Here you are.
And welcome back.
Now, in today's ruling, we'll be discussing about the Amazon Aurora.
Now, Aurora is a great piece of data was offering under the relational database service in Adaba is
now a recently database.
Is he pushing Aurora?
Quite a lot.
And specifically, this applies in terms of certification.
So when you discuss about certification, like cloud practitioner, you have solutions, architect,
associate solutions, architect, professional Dave OB's database specialty and others authorize all
of the common topics that you will see in all of them.
So if we learn this topic properly, it will really help you, not only the Alward scenarios, but also
in your certification journey.
So with this, let's go ahead and begin our journey in Aurora.
Now, when you discuss about the database offerings, the generally divided into two primary types.
One is the open source database.
And second is the commission or enterprise databases.
Now, when you compare both of them, commission database, Deskovic, certain aspects, it can be in
terms of performance, security and others that are not found in the open source database offerings.
So this is where the order steps in.
Now, Amazon, Aurora is a May sequel and Postgres sequel compatible relational database, Big for the
Globe that combines the performance and availability of a tradition in the press database with the simplicity
and cost effectiveness of the open source databases.
So this can be better understood with this diagram or you're there.
You have Amazon Aurora event.
Aurora tried to have a performance, availability, security, reliability of the enterprise database,
vett the simplicity and cost effectiveness of the open source database so it tries to be best of both
and provide you the database offering in eight movies.
So Amazon, Aurora is up to five times faster than this time that my secret database and three times
faster than the PostgreSQL database.
Now, again, it's not like out of the box.
It is five times faster and three times faster than Postgres.
But there are certain workloads where Aurora proves to be much more faster than the traditional myse
equal as well as their postgres equal.
Now, again, as we're discussing Aurora, it provides security, availability and reliability of a
commercial database offering at one tenth of the cost.
And this is what a lot of customers really need.
Now, let me quickly show you one interesting feature of Aurora.
In fact, there are a lot of them.
But let's begin with one now.
Generally in ideas.
Whenever you try to create a database, let's create with the mice equal engine failure.
So if you go down, you see that you have an option of storage take and you can go ahead and specify
the amount of storage that you need to wear.
So this is one example of mice equal.
However, when you select the Aurora one or you're.
And if you go big down, you see that there is no setting associated with storage at all.
So what happens in Aurora is that you do not select this, put it whenever required.
The storage of it automatically grow without hindering the ordered performance of the database.
So this is one of the really great features that Aurora offers.
Now, before we go ahead and discuss more in detail about the Aurora architecture, let's quickly have
a revision or a standard ideas might be easy and replica based architecture.
So this diagram will help you understand it in a quicker way.
So you have three availability zones or you add in the availability zone.
One, you have a primary ideas instance running.
And this ideas instance is not only mighty easy, but it also has the associated 3D replica.
So we do discuss about my ideas.
This works based on synchronous replication.
And when you discuss about ideas that you replicate, it works based on asynchronous replication.
And all of them in the UN deadline, they make use of the EPA storage.
So if you'll see or you have ideas so you can consider ideas with my Cequent and I'm dead Langley,
it makes use of the EPA as a volume for storage.
So now let's quickly look in too deep orderer architecture at a high level.
All of you know, when you look in to the architecture, there are two primary components that are involved.
First one is that DWC instance.
So this is basically the Aurora instance that is running.
And second primary component is the storage gless that volume.
So this storage cluster of Audu spans across multiple availability zones.
And the data is replicated between all of them.
Now, since the Aurora instance and the storage leered are Independent v. Kinsky, the storage we know
much more easier to eat when you compared with distribution, traditional ideas based architecture like
my Cequent, which basically makes use of the PBS volumes.
So let's quickly look in to these storage cluster wallaby's architecture.
So you have a storage cluster volume, which is basically a virtual stories volume which spans across
multiple availability zones.
So in this data, there are three availability zones and eat data is replicated six ways across the
availability zones.
So if you see all year.
So you have one data.
This data is available in easy one, easy two as well as easy three.
Similarly, if you look in to this green circle, the green circle is also available in all the three
availability zones.
Now, in case if one of the availability soon goes down, that is nothing much to get in because the
identify variability zones already have the same data that is replicated.
Now, with this specific architecture, the better storage cluster volume is used instead of a UBS volume.
You can add the database instance quickly because Aurora doesn't make a new copy of the table data instead.
Database instance, go next to this shared volume that already contains your data.
Now this can be better explained with this next diagram where you have three availability zones and
you know that that data is already replicated across the storage of the three availability zones.
So in case if you create a new Aurora replica, then all that happens is that the instance gets created
and it gets associated with the cluster storage volume.
That data is already present beforehand in a similar way.
If you create one more 3D replicator, different availability zone and maybe one more replica in a different
availability zone, all they have to do is they have to connect to the underlying cluster storage value.
And that's about it.
That data is already present across all the availability zones for the cluster storage volume.
And this is one of the great advantage.
And it really makes Aurora much more faster.
