pyspark catalog介绍
这里写自定义目录标题
catalog 是一个管理 Spark 元数据信息的接口，可以管理库、内部或外部表、函数、表列及临时视图
Catalog is the interface to work with a metastore, i.e. a data catalog of database(s), local and external tables, functions, table columns, and temporary views in Spark SQL.
可以做的事：
- 检查当前库以及切换库
- 创建永久表
- 创建删除临时视图
- 注册函数
使用示例：
from pyspark.sql import SparkSession
# 检查表是否存在
spark = SparkSession \
.builder \
.appName('tableExists') \
.config('spark.num.executors', '6') \
.config('spark.executor.memory', '12g') \
.config('spark.driver.memory', '2g') \
.config('spark.executor.cores', '4') \
.config('spark.default.parallelism', '50') \
.config('spark.executor.memoryOverhead', '2g') \
.config('spark.task.maxFailures', '10') \
.config("spark.dynamicAllocation.enabled", 'false') \
.config("spark.sql.broadcastTimeout", "3600") \
.enableHiveSupport() \
.getOrCreate()
# 例1
spark.catalog.tableExists("unexisting_table") # False
_ = spark.sql("DROP TABLE IF EXISTS tbl1")
_ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
spark.catalog.tableExists("tbl1") # True
# 例2 不同方式
spark.catalog.tableExists("default.tbl1")
spark.catalog.tableExists("spark_catalog.default.tbl1")
spark.catalog.tableExists("tbl1", "default")
_ = spark.sql("DROP TABLE tbl1")
源码定义
class Catalog:
"""User-facing catalog API, accessible through `SparkSession.catalog`.
This is a thin wrapper around its Scala implementation org.apache.spark.sql.catalog.Catalog.
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
def __init__(self, sparkSession: SparkSession) -> None:
"""Create a new Catalog that wraps the underlying JVM object."""
self._sparkSession = sparkSession
self._jsparkSession = sparkSession._jsparkSession
self._sc = sparkSession._sc
self._jcatalog = sparkSession._jsparkSession.catalog()
def currentCatalog(self) -> str:
"""Returns the current default catalog in this session.
.. versionadded:: 3.4.0
Examples
--------
>>> spark.catalog.currentCatalog()
'spark_catalog'
"""
return self._jcatalog.currentCatalog()
def setCurrentCatalog(self, catalogName: str) -> None:
"""Sets the current default catalog in this session.
.. versionadded:: 3.4.0
Parameters
----------
catalogName : str
name of the catalog to set
Examples
--------
>>> spark.catalog.setCurrentCatalog("spark_catalog")
"""
return self._jcatalog.setCurrentCatalog(catalogName)
def listCatalogs(self, pattern: Optional[str] = None) -> List[CatalogMetadata]:
"""Returns a list of catalogs in this session.
.. versionadded:: 3.4.0
Parameters
----------
pattern : str
The pattern that the catalog name needs to match.
.. versionchanged: 3.5.0
Added ``pattern`` argument.
Returns
-------
list
A list of :class:`CatalogMetadata`.
Examples
--------
>>> spark.catalog.listCatalogs()
[CatalogMetadata(name='spark_catalog', description=None)]
>>> spark.catalog.listCatalogs("spark*")
[CatalogMetadata(name='spark_catalog', description=None)]
>>> spark.catalog.listCatalogs("hive*")
[]
"""
if pattern is None:
iter = self._jcatalog.listCatalogs().toLocalIterator()
else:
iter = self._jcatalog.listCatalogs(pattern).toLocalIterator()
catalogs = []
while iter.hasNext():
jcatalog = iter.next()
catalogs.append(
CatalogMetadata(name=jcatalog.name(), description=jcatalog.description())
)
return catalogs
def currentDatabase(self) -> str:
"""
Returns the current default database in this session.
.. versionadded:: 2.0.0
Returns
-------
str
The current default database name.
Examples
--------
>>> spark.catalog.currentDatabase()
'default'
"""
return self._jcatalog.currentDatabase()
def setCurrentDatabase(self, dbName: str) -> None:
"""
Sets the current default database in this session.
.. versionadded:: 2.0.0
Examples
--------
>>> spark.catalog.setCurrentDatabase("default")
"""
return self._jcatalog.setCurrentDatabase(dbName)
def listDatabases(self, pattern: Optional[str] = None) -> List[Database]:
"""
Returns a list of databases available across all sessions.
