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import os
import sys
import tempfile
import warnings
from abc import ABCMeta, abstractmethod
from typing import Union, List, Tuple, Iterable

from py4j.java_gateway import get_java_class, get_method

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table.sources import TableSource

from pyflink.common.typeinfo import TypeInformation, WrapperTypeInfo
from pyflink.datastream.data_stream import DataStream

from pyflink.common import JobExecutionResult
from pyflink.dataset import ExecutionEnvironment
from pyflink.java_gateway import get_gateway
from pyflink.serializers import BatchedSerializer, PickleSerializer
from pyflink.table import Table, EnvironmentSettings, Module, Expression, ExplainDetail, TableSink
from pyflink.table.catalog import Catalog
from pyflink.table.descriptors import StreamTableDescriptor, BatchTableDescriptor, \
    ConnectorDescriptor, ConnectTableDescriptor
from pyflink.table.serializers import ArrowSerializer
from pyflink.table.statement_set import StatementSet
from pyflink.table.table_config import TableConfig
from pyflink.table.table_result import TableResult
from pyflink.table.types import _to_java_type, _create_type_verifier, RowType, DataType, \
    _infer_schema_from_data, _create_converter, from_arrow_type, RowField, create_arrow_schema, \
    _to_java_data_type
from pyflink.table.udf import UserDefinedFunctionWrapper, AggregateFunction, udaf, \
    UserDefinedAggregateFunctionWrapper
from pyflink.table.utils import to_expression_jarray
from pyflink.util import utils
from pyflink.util.utils import get_j_env_configuration, is_local_deployment, load_java_class, \
    to_j_explain_detail_arr

__all__ = [
    'BatchTableEnvironment',
    'StreamTableEnvironment',
    'TableEnvironment'
]


class TableEnvironment(object, metaclass=ABCMeta):
    """
    A table environment is the base class, entry point, and central context for creating Table
    and SQL API programs.

    It is unified for bounded and unbounded data processing.

    A table environment is responsible for:

        - Connecting to external systems.
        - Registering and retrieving :class:`~pyflink.table.Table` and other meta objects from a
          catalog.
        - Executing SQL statements.
        - Offering further configuration options.

    The path in methods such as :func:`create_temporary_view`
    should be a proper SQL identifier. The syntax is following
    [[catalog-name.]database-name.]object-name, where the catalog name and database are optional.
    For path resolution see :func:`use_catalog` and :func:`use_database`. All keywords or other
    special characters need to be escaped.

    Example: `cat.1`.`db`.`Table` resolves to an object named 'Table' (table is a reserved
    keyword, thus must be escaped) in a catalog named 'cat.1' and database named 'db'.

    .. note::

        This environment is meant for pure table programs. If you would like to convert from or to
        other Flink APIs, it might be necessary to use one of the available language-specific table
        environments in the corresponding bridging modules.

    """

    def __init__(self, j_tenv, serializer=PickleSerializer()):
        self._j_tenv = j_tenv
        self._is_blink_planner = TableEnvironment._judge_blink_planner(j_tenv)
        self._serializer = serializer
        # When running in MiniCluster, launch the Python UDF worker using the Python executable
        # specified by sys.executable if users have not specified it explicitly via configuration
        # python.executable.
        self._set_python_executable_for_local_executor()

    @staticmethod
    def _judge_blink_planner(j_tenv):
        if "getPlanner" not in dir(j_tenv):
            return False
        else:
            j_planner_class = j_tenv.getPlanner().getClass()
            j_blink_planner_class = get_java_class(
                get_gateway().jvm.org.apache.flink.table.planner.delegation.PlannerBase)
            return j_blink_planner_class.isAssignableFrom(j_planner_class)

    def from_table_source(self, table_source: 'TableSource') -> 'Table':
        """
        Creates a table from a table source.

