Thank you for your feedback Mich.
In general how can one optimise the cloud data warehouses (the sink part), to 
handle streaming Spark data efficiently, avoiding bottlenecks that discussed.

AK    On Monday, 9 October 2023 at 11:04:41 BST, Mich Talebzadeh 
<mich.talebza...@gmail.com> wrote:  
 
 Hi,
Please see my responses below:
1) In Spark Structured Streaming does commit mean streaming data has been 
delivered to the sink like Snowflake?

No. a commit does not refer to data being delivered to a sink like Snowflake or 
bigQuery. The term commit refers to Spark Structured Streaming (SS) internals. 
Specifically it means that a micro-batch of data has been processed by SSS. In 
the checkpoint directory there is a subdirectory called commits that marks the 
micro-batch process as completed.
2) if sinks like Snowflake  cannot absorb or digest streaming data in a timely 
manner, will there be an impact on spark streaming itself?

Yes, it can potentially impact SSS. If the sink cannot absorb data in a timely 
manner, the batches will start to back up in SSS. This can cause Spark to run 
out of memory and the streaming job to fail. As I understand, Spark will use a 
combination of memory and disk storage (checkpointing). This can also happen if 
the network interface between Spark and the sink is disrupted. On the other 
hand Spark may slow down, as it tries to process the backed-up batches of data. 
You want to avoid these scenarios.
HTH
Mich Talebzadeh,Distinguished Technologist, Solutions Architect & Engineer
London
United Kingdom



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On Sun, 8 Oct 2023 at 19:50, ashok34...@yahoo.com.INVALID 
<ashok34...@yahoo.com.invalid> wrote:

Hello team
1) In Spark Structured Streaming does commit mean streaming data has been 
delivered to the sink like Snowflake?
2) if sinks like Snowflake  cannot absorb or digest streaming data in a timely 
manner, will there be an impact on spark streaming itself?
Thanks

AK
  

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