need to ingest millions of csv files from aws s3

[Kumarashokjmu]

I have a need to ingest millions of csv files from aws s3 bucket. I am facing issue with aws s3 throttling issue and besides notebook process is running for 8 hours plus and sometimes failing. When looking at cluster performance, it is utilized 60%.

I need suggestions on avoiding throttling by aws and what should be source filesize if I have to combine small files to bigger for processing, speeding up ingestion and any other spark parameter needs tuning.

Thanks in advance.

Ash

[Kumarashokjmu]

Thank you so much Kaniz, I Really appreciate your response with detail reply on each topic. I will post more with time to get help from you.

Ashok

[jose_gonzalez]

Hi @Kumarashokjmu,

I would recommend to use Databricks auto loader to ingest your CSV files incrementally. You can find examples and more details here https://docs.databricks.com/en/ingestion/auto-loader/index.html#what-is-auto-loader

[jose_gonzalez]

Hi @Kumarashokjmu,

Just a friendly follow-up. Did you have time to test auto loader? do you have any follow-up questions? Please let us know

[kulkpd]

If you want to load all the data at once use autoloader or DLT pipeline with directory listing if files are lexically ordered. 

OR
If you want to perform incremental load, divide the load into two job like historic data load vs live data load:
Live data:
Use autoloader or delta live pipeline using fileNotification to load the data into Delta table. File Notification is scalable and recommended solution from Databricks. 
https://docs.databricks.com/en/ingestion/auto-loader/options.html#directory-listing-options

Historic Load: 
Use autoloader job to load all the data. if files are not lexically ordered then try using s3 inventory option to divide the workload into micro-batches. Using this approach multiple batches can be executed in parallel.

Handle S3 throttling issues:
if you can facing issue with s3 throttling. Try limit maxFilesPerTrigger to 10k-15k. 
Increase spark.network.timeout configuration in spark init block.

Let us know if you need more information