In Snowflake, micro-partitions are the fundamental unit of data storage. They contain a subset of columns from the table and are compressed and encoded for efficient storage and query processing.
When a query is executed on a virtual warehouse in Snowflake, only the micro-partitions containing the columns accessed during the query are cached. This approach optimizes caching resources by storing only the necessary data for the query, rather than caching all columns or all rows.
Its such a confusingquestion-
few point. 1its asking about cached in VM.So micro -partion information get stored at cloud layer level.So this out
2. For Data cache
it's header and column data are stored on SSD of virtual warehouse. Virtual warehouse first read locally available data (SSD of Virtual warehouse) and then read remaining from remote cloud storage (actual snowflake's storage system). Data keeps dropping on the bases of Least Used fashion when cache storages fills.
which means B and D 2 option left.Eliminating logic ...It does not cache all row data ,it caches only those column data which was output of previous query to ...
D is out and left with B
https://billigence.com/blog/snowflake-data-caching/ . There is no clear explanation from Snowflake on this. But this site tells that the entire Micro Partition accessed is cached. In that case Option A (All Columns from a Micropartition) is correct.
"The query will use all of the micro-partitions, which means neither the first or second query are actually pruning any micro-partitions in your table scan. It’s important to understand that the cache is made up of the entire micro-partitions that were fetched and not just the records that were selected in the first query"
As explained by a few, https://billigence.com/blog/snowflake-data-caching/ has more explanation on than other sites around the levels of caching and what the specific VIRTUAL WAREHOUSE CACHE does - It stores the whole micro partition used in the original query meaning that any other columns in that micro partition would also be stored.
Seems D:
https://billigence.com/blog/snowflake-data-caching/
Virtual Warehouse Cache
This cache is implemented in the compute layer and stores the micro-partitions that were used from the first query and leaves them on your warehouse for future queries. If the next query needs those same micro-partitions to complete its task, it’ll use the Warehouse Cache, rather than fetching the micro-partitions again.
The query will use all of the micro-partitions, which means neither the first or second query are actually pruning any micro-partitions in your table scan. It’s important to understand that the cache is made up of the entire micro-partitions that were fetched and not just the records that were selected in the first query. The Warehouse Cache, in this instance, can optimize the performance of the query
B is correct.
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse.
https://docs.snowflake.com/en/user-guide/warehouses-considerations
If I consider the sequence of the data scan:
1. according to the select command, find the specific columns, it means all columns accessed
2.according to the micro-partition metadata, find all micro partition that potentially have the values I needed. it means ALL rows in all the eligible micro-partitions.
3. get the rows I needed as the final results
4. cached it.
If it is work in this way, I guess the B is better than D as the final result cache may not include all rows accessed in step 2.
B
“As Snowflake is a columnar data warehouse, it automatically returns the columns needed rather then the entire row to further help maximize query performance”
https://community.snowflake.com/s/article/Caching-in-Snowflake-Data-Warehouse
Seems D.
https://community.snowflake.com/s/article/Caching-in-Snowflake-Data-Warehouse
Local Disk Cache: Which is used to cache data used by SQL queries. Whenever data is needed for a given query it's retrieved from the Remote Disk storage, and cached in SSD and memory.
No, because snowflake is column-based storage and can efficiently filter out columns not touched by a query.
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