Inconsistent Results When Writing to Oracle DB with Spark's dropDuplicates and foreachPartition

[UtkarshTrehan]

It's more a spark question then a databricks question, 

I'm encountering an issue when writing data to an Oracle database using Apache Spark. My workflow involves removing duplicate rows from a DataFrame and then writing the deduplicated DataFrame to the database. Here's a simplified version of my code:

val data = spark.read // ... load data

   val deduplicatedData = data.dropDuplicates("column_name")

   deduplicatedData.foreachPartition { partition =>

   // Code to write each partition to Oracle DB

   }

When I use the above approach with foreachPartition so I can use SQL INSERT statement with NEXTVAL, the written data still contains duplicates . However, if I replace foreachPartition with Spark's built-in JDBC writer, like so:

deduplicatedData.write

   .mode(SaveMode.Append) // You can change this to Append or other modes if needed

   .jdbc(jdbcUrl, ...)

The duplicates are removed correctly, and the data is written to the Oracle database as expected.

Here are my questions:

Why is there a difference in behavior between using foreachPartition and data.write.jdbc(...) after dropDuplicates()? What are the internal differences in how these methods handle partitioned data and deduplication? How can I ensure that duplicates are removed correctly when using foreachPartition? Any insights or explanations about these behaviors would be greatly appreciated!

[Sidhant07]

The difference in behaviour between using foreachPartition and data.write.jdbc(...) after dropDuplicates() could be due to how Spark handles data partitioning and operations on partitions. When you use foreachPartition, you are manually handling the process of writing each partition to the Oracle DB. This means that the deduplication (dropDuplicates) is performed on each partition separately, and not on the entire data. This could lead to duplicates if the same record exists in different partitions. On the other hand, when you use data.write.jdbc(...), Spark's built-in JDBC writer handles the writing process. It performs the deduplication operation on the entire data before writing it to the Oracle DB, which ensures that all duplicates are removed. To ensure that duplicates are removed correctly when using foreachPartition, you could try performing the dropDuplicates operation after repartitioning the data based on the column(s) you want to deduplicate. Here's an example:

scala
val data = spark.read // ... load data
val deduplicatedData = data.repartition($"column_name").dropDuplicates("column_name")
deduplicatedData.foreachPartition { partition =>
  // Code to write each partition to Oracle DB
}

In this code, repartition($"column_name") ensures that all records with the same column_name are in the same partition. Then, dropDuplicates("column_name") can correctly remove all duplicates. However, please note that this approach could have performance implications, as repartitioning can be an expensive operation.