        Example:
        ::

            >>> csv_table_source = CsvTableSource(
            ...     csv_file_path, ['a', 'b'], [DataTypes.STRING(), DataTypes.BIGINT()])
            >>> table_env.from_table_source(csv_table_source)

        :param table_source: The table source used as table.
        :return: The result table.
        """
        warnings.warn("Deprecated in 1.11.", DeprecationWarning)
        return Table(self._j_tenv.fromTableSource(table_source._j_table_source), self)

    def register_catalog(self, catalog_name: str, catalog: Catalog):
        """
        Registers a :class:`~pyflink.table.catalog.Catalog` under a unique name.
        All tables registered in the :class:`~pyflink.table.catalog.Catalog` can be accessed.

        :param catalog_name: The name under which the catalog will be registered.
        :param catalog: The catalog to register.
        """
        self._j_tenv.registerCatalog(catalog_name, catalog._j_catalog)

    def get_catalog(self, catalog_name: str) -> Catalog:
        """
        Gets a registered :class:`~pyflink.table.catalog.Catalog` by name.

        :param catalog_name: The name to look up the :class:`~pyflink.table.catalog.Catalog`.
        :return: The requested catalog, None if there is no
                 registered catalog with given name.
        """
        catalog = self._j_tenv.getCatalog(catalog_name)
        if catalog.isPresent():
            return Catalog(catalog.get())
        else:
            return None

    def load_module(self, module_name: str, module: Module):
        """
        Loads a :class:`~pyflink.table.Module` under a unique name. Modules will be kept
        in the loaded order.
        ValidationException is thrown when there is already a module with the same name.

        :param module_name: Name of the :class:`~pyflink.table.Module`.
        :param module: The module instance.

        .. versionadded:: 1.12.0
        """
        self._j_tenv.loadModule(module_name, module._j_module)

    def unload_module(self, module_name: str):
        """
        Unloads a :class:`~pyflink.table.Module` with given name.
        ValidationException is thrown when there is no module with the given name.

        :param module_name: Name of the :class:`~pyflink.table.Module`.

        .. versionadded:: 1.12.0
        """
        self._j_tenv.unloadModule(module_name)

    def create_java_temporary_system_function(self, name: str, function_class_name: str):
        """
        Registers a java user defined function class as a temporary system function.

        Compared to .. seealso:: :func:`create_java_temporary_function`, system functions are
        identified by a global name that is independent of the current catalog and current
        database. Thus, this method allows to extend the set of built-in system functions like
        TRIM, ABS, etc.

        Temporary functions can shadow permanent ones. If a permanent function under a given name
        exists, it will be inaccessible in the current session. To make the permanent function
        available again one can drop the corresponding temporary system function.

        Example:
        ::

            >>> table_env.create_java_temporary_system_function("func",
            ...     "java.user.defined.function.class.name")

        :param name: The name under which the function will be registered globally.
        :param function_class_name: The java full qualified class name of the function class
                                    containing the implementation. The function must have a
                                    public no-argument constructor and can be founded in current
                                    Java classloader.

        .. versionadded:: 1.12.0
        """
        gateway = get_gateway()
        java_function = gateway.jvm.Thread.currentThread().getContextClassLoader() \
            .loadClass(function_class_name)
        self._j_tenv.createTemporarySystemFunction(name, java_function)

    def create_temporary_system_function(self, name: str,
                                         function: Union[UserDefinedFunctionWrapper,
                                                         AggregateFunction]):
        """
        Registers a python user defined function class as a temporary system function.

        Compared to .. seealso:: :func:`create_temporary_function`, system functions are identified
        by a global name that is independent of the current catalog and current database. Thus,
        this method allows to extend the set of built-in system functions like TRIM, ABS, etc.

        Temporary functions can shadow permanent ones. If a permanent function under a given name
        exists, it will be inaccessible in the current session. To make the permanent function
        available again one can drop the corresponding temporary system function.

        Example:
        ::

            >>> table_env.create_temporary_system_function(
            ...     "add_one", udf(lambda i: i + 1, result_type=DataTypes.BIGINT()))

            >>> @udf(result_type=DataTypes.BIGINT())
            ... def add(i, j):
            ...     return i + j
            >>> table_env.create_temporary_system_function("add", add)

            >>> class SubtractOne(ScalarFunction):
            ...     def eval(self, i):
            ...         return i - 1
            >>> table_env.create_temporary_system_function(
            ...     "subtract_one", udf(SubtractOne(), result_type=DataTypes.BIGINT()))

        :param name: The name under which the function will be registered globally.
        :param function: The function class containing the implementation. The function must have a
                         public no-argument constructor and can be founded in current Java
                         classloader.

