Challenges in Scaling StarRocks for High-Concurrency Real-Time Analytics

Hello

I’ve been working on scaling StarRocks for a high-concurrency real-time analytics environment, but I’m facing performance bottlenecks as the query load increases. :upside_down_face:

Despite optimizing the hardware infrastructure and fine-tuning the query settings, I’m still experiencing slow response times and occasional failures under heavy traffic. :innocent:

I suspect that the issue might be related to improper resource allocation and partitioning strategies. I’m particularly interested in understanding the best practices for managing distributed data nodes, caching mechanisms, and load balancing to improve query performance. :thinking:

Additionally, insights into memory management and indexing strategies would be incredibly helpful. :thinking: Checked Performance Optimization | StarRocks and CISSP Course Online guide related to this and found it quite informative.

Has anyone here dealt with similar scaling challenges? :upside_down_face: Any recommendations on monitoring tools or performance tuning techniques would be highly appreciated.

Thank you!! :slightly_smiling_face:

  1. What’s the monitor of CPU, I/O, Memory usage?
  2. What’s the table schema?
  3. What’s the ingestion frequency?