.. versionadded:: 2.0.0
Parameters
----------
pattern : str
The pattern that the database name needs to match.
.. versionchanged: 3.5.0
Adds ``pattern`` argument.
Returns
-------
list
A list of :class:`Database`.
Examples
--------
>>> spark.catalog.listDatabases()
[Database(name='default', catalog='spark_catalog', description='default database', ...
>>> spark.catalog.listDatabases("def*")
[Database(name='default', catalog='spark_catalog', description='default database', ...
>>> spark.catalog.listDatabases("def2*")
[]
"""
if pattern is None:
iter = self._jcatalog.listDatabases().toLocalIterator()
else:
iter = self._jcatalog.listDatabases(pattern).toLocalIterator()
databases = []
while iter.hasNext():
jdb = iter.next()
databases.append(
Database(
name=jdb.name(),
catalog=jdb.catalog(),
description=jdb.description(),
locationUri=jdb.locationUri(),
)
)
return databases
def getDatabase(self, dbName: str) -> Database:
"""Get the database with the specified name.
This throws an :class:`AnalysisException` when the database cannot be found.
.. versionadded:: 3.4.0
Parameters
----------
dbName : str
name of the database to get.
Returns
-------
:class:`Database`
The database found by the name.
Examples
--------
>>> spark.catalog.getDatabase("default")
Database(name='default', catalog='spark_catalog', description='default database', ...
Using the fully qualified name with the catalog name.
>>> spark.catalog.getDatabase("spark_catalog.default")
Database(name='default', catalog='spark_catalog', description='default database', ...
"""
jdb = self._jcatalog.getDatabase(dbName)
return Database(
name=jdb.name(),
catalog=jdb.catalog(),
description=jdb.description(),
locationUri=jdb.locationUri(),
)
def databaseExists(self, dbName: str) -> bool:
"""Check if the database with the specified name exists.
.. versionadded:: 3.3.0
Parameters
----------
dbName : str
name of the database to check existence
.. versionchanged:: 3.4.0
Allow ``dbName`` to be qualified with catalog name.
Returns
-------
bool
Indicating whether the database exists
Examples
--------
Check if 'test_new_database' database exists
>>> spark.catalog.databaseExists("test_new_database")
False
>>> _ = spark.sql("CREATE DATABASE test_new_database")
>>> spark.catalog.databaseExists("test_new_database")
True
Using the fully qualified name with the catalog name.
>>> spark.catalog.databaseExists("spark_catalog.test_new_database")
True
>>> _ = spark.sql("DROP DATABASE test_new_database")
"""
return self._jcatalog.databaseExists(dbName)
def listTables(
self, dbName: Optional[str] = None, pattern: Optional[str] = None
) -> List[Table]:
"""Returns a list of tables/views in the specified database.
.. versionadded:: 2.0.0
Parameters
----------
dbName : str
name of the database to list the tables.
.. versionchanged:: 3.4.0
Allow ``dbName`` to be qualified with catalog name.
pattern : str
The pattern that the database name needs to match.
.. versionchanged: 3.5.0
Adds ``pattern`` argument.
Returns
-------
list
A list of :class:`Table`.
Notes
-----
If no database is specified, the current database and catalog
are used. This API includes all temporary views.
Examples
--------
>>> spark.range(1).createTempView("test_view")
>>> spark.catalog.listTables()
[Table(name='test_view', catalog=None, namespace=[], description=None, ...
>>> spark.catalog.listTables(pattern="test*")
[Table(name='test_view', catalog=None, namespace=[], description=None, ...
>>> spark.catalog.listTables(pattern="table*")
[]
>>> _ = spark.catalog.dropTempView("test_view")
>>> spark.catalog.listTables()
[]
"""
if dbName is None:
dbName = self.currentDatabase()
if pattern is None:
iter = self._jcatalog.listTables(dbName).toLocalIterator()
else:
iter = self._jcatalog.listTables(dbName, pattern).toLocalIterator()
tables = []
while iter.hasNext():
jtable = iter.next()
jnamespace = jtable.namespace()
if jnamespace is not None:
namespace = [jnamespace[i] for i in range(0, len(jnamespace))]
else:
namespace = None
tables.append(
Table(
name=jtable.name(),
catalog=jtable.catalog(),
namespace=namespace,
description=jtable.description(),
tableType=jtable.tableType(),
isTemporary=jtable.isTemporary(),
)
)
return tables
def getTable(self, tableName: str) -> Table:
"""Get the table or view with the specified name. This table can be a temporary view or a
table/view. This throws an :class:`AnalysisException` when no Table can be found.