        .. versionadded:: 1.12.0
        """
        function = self._wrap_aggregate_function_if_needed(function)
        java_function = function.java_user_defined_function()
        self._j_tenv.createTemporarySystemFunction(name, java_function)

    def drop_temporary_system_function(self, name: str) -> bool:
        """
        Drops a temporary system function registered under the given name.

        If a permanent function with the given name exists, it will be used from now on for any
        queries that reference this name.

        :param name: The name under which the function has been registered globally.
        :return: true if a function existed under the given name and was removed.

        .. versionadded:: 1.12.0
        """
        return self._j_tenv.dropTemporarySystemFunction(name)

    def create_java_function(self, path: str, function_class_name: str,
                             ignore_if_exists: bool = None):
        """
        Registers a java user defined function class as a catalog function in the given path.

        Compared to system functions with a globally defined name, catalog functions are always
        (implicitly or explicitly) identified by a catalog and database.

        There must not be another function (temporary or permanent) registered under the same path.

        Example:
        ::

            >>> table_env.create_java_function("func", "java.user.defined.function.class.name")

        :param path: The path under which the function will be registered.
                     See also the :class:`~pyflink.table.TableEnvironment` class description for
                     the format of the path.
        :param function_class_name: The java full qualified class name of the function class
                                    containing the implementation. The function must have a
                                    public no-argument constructor and can be founded in current
                                    Java classloader.
        :param ignore_if_exists: If a function exists under the given path and this flag is set,
                                 no operation is executed. An exception is thrown otherwise.

        .. versionadded:: 1.12.0
        """
        gateway = get_gateway()
        java_function = gateway.jvm.Thread.currentThread().getContextClassLoader() \
            .loadClass(function_class_name)
        if ignore_if_exists is None:
            self._j_tenv.createFunction(path, java_function)
        else:
            self._j_tenv.createFunction(path, java_function, ignore_if_exists)

    def drop_function(self, path: str) -> bool:
        """
        Drops a catalog function registered in the given path.

        :param path: The path under which the function will be registered.
                     See also the :class:`~pyflink.table.TableEnvironment` class description for
                     the format of the path.
        :return: true if a function existed in the given path and was removed.

        .. versionadded:: 1.12.0
        """
        return self._j_tenv.dropFunction(path)

    def create_java_temporary_function(self, path: str, function_class_name: str):
        """
        Registers a java user defined function class as a temporary catalog function.

        Compared to .. seealso:: :func:`create_java_temporary_system_function` with a globally
        defined name, catalog functions are always (implicitly or explicitly) identified by a
        catalog and database.

        Temporary functions can shadow permanent ones. If a permanent function under a given name
        exists, it will be inaccessible in the current session. To make the permanent function
        available again one can drop the corresponding temporary function.

        Example:
        ::

            >>> table_env.create_java_temporary_function("func",
            ...     "java.user.defined.function.class.name")

        :param path: The path under which the function will be registered.
                     See also the :class:`~pyflink.table.TableEnvironment` class description for
                     the format of the path.
        :param function_class_name: The java full qualified class name of the function class
                                    containing the implementation. The function must have a
                                    public no-argument constructor and can be founded in current
                                    Java classloader.

        .. versionadded:: 1.12.0
        """
        gateway = get_gateway()
        java_function = gateway.jvm.Thread.currentThread().getContextClassLoader() \
            .loadClass(function_class_name)
        self._j_tenv.createTemporaryFunction(path, java_function)

    def create_temporary_function(self, path: str, function: Union[UserDefinedFunctionWrapper,
                                                                   AggregateFunction]):
        """
        Registers a python user defined function class as a temporary catalog function.

        Compared to .. seealso:: :func:`create_temporary_system_function` with a globally defined
        name, catalog functions are always (implicitly or explicitly) identified by a catalog and
        database.

