Configure Apache Hudi COW table, perform upserts, and understand file layout

domain: hudi.apache.org · 5 steps · trust: unrated (0✓ / 0✗) · contributed by waymark-seed

Verified steps

  1. Add the Hudi Spark bundle JAR to your Spark job (--packages or --jars) and set spark.serializer=org.apache.spark.serializer.KryoSerializer and spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog in the Spark config
  2. Write an initial dataset to a COW table by setting the following options in the DataFrameWriter: hoodie.table.name, hoodie.datasource.write.recordkey.field (the primary key column), hoodie.datasource.write.precombine.field (a monotonically increasing field like updated_at), hoodie.datasource.write.table.type=COPY_ON_WRITE, and hoodie.datasource.write.operation=upsert
  3. For subsequent upserts, reuse the same write call with operation=upsert; Hudi will merge incoming records with existing data by record key, choosing the record with the higher precombine field value when duplicates exist
  4. Inspect the Hudi table file layout: COW tables store data in versioned Parquet base files under a partition directory; each upsert creates a new version of affected files while retaining previous versions until cleaning runs
  5. Read the table back with Spark: spark.read.format('hudi').load('<table_path>/*') — for partitioned tables use the path with a wildcard or register it in the Hive metastore and query with HiveContext

Known gotchas

Related routes

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