# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys import warnings from typing import Any, Callable, NamedTuple, List, Optional, TYPE_CHECKING from pyspark import since from pyspark.sql.dataframe import DataFrame from pyspark.sql.session import SparkSession from pyspark.sql.types import StructType if TYPE_CHECKING: from pyspark.sql._typing import UserDefinedFunctionLike from pyspark.sql.types import DataType class Database(NamedTuple): name: str description: Optional[str] locationUri: str class Table(NamedTuple): name: str database: Optional[str] description: Optional[str] tableType: str isTemporary: bool class Column(NamedTuple): name: str description: Optional[str] dataType: str nullable: bool isPartition: bool isBucket: bool class Function(NamedTuple): name: str description: Optional[str] className: str isTemporary: bool [docs]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. """ def __init__(self, sparkSession: SparkSession) -> None: """Create a new Catalog that wraps the underlying JVM object.""" self._sparkSession = sparkSession self._jsparkSession = sparkSession._jsparkSession self._jcatalog = sparkSession._jsparkSession.catalog() [docs] @since(2.0) def currentDatabase(self) -> str: """Returns the current default database in this session.""" return self._jcatalog.currentDatabase() [docs] @since(2.0) def setCurrentDatabase(self, dbName: str) -> None: """Sets the current default database in this session.""" return self._jcatalog.setCurrentDatabase(dbName) [docs] @since(2.0) def listDatabases(self) -> List[Database]: """Returns a list of databases available across all sessions.""" iter = self._jcatalog.listDatabases().toLocalIterator() databases = [] while iter.hasNext(): jdb = iter.next() databases.append( Database( name=jdb.name(), description=jdb.description(), locationUri=jdb.locationUri() ) ) return databases [docs] 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 Returns ------- bool Indicating whether the database exists Examples -------- >>> spark.catalog.databaseExists("test_new_database") False >>> df = spark.sql("CREATE DATABASE test_new_database") >>> spark.catalog.databaseExists("test_new_database") True >>> df = spark.sql("DROP DATABASE test_new_database") """ return self._jcatalog.databaseExists(dbName) [docs] @since(2.0) def listTables(self, dbName: Optional[str] = None) -> List[Table]: """Returns a list of tables/views in the specified database. If no database is specified, the current database is used. This includes all temporary views. """ if dbName is None: dbName = self.currentDatabase() iter = self._jcatalog.listTables(dbName).toLocalIterator() tables = [] while iter.hasNext(): jtable = iter.next() tables.append( Table( name=jtable.name(), database=jtable.database(), description=jtable.description(), tableType=jtable.tableType(), isTemporary=jtable.isTemporary(), ) ) return tables [docs] @since(2.0) def listFunctions(self, dbName: Optional[str] = None) -> List[Function]: """Returns a list of functions registered in the specified database. If no database is specified, the current database is used. This includes all temporary functions. """ if dbName is None: dbName = self.currentDatabase() iter = self._jcatalog.listFunctions(dbName).toLocalIterator() functions = [] while iter.hasNext(): jfunction = iter.next() functions.append( Function( name=jfunction.name(), description=jfunction.description(), className=jfunction.className(), isTemporary=jfunction.isTemporary(), ) ) return functions [docs] 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 dbName : str, optional name of the database to check function existence in. If no database is specified, the current database is used Returns ------- bool Indicating whether the function exists Examples -------- >>> spark.catalog.functionExists("unexisting_function") False """ if dbName is None: dbName = self.currentDatabase() return self._jcatalog.functionExists(dbName, functionName) [docs] 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. If no database is specified, the current database is used. .. versionadded:: 2.0.0 Notes ----- the order of arguments here is different from that of its JVM counterpart because Python does not support method overloading. """ if dbName is None: dbName = self.currentDatabase() 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 [docs] 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 dbName : str, optional name of the database to check table existence in. If no database is specified, the current database is used 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 >>> df = spark.sql("CREATE TABLE tab1 (name STRING, age INT) USING parquet") >>> spark.catalog.tableExists("tab1") True >>> df = spark.sql("DROP TABLE tab1") >>> spark.catalog.tableExists("unexisting_table") False It also works for views: >>> spark.catalog.tableExists("view1") False >>> df = spark.sql("CREATE VIEW view1 AS SELECT 1") >>> spark.catalog.tableExists("view1") True >>> df = spark.sql("DROP VIEW view1") >>> spark.catalog.tableExists("view1") False And also for temporary views: >>> df = 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 """ return self._jcatalog.tableExists(dbName, tableName) [docs] 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) [docs] 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. It returns the DataFrame associated with the 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. When ``path`` is specified, an external table is created from the data at the given path. Otherwise a managed table is created. Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and created table. .. versionadded:: 2.2.0 Returns ------- :class:`DataFrame` .. versionchanged:: 3.1 Added the ``description`` parameter. """ 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) [docs] def dropTempView(self, viewName: str) -> None: """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 Notes ----- The return type of this method was None in Spark 2.0, but changed to Boolean in Spark 2.1. Examples -------- >>> spark.createDataFrame([(1, 1)]).createTempView("my_table") >>> spark.table("my_table").collect() [Row(_1=1, _2=1)] >>> spark.catalog.dropTempView("my_table") True >>> spark.table("my_table") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... AnalysisException: ... """ return self._jcatalog.dropTempView(viewName) [docs] def dropGlobalTempView(self, viewName: str) -> None: """Drops the global 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:: 2.1.0 Examples -------- >>> spark.createDataFrame([(1, 1)]).createGlobalTempView("my_table") >>> spark.table("global_temp.my_table").collect() [Row(_1=1, _2=1)] >>> spark.catalog.dropGlobalTempView("my_table") True >>> spark.table("global_temp.my_table") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... AnalysisException: ... """ return self._jcatalog.dropGlobalTempView(viewName) [docs] 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. """ warnings.warn("Deprecated in 2.3.0. Use spark.udf.register instead.", FutureWarning) return self._sparkSession.udf.register(name, f, returnType) [docs] @since(2.0) def isCached(self, tableName: str) -> bool: """Returns true if the table is currently cached in-memory.""" return self._jcatalog.isCached(tableName) [docs] @since(2.0) def cacheTable(self, tableName: str) -> None: """Caches the specified table in-memory.""" self._jcatalog.cacheTable(tableName) [docs] @since(2.0) def uncacheTable(self, tableName: str) -> None: """Removes the specified table from the in-memory cache.""" self._jcatalog.uncacheTable(tableName) [docs] @since(2.0) def clearCache(self) -> None: """Removes all cached tables from the in-memory cache.""" self._jcatalog.clearCache() [docs] @since(2.0) def refreshTable(self, tableName: str) -> None: """Invalidates and refreshes all the cached data and metadata of the given table.""" self._jcatalog.refreshTable(tableName) [docs] @since("2.1.1") def recoverPartitions(self, tableName: str) -> None: """Recovers all the partitions of the given table and update the catalog. Only works with a partitioned table, and not a view. """ self._jcatalog.recoverPartitions(tableName) [docs] @since("2.2.0") 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. """ 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() def _test() -> None: import os import doctest from pyspark.sql import SparkSession import pyspark.sql.catalog os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.sql.catalog.__dict__.copy() spark = SparkSession.builder.master("local[4]").appName("sql.catalog tests").getOrCreate() globs["sc"] = spark.sparkContext globs["spark"] = spark (failure_count, test_count) = doctest.testmod( pyspark.sql.catalog, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE, ) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()