.. versionadded:: 3.4.0
Parameters
----------
tableName : str
name of the table to get.
.. versionchanged:: 3.4.0
Allow `tableName` to be qualified with catalog name.
Returns
-------
:class:`Table`
The table found by the name.
Examples
--------
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
>>> spark.catalog.getTable("tbl1")
Table(name='tbl1', catalog='spark_catalog', namespace=['default'], ...
Using the fully qualified name with the catalog name.
>>> spark.catalog.getTable("default.tbl1")
Table(name='tbl1', catalog='spark_catalog', namespace=['default'], ...
>>> spark.catalog.getTable("spark_catalog.default.tbl1")
Table(name='tbl1', catalog='spark_catalog', namespace=['default'], ...
>>> _ = spark.sql("DROP TABLE tbl1")
Throw an analysis exception when the table does not exist.
>>> spark.catalog.getTable("tbl1")
Traceback (most recent call last):
...
AnalysisException: ...
"""
jtable = self._jcatalog.getTable(tableName)
jnamespace = jtable.namespace()
if jnamespace is not None:
namespace = [jnamespace[i] for i in range(0, len(jnamespace))]
else:
namespace = None
return Table(
name=jtable.name(),
catalog=jtable.catalog(),
namespace=namespace,
description=jtable.description(),
tableType=jtable.tableType(),
isTemporary=jtable.isTemporary(),
)
def listFunctions(
self, dbName: Optional[str] = None, pattern: Optional[str] = None
) -> List[Function]:
"""
Returns a list of functions registered in the specified database.
.. versionadded:: 3.4.0
Parameters
----------
dbName : str
name of the database to list the functions.
``dbName`` can be qualified with catalog name.
pattern : str
The pattern that the function name needs to match.
.. versionchanged: 3.5.0
Adds ``pattern`` argument.
Returns
-------
list
A list of :class:`Function`.
Notes
-----
If no database is specified, the current database and catalog
are used. This API includes all temporary functions.
Examples
--------
>>> spark.catalog.listFunctions()
[Function(name=...
>>> spark.catalog.listFunctions(pattern="to_*")
[Function(name=...
>>> spark.catalog.listFunctions(pattern="*not_existing_func*")
[]
"""
if dbName is None:
dbName = self.currentDatabase()
iter = self._jcatalog.listFunctions(dbName).toLocalIterator()
if pattern is None:
iter = self._jcatalog.listFunctions(dbName).toLocalIterator()
else:
iter = self._jcatalog.listFunctions(dbName, pattern).toLocalIterator()
functions = []
while iter.hasNext():
jfunction = iter.next()
jnamespace = jfunction.namespace()
if jnamespace is not None:
namespace = [jnamespace[i] for i in range(0, len(jnamespace))]
else:
namespace = None
functions.append(
Function(
name=jfunction.name(),
catalog=jfunction.catalog(),
namespace=namespace,
description=jfunction.description(),
className=jfunction.className(),
isTemporary=jfunction.isTemporary(),
)
)
return functions
def functionExists(self, functionName: str, dbName: Optional[str] = None) -> bool:
"""Check if the function with the specified name exists.
This can either be a temporary function or a function.
.. versionadded:: 3.3.0
Parameters
----------
functionName : str
name of the function to check existence
.. versionchanged:: 3.4.0
Allow ``functionName`` to be qualified with catalog name
dbName : str, optional
name of the database to check function existence in.
Returns
-------
bool
Indicating whether the function exists
Notes
-----
If no database is specified, the current database and catalog
are used. This API includes all temporary functions.
Examples
--------
>>> spark.catalog.functionExists("count")
True
Using the fully qualified name for function name.
