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

UtkarshTrehan
New Contributor

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!

3 REPLIES 3

Kaniz
Community Manager
Community Manager

Hi @UtkarshTrehan , 

The difference in the behaviour you're seeing between using foreachPartition and write.jdbc may be due to how Spark processes and writes the data.

When you use foreachPartition, you are processing each partition of the data separately and writing to the database using the custom code that you provide. This means that the deduplication logic is applied to each partition independently, so duplicates may exist within each partition. When you use write.jdbc, Spark automatically handles partitioning and writing the data using its built-in JDBC writer. This means that Spark applies deduplication logic across all partitions before writing to the database, ensuring that duplicates are removed correctly.

To ensure that duplicates are removed correctly when using foreachPartition, you can either modify your custom code to apply deduplication logic within each partition or collect all rows in memory and de-duplicate before writing to the database.

Sidhant07
New Contributor III
New Contributor III

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.

Kaniz
Community Manager
Community Manager

Hi @UtkarshTrehan , To help us provide you with the most accurate information, could you please take a moment to review the responses and select the one that best answers your question?

This will also help other community members who may have similar questions in the future. Thank you for your participation and let us know if you need any further assistance! 
 

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