Holds a table, or the output of a query, in Spark's cache. df.persist() and df.cache() can still be used on serverless compute, but this statement, df.checkpoint() and spark.catalog.cacheTable() all throw exceptions.
Read more: Databricks docs
In the Ultra Transcenders books
Each book explains CACHE TABLE in context, with comparison tables and the common traps.
Terms in this definition
- Event
Table in Log Analytics where entries from Windows event logs are kept.
- Serverless compute
Azure Machine Learning compute provided on demand whenever a job names no compute target. No cluster needs creating or managing, and jobs do not wait in a queue behind one another.
- ALL
A DAX function that ignores any filters and gives back every row of a table or every value of the named columns. Used within CALCULATE, it works as a modifier that clears filters, although REMOVEFILTERS states that intent more clearly where it is available.
- THROW
A T-SQL statement for raising an error at severity 16; used on its own inside a CATCH block, it re-raises the error that was caught. Whatever statement comes before it needs a terminating semicolon.
Related terms
- Apache Spark cache
A cache you request explicitly, in memory or on disk, for a table or DataFrame using
df.cache(),df.persist()orCACHE TABLE, and release withunpersist. It differs from the automatic disk cache and cannot be used on serverless compute. - CLEAR CACHE
Empties the Apache Spark cache of every cached table and view. Serverless compute doesn't support it, nor SQL cache statements like
CACHE TABLE.