https://docs.snowflake.com/en/user-guide/data-load-considerations-load
Tip
Of the three options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
he COPY command supports several options for loading data files from a stage:
By path (internal stages) / prefix (Amazon S3 bucket). See Organizing data by path for information.
Specifying a list of specific files to load.
Using pattern matching to identify specific files by pattern.
Of the three options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
Of the three options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
Option A, "Specifying a list of specific files to load," can also be fast but is generally less efficient for bulk data loading compared to option B, "Loading by path (internal stages) / prefix."
The reason is that specifying a list of specific files means you need to provide each individual file name to be loaded. If you have a large number of files to load, it can be time-consuming and less efficient to list them all explicitly. Additionally, if new files are added frequently, you would need to update the list regularly.
On the other hand, option B allows you to load all files with a common path or prefix, which means you can load multiple files in a single operation without needing to specify each file individually. This approach is more scalable and efficient for bulk data loading, especially when dealing with large datasets or frequent data updates.
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