Hi @Murlidhar Patil
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When optimizing the performance of an Azure Data Factory (ADF) data flow pipeline, especially during the insert/update sink stage to an Azure SQL database table with a large volume of data, there are several methods to consider. Here’s an explanation with examples, pros, cons, and suitable methods for this scenario:
Example: A data flow pipeline is designed to move data from a Parquet file to an Azure SQL database table with 1.1 billion rows. Despite increasing the Azure SQL Server cores to 32 GB and running the ADF data flow on a 32 cores integration runtime, the insert/update sink stage is slow.
Pros and Cons of Performance Optimization Methods:
Data Integration Units (DIUs):
Pros: Can increase parallelism and throughput.
Suitability: Adjusting DIUs can be beneficial in scenarios where the default parallelism is not sufficient for the data volume.
Self-hosted Integration Runtime Scalability:
Pros: Allows for more granular control over resources.
Suitability: Suitable for scenarios where cloud integration runtime does not meet performance needs.
Parallel Copy:
Pros: Can significantly reduce the time required for data movement.
Suitability: Useful when the bottleneck is data movement rather than compute resources.
Bulk Insert Techniques:
Pros: Minimizes roundtrips and log writes, maximizing throughput.
Suitability: Highly suitable for large batch inserts like in the given scenario.
Incremental Data Loading:
Pros: Reduces data movement by only processing delta changes.
Suitability: Not suitable for the initial load but can be considered for subsequent updates.
Source Partitioning:
Pros: Improves parallel processing capabilities.
Suitability: Suitable for large datasets to improve performance during data movement.
Recommended Method for the Scenario:
Given the large volume of data and the structure of the destination table, leveraging bulk insert techniques would be the most suitable method. This approach would allow for the entire dataset to be uploaded to the Azure SQL Database and then execute all the INSERT/UPDATE operations within a single batch, minimizing roundtrips and log writes, thus maximizing throughput.
In addition to the above method, it’s also recommended to review the partitioning strategy and ensure that the data is partitioned effectively to maximize parallel processing capabilities during the data movement.
Each method has its trade-offs, and the best approach depends on the specific requirements and constraints of your data flow pipeline. It’s essential to monitor the performance after applying these optimizations and adjust the strategy as needed.
https://devblogs.microsoft.com/azure-sql/optimize-azure-sql-upsert-scenarios/
https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-performance-features
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