Welcome to the Cloud Computing Applications course, the first part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this first course we cover a multitude of technologies that comprise the modern concept of cloud computing. Cloud computing is an information technology revolution that has just started to impact many enterprise computing systems in major ways, and it will change the face of computing in the years to come. We start the first week by introducing some major concepts in cloud computing, the economics foundations of it and we introduce the concept of big data. We also cover the concept of software defined architectures, and how virtualization results in cloud infrastructure and how cloud service providers organize their offerings. In week two, we cover virtualization and containers with deeper focus, including lectures on Docker, JVM and Kubernates. We finish up week two by comparing the infrastructure as a service offering by the big three: Amazon, Google and Microsoft. Week three moves to higher level of cloud offering, including platform as a service, mobile backend as a service and even serverless architectures. We also talk about some of the cloud middleware technologies that are fundamental to cloud based applications such as RPC and REST, JSON and load balancing. Week three also covers metal as a service (MaaS), where physical machines are provisioned in a cloud environment. Week four introduces higher level cloud services with special focus on cloud storage services. We introduce Hive, HDFS and Ceph as pure Big Data Storage and file systems, and move on to cloud object storage systems, virtual hard drives and virtual archival storage options. As discussion on Dropbox cloud solution wraps up week 4 and the course.
3.3.8 Protocol Buffers and Thrift
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Cloud Infrastructure, Cloud Applications, Cloud Storage, Cloud Computing
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Module 3: MAAS, PAAS, Web Services
Welcome to the third module, where we introduce Metal as a Service (provision real hardware in the cloud), Platform as a Service (provide a platform to run user code on) and Web Middleware as the glue technology that empowers cloud computing.
教學方
Roy H. Campbell
Professor of Computer Science
Reza Farivar
Data Engineering Manager at Capital One, Adjunct Research Assistant Professor of Computer Science
[SOUND] Okay,
we've looked at RPCs at the web, how we can use
the web to support RPCs with things like SOAP.
We've looked at formats for data over the net and we've looked at load balancing.
In this last lecture, we put it all together with how
do you very effectively, make development of web services and
how do you ease, if you like, all the complexity.
You've already seen there's some complexity creeping in
with load balancing.
There's a little bit more that we can deal with.
As we go forward, the reason for doing this is so
that you don't have to, later on in the course, worry about this.
What you will have learned in this last section is just how we get
the data from the client to the application in a very reliable way.
And so, from now on, after this, you don't have to worry about
all the complexities of the Cloud services that we're going to describe.
So, let's deal with this straightforwardly.
Protocol buffers are an invention by Google.
They're based off RPCs, they're very similar to RPCs.
What they're doing is, for web requests, they're doing what RPCs requested.
And RPCs were implemented initially sort of carver and .net and so on.
What's happened is those ideas have been used to
implement the same Keiper support, but
this time at the web level with protocol buffers.
So, it's a language-neutral, platform-neutral scheme.
It's extensible, and what it does is to take the client requests and
it serializes it, transmits it to the server.
So, all of that mechanism we were describing and delivers it to the server.
The server can use the serialized stretch of data to
extract the data and then perform the various tasks.
And we described some of the distributed services that can be
provided like an RPC or just a web request, or anywhere in between.
All of these are taken into account inside this sort of
protocol buffer organization automation.
What we're going to see is the protocol buffers actually give you the code for
the client and they give you the code for the server,
so you just need to know what you want to do, and then the protocol buffer
system will actually build for the client, the mechanism to do it, and
for the server, the skeletons to actually accomplish the task.
So, here's an example schema for
sending a request about a person.
And, this can be embedded in HTTP.
It could be an RPC type of request that is being made.
But, whatever it is, it needs to pass this data structure.
And, this data structure is going to be a person.
So, it looks like a record, if you like.
There's going to be an identifier required and it's an integer 32-bit and
there's a required name for it, and there's an optional string.
And in the Google protocol buffers, they're actually labeled, so
you can easily access them one, two, three, four.
And you can see what those various parameters are,
and that's the way the client and the server deal with those strings.
But the software, the actual encoding for the packet and
the decoding at the other end, is all done magically for
you via a compiler that takes the protocol buffers and
produces those pieces of the code that you want to use.
Now, we know that the web is so big, can be Json,
it can have different programming languages,
C++, or whatever you chose to name.
This particular approach allows you to generate the data
serialization for any of those languages.
And so, it's very powerful, very useful, keeps everything consistent.
You can start off in C++, and you can end up in Java,
or some other language, you don't care.
It's all going to be taken care of for you by this particular system.
This is an example of what it'll translate into.
