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• Even though we can force Spark to fallback to using the InputFormat class, we could lose ability to use Spark's optimized parquet reader path by doing so. 
• To keep benefits of native parquet read performance, we set the  `HoodieROTablePathFilter` as a path filter, explicitly set this in the Spark Hadoop Configuration.There is logic in the file: to ensure that folders (paths) or files for Hoodie related files always ensures that latest file slice is selected. This filters out duplicate entries and shows latest entries for each record. 

I have an existing dataset and want to evaluate Hudi using portion of that data ?

You can bulk import portion of that data to a new hudi table.  For example, if you want to try on  a month of data -

spark.read.parquet("your_data_set/path/to/month")
     .write.format("org.apache.hudi")
     .option("hoodie.datasource.write.operation", "bulk_insert")
     .option("hoodie.datasource.write.storage.type", "storage_type") // COPY_ON_WRITE or MERGE_ON_READ
     .option(RECORDKEY_FIELD_OPT_KEY, "<your key>").
     .option(PARTITIONPATH_FIELD_OPT_KEY, "<your_partition>")
     ...
     .mode(SaveMode.Append)
     .save(basePath);

Once you have the initial copy, you can simply run upsert operations on this by selecting some sample of data every round

spark.read.parquet("your_data_set/path/to/month").limit(n) // Limit n records 
     .write.format("org.apache.hudi")
     .option("hoodie.datasource.write.operation", "upsert")
     .option(RECORDKEY_FIELD_OPT_KEY, "<your key>").
     .option(PARTITIONPATH_FIELD_OPT_KEY, "<your_partition>")
     ...
     .mode(SaveMode.Append)
     .save(basePath);

For merge on read table, you may want to also try scheduling and running compaction jobs. You can run compaction directly using spark submit on org.apache.hudi.utilities.HoodieCompactor or by using HUDI CLI

Contributing to FAQ 

A good and usable FAQ should be community-driven and crowd source questions/thoughts across everyone. 

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