When working with large datasets, schema changes—whether it’s adding new fields, modifying data types, or removing columns—are inevitable. This is where schema evolution comes into play. In this post, we’ll dive into how Parquet handles schema changes and why this flexibility is essential in dynamic data environments. We’ll also explore how Parquet's schema evolution compares to other file formats and the practical implications for data engineers.

What is Schema Evolution?

In data management, a schema defines the structure of your data, including the types, names, and organization of fields in a dataset. Schema evolution refers to the ability to handle changes in the schema over time without breaking compatibility with the data that’s already stored. In other words, schema evolution allows you to modify the structure of your dataset without needing to rewrite or discard existing data.

In Parquet, schema evolution is supported in a way that maintains backward and forward compatibility, allowing applications to continue reading data even when the schema changes. This is particularly useful in situations where data models evolve as new features are added, or as datasets are refined.

How Schema Evolution Works in Parquet

Parquet’s ability to handle schema evolution is one of its key advantages. When a Parquet file is written, the schema of the data is embedded in the file’s metadata. This schema is checked when data is read, ensuring that any discrepancies between the stored data and the expected structure are handled gracefully.

Common Schema Evolution Scenarios

Here are some common schema evolution scenarios and how Parquet handles them:

1. Adding New Columns

One of the most common schema changes is the addition of new columns to a dataset. For example, imagine you have a Parquet file that originally contains the columns Name, Age, and Salary. Later, you decide to add a Department column.

In this case, Parquet handles the new column without any issues. Older Parquet files that do not have the Department column will simply read that column as null when queried. This is known as forward compatibility, where the old data remains readable even after the schema has been updated.

2. Removing Columns

In some cases, you may want to remove a column that is no longer relevant. If you remove a column from the schema, Parquet will continue to read the old data, but the removed column will not be included in queries. This is known as backward compatibility, meaning that even though the schema has changed, the old data can still be accessed.

However, be cautious when removing columns, as some downstream applications or queries may still rely on that data. Parquet ensures that no data is lost, but the removed column will no longer appear in new data written after the schema change.

3. Changing Data Types

Changing the data type of an existing column can be trickier, but Parquet provides mechanisms to handle this scenario. If you change the data type of a column (for example, changing an int column to a float), Parquet ensures that old data can still be read by performing necessary type conversions.

While this approach preserves compatibility, it's important to note that changing data types can sometimes lead to unexpected results in queries, especially if precision is lost during conversion. It's always a good practice to carefully consider the implications of changing data types.

4. Renaming Columns

Renaming a column is another common schema change. Parquet does not natively support renaming columns, but you can achieve this by adding a new column with the desired name and removing the old column. As a result, the renamed column will appear as a new addition in the schema, and older files will treat it as a missing column (reading it as null).

While this is not true "schema evolution" in the traditional sense, it is a common workaround in systems that rely on Parquet.

5. Reordering Columns

In Parquet, the order of columns in the schema does not affect the ability to read the data. This means that if you change the order of columns, Parquet will still be able to read the file without any issues. Column order is not enforced when querying, allowing flexibility in how data is structured.

Schema Evolution in Other Formats

Compared to other file formats like CSV or Avro, Parquet’s schema evolution capabilities are particularly robust:

CSV: Since CSV lacks a formal schema definition, it doesn’t support schema evolution. If the structure of your CSV file changes, you’ll need to rewrite the entire file or deal with errors when parsing the data.
Avro: Like Parquet, Avro supports schema evolution. However, Avro focuses on row-based storage, making it more suitable for transactional systems than analytical workloads. Parquet’s columnar nature makes it more efficient for large-scale analytics, particularly when the schema evolves over time.
ORC: ORC, another columnar storage format, also supports schema evolution. However, Parquet is generally considered more flexible and is widely used in a variety of data processing systems.

Best Practices for Schema Evolution

Here are a few best practices to follow when working with schema evolution in Parquet:

Plan for Schema Changes Early

It’s always a good idea to anticipate potential schema changes when designing your data models. Adding new columns or changing data types is easier to manage if your data model is flexible from the start.
Use Nullable Fields

Adding new columns to a dataset is one of the most common schema changes. By making new fields nullable, you ensure that old data remains compatible with the updated schema.
Test Schema Changes in Staging Environments

Before deploying schema changes to production, test them in a staging environment. This allows you to catch potential issues related to backward or forward compatibility before they impact production systems.
Document Schema Changes

Keep detailed documentation of schema changes, especially if you are working in a team. This ensures that everyone understands the evolution of the data model and how to handle older versions of the data.
Leverage Data Catalogs

Using a data catalog or schema registry can help manage schema evolution across multiple Parquet files and datasets. Tools like Apache Hive Metastore or Nessie Catalog allow you to track schema versions and ensure compatibility.

Conclusion

Schema evolution is a powerful feature of the Parquet file format, enabling data engineers to adapt to changing data models without losing compatibility with existing datasets. By supporting the addition, removal, and modification of columns, Parquet provides flexibility and ensures that data remains accessible even as it evolves.

Understanding how Parquet handles schema evolution allows you to build data pipelines that are resilient to change, helping you future-proof your data architecture.

In the next post, we’ll explore the various compression techniques used in Parquet and how they help reduce file sizes while improving query performance.

Stay tuned for part 5: Compression Techniques in Parquet.

All About Parquet Part 04 - Schema Evolution in Parquet