Python Kafka Integration: Developers Guide
0 likes90 views
The document serves as a guide for integrating Python with Apache Kafka, an open-source platform for real-time data streaming and processing. It describes key components of Kafka such as producers, consumers, brokers, and topics, and highlights the differences between the two Python clients available: confluent-kafka and kafka-python. The guide also emphasizes the importance of hiring a skilled Python developer to effectively implement Kafka in projects, enhancing data processing and operational efficiency.
Python Kafka Integration: Developers Guide
- 1. Python Kafka Integration: Developers Guide Apache Kafka is an open-source platform that is free to use. It is mainly written in Java and Scala. It is mainly used for streaming data(pub, sub). We will discuss this later about pub and sub here. We can use Kafka especially when we build microservice projects. In that scenario, Kafka fits well. In this scenario, Kafka comes in use for sending data in the form of messages from one service to another. What is Kafka? Apache Kafka is a robust distributed data platform that manages and facilitates real- time stream processing. This data, which is constantly generated by a variety of sources, arrives in a single stream, necessitating a system capable of handling, storing, and analyzing data as it is received—sequentially and incrementally.
- 2. Kafka shines in this field by providing important functionality: it allows users to publish and subscribe to streams of records, keeps these records in the order they were created, and allows for real-time processing of these data streams. Its value extends to the creation of real-time streaming data pipelines and applications, offering a complete solution that combines messaging, storage, and stream processing. This integration allows for data storage and analysis, meeting historical insights and rapid, real-time data requirements. Kafka Architect Producer: Clients that send data to Kafka topics, which are categories or feeds for records. They support sending data to multiple topics and partitioning within topics for enhanced scalability. Consumer: Entities that read data from subscribed topics, optionally organized into consumer groups for distributed processing, allowing efficient parallel data handling. Broker: Server processes managing data storage and distribution, handling high volumes of data across Kafka cluster nodes. Brokers organize data into topics and partitions for efficient access and scalability. Zookeeper: A service for managing and coordinating Kafka brokers, maintaining cluster node status, and facilitating configuration management and leader election for partitions. Topic: Named categories or feeds where records are published, supporting multiple subscribers through consumers and consumer groups. Partition: The subdivision of topics for spreading data across the cluster, enhancing throughput and scalability. Each partition holds an ordered, immutable sequence of records.
- 3. Basically, we have two Python clients available to use with Kafka. confluent-kafka kafka-python By having two options one may be confused in selecting one for their use. So, let’s clear some technical doubts. In Performance, • confluent-kafka is ahead in this part in comparison to kafka python. • Confluent-Kafka is ahead in performance due to underlying C-based implementation. • kafka-python is a pure Python based library. • confluent-kafka provides more functionalities than kafka-python To install • pip install confluent-kafka • pip install kafka-python Let’s discuss some important parts of Kafka • Producer • Consumer • Topic • Broker Producer • The producer is responsible for publishing the message. Let’s try to understand it in a simple way:
- 4. • As we see in this image, the Producer produces data on a topic. Topic can be created in each Broker’s. We can imagine a broker as a server of Kafka. • One Broker may have multiple topics. Topic • It is used to categorize messages in an organized way. • We can create multiple topics and use them in Producer. Consumer • Consumers are used to subscribing to topics to fetch data from specific topics. • Always mention group id in consumer configuration. • We can consume data from multiple brokers.
- 5. No, we see the configuration For now, we see how to use Kafka from our local • First download Kafka • https://kafka.apache.org/downloads • Always try to download the binary version • Kafka_2.13-3.6.1.tgz • Go to your path in the terminal where you have downloaded the Kafka binary file • tar Kafka_2.13-3.6.1.tgz • mv Kafka_2.13-3.6.1 • Also, Install Java JDK version 11 After downloading Kafka, we have to start Kafka Zookeeper and Kafka server Go to the same Kafka path Start Kafka Zookeeper /kafka_2.13-3.6.1/bin/zookeeper-server-start.sh ~/kafka_2.13- 3.6.1/config/zookeeper.properties Now start the Kafka Server /kafka_2.13-3.6.1/bin/kafka-server-start.sh ~/kafka_2.13- 3.6.1/config/server.properties Now after successfully running this server, we can move forward with the coding part: Producer
- 6. Consumer
- 7. Conclusion To summarise, learning the complexities of Apache Kafka and fully utilizing its abilities to handle high-throughput, real-time data flows necessitates a specialized skill set. Think about the invaluable assistance an experienced Python developer can bring in harnessing Kafka’s power and effectively integrating it into your applications. Hire a Python developer to ensure that Kafka is implemented swiftly and allows you to innovate and stay competitive in the digital industry. Their experience can help
- 8. you optimize data processing, streamline operations, and improve the general efficiency of your applications. As a result, if you want to get the most out of Kafka for your next project, hiring a skilled Python developer could be the key to realizing its full potential. Originally published by: Python Kafka Integration: Developers Guide