Optimal Strategies for downloading large query results with Databricks API

[rafal_walisko]

Hi everyone,

I'm currently facing an issue with handling a large amount of data using the Databricks API. Specifically, I have a query that returns a significant volume of data, sometimes resulting in over 200 chunks.

My initial approach was to retrieve the external_link for each chunk within a loop and then download the .csv file containing the data. However, I've encountered a bottleneck where obtaining the external link alone takes a considerable amount of time, leading to many files expiring before they can be downloaded.

I'm wondering if anyone has found an optimal strategy or method for dealing with this problem. For instance, is it feasible to generate and retrieve all the links at once and then download the files in parallel?

Any insights or suggestions would be greatly appreciated.

[Kaniz_Fatma]

Hi @rafal_walisko, Handling large volumes of data using the Databricks API can indeed be challenging, especially when dealing with numerous chunks.

Let’s explore some strategies that might help you optimize your approach:

1. Rate Limits and Parallelization:

   • Databricks enforces rate limits for all REST API calls to ensure high-quality service under heavy lo...¹. Be aware of these limits and design your solution accordingly.
   • Instead of retrieving external links one by one in a loop, consider fetching them in parallel. You can create a pool of worker nodes, each responsible for processing a chunk of records. As a worker finishes processing one chunk, assign it another chunk until all chunks are processed².
   • Parallelization can significantly improve efficiency, especially when dealing with a large number of files.

2. Partitioning and Clustering:

   • Ensure that your table is properly partitioned and clustered. This can enhance query performance and reduce the time spent retrieving external links.
   • Partitioning involves dividing your data into smaller, manageable chunks based on specific columns (e.g., date, region, etc.). Clustering, on the other hand, organizes data within each partition to optimize query execution.
   • By choosing appropriate partition and clustering keys, you can minimize the amount of data scanned d...³.

3. Batch Processing:

   • Consider batching your requests. Instead of fetching all external links at once, break them down into smaller batches and retrieve links for each batch sequentially.
   • This approach can help avoid overloading the system and reduce the risk of files expiring before they can be downloaded.

4. Caching and Memoization:

   • If your query results are relatively stable over time, consider caching the external links. Cache the links locally or in a distributed storage system (e.g., Databricks File System, Azure Blob Storage).
   • Memoization (caching intermediate results) can save time by avoiding redundant API calls. When a link is requested, check if it’s already cached before making a new API call.

5. Monitoring and Error Handling:

   • Implement robust error handling mechanisms. Monitor the status of your API requests and handle any failures gracefully.
   • Keep track of the expiration time for each link. If a link is about to expire, prioritize downloading it promptly.

Good luck! 😊

If you’d like more detailed examples or have additional questions, feel free to ask ²¹³