        Temporary functions can shadow permanent ones. If a permanent function under a given name
        exists, it will be inaccessible in the current session. To make the permanent function
        available again one can drop the corresponding temporary function.

        Example:
        ::

            >>> table_env.create_temporary_function(
            ...     "add_one", udf(lambda i: i + 1, result_type=DataTypes.BIGINT()))

            >>> @udf(result_type=DataTypes.BIGINT())
            ... def add(i, j):
            ...     return i + j
            >>> table_env.create_temporary_function("add", add)

            >>> class SubtractOne(ScalarFunction):
            ...     def eval(self, i):
            ...         return i - 1
            >>> table_env.create_temporary_function(
            ...     "subtract_one", udf(SubtractOne(), result_type=DataTypes.BIGINT()))

        :param path: The path under which the function will be registered.
                     See also the :class:`~pyflink.table.TableEnvironment` class description for
                     the format of the path.
        :param function: The function class containing the implementation. The function must have a
                         public no-argument constructor and can be founded in current Java
                         classloader.

        .. versionadded:: 1.12.0
        """
        function = self._wrap_aggregate_function_if_needed(function)
        java_function = function.java_user_defined_function()
        self._j_tenv.createTemporaryFunction(path, java_function)

    def drop_temporary_function(self, path: str) -> bool:
        """
        Drops a temporary system function registered under the given name.

        If a permanent function with the given name exists, it will be used from now on for any
        queries that reference this name.

        :param path: The path under which the function will be registered.
                     See also the :class:`~pyflink.table.TableEnvironment` class description for
                     the format of the path.
        :return: true if a function existed in the given path and was removed.

        .. versionadded:: 1.12.0
        """
        return self._j_tenv.dropTemporaryFunction(path)

    def register_table(self, name: str, table: Table):
        """
        Registers a :class:`~pyflink.table.Table` under a unique name in the TableEnvironment's
        catalog. Registered tables can be referenced in SQL queries.

        Example:
        ::

            >>> tab = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['a', 'b'])
            >>> table_env.register_table("source", tab)

        :param name: The name under which the table will be registered.
        :param table: The table to register.

        .. note:: Deprecated in 1.10. Use :func:`create_temporary_view` instead.
        """
        warnings.warn("Deprecated in 1.10. Use create_temporary_view instead.", DeprecationWarning)
        self._j_tenv.registerTable(name, table._j_table)

    def register_table_source(self, name: str, table_source: TableSource):
        """
        Registers an external :class:`~pyflink.table.TableSource` in this
        :class:`~pyflink.table.TableEnvironment`'s catalog. Registered tables can be referenced in
        SQL queries.

        Example:
        ::

            >>> table_env.register_table_source("source",
            ...                                 CsvTableSource("./1.csv",
            ...                                                ["a", "b"],
            ...                                                [DataTypes.INT(),
            ...                                                 DataTypes.STRING()]))

        :param name: The name under which the table source is registered.
        :param table_source: The table source to register.

        .. note:: Deprecated in 1.10. Use :func:`execute_sql` instead.
        """
        warnings.warn("Deprecated in 1.10. Use connect instead.", DeprecationWarning)
        self._j_tenv.registerTableSourceInternal(name, table_source._j_table_source)

    def register_table_sink(self, name: str, table_sink: TableSink):
        """
        Registers an external :class:`~pyflink.table.TableSink` with given field names and types in
        this :class:`~pyflink.table.TableEnvironment`'s catalog. Registered sink tables can be
        referenced in SQL DML statements.

        Example:
        ::

            >>> table_env.register_table_sink("sink",
            ...                               CsvTableSink(["a", "b"],
            ...                                            [DataTypes.INT(),
            ...                                             DataTypes.STRING()],
            ...                                            "./2.csv"))

        :param name: The name under which the table sink is registered.
        :param table_sink: The table sink to register.