>>> spark.catalog.functionExists("default.unexisting_function")
False
>>> spark.catalog.functionExists("spark_catalog.default.unexisting_function")
False
"""
if dbName is None:
return self._jcatalog.functionExists(functionName)
else:
warnings.warn(
"`dbName` has been deprecated since Spark 3.4 and might be removed in "
"a future version. Use functionExists(`dbName.tableName`) instead.",
FutureWarning,
)
return self._jcatalog.functionExists(dbName, functionName)
def getFunction(self, functionName: str) -> Function:
"""Get the function with the specified name. This function can be a temporary function or a
function. This throws an :class:`AnalysisException` when the function cannot be found.
.. versionadded:: 3.4.0
Parameters
----------
functionName : str
name of the function to check existence.
Returns
-------
:class:`Function`
The function found by the name.
Examples
--------
>>> _ = spark.sql(
... "CREATE FUNCTION my_func1 AS 'test.org.apache.spark.sql.MyDoubleAvg'")
>>> spark.catalog.getFunction("my_func1")
Function(name='my_func1', catalog='spark_catalog', namespace=['default'], ...
Using the fully qualified name for function name.
>>> spark.catalog.getFunction("default.my_func1")
Function(name='my_func1', catalog='spark_catalog', namespace=['default'], ...
>>> spark.catalog.getFunction("spark_catalog.default.my_func1")
Function(name='my_func1', catalog='spark_catalog', namespace=['default'], ...
Throw an analysis exception when the function does not exists.
>>> spark.catalog.getFunction("my_func2")
Traceback (most recent call last):
...
AnalysisException: ...
"""
jfunction = self._jcatalog.getFunction(functionName)
jnamespace = jfunction.namespace()
if jnamespace is not None:
namespace = [jnamespace[i] for i in range(0, len(jnamespace))]
else:
namespace = None
return Function(
name=jfunction.name(),
catalog=jfunction.catalog(),
namespace=namespace,
description=jfunction.description(),
className=jfunction.className(),
isTemporary=jfunction.isTemporary(),
)
def listColumns(self, tableName: str, dbName: Optional[str] = None) -> List[Column]:
"""Returns a list of columns for the given table/view in the specified database.
.. versionadded:: 2.0.0
Parameters
----------
tableName : str
name of the table to list columns.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name when ``dbName`` is None.
dbName : str, optional
name of the database to find the table to list columns.
Returns
-------
list
A list of :class:`Column`.
Notes
-----
The order of arguments here is different from that of its JVM counterpart
because Python does not support method overloading.
If no database is specified, the current database and catalog
are used. This API includes all temporary views.
Examples
--------
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tblA (name STRING, age INT) USING parquet")
>>> spark.catalog.listColumns("tblA")
[Column(name='name', description=None, dataType='string', nullable=True, ...
>>> _ = spark.sql("DROP TABLE tblA")
"""
if dbName is None:
iter = self._jcatalog.listColumns(tableName).toLocalIterator()
else:
warnings.warn(
"`dbName` has been deprecated since Spark 3.4 and might be removed in "
"a future version. Use listColumns(`dbName.tableName`) instead.",
FutureWarning,
)
iter = self._jcatalog.listColumns(dbName, tableName).toLocalIterator()
columns = []
while iter.hasNext():
jcolumn = iter.next()
columns.append(
Column(
name=jcolumn.name(),
description=jcolumn.description(),
dataType=jcolumn.dataType(),
nullable=jcolumn.nullable(),
isPartition=jcolumn.isPartition(),
isBucket=jcolumn.isBucket(),
)
)
return columns
def tableExists(self, tableName: str, dbName: Optional[str] = None) -> bool:
"""Check if the table or view with the specified name exists.
This can either be a temporary view or a table/view.
.. versionadded:: 3.3.0
Parameters
----------
tableName : str
name of the table to check existence.
If no database is specified, first try to treat ``tableName`` as a
multi-layer-namespace identifier, then try ``tableName`` as a normal table
name in the current database if necessary.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name when ``dbName`` is None.
dbName : str, optional
name of the database to check table existence in.
Returns
-------
bool
Indicating whether the table/view exists
Examples
--------
This function can check if a table is defined or not:
>>> spark.catalog.tableExists("unexisting_table")
False
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
>>> spark.catalog.tableExists("tbl1")
True
Using the fully qualified names for tables.
