How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud

Summary

Eisan System Development moved a retail analytics workload from a QlikView-based platform to ClickHouse Cloud after rising ID-POS data volumes caused slower processing, failed analyses, and increasing licensing and server costs. Client records from supermarkets and drugstores support basket analysis, cross analysis, and real-time personalization, with some datasets nearing 200 million records annually. The team compared open-source options including PostgreSQL and YugabyteDB, then tested ClickHouse configurations, including a 30-core, 120-GB setup, while accounting for cache memory usage through load testing. In comparison, ClickHouse ran on slightly lower-spec hardware under high-load conditions without observed performance deterioration, and Eisan reports no cases so far where it failed to return a result; the team is now targeting 100 billion records and exploring off-hours processing and scheduled scaling.

Takeaways

1. 1

   ClickHouse maintained stable performance under high-load settings on a server with slightly lower specifications than typical setups, while QlikView increasingly failed to return some analysis results.

2. 2

   Eisan’s configuration testing showed that apparently low memory utilization included memory used for caching; accounting for cache usage made the 30-core, 120-GB setup appear more balanced.

3. 3

   The team aims to support 100 billion records and is exploring off-hours workloads, autoscaling, and possibly shutting down operations during low-use periods such as Sundays.