For Ruby, for example, I'm just giving you the data definition here,
I'm not going to go into what the server and the client look like yet, but
here what you see is the translation if you wanted to implement this in Ruby, what
it would actually translate to according to those translators from Google.
Okay, now, because this was so successful, it had imitators.
Thrift, I won't say it's a straight imitation because it also
introduces some innovation, Thrift built on this idea, and
it created more of a network stack that could be used for other things.
In particular, what it allows you to do to is to transmit files between clients and
servers, so that you can actually access directly, distributed file systems.
That's a big advantage if you think of now you've got the web, and
you've got files perhaps with details, FDP or whatever.
If you can actually use some sort of file access mechanisms,
distributive file access mechanisms, then you can simplify everything.
And if you can put all that into a package, so
that you don't have to actually program it, but what you do is to specify
to the thrift compiler what you want done, it does it, then enormous benefits.
So, that is exactly what Hortonworks,
and Yahoo, and Apache have put together.
You can read all about it, thrift.apache.org and
go to that web address at the bottom.
It will explain, there's a paper which explains the concepts underlying thrift
but I'm going to tell you about them anyway.
So, what Thrift does is to deal with servers.
It would deal with single-threaded, event-driven servers,
that's its specialty.
It would deal with processes.
You can say, I want an RPC, or you can say, well, I just want a video content.
Just get it to the screen.
You can specify events, asynchronous events coming in
like sort of notifications of the news.
So, you've got various different processes you can use.
You can use various different protocols if you want it to be sent in Json, or
if you want it to be sent in clear text, or you want it compressed.
There are options for all of these.
And then for the transport, well, you can use raw Ethernet if you want.
You can use TCP, you can use HTTP, in fact,
any future protocols will also go there too.
So, you get flexibility in this generating system.
And what it's going to deliver to you is a serialized
stretch of data specifically, for your application and
for your server, given those specifications.
So, just to recap, I mentioned Hortonworks, and
all these other companies.
But the idea, this sort of knitting together of everything,
we have to actually acknowledge Facebook.
So, transport methods, as that's a topic that we haven't actually addressed.
You'll want to open, close, read, write, flush a file.
The server transport has actually the ability to listen to see if
anything's happened to the file, accept and close.
And it will allow you to read, write, to and
from the file on disc, on the remote disc.
It will also, of course, let you do HTTP and all those other types of things.
But having that richness for transportation really sort of makes you
much more productive development wise in building Cloud services.
Same thing with File Transport.
You can transport a file.
You can transport it framed which is for non-blocking service.
Just imagine a video stream.
And you know with a block,
you just want to take what you can get out of the video stream and present it.
Then, you would use a TFramedTransport for it.
If you're building a complicated distributed memory system,
you were to use a TMemoryTransport.
It uses memory for I/O.
If you're going to do blocking, which is what mostly we do.
You would be using a TSocket.
If you were going to do compressed transport,
then you would use the TZlib library.
Server codes, what can we do with servers?
Well, you can have servers that are non-blocking to match that transport.
You could build muti-threaded servers
using the non-blocking I/O of systems.
Well, it has a non-blocking server all built in to this.
You have a simple server, you also have a thread pool server, so you create so
many threads.
What it's going to do is just select one of those threads to operate for
the moment as a poured device to help you communicate, and then when it's finished
it's going to release it, so that the actual creation of a process isn't needed.
You just use threads.
So, lots of different server codes you can have.
But they're all encoded as type within this system.
And you can pick and chose, and it will generate the code for you.
So, again, the data structures get translated.
This is an example schema for Thrift.
Now, you notice that I'm talking about what is Thrift's schema?
This is the way it would look.
It's a bit simpler than the protocol buffers but
it does exactly the same thing.
In this case, it's a struct.
It's very familiar if you know C++ or something equivalent.
Okay, so that's your data structure.
Now, you're going to build skeleton for the server and the code for the client.
Thrift will do that automatically for you once you choose the different
mechanisms for transport and different servers.
I'm not going to go into great amount of detail, you can read about it.
But essentially, what happens is that you would say well,
I want this type of communication.
The Thrift compiler would produce a stub for the client.
You take the stub, you add your code to that stub.
The stub does everything,
in the way of doing the serialization of the data structure and transmitting it.
You also take the skeleton, you put that into your server.
The server code then needs some development as to how you're
going to process that data.
But all of the threading and all of the communication,
all of the march link getting day trained to the packets.
All of that's going to be taking care of you, so it becomes really simple.
So, that's my quick update on Thrift, but it brings together all of these packages.
What you see from client to server, through the Cloud, for
any level of complexity, we have a bunch of tools that
really helps in organizing and building these systems.
[MUSIC]
