In this course, Google Developer Expert Lynn Langit introduces you to Google's Cloud technologies for hosted virtual machines or Google Compute Engine. This course will get you up and running with the definitions and technologies you need to know. By the end of the course, you’ll know what Google has to offer in the infrastructure as a service cloud arena and how to devise a strategy for adopting Google Compute Engine. The 'Introduction to Google cloud' Pluralsight course is a prerequisite. This course is aimed at developers and business decision makers, and is actionable for executives as well. It includes a demos of using the various tools and APIs to work with GCE.
Introduction to Google Compute Engine Hi. I'm Lynn Langit from Pluralsight. In this course we are going to take a look at using Google's Compute Engine which is -- this is part of the series I'm creating on using Google's Cloud for developers. In this course we're going to cover a number of aspects of using Google's Compute Engine or GCE. First is we're going to understand what the service itself is and why you as developers might choose to use Google's cloud-based virtual machines and that's a one sentence definition of what GCE is. We're going to look at some other considerations of the possibility of using the service, so features. What is the speed; what's the scalability, operating choices, systems cost, and so on and so forth. We're going to also have a lightweight comparison to the competition. Amazon, Azure, Rackspace, some of the other VMs out there available in the cloud. However, the core of the course is going to be around implementation details. So we're going to look at what languages are available for you to use to interact with GCE. What kind of tools whether they're command line, whether they're console or API's and what editors. So we'll get into the nitty-gritty of how to work with it. We will also talk about some use cases of what you might want to use this service for and some things that I've seen with customers that I've worked with. As you can see on the bottom side here this is one in a series of many different services that Google has available. So we will be focusing on Compute Engine in this particular course.
GCE Implementation Lynn Langit: We've been talking about disk storage in GCE, and, again, I wanted to put a slide to pull in some key details. I'll remind you that the services are in limited preview, and you need to verify before you work with the service if you're watching this, you know, sometime later than when I've recorded it, which is January 2013. So the default storage for GCE's are ephemeral or temporary. Ten gigabyte disk by default, but you can have up to two disks of the size 1770 gigs. You can use permanent or persistent storage. You will be charged more for that. You can have a read/write persistent storage with a single instance. You can have a copy of that information as read only with multiple instances. And there is currently a maximum of 16 permanent disks attached to an instance. This is, obviously, limiting when we're looking at Cloud scaling, and I know there's been a lot of feedback from the community pressure on Google to increase that. I would verify that before I looked into any production workloads. As I mentioned, you can attach persistent only on create of an instance. The max size of the disks for the persistent varies by the instance type. And now you can have snapshots for reuse. You can also use source or persistent disk data from Google Cloud Storage. So this is similar to using an existing configured VM. Now, again, to be clear, Google has quite a bit of control over the OS. You can add additional functionality on top. So the Java run time, as an example, but they have particular OS images that they have, that they approve for use on, running on their Cloud. Again, all data is encrypted by default. It's done transparently when data is at rest.
Next steps and futures with GCE Lynn Langit: Now that you've seen how to program against GCE, some things for you to think about when you're going to either try it out or possibly implement some production work loads. Very important consideration is disks. I said it before, but I'm going to say it again. What I'm finding with, comparison to other vendors this fact that Google has the ability to use ephemeral disks makes the service flexible in that you can spin up VM's, maybe for, you know, running MapReduces or something, and then when you're done, just get rid of them. There's no deleting any persistent storage. Because I have found that I've incurred charges from other vendors, notably Amazon, because I have persistent storage, and I forget to delete it. Now on the same hand, you have to be cognizant that if you want persistent storage you have to approach it in the ways that I showed you in this presentation. So you can either use a persistent disk that you just spin up and is empty or, more commonly, you will use Google Cloud Storage and do your, you know, whatever you want to install on there so that it's a configured image. Google App Engine Integration, very, very tight integration between GCE and GAE. So something for you to think about for projects. All the consoles themselves are hosted in GAE. So Google eating its own dog food. If you are going to be doing a GAE project, then there's all kinds of considerations around data and data storage, caching, routing, which we'll talk about in subsequent courses. And then, again, the possible use of other Google API's. For me, the production use of GCE is around data projects. So what I'm finding with these data projects is they're a combination of, say, MapReduces and data mining some prediction. Possibly even big quarry. So this is sort of my real world of GCE. I'll be very interested in hearing what yours is. So please, you know, use the Pluralsight blogs and feedback mechanisms so I can get comments on how you're using GCE. I'll be really interested to hear.