Yeah, we haven't moved to-- and there's another thing that we're working on, which is to move some of our data or a lot of our most important tables from Postgres into BigQuery.

And the main reason we want to do that is for doing analytics. So turns out we have too much data to be able to query the entire thing at a reasonable speed on Postgres. So we've got coming up to or gigs of data, which actually doesn't seem like that much. But turns out, is enough. It's expensive to run at a fast speed when we're doing stupid queries.

So anyway, short version, we're working on a way of exporting that data direct into BigQuery, which will mean that we can do the more insane queries faster, which is going to be cool. Do you have any other piece of advice for people that are starting to migrate, that they have a huge database relational. And they're like, should I move to BigQuery?

Yeah, basically the biggest limitation when I started doing it was-- well, I guess I did read the docs. But I just didn't bother doing a bunch of stuff because I wanted to get started. But the biggest thing I would say now is make sure you read the docs properly and probably go with the standard SQL stuff, which seems to be the new version.

I'm still on the older version. The standard SQL seems super awesome because it removes one of the major limitations of BigQuery, which is updating stuff. Now you can treat it almost like a proper database. I don't mean that meanly, by the way. It's not just depend only now. You can actually update things. It totally makes sense if you're talking about something to analyze logs.

RUSSELL: Perfect use case for us, but horrible use case for the analytic stuff for us, because we need to just drop it every time we want to update it again. Which, I guess, is also possible. I would say definitely look at the streaming API. It's pretty awesome.

It also means stuff's really fast. There's a really good go library for it that I found, which I will get put in the podcast notes. And then I guess the only other thing-- what do you want to do to wrap up? The only other thing I was going say was I'm in Reinvent. I'm speaking at Reinvent about MTurk. So if anyone wants to come say hi and do that. So if anyone wants to say hi there, do it.

Well, thank you so much for taking the time today to talk with us. That's a very interesting interview. I will have a bunch of links to all the different projects that you have mentioned. And hopefully we'll also have links to the open source versions of your projects.

MARK: Awesome. Well, thank you. Thanks, guys. MARK: Thanks. It was a pleasure and a joy to have Russell Smith on with us today. I definitely appreciate hearing about people building interesting things. Especially, I think, across cloud, I think that's really interesting to see how people are doing that sort of stuff. And it was really good. And the amount of data he's processing, it's decent. It's a decent amount. Yeah, when he was walking away we had a quick chat. And one of the things that I was saying is, oh so, you have 25 billion rows.

Are they very short rows? It's like, oh no. They have ten or more fields. Oh wow. And some of the fields have JSON inside. So there's a decent amount of data. Yeah, very cool. So yeah, looking forward to all the open sourcing of those tools. MARK: Yes. Very good. They make you very happy. All right cool. So let's get into our question-- apparently questions-- of the week.

Yeah, you raised the first one, which I think is an interesting one and got me very excited. Which is, how to react to email from Google Cloud. So say I-- I don't know. This is always a fun one. I'm going to get sent an email by some third party, maybe you for example. And I want to be able to automatically do something about it once I receive it. How can I find out when that email comes to me, so I can pause it's data and do stuff like that?

And it was pretty funny because they need to do something very complex on the compute engine. And that very complex job needs to be done every single time that someone could update a database, that it was publicly available, built by NASA, actually. The impressive thing is that the way they going to notify people there's new data in the database is by sending emails.

MARK: Of course. So you have someone that was receiving that email that then could go and run a script that could start everything. And I was like, I'm pretty sure there's a better way to do this. And there's many ways of doing it. One of them could be, run your own web server or run your own mail server. MARK: Let's don't do that. That doesn't sound like fun. Then another thing you could do is basically use IMAP to connect from your script that runs regularly.

Like, for instance, using Chrome. And every hour it checks if there's new email. And then the email starts doing stuff. But then the problem is that, how often do you check? MARK: You're basically polling. So it's going to not be perfect. There is actually a very, very easy solution, which is just using app engine. MARK: So app engine can receive email? So, you actually get the same way-- you get a URL to host your web server, which is like your appid.

You also get an email, which is whatever you want at yourappid. So if you send an email to that address, what will happen is that the email will be sent as a post, HTTP request, to your application. So now if your instance is not running-- like your application is not running because you don't have any traffic, which is probably normal if you're only receiving mail once a day.

