Apache Druid is a real-time analytics database built for speed and scale, capable of executing complex queries against billions of rows and getting sub-second answers. Druid thrives on highly concurrent workloads, making it ideal for applications like website clickstream analysis, network performance monitoring, or handling vast IoT metrics. By using pre-aggregated data, lightning-fast columnar storage, and parallel processing we can gain insights in real-time.
In this talk, we will share techniques for improving query concurrency and achieving sub-second responses. We will cover the following topics:
- How Druid’s architecture is designed for high concurrency
- How to scale Druid to handle large volumes of data and highly concurrent query workloads
- How to deal with the specific requirements of high concurrency on recently ingested data
- How to monitor and troubleshoot Druid performance
We will also present some case studies of how Druid has been used to achieve high concurrency real-time queries in real-world applications.
This talk is intended for developers and architects who are interested in using Apache Druid for real-time analytics. No prior knowledge of Druid is required.
