Apache Hudi enables efficient incremental data ingestion and real-time analytics in data lakes. As your datasets evolve, managing schema changes becomes a critical part of maintaining stable, accurate, and performant pipelines.

In this post, we explore the best practices for handling schema evolution in Hudi pipelines, including field additions, type changes, compatibility strategies, Hive syncing, and metadata tracking — all while ensuring downstream query stability and data consistency.

What is Schema Evolution in Hudi?

Schema evolution allows modifying the structure of a dataset over time while retaining compatibility with existing records. In Hudi, this is primarily handled through:

  • Avro schemas (used for reading and writing)
  • Hive schema syncing (if Hive integration is enabled)
  • Schema validation and compatibility rules

Common schema changes include:

  • Adding new fields
  • Changing data types
  • Renaming or removing fields (advanced, riskier)

1. Use Evolved Avro Schemas for Ingestion

All Hudi tables use Avro format for row-level serialization. The best way to manage schema evolution is to:

  • Define a base schema
  • Update the schema file as new fields are added
  • Use this schema consistently across your ingestion tools (e.g., Spark, Flink)

Example Avro schema change (adding a nullable field):

{
"name": "user_name",
"type": ["null", "string"],
"default": null
}

Using a nullable field with a default ensures backward compatibility.

2. Enable Schema Validation in Hudi

Hudi can validate schema changes at write time to prevent breaking changes.

Enable this with:

.option("hoodie.avro.schema.validate", "true")

This enforces that the incoming schema is backward compatible with existing schema history.

3. Manage Schema History with Metadata Table

Starting with Hudi 0.9.0+, schema evolution is tracked via the metadata table, which stores:

  • Historical schemas
  • Commit logs
  • Table-level metadata (files, partitions, etc.)

Use:

hudi-cli
schema show --path /path/to/hudi/table

This helps in debugging or rolling back schema-related issues.

4. Avoid Incompatible Type Changes

Changing column data types (e.g., int to string) may lead to corruption or query errors.

✅ Safe changes:

  • Adding a field (nullable)
  • Adding nested fields in a struct (with defaults)

❌ Risky changes:

  • Changing type (int → string)
  • Renaming or removing fields
  • Changing nested field structure

Best practice: version your schemas and test downstream compatibility before deployment.

5. Use Hive Sync for Schema Alignment

If you’re syncing Hudi tables to Hive Metastore, make sure to update Hive schema during writes:

.option("hoodie.datasource.hive_sync.enable", "true")
.option("hoodie.datasource.hive_sync.support_timestamp", "true")
.option("hoodie.datasource.hive_sync.mode", "hms")

This ensures Hive reflects your evolved schema and avoids query mismatches.

6. Partitioning and Schema Evolution

Avoid evolving schema in partition columns — it’s not supported and can cause inconsistencies.

If your use case demands dynamic partitions, use non-evolving fields for partitioning:

.option("hoodie.datasource.write.partitionpath.field", "event_date")

Avoid nested fields or frequently updated columns as partition keys.

7. Test with Schema Registry (Optional)

If you’re using a schema registry (like Confluent or AWS Glue), register your Avro schemas and enforce compatibility rules.

This adds governance and automates schema checks at ingestion.

8. Monitor with CLI and Tools

Use Hudi CLI for quick schema validation:

hudi-cli
connect --path /path/to/table
schema show

You can also integrate with monitoring tools to track:

  • Schema mismatches
  • Failed writes due to schema validation
  • Hive syncing errors

Conclusion

Schema evolution is inevitable in fast-changing data pipelines. Apache Hudi makes it possible to manage these changes safely and efficiently — if you follow best practices around schema validation, Avro compatibility, Hive integration, and metadata tracking.

By designing your schemas carefully and evolving them with discipline, you can build long-lasting Hudi pipelines that support real-time analytics and complex transformations without breaking downstream systems.