        .. note:: Deprecated in 1.10. Use :func:`execute_sql` instead.
        """
        warnings.warn("Deprecated in 1.10. Use connect instead.", DeprecationWarning)
        self._j_tenv.registerTableSinkInternal(name, table_sink._j_table_sink)

    def scan(self, *table_path: str) -> Table:
        """
        Scans a registered table and returns the resulting :class:`~pyflink.table.Table`.
        A table to scan must be registered in the TableEnvironment. It can be either directly
        registered or be an external member of a :class:`~pyflink.table.catalog.Catalog`.

        See the documentation of :func:`~pyflink.table.TableEnvironment.use_database` or
        :func:`~pyflink.table.TableEnvironment.use_catalog` for the rules on the path resolution.

        Examples:

        Scanning a directly registered table
        ::

            >>> tab = table_env.scan("tableName")

        Scanning a table from a registered catalog
        ::

            >>> tab = table_env.scan("catalogName", "dbName", "tableName")

        :param table_path: The path of the table to scan.
        :throws: Exception if no table is found using the given table path.
        :return: The resulting table.

        .. note:: Deprecated in 1.10. Use :func:`from_path` instead.
        """
        warnings.warn("Deprecated in 1.10. Use from_path instead.", DeprecationWarning)
        gateway = get_gateway()
        j_table_paths = utils.to_jarray(gateway.jvm.String, table_path)
        j_table = self._j_tenv.scan(j_table_paths)
        return Table(j_table, self)

    def from_path(self, path: str) -> Table:
        """
        Reads a registered table and returns the resulting :class:`~pyflink.table.Table`.

        A table to scan must be registered in the :class:`~pyflink.table.TableEnvironment`.

        See the documentation of :func:`use_database` or :func:`use_catalog` for the rules on the
        path resolution.

        Examples:

        Reading a table from default catalog and database.
        ::

            >>> tab = table_env.from_path("tableName")

        Reading a table from a registered catalog.
        ::

            >>> tab = table_env.from_path("catalogName.dbName.tableName")

        Reading a table from a registered catalog with escaping. (`Table` is a reserved keyword).
        Dots in e.g. a database name also must be escaped.
        ::

            >>> tab = table_env.from_path("catalogName.`db.Name`.`Table`")

        :param path: The path of a table API object to scan.
        :return: Either a table or virtual table (=view).

        .. seealso:: :func:`use_catalog`
        .. seealso:: :func:`use_database`
        .. versionadded:: 1.10.0
        """
        return Table(get_method(self._j_tenv, "from")(path), self)

    def insert_into(self, target_path: str, table: Table):
        """
        Instructs to write the content of a :class:`~pyflink.table.Table` API object into a table.

        See the documentation of :func:`use_database` or :func:`use_catalog` for the rules on the
        path resolution.

        Example:
        ::

            >>> tab = table_env.scan("tableName")
            >>> table_env.insert_into("sink", tab)

        :param target_path: The path of the registered :class:`~pyflink.table.TableSink` to which
                            the :class:`~pyflink.table.Table` is written.
        :param table: The Table to write to the sink.

        .. versionchanged:: 1.10.0
            The signature is changed, e.g. the parameter *table_path_continued* was removed and
            the parameter *target_path* is moved before the parameter *table*.

        .. note:: Deprecated in 1.11. Use :func:`execute_insert` for single sink,
                  use :func:`create_statement_set` for multiple sinks.
        """
        warnings.warn("Deprecated in 1.11. Use Table#execute_insert for single sink,"
                      "use create_statement_set for multiple sinks.", DeprecationWarning)
        self._j_tenv.insertInto(target_path, table._j_table)

    def list_catalogs(self) -> List[str]:
        """
        Gets the names of all catalogs registered in this environment.

        :return: List of catalog names.
        """
        j_catalog_name_array = self._j_tenv.listCatalogs()
        return [item for item in j_catalog_name_array]

    def list_modules(self) -> List[str]:
        """
        Gets the names of all modules registered in this environment.

        :return: List of module names.

        .. versionadded:: 1.10.0
        """
        j_module_name_array = self._j_tenv.listModules()
        return [item for item in j_module_name_array]

    def list_databases(self) -> List[str]:
        """
        Gets the names of all databases in the current catalog.