What will happen is that when you receive that request, your new instance will start. And you will treat that and then you do whatever you want. And one of the things that you can, as we said at the beginning of the podcast, was that you can start a new computer engine instance. And then from there you do whatever you need. MARK: I'm just actually genuinely blown away that app engine can send me email. I didn't realize it could do this. And it's so cool. It is really not new. It is very simple.

And if you know it's there it is good. MARK: My mind is literally blown. It is Literally blown. And talking about app engine, we also have a second question of the week coming from our friend, Stu Swartz, or Schwartz. Tell us how you pronounce it. So, it is about app engine services, which for people who have been using app engine for awhile, you might have known as app engine models.

And he's asking when should I use app engine vs. Is this a scale issue, an approach issue, implementation issue? And then the second question, which is more related to app engine services, is should I have a bunch of services? Should I have a bunch of projects and one service per project? The answer to the second part of the question is, well, have as many services as you need.

Micro services are awesome. MARK: And when you say services, we're talking about modules. So you can have, basically, a different app-- it's basically a different app engine application. But they share the same resources. So if you want to make them communicate through You can.

But also you can use task queues and all of these things. And it's very easy to communicate because they're all in the same project. When to use more than one project. Normally, I could reserve that to a different environment. So you have like prod, QA, stuff like that. Then those are good because that way you're not sharing the data store, which is probably a good idea. So it is a good place to separate. But you should have all your production services in one single project.

It's much easier to handle. And then about the question, when to use app engine versus GKE, maybe you can give us a little bit of background on that. MARK: Yeah, so this is an interesting question. I'll hearken back for a more detailed response to our episode number two, Compute as a Continuum, where we go into much nuance detail about it.

But I feel like it's really more a question of how much flexibility and control do you want. App engine, standard and app engine flexible will give you slightly different versions of that. Whereas app engine standard platform as a service, we dictate a lot of things about how you do stuff. But that gives you great scale. Which is really powerful. Flex, we give you a few more configuration options, a few more bits and pieces so you can run pretty much anything you want on it.

But now you can't scale to zero. We don't spin up quite as fast. So there's the trade off. MARK: But if you want, still, the sort of capabilities such that things stay up. If they fall over, they come back. You still want to be able to deploy with a rolling update, things like that, GKE is a great place for that. But you now have even more tweaks and knobs that you can pull. So you'll have a cluster of machines, in that case. You pay for that cluster of machines.

You can order or scale it to a degree. But then you can do things like resource management or have jobs or scheduled jobs, as well, once that comes out of alpha. But you have way more control. And you can say, maybe I want to have stuff with a whole bunch of memory in it, which is way more than I'd be able to get on app engine or something like that. Persistent volume amounts, if there's state that I want to be able to put in places, things that you can't necessarily do as easy on app engine.

So it's probably really down to what sort of application you're building, and then what sort of capabilities you really want to be able to have at hand. That way I can forget about all the DevOps. And I just deploy it, and I know it will be running there forever. And that makes me happy. But if it's something that I'm going to need to be like, oh no, I have all of these services and there's dependencies across them. And I want them to be able to scale them independently and update them independently, and all that stuff, then GKE is probably the best place.

MARK: Seems pretty reasonable. MARK: I've got a lot of trouble. I say that with some sadness. But I'm doing some fun events, so that's pretty cool. So I will be at Siege in Atlanta on the 8th of October. That's going to be a fun gaming conference. After that, I'll be at Connect Tech on the 20th. I'll be back in Atlanta, again, on the 20th. That's a general web dev conference.

And then I'll be at Game a Con, another game conference on the 27th. So I have a fair bit of travel in October. MARK: And yourself? What are you doing? And I'm going to be running a workshop. And you're all invited. Yeah, it's about how to be a highly scalable web applications with go on a pension. Everyone is welcome. So we'll have a link on the show notes to that. And then I'm also very excited about something else, which is not traveling but kind of.

I'm going to be doing a remote, a remote meet up. That will be on Monday after that, so on October 10th. And it's going to be kind of the same. So it's a mini workshop. It's how to build a web application with Francesc.

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