In the landscape of observability, logs reign as a fundamental pillar. Undoubtedly, they are among the most extensively employed telemetry signals. However, beneath their widespread usage by developers lies a complexity that cannot be ignored - logs are verbose, lack structure, and are hard to search and analyze. The pursuit of advanced analytics on this foundation can lead down a costly and intricate path. While tools like OpenSearch offer analytic features, their cost and operational complexity can be prohibitive. Conversely, tools like Loki, while cost-effective, fall short in delivering analytics capabilities.
Enter ClickHouse, a formidable solution boasting exceptional analytical prowess and remarkable data compression capabilities, offering a compelling and cost-effective alternative. In this session, we will look at straightforward methods for harnessing the potential of ClickHouse and Kafka to construct an advanced log analytics solution.
During our discussion, we will focus on key components of ClickHouse, including the JSON data type, Kafka table engine, and materialized views. We will also demonstrate how Kafka can be utilized for log ingestion into ClickHouse. Additionally, we’ll highlight ClickHouse’s impressive compression capabilities, which contribute to a cost-effective infrastructure.. This method can easily be scaled to handle several Billion log events.
Throughout the session, we will provide a comprehensive, end-to-end working example to illustrate the practical application of ClickHouse and Kafka in the context of advanced log analytics.