Workflow:Apache Hudi Flink Table Clustering
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Lake, Table_Management |
| Last Updated | 2026-02-08 20:00 GMT |
Overview
End-to-end process for optimizing the data layout of Apache Hudi tables using Flink-based clustering, reorganizing file groups to improve query performance through better data locality.
Description
This workflow covers the clustering table service for Hudi tables in Flink. Clustering reorganizes data within a table by rewriting file groups to improve data locality, reduce small files, and optimize for common query patterns. It supports multiple strategies including linear sorting, space-filling curves (Z-order, Hilbert), and consistent hashing bucket resizing. The Flink clustering pipeline reads existing file groups, sorts or repartitions the data, writes new optimized file groups, and atomically replaces the old ones. Clustering can run inline during writes or as a standalone Flink job.
Usage
Execute this workflow when query performance is degraded due to suboptimal data layout, excessive small files, or when data needs to be colocated by specific columns for range query efficiency. Clustering is particularly useful after bulk loads that create many small files, or when the query access pattern has changed and data needs to be reorganized around new sort columns.
Execution Steps
Step 1: Analyze Current Data Layout
Evaluate the current file group structure of the Hudi table to determine if clustering would be beneficial. Check for small files below the target file size, assess data distribution across partitions, and identify columns commonly used in query predicates that would benefit from data sorting.
Key considerations:
- Small file proliferation increases query planning overhead and reduces I/O efficiency
- Uneven file sizes indicate suboptimal data distribution
- ClusteringUtil provides methods to check pending clustering plans and table state
Step 2: Generate Clustering Plan
Create a clustering plan that selects which file groups to reorganize and how to group them. The clustering plan strategy identifies candidate file groups based on size, age, or number of records. File groups are grouped into clustering groups that will be rewritten together.
Key considerations:
- Size-based strategy targets file groups below a threshold for merging
- Time-based strategy clusters recently written file groups
- The plan limits the number of file groups per run to control resource usage
- Plans are recorded on the Hudi timeline for crash recovery
Step 3: Configure Clustering Strategy
Select the clustering execution strategy that determines how data is reorganized within file groups. Linear sort orders data by specified columns. Space-filling curves (Z-order or Hilbert) optimize for multi-dimensional range queries. Consistent hashing clustering dynamically resizes buckets for bucket-indexed tables.
Key considerations:
- Linear sort is optimal for single-column range queries
- Z-order and Hilbert curves balance locality across multiple columns
- Consistent hashing allows bucket splitting or merging without full rewrite
- Sort columns should match common query filter predicates
Step 4: Execute Clustering Job
Run the Flink clustering pipeline which reads the selected file groups, applies the chosen sorting or partitioning strategy, and writes new optimized file groups. The ClusteringCommitSink aggregates results from all clustering tasks and performs the atomic commit replacing old file groups with new ones.
Key considerations:
- Clustering tasks run in parallel across the Flink cluster
- Each clustering group is processed independently
- Memory requirements depend on the clustering group size and sort buffer
- Concurrent readers continue to see the old file groups until commit completes
Step 5: Commit and Validate
Commit the clustering result atomically on the Hudi timeline. The old file groups are marked for replacement and will be cleaned up by the cleaner service. Validate the clustering result by comparing file sizes, checking sort order of output files, and measuring query performance improvement.
Key considerations:
- The replace commit atomically swaps old file groups for new ones
- Pending readers on old file groups are not affected
- File sizes should be more uniform after clustering
- Run representative queries to verify performance improvement