Kafka Event

Introduction to Kafka Event

In Kafka, the Kafka events would be some sort of an action (read or write) that can perform on the Kafka partition when any action or task was executed. The action will be from the internal Kafka environment or from the external world. As per the requirement, we can choose any implement strategies for the Kafka events. The Kafka event majorly distributed into three major strategies like

• Single Topic
• topic-per-entity-type
• topic-per-entity

As per the requirement, we can select the above strategies. But before selecting it, we need to consider multiple factors like the data size, access request, and topic classification (it would be the single topic or separate topic for the Kafka events).

Syntax:

As such, there is no specific syntax available for the Kafka Event. Generally, we are using the Kafka Event value while creating new events or defining the new event strategies on the number of Kafka topics.

Note:

1) While working with the Kafka Event. We are using the core Kafka commands and Kafka Event command for the troubleshooting front.

2) At the time of Kafka Event configuration; we are using the CLI method. But generally, we can the Kafka logs as well for further troubleshooting.

How Kafka Event Works?

The Kafka Event is useful when any specific task or activity may occur and with respect to this activity, we need to execute the Kafka event. To manage this, we are having multiple ways to handle this event like

• Single Topic: It will store all the events with respect to all the entities in a single Kafka topic. In the same single topic, we can define multiple partitions.
• Topic per entity category: We can select a different topic for all the users associated with the events and all the entity related events.
• Topic per entity: We can select a separate topic for a single user and a single entity.

Below are the list of property and its value that we can use in the Kafka Event.

Sr No	Configuration Parameter	Prominence	Description	Default Value of the  Configuration
1	application.server	Low	We need to define the combination of the hostname and the port of the server. It is pointing to the embedded user that would be defined in the endpoint. It would help for discovering the locations of state stores of the single or multiple Kafka streams job or application. The configuration value may vary from the different Kafka architecture and server configuration.	the empty string
2	buffered.records.per.partition	Low	This configuration property will help the maximum number of records to the buffer per Kafka Events.	1000
3	cache.max.bytes.buffering	Medium	This configuration property will help to the maximum number of memory (in bytes) to be used for record caches (It will apply for all the all threads).	10485760 bytes
4	client.id	Medium	The client id will be in the string format. While doing a request, it will help to pass to the server. The same configuration will pass to the consumer/producer clients in the Kafka streaming application.	the empty string
5	commit.interval.ms	Low	As per the configuration value, the frequency in which to save the position of Kafka events.	30000 milliseconds
6	default.deserialization.exception.handler	Medium	This configuration value details with the exception handling class that implements the Deserialization ExceptionHandler interface.	LogAndContinueExceptionHandler
7	default.production.exception.handler	Medium	This configuration value details with the exception handling class that implements in the Production Exception Handler interface.	DefaultProductionExceptionHandler
8	key.serde	Medium	It will be the default deserializer and serialize class for record keys. It will implement the serde interface. The same value will be inherited from the key.serde configuration property.	serdes.ByteArray().getClass().getName()
9	metric.reporters	Low	This configuration property helps to list out the classes to use the metrics reporters.	the empty list
10	metrics.num.samples	Low	This configuration property helps to the number of samples to maintain the compute metrics.	2
11	metrics.recording.level	Low	This configuration property helps to the highest recording level for metrics.	Info
12	metrics.sample.window.ms	Low	This configuration property helps to the window of time a metrics sample (in computed over).	30000 milliseconds
13	num.standby.replicas	Medium	It will help to define the number of standby replicas for each application or job.	0
14	num.stream.threads	Medium	It will help to define the number of threads to execute in the streaming processing.	1
15	partition.grouper	Low	This configuration property helps to the partition the grouper class that helps to implement the Partition Grouper interface.	See Partition Grouper
16	processing.guarantee	Low	This configuration property helps for the processing mode. It is having two different values like “at_least_once” (it will be the default) or the “exactly_once” value.	See Processing Guarantee
17	poll.ms	Low	This configuration property helps to define the amount of time in milliseconds. To block the waiting for input.	100 milliseconds
18	replication.factor	High	It will help to define the replication factor for change log topics. The repartition topics were created by the application.	1
19	retries	Medium	It will be the value or the number of retries for broker requests. It will lead the return to the retryable error.	0
20	retry.backoff.ms	Medium	This configuration property helps to define the amount of time in milliseconds, before any request was retried. It will apply when the retries parameter was configured. But the value should be garter than zero.	100
21	state.cleanup.delay.ms	Low	This configuration property helps to define the amount of time in milliseconds. When the partition has migrated then it will wait before deleting state.	600000 milliseconds
22	state.dir	High	The state.dir property will define the directory location for state stores.	/tmp/kafka-streams
23	timestamp.extractor	Medium	We can define the timestamp extractor class; it will help to implement the Timestamp Extractor interface.	See Timestamp Extractor

Examples to Implement or Check Kafka Event

Create a Topic to Store the Events

In the Kafka environment, we can store the events.

Command :

Consume the Kafka Events:

./kafka-console-consumer.sh --bootstrap-server 10.10.132.70:6667 --topic kafka_events --from-beginning

Explanation :

As per the below screenshot, we have created “Kafka_events” topic and consume the events on the same topics.

Output :

 

Conclusion

We have seen the uncut concept of “Kafka Event” with the proper example, explanation, and methods with different outputs. As per the requirement, we can choose the Kafka strategies for the Kafka event handling like Single Topic, topic-per-entity-type, and topic-per-entity. If we want to execute and task, as per the previous event was happed in Kafka. In such cases, we are using Kafka events.

Recommended Articles

This is a guide to Kafka Event. Here we discuss the definition, How Kafka Event Works, and Examples to Implement or Check Kafka Event. You may also have a look at the following articles to learn more –

1. Kafka Zookeeper
2. Kafka Replication
3. Kafka Node
4. Kafka Console Consumer