Now, the same can be explained with this data.
In fact, this diagram I have directly decoded from the end of this documentation there you have a primary
instance and you have multiple 3D replica in a different variabilities zones.
Now you'll see that you have a storage cluster of all you.
And each availability zone has its own set of data piece for it, availability point of view.
Now, then you discuss about Aurora.
The concept of endpoint is something that we need to understand.
So you can connect to Aurora cluster through endpoints.
Now, Endpoint is basically a Aurora specific EU idea, which consists of two parts.
First one is a host and second one is the port.
Now, there are three primary type of endpoints that are available that are more as one.
So the primary date is you have a cluster levelling point.
You have a debt limit endpoint and you have the instance limit endpoint.
So when you're working with the Aurora.
Understanding this concept is very important.
In fact, we'll be discussing about it in detail in a moment.
So let's go ahead and understand this with a simple diagram.
Very.
You have an application.
Now, this application can connect to a clustering point.
It can also connect to a dead end point.
So cluster in point is the end point where you will be performing the right operation.
And the reader level endpoint is the end point where you will be performing lead level operation.
So you can consider that a point as the end point for 3D replicas and declasse thirteen point as the
end point for the right specific operation.
So if the application wants to write something into their database, then you will have to supply the
cluster level endpoint for this thing.
And if there are certain use cases where you only want to perform read-only operation, then you will
have to configure dirty dairying.
Point in to that application.
So speaking about the order, eight points, let's discuss about the four types of endpoints that are
available.
First one is the class, the level endpoint.
So cluster level endpoints are used when you want to connect to the primary database instance within
the cluster.
And it is used for performing the right operation.
Then you have a leader level endpoint.
This at a building and points for the replicates.
Now for multiple Reider because this endpoint will also load balance though for Claude Ibom, all the
3D replicas.
So consider this data very.
You have to read replicas that are available now.
It is not a best practice to basically add that you are not the read replicate within your application.
Instead, you can Dave there you are allowed to read and point ported within the application and the
reader endpoint will also lower the balance between the multiple three particles that are available
within the Aurora instance.
The thought of it is the instancing point.
So instance endpoint allows you to connect directly to the.
So if you Selya data to read replica instances where you.
So instance input directly allow you to connect to the instance in case if you needed.
But the best practice space that you should not directly connect to the instance.
You should directly connect to either the clustering point or the reader endpoint.
And then you also have the option of custom endpoints, which allows you to create custom made points
for your own requirement.
So that's the high level, all of you, as far as the theoretical perspective is concerned.
So let's do one thing.
Let's quickly have a demo so that this can be better understood.
So, I mean, the ideas console and I already have Aurora instance running.
Now, if I maximize this, this architecture is very similar to the diagram that we were discussing
where you have one primary instance.
And there are two replicas that are available.
So within this, the first one is the primary instance.
So if you look at the rule, it is right there.
And so this is where you actually perform the right operation.
And then you have two other instance which has a rule of reader.
So these are basically the heat replicas all year.
So let me, in fact, open up Aurora.
Now, if you look into the end point, Sawyer, you would see that there are two end points that are
available.
One is the cluster either.
And second one is the reader endpoint.
So if we look into the tapir, this is that right there.
So that right there is nothing.
But this cluster in point.
So within the documentation, you would not see it as a writer.
You would see it as a clustering point.
So this is where you perform that aid operation.
And the second date is the reader.
And we've already seen.
So this reader is nothing but dead.
You that end point, which in turn has a connectivity to already replicas that that are relevant.
And it will also reload the balance, the workload.
Now, within the aurora, you will see that direct to Aurora 3D replicas that are already available.
So instead of using the business of each Reed replica, you can directly make use of this at either
end point.
Now, in fact, let me also show you the instancing point all year.
So let's click on one among them.
And you will see that each instance, Sawyer has its own endpoint.
So this is the first one.
Then you have the 3D replica.
3D replica also has its own endpoint.
And you have read the replica to 3D replica to also has its own endpoint.
So when you compare it with the table.
So you have a class the level point.
You have a reading level endpoint.
And then you have the instance level endpoint, which I'm nothing but the ones that we are exploring
now.
So these are also effort as the instance eleven endpoints.
So that's the high level.
What do you know about the Aurora in points now when you discuss about the features that Aurora off
was dead out of Vibe dreamed of?
Very interesting features that Aurora has to offer.
Some of these include Global Database.
Cervalis is a very interesting feature here.
Again, you have glossaries and application.
You have auto scaling, which is also very interesting when you have Aurora.
That track you have, I am deeply authentication.
You have a feature for sharing a DBE cluster ideas proxy and so on.
So there are a lot of interesting features that are available for Aurora, however.
We'll be discussing them in the relevant section, Vente.
It is required.
So this video was a very high level or view video for Aurora.
The purpose for which the aurora is based and at a high level or view the understanding about the Aurora
endpoints.
So this will conclude this video.
I hope this video has been informative for you.
And I look forward to seeing the next video.