>>> spark.catalog.tableExists("default.tbl1")
True
>>> spark.catalog.tableExists("spark_catalog.default.tbl1")
True
>>> spark.catalog.tableExists("tbl1", "default")
True
>>> _ = spark.sql("DROP TABLE tbl1")
Check if views exist:
>>> spark.catalog.tableExists("view1")
False
>>> _ = spark.sql("CREATE VIEW view1 AS SELECT 1")
>>> spark.catalog.tableExists("view1")
True
Using the fully qualified names for views.
>>> spark.catalog.tableExists("default.view1")
True
>>> spark.catalog.tableExists("spark_catalog.default.view1")
True
>>> spark.catalog.tableExists("view1", "default")
True
>>> _ = spark.sql("DROP VIEW view1")
Check if temporary views exist:
>>> _ = spark.sql("CREATE TEMPORARY VIEW view1 AS SELECT 1")
>>> spark.catalog.tableExists("view1")
True
>>> df = spark.sql("DROP VIEW view1")
>>> spark.catalog.tableExists("view1")
False
"""
if dbName is None:
return self._jcatalog.tableExists(tableName)
else:
warnings.warn(
"`dbName` has been deprecated since Spark 3.4 and might be removed in "
"a future version. Use tableExists(`dbName.tableName`) instead.",
FutureWarning,
)
return self._jcatalog.tableExists(dbName, tableName)
def createExternalTable(
self,
tableName: str,
path: Optional[str] = None,
source: Optional[str] = None,
schema: Optional[StructType] = None,
**options: str,
) -> DataFrame:
"""Creates a table based on the dataset in a data source.
It returns the DataFrame associated with the external table.
The data source is specified by the ``source`` and a set of ``options``.
If ``source`` is not specified, the default data source configured by
``spark.sql.sources.default`` will be used.
Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and
created external table.
.. versionadded:: 2.0.0
Returns
-------
:class:`DataFrame`
"""
warnings.warn(
"createExternalTable is deprecated since Spark 2.2, please use createTable instead.",
FutureWarning,
)
return self.createTable(tableName, path, source, schema, **options)
def createTable(
self,
tableName: str,
path: Optional[str] = None,
source: Optional[str] = None,
schema: Optional[StructType] = None,
description: Optional[str] = None,
**options: str,
) -> DataFrame:
"""Creates a table based on the dataset in a data source.
.. versionadded:: 2.2.0
Parameters
----------
tableName : str
name of the table to create.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name.
path : str, optional
the path in which the data for this table exists.
When ``path`` is specified, an external table is
created from the data at the given path. Otherwise a managed table is created.
source : str, optional
the source of this table such as 'parquet, 'orc', etc.
If ``source`` is not specified, the default data source configured by
``spark.sql.sources.default`` will be used.
schema : class:`StructType`, optional
the schema for this table.
description : str, optional
the description of this table.
.. versionchanged:: 3.1.0
Added the ``description`` parameter.
**options : dict, optional
extra options to specify in the table.
Returns
-------
:class:`DataFrame`
The DataFrame associated with the table.
Examples
--------
Creating a managed table.
>>> _ = spark.catalog.createTable("tbl1", schema=spark.range(1).schema, source='parquet')
>>> _ = spark.sql("DROP TABLE tbl1")
Creating an external table
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
... _ = spark.catalog.createTable(
... "tbl2", schema=spark.range(1).schema, path=d, source='parquet')
>>> _ = spark.sql("DROP TABLE tbl2")
"""
if path is not None:
options["path"] = path
if source is None:
c = self._sparkSession._jconf
source = c.defaultDataSourceName()
if description is None:
description = ""
if schema is None:
df = self._jcatalog.createTable(tableName, source, description, options)
else:
if not isinstance(schema, StructType):
raise TypeError("schema should be StructType")
scala_datatype = self._jsparkSession.parseDataType(schema.json())
df = self._jcatalog.createTable(tableName, source, scala_datatype, description, options)
return DataFrame(df, self._sparkSession)
def dropTempView(self, viewName: str) -> bool:
"""Drops the local temporary view with the given view name in the catalog.
If the view has been cached before, then it will also be uncached.
Returns true if this view is dropped successfully, false otherwise.
.. versionadded:: 2.0.0
Parameters
----------
viewName : str
name of the temporary view to drop.
