What happened
The article shows that the physical organisation of data in a lakehouse can strongly affect query performance. Across four workloads on Delta Lake, the author compares Liquid Clustering, ZOrdering and partitioning, and concludes that the best option depends on the query pattern, with no single rule.
Liquid Clustering recorded the lowest latency in three of the four scenarios, especially when queries are varied or change over time. ZOrdering only stood out when filters matched a stable set of columns very precisely. The text also points to two production risks: date-range queries may worsen when date is not the first key in ZOrdering, and very selective queries may lose efficiency with Liquid Clustering.
The recommendation is to test the three approaches with the organisation’s main queries before deciding. The choice should consider workload type, filter stability and the balance between reading less data and improving read locality within files.
Key points
- Liquid Clustering performed best in three of the four scenarios.
- ZOrdering only won on stable, highly selective multicolumn filters.
- The position of date in the ZOrdering key can affect range queries.
- Layout choice should be tested against an organisation’s real queries.
Why it matters for your organisation
For data teams, the physical layout decision can affect latency, compute cost and operational effort. The text shows that a choice made by rule of thumb can quietly degrade important queries without being obvious at first glance.