Exporting delta table to one CSV

[561064]

Process to export a delta table is taking ~2hrs.

Delta table has 66 partitions with total size of ~6gb, 4million rows and 270 columns.

Used below command

df.coalesce(1).write.csv("path")

what are my options to reduce the time?

[Dribka]

A very interesting task in front of you.... let me know how you solve it!

[Kaniz_Fatma]

Hi @561064, Exporting a Delta table can indeed be time-consuming, especially when dealing with large datasets.

Let’s explore some strategies to optimize the export process and reduce the time:

Partitioning:

• Choose an appropriate partition column for your Delta table. The most commonly used partition column is date.
• Avoid partitioning by columns with very high cardinality (e.g., user IDs with millions of distinct values).
• Ensure that each partition contains at least 1 GB of data.
• You can repartition the table to a smaller number of files using the repartition method. For example:df.repartition(numFiles).write.csv("path")
• If you want to repartition just one partition based on a predicate, use where and replaceWhere.

Compaction:

• Over time, a Delta table accumulates a large number of files due to continuous writes. Compaction helps consolidate these files into a smaller number of larger files.
• Use the OPTIMIZE command to compact the table:OPTIMIZE '/path/to/delta/table'
• You can also specify an optional partition predicate using WHERE if you want to optimize only a subset of the data.

V-Order Optimization:

• V-Order is a write-time optimization for the Parquet file format.
• It enables lightning-fast reads under Microsoft Fabric compute engines (e.g., Power BI, SQL, Spark).
• V-Order works by applying special sorting, row group distribution, dictionary encoding, and compression to Parquet files.
• To enable or disable V-Order:
  • In Apache Spark™ session:SET spark.sql.parquet.vorder.enabled=TRUE  -- Enable SET spark.sql.parquet.vorder.enabled=FALSE -- Disable
  • All Parquet writes will be made with V-Order enabled when set at the session level.

Delta Table Properties:

• Use table properties to fine-tune performance.
• Set properties like spark.databricks.delta.optimizeWrite.enabled and spark.databricks.delta.retentionDurationCheck.enabled.

Remember that these optimizations can significantly improve export times, but the actual impact may vary based on your specific use case.

Experiment with different approaches to find the best combination for your Delta table export process. 🚀

[561064]

Hi Kainz,

None of the options I tried helped as the challenge is not reading but writing it to a one CSV file. df.repartition(numFiles).write.csv("path") has consumed the same amount of time as 'df.coalesce(1).write.csv("path")' in my case.

any other options I can explore?

[Kaniz_Fatma]

Thank you for posting your question in our community! We are happy to assist you.

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! 
 

[Kaniz_Fatma]

Thank you for posting your question in our community! We are happy to assist you.

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!