Duplicates in CSV Export to ADLS

[pankajshaw]

Hello everyone,

I'm facing an issue when writing data in CSV format to Azure Data Lake Storage (ADLS). Before writing, there are no duplicates in the DataFrame, and all the records look correct. However, after writing the CSV files to ADLS, I notice duplicates in the node_id:ID(MatlBatchPlant){label:MatlBatchPlant} column. 

Here's a summary of my observations:

• DataFrame count before writing to CSV matches the CSV count after writing, yet I still find duplicate rows in the CSV.
• Some records are missing from the CSV despite the DataFrame and CSV file having the same number of rows.

Debugging Steps I've Tried:

1. Writing to Parquet format – No duplicates found in the Parquet file; the count matches as expected.
2. Writing using a single partition – The problem persists.
3. Loading the DataFrame to a Postgres database – No duplicates in the node_id_colin Postgres.
4. Reading the Parquet file and converting it to CSV – The duplicate issue reappears in the CSV file.

It seems like the issue only occurs when writing to CSV format, and I haven’t encountered the same behavior with other formats (Parquet or Postgres).

Any insights or suggestions on how to address this issue would be greatly appreciated. Thank you!

[Kaniz_Fatma]

Hello ,I appreciate you sharing your challenge regarding the duplication of entries in the node_id:ID(MatlBatchPlant) column when writing data to Azure Data Lake Storage (ADLS) in CSV format. This is indeed a nuanced issue, and I’d like to offer some insights and strategies that might help address it effectively.

Understanding the Issue:

Duplicates in CSV files, especially when your original DataFrame contains unique entries, can stem from several factors:

1. Concurrency in Writing: When multiple processes or partitions attempt to write to the same CSV simultaneously, it can lead to race conditions, resulting in duplicate entries.
2. File Fragmentation: Parallel writes may cause fragmentation within the CSV, where overlapping writes lead to unintended duplication of records.
3. DataFrame Integrity: It’s crucial to verify the integrity of the DataFrame right before the write operation. Even minor transformations or filtering applied post-creation could inadvertently introduce duplicates.
4. Limitations of CSV Format: Unlike Parquet or other binary formats, CSV lacks built-in mechanisms for deduplication and schema management, which may lead to challenges during data merges or appends.

   Recommended Strategies:

   To mitigate these issues, I recommend the following approaches:

   • Implement drop_duplicates(): Before writing the DataFrame to CSV, ensure uniqueness by applying:
     df = df.drop_duplicates(subset=['node_id:ID(MatlBatchPlant)']) 

     This proactive step helps eliminate duplicates before the data is persisted.

   • Consolidate Write Operations: Whenever possible, perform the write operation as a single process rather than partitioning the DataFrame. This reduces the risk of overlapping writes:

     df.to_csv('path/to/adls/file.csv', index=False)

     Monitor Concurrent Writes: Confirm that no other processes are writing to the same file at the same time. In distributed environments like Spark, consider implementing a mechanism to control write access sequentially.

   • Ensure Unique File Naming: If your filenames are dynamically generated (e.g., based on timestamps), verify their uniqueness to prevent overwrites.

   • Evaluate Data Pipeline Logic: If using data orchestration tools like Azure Data Factory, review the pipeline configurations to ensure they are not duplicating writes inadvertently.

   • Validate Parquet Files: Since you mentioned that converting Parquet files to CSV reproduces the issue, ensure that the Parquet files are validated for duplicates prior to conversion. Tools such as Apache Arrow or Pandas can be useful in this context.

     Conclusion:

     The duplication issue you're experiencing likely arises from the inherent behavior of the CSV format, especially in relation to concurrent writes and the lack of deduplication capabilities. By implementing these strategies, you should be well-equipped to resolve the challenges you’re facing.

     I hope these insights prove valuable, and I look forward to hearing about your progress!

     Best regards,

     Kaniz Fatma

[bhanu_gautam]

@Kaniz_Fatma , Great explanation