Till now we have seen basics of Apache Kafka and created Producer and Consumer using Java. In this tutorial, we are going to build Kafka Producer and Consumer in Python. Along with that, we are going to learn about how to set up configurations and how to use group and offset concepts in Kafka.

Set up

We should have python installed on our machine for this tutorial. Also, we need to have access for Apache Kafka running on our device or some server. You can check how to install Apache Kafka on windows. Apart from this, we need pythons kafka library to run our code. To fix this, on system run following command

Kafka Producer

Let us start creating our own Kafka Producer. We have to import KafkaProducer from kafka library. We also need to give broker list of our Kafka server to Producer so that it can connect to Kafka server. We also need to provide a topic name to which we want to publish messages. That is the minimal configuration that we need to give to create Producer.

We can start sending messages to this topic using the following code.

Above code sends a message to the topic named ‘myTopic’ in Kafka server. However, what if that topic is not already present in the Kafka server? In such case, Kafka creates a new topic with this name and publish messages to it. Convenient isn’t it? But you should remember to check for any spelling mistakes in topic names.

If you want to set some more properties for your Producer or change its serialization format you can use following lines of code.

Kafka Consumer

As we are finished with creating Producer, let us now start building Consumer in python and see if that will be equally easy. After importing KafkaConsumer, we need to set up provide bootstrap server id and topic name to establish a connection with Kafka server.

As we can see we need to setup which group consumer belongs to. Also, we need to specify offset from which this consumer should read messages from the topic. In above case, we have specified auto_offset_reset to earliest which means this consumer will start reading messages from the beginning of the topic.

After this, we can start reading messages from a topic. Along with each message, we get some additional information like which partition that message belongs to, its offset in that partition and its key.

This will print output in the following format.

Kafka consumer output

This is it. We have created our first Kafka consumer in python. We can see this consumer have read messages from the topic and printed it on a console.

Conclusion

We have learned how to create Kafka producer and Consumer in python. In the next articles, we will learn to the practical use case when we will read live stream data from Twitter. Until then, keep learning.

I am passionate about data analytics, machine learning, and artificial intelligence. Recently I have started blogging about my experience while learning these exciting technologies.

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