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{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"Spark Materialization Engine Cannot Infer Schema","articleBody":"## Expected Behavior \nSpark engine when enabled should materialize features to the online store.\n\n## Current Behavior\nMaterialization fails with the following error:\n```\npyspark.errors.exceptions.base.PySparkTypeError: [CANNOT_INFER_SCHEMA_FOR_TYPE] Can not infer schema for type: `ChunkedArray`.\n```\n\n## Steps to reproduce\nSee [this repo](https://github.com/danbaron63/feast-spark-materialisation) that should reproduce the error locally for you. Ensure your `JAVA_HOME` is pointing to a java 17 installation and run `make run` to run the workflow. Optionally inspect the Makefile and run the commands yourself.\n\nThe repo has the default `feast init` repo created and will run a `feast plan`, `feast apply` and finally a `materialize.py` script which should trigger a Spark materialization job.\n\n### Specifications\n\n- Version: 0.53.0\n- Platform: Python 3.12, PySpark 3.5.5, Java 17\n- Subsystem: MacOS 15.6.1\n\n## Possible Solution\nI believe this happens when an arrow dataframe is converted to a Spark dataframe. Why this happens I am unsure, it may be my configuration or it may be a bug. If someone could advise on a solution that would be great.\n\nThanks!","author":{"url":"https://github.com/danbaron63","@type":"Person","name":"danbaron63"},"datePublished":"2025-09-02T16:36:05.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":4},"url":"https://github.com/5594/feast/issues/5594"}