When Should You Use Liquid Clustering? Benchmarking Lakehouse Layout Strategies

Key Takeaways

No single storage layout is universally optimal for lakehouse analytics. The right choice depends on the workload, as improper use of Liquid Clustering, ZOrder, or Partitioning can significantly impact cost and latency.

ZOrder has a hidden failure mode; when a date column is a secondary or tertiary clustering key, time range queries can perform significantly worse than on a simple date-partitioned table.

Bytes scanned can be a misleading performance metric. Liquid Clustering provides the lowest latency for point lookups, even though it may scan more raw data than ZOrder.

Liquid Clustering should be the default choice for teams with diverse or evolving query patterns, as it generally outperforms ZOrder across most workloads while eliminating the need for manual OPTIMIZE jobs and periodic full table rewrites.

Before finalizing a layout strategy, consider whether your top queries consistently filter on the same two or three columns. If so, ZOrder those columns; otherwise, choose Liquid Clustering.

Introduction

The optimal storage layout for lakehouse analytical systems is not one-size-fits-all. In tests across four workload types on Delta Lake, Liquid Clustering consistently delivered the lowest latency for point lookups, time-range aggregations, and large aggregations. ZOrdering only excelled in scenarios with stable, highly selective multicolumn predicate workloads where the filter columns matched the clustering key.

Partitioning, ZOrdering, and Liquid Clustering each represent distinct approaches to data organization on disk. Understanding their trade-offs is essential for selecting the right strategy for your workload. Notably, two findings challenge the conventional understanding of these layouts and could be silently degrading production systems.

ZOrder’s Hidden Failure Mode on Date Ranges

When a date column is a secondary or tertiary key in a ZOrder clustering expression, time-range queries can be up to seventy-eight percent slower than those on a simple date-partitioned table. This occurs because sequential date values become dispersed across files, undermining the efficiency of range scans that partitioning provides.

Liquid Clustering’s Precision Gap on Multicolumn Filters

For queries that precisely match a stable set of filter columns, ZOrder’s file pruning is nearly optimal, while Liquid Clustering can be nearly five times slower and may scan ten times more data. Liquid Clustering’s design prioritizes workload flexibility over deterministic pruning, which can be disadvantageous when queries are narrow and predictable.

Experiment Setup

The analysis utilized a synthetic transactional dataset of fifty million records, simulating a retail purchase environment. The dataset included various fields such as customer_id, txn_date, region, amount, and product_cat, with distributions calibrated against real-world access patterns.

Layout Configuration

The configurations tested included a partitioned table, a ZOrdered table, and a Liquid Clustered table, each set up to assess their performance under different workloads.

Results

Point Lookup

Liquid Clustering achieved the lowest latency at 1.003 seconds, outperforming the partitioned baseline by forty-nine percent, despite scanning more raw data. This result highlights the importance of intra-file sequential locality, which reduces seek overhead.

Time Range Aggregation

Liquid Clustering again delivered the best performance with a latency of 659 milliseconds. In contrast, ZOrder took 1.845 seconds, significantly underperforming compared to the partitioned baseline due to the inefficiency of dispersing sequential date values.

MultiColumn Selective Filter

This workload showed ZOrder achieving 615 milliseconds with only 59.03 megabytes read, while Liquid Clustering took 2.967 seconds and read 639.95 megabytes. ZOrder’s precise file pruning made it the superior choice in this scenario.

Large Aggregation

For a full dataset SUM aggregation, Liquid Clustering again recorded the lowest latency at 659 milliseconds, while ZOrder was the slowest at 1.845 seconds, emphasizing the importance of intra-file read efficiency.

Discussion

The results indicate that file-pruning efficiency and intra-file locality are the two primary mechanisms influencing performance across workloads. ZOrder and partitioning excel in pruning, while Liquid Clustering shines in locality, even when it reads more data. The hidden failure mode of ZOrder regarding date ranges further illustrates the importance of layout decisions.

Caveats

These benchmarks were conducted on a synthetic dataset to ensure reproducibility. Real production datasets may introduce complexities that could shift the relative performance of each layout. Therefore, the findings should be validated against your specific workload profile before making irreversible schema decisions.

 

Source: infoq.com