xushiyan commented on issue #3699: URL: https://github.com/apache/hudi/issues/3699#issuecomment-927230026
Ok @ZeMirella so basically it's about bulk insert a chunk of data. Was `bulk_insert_shuffle_parallelism` set to 2000 in this case? given the data size, you may want to try partitioning the output dataset. Say you choose a field `foo` from the schema as the partition field, and roughly it gives 200 partitions. A few configs you may try changing accordingly - hoodie.bulkinsert.shuffle.parallelism = 200 - hoodie.datasource.write.row.writer.enable = true // why disabled it explicitly? - Use a proper class for hoodie.datasource.write.keygenerator.class and hoodie.datasource.hive_sync.partition_extractor_class. It should fit the partitioning strategy you chose. Check out [this blog](https://hudi.incubator.apache.org/blog/2021/02/13/hudi-key-generators/). - the code for spark write should be sth like ```python spark_df.repartition(200, spark_df.col("foo")).write.format("hudi").options(**hudi_options).mode("overwrite").partitionBy("foo").save(self.table_path) ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
