I see the option to enable Photon when creating a new SQL Endpoint. The description says that enabling it helps speed up up queries, which sounds good, but are there any downsides I need to be aware of?
Generally, yes you should enable photon. The majority of functionality is available and will perform extremely well. There are some limitations with it that can be found here.
Limitations:
Works on Delta and Parquet tables only for both read and write.
Does not support the following data types:
Map
Array
Does not support window and sort operators
Does not support Spark Structured Streaming.
Does not support UDFs.
Not expected to improve operations bottlenecked by network or scan I/O.
Not expected to improve short-running queries (<2 seconds), for example, against small data.
Advantages:
Supports SQL and equivalent DataFrame operations against Delta and Parquet tables.
Expected to accelerate queries that process a significant amount of data (100GB+) and include aggregations and joins.
Data is accessed repeatedly and likely in the Delta Lake cache.
More robust scan performance on tables with many columns and many small files.
Faster Delta and Parquet writing using update, delete, merge into, and create table as select, especially for wide tables (hundreds to thousands of columns).
Generally, yes you should enable photon. The majority of functionality is available and will perform extremely well. There are some limitations with it that can be found here.
Limitations:
Works on Delta and Parquet tables only for both read and write.
Does not support the following data types:
Map
Array
Does not support window and sort operators
Does not support Spark Structured Streaming.
Does not support UDFs.
Not expected to improve operations bottlenecked by network or scan I/O.
Not expected to improve short-running queries (<2 seconds), for example, against small data.
Advantages:
Supports SQL and equivalent DataFrame operations against Delta and Parquet tables.
Expected to accelerate queries that process a significant amount of data (100GB+) and include aggregations and joins.
Data is accessed repeatedly and likely in the Delta Lake cache.
More robust scan performance on tables with many columns and many small files.
Faster Delta and Parquet writing using update, delete, merge into, and create table as select, especially for wide tables (hundreds to thousands of columns).
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