Yong Jie Fong
Data engineer at Jupiter. I work on real-time analytics, distributed systems, and ClickHouse.
Writing
Stop polling ClickHouse for changes
Using materialized views, HTTP, and Kafka to turn inserts into recomputation signals.
Protecting early LIMIT in ClickHouse
A before-and-after rewrite that separates page selection from optional enrichment.
A ClickHouse TTL is scheduled merge work
Why retention and rollups create merge pressure—and when the cost is worth it.
FINAL vs argMax in ClickHouse
Choosing query-time deduplication by part overlap, key cardinality, and correctness.
When IN beats a JOIN in ClickHouse
Why a small IN filter pruned granules that a hash join still read.
Ordering keys, projections, or skip indexes?
Choose each physical structure by access pattern and write cost.
About
I lead the data team at Jupiter. This site is where I write down notes on ClickHouse and data systems. Examples use synthetic data and simplified schemas.