No, you know what happened in coffeecake last hour under the hood when we have entered this command
that actually created Utopia, let's not try to read the details about this city.
Storbeck For that, let's Sidis again to Kafka, FALDER City to Kafka.
Clear terminal and no, let's run again, same script topics, but with another option, that's the
least been topics Topix as age and here will be optioned list and afterwards we need to specify, same
as before, a bootstrap.
Sabra, also keep ourselves.
Let's try to specify connections between of Zookeeper.
For that, let's use zookeeper option.
And here will be address of connection to Ozaukee Prospero, remember that the zookeeper is running
at Port two one eight one, so he'll type two one eight one.
And let's press enter and you'll see a list of the topics in Kafka class.
And now there was just a single topic called seeds.
That's what this command does.
You could always use this list option in order to list all available topics that exist in Kafka.
Gloucester also noticed that we have just used Zookeeper option instead of bootstrap Sarah.
And that means that you are able to connect to Kafka Gloster either via Zookeeper or any of the Kafka
Saras.
OK, let's now read the details about this topic called Citius.
For that, let's use another option.
Does best describe type same command Beenz less COFCO.
Topics nutsedge.
Next comes describe.
Next comes same as before, as a sort of bootstrap, Sarah, let's again specify zookeeper.
Localhost.
Two one eight one, and the next there comes another named argument, name of the topic you want to
read details about.
So let's look at this next topic.
And here comes the name of the topic, Cities and Lesperance Enter.
And here with our configuration details for this topic, let me make it a bit smaller to feed it to
the screen.
Like this and do we see such information topic name cities next competition, count one, and you already
know what is that?
That is actually quantity of different folders where messages of this topic will be stored and by default,
this quantity is one.
Next comes and others are new for you.
Parameter replication factor one.
What does this parameter mean at the moment?
Our coffeecake last year has only single broker and if the seller will become unavailable for some reason,
then nobody will be able to store new messages that will be arrive from producers and consumers will
be able to read data from the server.
And that's why replication factor is used.
If there are multiple brokers in the Gloucester, you are able to replicate every message that arrives
to topic in our case, see this topic and replication factor of how many times each message will be
replicated.
And in our case, this parameter is set to default value one because we have only one server and only
one server will save every single message in the topic.
But if there were, for example, three s in this cluster, you can set replication a factor to three
and every message in this topic will be stored on every of three servers for the purposes, of course.
OK, that's replication facto below.
After this line, you'll see this line with details.
For every partition, here comes number of partition.
And you know that partition numbers start from zero zero one two and so on.
Next comes information about leader grammaticus and in-sync replicas.
And everywhere you see here, Namba zero.
And it is basically number of the broker.
And in our case, we have only a single broker with Zero Raqib.
This configuration parameter broker ID zero, this one.
And that is exactly that number that you see here in those fields.
And that means that every partition must have at least one leader, leader, broker in our case.
Again, we have only a single broker, and that's why he is leader for this partition partition number
zero.
We will get back to this later on for sure.
But no, you should remember that we describe argument.
You are able to read the details about each topic.
In our case, we have only a single topic and you could list all topics in the Kafka cluster by using
the least option.
OK, guys, that's all for this one.
And the next day we could try to send a very strong message to this topic, see this topic.
And for that, we need to have producer, luckily Kafka sheep, Swiss built in consumers and producers
that allocated in Benfold and they are also producer and Kafka Council consumer.
And we are able easily use them in order to produce and consume Kafka messages.
And let's produce some messages for C this topic in the next lecture.
Bye bye.