Apache Kafka Guide #46 Logs Aggregation Application example
Hi, this is Paul, and welcome to the #46 part of my Apache Kafka guide. Today we will discuss the Logs Aggregation example as practical training in learning Apache Kafka.
One of the initial use cases for Apache Kafka was in logging and metrics applications. All these applications’ data were sent to Kafka. In such deployments, high throughput is usually desired, with fewer restrictions concerning data loss or data replication. This is why settings like acks=0 and a lower replication factor were common. From there, the aggregated logging and metrics data would be sent to preferred logging solutions, including Splunk, CloudWatch, and the ELK stack, among others.
The architecture we’re discussing is straightforward. Central to the design is Apache Kafka.
Primarily, you would establish topics in Kafka for different data types, such as application logs and metrics. Your application will include a log forwarder, acting as a producer, which channels all log data directly into Kafka. This log forwarder is designed for high throughput, ensuring efficient data handling.
Additionally, there will be metrics collectors in your system. These also function as producers, tasked with sending all metrics data into the designated metrics topic within Kafka.
For data integration, tools like Splunk will be utilized. You can employ Kafka Connect Sink to seamlessly transfer data from both the logs and metrics topics into Splunk. This setup ensures that all data flows smoothly from your application into appropriate analysis tools.
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