Let's summarize key points of this example.
We have started the single Brylcreem and greet the topic with five partitions and then of the topic
was Nomura's replication factor was set to default equal to one next week of Loung.
Several consumers in the same consumer group using this command and a group name was NOM's.
And we have started just with a single consumer and ended up with six concurrent consumers in the same
group and while having multiple consumers in the same group running, we have read the tales about specific
consumer group, and in this output we're able to see which partitions were assigned to different consumers.
And Kafka tries to spread the different partitions across all available consumers in the same group
equally.
And also in this output, you always see current of sales that are committed offsets by specific consumers
in specific partition look end and offsets of the messages in each partition.
And also you may select and we will try to make this number non-zero in the next example.
And main outcome from this example is that if there are more consumers, then available partitions,
then some of the consumers will be idle and will not consume any messages at all.
That's all for this example.
And the next Dalitz Lounge Kafka performance producer that will produce a bunch of messages instead
of this is a built in producer.
And we will actually try to overload our consumers and we will try to observe how they will be able
to catch up with increased load.
So see you in the next example.
By by.