Merk
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Caches the specified table in-memory or with given storage level. Default MEMORY_AND_DISK.
Syntax
cacheTable(tableName: str, storageLevel: StorageLevel = None)
Parameters
| Parameter | Type | Description |
|---|---|---|
tableName |
str | Name of the table to get. Can be qualified with catalog name. |
storageLevel |
StorageLevel, optional |
Storage level to set for persistence. |
Notes
Cached data is shared across all Spark sessions on the cluster.
Examples
_ = spark.sql("DROP TABLE IF EXISTS tbl1")
_ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
spark.catalog.cacheTable("tbl1")
# or
spark.catalog.cacheTable("tbl1", StorageLevel.OFF_HEAP)
# Throw an analysis exception when the table does not exist.
spark.catalog.cacheTable("not_existing_table")
# Traceback (most recent call last):
# ...
# AnalysisException: ...
# Using the fully qualified name for the table.
spark.catalog.cacheTable("spark_catalog.default.tbl1")
spark.catalog.uncacheTable("tbl1")
_ = spark.sql("DROP TABLE tbl1")