        :return: List of database names in the current catalog.
        """
        j_database_name_array = self._j_tenv.listDatabases()
        return [item for item in j_database_name_array]

    def list_tables(self) -> List[str]:
        """
        Gets the names of all tables and views in the current database of the current catalog.
        It returns both temporary and permanent tables and views.

        :return: List of table and view names in the current database of the current catalog.
        """
        j_table_name_array = self._j_tenv.listTables()
        return [item for item in j_table_name_array]

    def list_views(self) -> List[str]:
        """
        Gets the names of all views in the current database of the current catalog.
        It returns both temporary and permanent views.

        :return: List of view names in the current database of the current catalog.

        .. versionadded:: 1.11.0
        """
        j_view_name_array = self._j_tenv.listViews()
        return [item for item in j_view_name_array]

    def list_user_defined_functions(self) -> List[str]:
        """
        Gets the names of all user defined functions registered in this environment.

        :return: List of the names of all user defined functions registered in this environment.
        """
        j_udf_name_array = self._j_tenv.listUserDefinedFunctions()
        return [item for item in j_udf_name_array]

    def list_functions(self) -> List[str]:
        """
        Gets the names of all functions in this environment.

        :return: List of the names of all functions in this environment.

        .. versionadded:: 1.10.0
        """
        j_function_name_array = self._j_tenv.listFunctions()
        return [item for item in j_function_name_array]

    def list_temporary_tables(self) -> List[str]:
        """
        Gets the names of all temporary tables and views available in the current namespace
        (the current database of the current catalog).

        :return: A list of the names of all registered temporary tables and views in the current
                 database of the current catalog.

        .. seealso:: :func:`list_tables`
        .. versionadded:: 1.10.0
        """
        j_table_name_array = self._j_tenv.listTemporaryTables()
        return [item for item in j_table_name_array]

    def list_temporary_views(self) -> List[str]:
        """
        Gets the names of all temporary views available in the current namespace (the current
        database of the current catalog).

        :return: A list of the names of all registered temporary views in the current database
                 of the current catalog.

        .. seealso:: :func:`list_tables`
        .. versionadded:: 1.10.0
        """
        j_view_name_array = self._j_tenv.listTemporaryViews()
        return [item for item in j_view_name_array]

    def drop_temporary_table(self, table_path: str) -> bool:
        """
        Drops a temporary table registered in the given path.

        If a permanent table with a given path exists, it will be used
        from now on for any queries that reference this path.

        :param table_path: The path of the registered temporary table.
        :return: True if a table existed in the given path and was removed.

        .. versionadded:: 1.10.0
        """
        return self._j_tenv.dropTemporaryTable(table_path)

    def drop_temporary_view(self, view_path: str) -> bool:
        """
        Drops a temporary view registered in the given path.

        If a permanent table or view with a given path exists, it will be used
        from now on for any queries that reference this path.

        :return: True if a view existed in the given path and was removed.

        .. versionadded:: 1.10.0
        """
        return self._j_tenv.dropTemporaryView(view_path)

    def explain(self, table: Table = None, extended: bool = False) -> str:
        """
        Returns the AST of the specified Table API and SQL queries and the execution plan to compute
        the result of the given :class:`~pyflink.table.Table` or multi-sinks plan.

        :param table: The table to be explained. If table is None, explain for multi-sinks plan,
                      else for given table.
        :param extended: If the plan should contain additional properties.
                         e.g. estimated cost, traits
        :return: The table for which the AST and execution plan will be returned.

        .. note:: Deprecated in 1.11. Use :class:`Table`#:func:`explain` instead.
        """
        warnings.warn("Deprecated in 1.11. Use Table#explain instead.", DeprecationWarning)
        if table is None:
            return self._j_tenv.explain(extended)
        else:
            return self._j_tenv.explain(table._j_table, extended)

    def explain_sql(self, stmt: str, *extra_details: ExplainDetail) -> str:
        """
        Returns the AST of the specified statement and the execution plan.