Returns
-------
bool
If the temporary view was successfully dropped or not.
.. versionadded:: 2.1.0
The return type of this method was ``None`` in Spark 2.0, but changed to ``bool``
in Spark 2.1.
Examples
--------
>>> spark.createDataFrame([(1, 1)]).createTempView("my_table")
Dropping the temporary view.
>>> spark.catalog.dropTempView("my_table")
True
Throw an exception if the temporary view does not exists.
>>> spark.table("my_table")
Traceback (most recent call last):
...
AnalysisException: ...
"""
return self._jcatalog.dropTempView(viewName)
def dropGlobalTempView(self, viewName: str) -> bool:
"""Drops the global temporary view with the given view name in the catalog.
.. versionadded:: 2.1.0
Parameters
----------
viewName : str
name of the global view to drop.
Returns
-------
bool
If the global view was successfully dropped or not.
Notes
-----
If the view has been cached before, then it will also be uncached.
Examples
--------
>>> spark.createDataFrame([(1, 1)]).createGlobalTempView("my_table")
Dropping the global view.
>>> spark.catalog.dropGlobalTempView("my_table")
True
Throw an exception if the global view does not exists.
>>> spark.table("global_temp.my_table")
Traceback (most recent call last):
...
AnalysisException: ...
"""
return self._jcatalog.dropGlobalTempView(viewName)
def registerFunction(
self, name: str, f: Callable[..., Any], returnType: Optional["DataType"] = None
) -> "UserDefinedFunctionLike":
"""An alias for :func:`spark.udf.register`.
See :meth:`pyspark.sql.UDFRegistration.register`.
.. versionadded:: 2.0.0
.. deprecated:: 2.3.0
Use :func:`spark.udf.register` instead.
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
warnings.warn("Deprecated in 2.3.0. Use spark.udf.register instead.", FutureWarning)
return self._sparkSession.udf.register(name, f, returnType)
def isCached(self, tableName: str) -> bool:
"""
Returns true if the table is currently cached in-memory.
.. versionadded:: 2.0.0
Parameters
----------
tableName : str
name of the table to get.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name.
Returns
-------
bool
Examples
--------
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
>>> spark.catalog.cacheTable("tbl1")
>>> spark.catalog.isCached("tbl1")
True
Throw an analysis exception when the table does not exist.
>>> spark.catalog.isCached("not_existing_table")
Traceback (most recent call last):
...
AnalysisException: ...
Using the fully qualified name for the table.
>>> spark.catalog.isCached("spark_catalog.default.tbl1")
True
>>> spark.catalog.uncacheTable("tbl1")
>>> _ = spark.sql("DROP TABLE tbl1")
"""
return self._jcatalog.isCached(tableName)
def cacheTable(self, tableName: str, storageLevel: Optional[StorageLevel] = None) -> None:
"""Caches the specified table in-memory or with given storage level.
Default MEMORY_AND_DISK.
.. versionadded:: 2.0.0
Parameters
----------
tableName : str
name of the table to get.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name.
storageLevel : :class:`StorageLevel`
storage level to set for persistence.
.. versionchanged:: 3.5.0
Allow to specify storage level.
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")
"""
if storageLevel:
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jcatalog.cacheTable(tableName, javaStorageLevel)
else:
self._jcatalog.cacheTable(tableName)
def uncacheTable(self, tableName: str) -> None:
"""Removes the specified table from the in-memory cache.
.. versionadded:: 2.0.0
Parameters
----------
tableName : str
name of the table to get.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name.
Examples
--------
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
>>> spark.catalog.cacheTable("tbl1")
>>> spark.catalog.uncacheTable("tbl1")
>>> spark.catalog.isCached("tbl1")
False
Throw an analysis exception when the table does not exist.
>>> spark.catalog.uncacheTable("not_existing_table")
Traceback (most recent call last):
...
AnalysisException: ...
Using the fully qualified name for the table.
>>> spark.catalog.uncacheTable("spark_catalog.default.tbl1")
>>> spark.catalog.isCached("tbl1")
False
>>> _ = spark.sql("DROP TABLE tbl1")
"""
self._jcatalog.uncacheTable(tableName)
def clearCache(self) -> None:
"""Removes all cached tables from the in-memory cache.
