Topic

Liquid Clustering

1 post about Liquid Clustering. Every summary links to the original.

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Databricks
Debunking 8 data layout myths: why Liquid Clustering outperforms partitioning
Partitioning has long been the standard physical layout for Hadoop- and Hive-era processing, but the post argues that changing Lakehouse workloads make fixed choices brittle. Liquid Clustering treats keys as guidance for file organization, allowing layouts to evolve, supporting low- and high-cardinality filters, maintaining file sizes, and enabling row-level concurrency. Using transaction-log metadata and per-column statistics, the post says modern Delta and Iceberg engines prune files rather than directories, and reports 35% lower clustering time, 22% faster queries, roughly 90% faster metadata-only deletes, and 23-minute planning for a 10-PB table. Conversion examples include Bolt’s 138% higher write throughput and up to 63% faster reads, while a 1.1-PB workload saw 5.9x faster queries, 86% fewer bytes read, and a 27% smaller table after clustering by date, hour, source, and id.

The best engineering reads, once a week.

Topic

1 post about Liquid Clustering. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Databricks
Debunking 8 data layout myths: why Liquid Clustering outperforms partitioning
Partitioning has long been the standard physical layout for Hadoop- and Hive-era processing, but the post argues that changing Lakehouse workloads make fixed choices brittle. Liquid Clustering treats keys as guidance for file organization, allowing layouts to evolve, supporting low- and high-cardinality filters, maintaining file sizes, and enabling row-level concurrency. Using transaction-log metadata and per-column statistics, the post says modern Delta and Iceberg engines prune files rather than directories, and reports 35% lower clustering time, 22% faster queries, roughly 90% faster metadata-only deletes, and 23-minute planning for a 10-PB table. Conversion examples include Bolt’s 138% higher write throughput and up to 63% faster reads, while a 1.1-PB workload saw 5.9x faster queries, 86% fewer bytes read, and a 27% smaller table after clustering by date, hour, source, and id.

The best engineering reads, once a week.