Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation, designed for building real-time data pipelines and streaming applications. It is capable of handling high-throughput, low-latency data streams, making it ideal for use cases that require processing of large volumes of data in real-time.
- GO: kafka, to wrap and simplify segmentio/kafka-go, IBM/sarama and confluent-kafka-go. Example is at go-kafka-sample.
- nodejs: kafka-plus, to wrap and simplify kafkajs. Example is at kafka-sample.
- The libraries to implement this flow are:
- mq for GOLANG. Example is at go-kafka-sample
- mq-one for nodejs. Example is at kafka-sample
- Capable of handling millions of messages per second with low latency.
- Scales horizontally by adding more brokers to the cluster.
- Ensures data is stored reliably with configurable retention policies.
- Provides replication of data across multiple brokers, ensuring resilience and fault tolerance.
- Ensures continuous availability and reliability through distributed architecture.
- Includes Kafka Streams API for building stream processing applications.
- Allows multiple consumers to read messages independently, supporting various use cases like real-time analytics and log aggregation.
Kafka operates using the following core concepts:
- An application that sends records (messages) to Kafka topics.
- An application that reads records from Kafka topics.
- A category or feed name to which records are sent by producers. Topics are partitioned and replicated across brokers.
- A division of a topic that allows for parallel processing. Each partition is an ordered, immutable sequence of records.
- A Kafka server that stores data and serves clients. Kafka clusters are composed of multiple brokers.
- A collection of Kafka brokers working together to provide scalability and fault tolerance.
- A coordination service used by Kafka to manage brokers, maintain configurations, and track topic partitions.
- A unique identifier assigned to each record within a partition, used by consumers to keep track of their position in the partition.
- Kafka: Stores data for a configurable amount of time, allowing consumers to reprocess or analyze historical data.
- Traditional Message Queues (e.g., RabbitMQ): Typically remove messages once they are consumed, focusing on point-to-point communication.
- Kafka: Designed for horizontal scalability, handling large-scale data streams with ease.
- Traditional Message Queues: May require more complex configurations for scaling, often using clustering or sharding techniques.
- Kafka: Suited for real-time stream processing and analytics, allowing multiple consumers to read the same data independently.
- Traditional Message Queues: Focus on ensuring message delivery to one or more consumers, often used for task distribution.
- Kafka: Optimized for high throughput and low latency, making it ideal for big data applications.
- Traditional Message Queues: Generally optimized for reliable message delivery and simpler use cases.
- Capable of handling large volumes of data with minimal delay, suitable for real-time applications.
- Easily scales horizontally by adding more brokers and partitions, supporting the growth of data-intensive applications.
- Ensures data reliability through replication and configurable retention policies, making it robust against failures.
- Allows multiple consumers to independently read and process data, enabling various analytics and processing use cases.
- Integrates seamlessly with other big data tools like Hadoop, Spark, and Flink, providing a comprehensive data processing pipeline.
- Requires careful configuration and management, including the use of Zookeeper, which adds to the complexity.
- High throughput and durability features can demand significant computational and storage resources.
- Best suited for high-throughput scenarios; may be overkill for applications with low message volumes or small message sizes.
- Processing and analyzing streaming data in real-time, such as monitoring user activities on a website.
- Collecting and centralizing logs from various services for monitoring and analysis.
- Storing events as a sequence of state changes, enabling complex event-driven architectures.
- Collecting and processing metrics from distributed systems for monitoring and alerting.
- Integrating data from various sources into data lakes or warehouses for further analysis.
In a real-time user activity tracking system, Kafka can be used to collect and process user interactions from a website or application.
- Web applications and mobile apps send user interaction data (e.g., clicks, page views) to Kafka topics.
- Different topics are created for different types of interactions (e.g., "page_views", "clicks").
- Analytics services consume data from these topics to generate real-time dashboards and reports.
- Storage services consume data to store historical user interaction data in data lakes or warehouses.
- Kafka Streams or other stream processing tools like Apache Flink process the data in real-time to detect patterns, anomalies, or trigger actions (e.g., personalized recommendations).
Apache Kafka is a powerful and scalable stream processing platform designed to handle high-throughput, low-latency data streams. Its robust architecture and extensive feature set make it suitable for a wide range of use cases, from real-time analytics to log aggregation and event-driven architectures. While it introduces some complexity and resource demands, its benefits in terms of scalability, durability, and flexibility make it a valuable tool for modern data-intensive applications. Understanding Kafka's core concepts and capabilities can help organizations build efficient and reliable data pipelines and streaming applications.
Please make sure to initialize a Go module before installing core-go/kafka:
go get -u github.com/core-go/kafkaImport:
import "github.com/core-go/kafka"