.. versionadded:: 2.0.0
Examples
--------
>>> _ = spark.sql("DROP TABLE IF EXISTS tbl1")
>>> _ = spark.sql("CREATE TABLE tbl1 (name STRING, age INT) USING parquet")
>>> spark.catalog.clearCache()
>>> spark.catalog.isCached("tbl1")
False
>>> _ = spark.sql("DROP TABLE tbl1")
"""
self._jcatalog.clearCache()
def refreshTable(self, tableName: str) -> None:
"""Invalidates and refreshes all the cached data and metadata of the given table.
.. versionadded:: 2.0.0
Parameters
----------
tableName : str
name of the table to get.
.. versionchanged:: 3.4.0
Allow ``tableName`` to be qualified with catalog name.
Examples
--------
The example below caches a table, and then removes the data.
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
... _ = spark.sql("DROP TABLE IF EXISTS tbl1")
... _ = spark.sql(
... "CREATE TABLE tbl1 (col STRING) USING TEXT LOCATION '{}'".format(d))
... _ = spark.sql("INSERT INTO tbl1 SELECT 'abc'")
... spark.catalog.cacheTable("tbl1")
... spark.table("tbl1").show()
+---+
|col|
+---+
|abc|
+---+
Because the table is cached, it computes from the cached data as below.
>>> spark.table("tbl1").count()
1
After refreshing the table, it shows 0 because the data does not exist anymore.
>>> spark.catalog.refreshTable("tbl1")
>>> spark.table("tbl1").count()
0
Using the fully qualified name for the table.
>>> spark.catalog.refreshTable("spark_catalog.default.tbl1")
>>> _ = spark.sql("DROP TABLE tbl1")
"""
self._jcatalog.refreshTable(tableName)
def recoverPartitions(self, tableName: str) -> None:
"""Recovers all the partitions of the given table and updates the catalog.
.. versionadded:: 2.1.1
Parameters
----------
tableName : str
name of the table to get.
Notes
-----
Only works with a partitioned table, and not a view.
Examples
--------
The example below creates a partitioned table against the existing directory of
the partitioned table. After that, it recovers the partitions.
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
... _ = spark.sql("DROP TABLE IF EXISTS tbl1")
... spark.range(1).selectExpr(
... "id as key", "id as value").write.partitionBy("key").mode("overwrite").save(d)
... _ = spark.sql(
... "CREATE TABLE tbl1 (key LONG, value LONG)"
... "USING parquet OPTIONS (path '{}') PARTITIONED BY (key)".format(d))
... spark.table("tbl1").show()
... spark.catalog.recoverPartitions("tbl1")
... spark.table("tbl1").show()
+-----+---+
|value|key|
+-----+---+
+-----+---+
+-----+---+
|value|key|
+-----+---+
| 0| 0|
+-----+---+
>>> _ = spark.sql("DROP TABLE tbl1")
"""
self._jcatalog.recoverPartitions(tableName)
def refreshByPath(self, path: str) -> None:
"""Invalidates and refreshes all the cached data (and the associated metadata) for any
DataFrame that contains the given data source path.
.. versionadded:: 2.2.0
Parameters
----------
path : str
the path to refresh the cache.
Examples
--------
The example below caches a table, and then removes the data.
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
... _ = spark.sql("DROP TABLE IF EXISTS tbl1")
... _ = spark.sql(
... "CREATE TABLE tbl1 (col STRING) USING TEXT LOCATION '{}'".format(d))
... _ = spark.sql("INSERT INTO tbl1 SELECT 'abc'")
... spark.catalog.cacheTable("tbl1")
... spark.table("tbl1").show()
+---+
|col|
+---+
|abc|
+---+
Because the table is cached, it computes from the cached data as below.
>>> spark.table("tbl1").count()
1
After refreshing the table by path, it shows 0 because the data does not exist anymore.
>>> spark.catalog.refreshByPath(d)
>>> spark.table("tbl1").count()
0
>>> _ = spark.sql("DROP TABLE tbl1")
"""
self._jcatalog.refreshByPath(path)
def _reset(self) -> None:
"""(Internal use only) Drop all existing databases (except "default"), tables,
partitions and functions, and set the current database to "default".
This is mainly used for tests.
"""
self._jsparkSession.sessionState().catalog().reset()
github源码
Exploring Spark Catalog
Catalog