Online Analytical Processing (OLAP) workloads typically require low-latency reads, high query throughput, and efficient aggregation across large datasets. As cloud data lakes replace traditional data warehouses, technologies like Apache Hudi enable scalable OLAP workloads by combining storage optimization with transactional capabilities.

In this blog, we’ll explore how Apache Hudi can be used to optimize OLAP performance on data lakes — covering table formats, query acceleration, clustering, partitioning, and metadata indexing techniques.

Why Use Hudi for OLAP?

Apache Hudi provides key advantages for OLAP workloads:

  • Fast query response times using Copy-on-Write (COW) tables
  • Efficient upserts and deletes without reprocessing entire datasets
  • Column-level statistics for predicate pushdown
  • Clustering to reduce small files and enable sorted access
  • Metadata indexing for faster table scans and partition pruning

When configured properly, Hudi turns your data lake into a high-performance OLAP system.

Choosing the Right Table Type: COW vs MOR

Table Type Use Case OLAP Suitability
Copy-on-Write Frequent reads, BI dashboards ✅ Best for OLAP queries
Merge-on-Read Heavy writes, streaming ingest ❌ Slower queries

For OLAP workloads, always use Copy-on-Write to store data directly in query-ready formats (e.g., Parquet).

hoodie.table.type=COPY_ON_WRITE

Optimize File Size and Layout

Hudi splits data into base files based on target size. For OLAP workloads:

  • Set optimal file size for scan performance:
hoodie.parquet.max.file.size=134217728  # 128MB
hoodie.copyonwrite.record.size.estimate=1024
  • Avoid generating too many small files by batching inserts and using clustering.

Use Clustering to Improve Query Performance

Clustering reorganizes files to:

  • Combine small files
  • Sort by query-friendly columns
  • Improve scan performance in engines like Athena, Presto, or Spark SQL

Enable inline clustering:

hoodie.clustering.inline=true
hoodie.clustering.sort.columns=query_date,event_type
hoodie.clustering.execution.strategy.class=org.apache.hudi.client.clustering.run.strategy.SparkSortAndSizeExecutionStrategy

Clustering allows OLAP queries to benefit from fewer, larger, and sorted files.

Enable Column Stats Indexing

Column-level statistics help engines skip blocks that don’t match filters.

hoodie.metadata.enable=true
hoodie.metadata.index.column.stats.enable=true
hoodie.metadata.index.bloom.enable=true

This allows Spark, Presto, or Trino to use predicate pushdown, speeding up queries like:

SELECT COUNT(*) FROM sales
WHERE event_type = 'purchase' AND region = 'US';

Leverage Partitioning for Pruning

Choose partition keys aligned with query patterns — e.g., event_date, region, or platform.

hoodie.datasource.write.partitionpath.field=event_date
hoodie.datasource.write.hive_style_partitioning=true

Avoid over-partitioning (e.g., by user_id), which can slow down query planning and file access.

Incremental Aggregation for OLAP Cubes

Hudi supports incremental queries, allowing OLAP engines to:

  • Read only new records since last query
  • Refresh dashboards without scanning entire tables

Example Spark read:

spark.read.format("hudi") \
.option("hoodie.datasource.query.type", "incremental") \
.option("hoodie.datasource.read.begin.instanttime", "20240411000000") \
.load("s3://lake/sales_data")

This dramatically reduces query latency and cost in real-time BI scenarios.

Querying Hudi Tables from OLAP Engines

Presto, Athena, Hive, Trino, Spark SQL all support querying Hudi COW tables.

Example Athena query:

SELECT region, COUNT(*) FROM default.sales_data
WHERE event_date = '2024-04-10'
GROUP BY region;

For best performance:

  • Use COW tables
  • Enable Glue Catalog sync
  • Compact and cluster periodically

Best Practices Summary

Practice Benefit
Use Copy-on-Write tables Fast query response
Enable clustering Reduce small files and sort data
Use column stats indexing Enable predicate pushdown
Align partitioning with queries Reduce scan cost
Compact and clean old data Maintain performance
Leverage incremental reads Power real-time dashboards

Conclusion

Apache Hudi is not just a data ingestion framework — it’s a high-performance lakehouse engine capable of powering enterprise-grade OLAP workloads. By using Copy-on-Write tables, clustering, and metadata indexing, you can deliver sub-second query latency on massive datasets stored in Amazon S3 or HDFS.

Whether you’re using Athena, Presto, or Spark for interactive analytics, Hudi provides the foundation for fast, reliable, and cost-efficient OLAP on the data lake.