        :param stmt: The statement for which the AST and execution plan will be returned.
        :param extra_details: The extra explain details which the explain result should include,
                              e.g. estimated cost, changelog mode for streaming
        :return: The statement for which the AST and execution plan will be returned.

        .. versionadded:: 1.11.0
        """

        j_extra_details = to_j_explain_detail_arr(extra_details)
        return self._j_tenv.explainSql(stmt, j_extra_details)

    def sql_query(self, query: str) -> Table:
        """
        Evaluates a SQL query on registered tables and retrieves the result as a
        :class:`~pyflink.table.Table`.

        All tables referenced by the query must be registered in the TableEnvironment.

        A :class:`~pyflink.table.Table` is automatically registered when its
        :func:`~Table.__str__` method is called, for example when it is embedded into a String.

        Hence, SQL queries can directly reference a :class:`~pyflink.table.Table` as follows:
        ::

            >>> table = ...
            # the table is not registered to the table environment
            >>> table_env.sql_query("SELECT * FROM %s" % table)

        :param query: The sql query string.
        :return: The result table.
        """
        j_table = self._j_tenv.sqlQuery(query)
        return Table(j_table, self)

    def execute_sql(self, stmt: str) -> TableResult:
        """
        Execute the given single statement, and return the execution result.

        The statement can be DDL/DML/DQL/SHOW/DESCRIBE/EXPLAIN/USE.
        For DML and DQL, this method returns TableResult once the job has been submitted.
        For DDL and DCL statements, TableResult is returned once the operation has finished.

        :return content for DQL/SHOW/DESCRIBE/EXPLAIN,
                the affected row count for `DML` (-1 means unknown),
                or a string message ("OK") for other statements.

        .. versionadded:: 1.11.0
        """
        self._before_execute()
        return TableResult(self._j_tenv.executeSql(stmt))

    def create_statement_set(self) -> StatementSet:
        """
        Create a StatementSet instance which accepts DML statements or Tables,
        the planner can optimize all added statements and Tables together
        and then submit as one job.

        :return statement_set instance

        .. versionadded:: 1.11.0
        """
        _j_statement_set = self._j_tenv.createStatementSet()
        return StatementSet(_j_statement_set, self)

    def sql_update(self, stmt: str):
        """
        Evaluates a SQL statement such as INSERT, UPDATE or DELETE or a DDL statement

        .. note::

            Currently only SQL INSERT statements and CREATE TABLE statements are supported.

        All tables referenced by the query must be registered in the TableEnvironment.
        A :class:`~pyflink.table.Table` is automatically registered when its
        :func:`~Table.__str__` method is called, for example when it is embedded into a String.
        Hence, SQL queries can directly reference a :class:`~pyflink.table.Table` as follows:
        ::

            # register the table sink into which the result is inserted.
            >>> table_env.register_table_sink("sink_table", table_sink)
            >>> source_table = ...
            # source_table is not registered to the table environment
            >>> table_env.sql_update("INSERT INTO sink_table SELECT * FROM %s" % source_table)

        A DDL statement can also be executed to create/drop a table:
        For example, the below DDL statement would create a CSV table named `tbl1`
        into the current catalog::

            create table tbl1(
                a int,
                b bigint,
                c varchar
            ) with (
                'connector.type' = 'filesystem',
                'format.type' = 'csv',
                'connector.path' = 'xxx'
            )

        SQL queries can directly execute as follows:
        ::

            >>> source_ddl = \\
            ... '''
            ... create table sourceTable(
            ...     a int,
            ...     b varchar
            ... ) with (
            ...     'connector.type' = 'kafka',
            ...     'update-mode' = 'append',
            ...     'connector.topic' = 'xxx',
            ...     'connector.properties.bootstrap.servers' = 'localhost:9092'
            ... )
            ... '''

            >>> sink_ddl = \\
            ... '''
            ... create table sinkTable(
            ...     a int,
            ...     b varchar
            ... ) with (
            ...     'connector.type' = 'filesystem',
            ...     'format.type' = 'csv',
            ...     'connector.path' = 'xxx'
            ... )
            ... '''

            >>> query = "INSERT INTO sinkTable SELECT FROM sourceTable"
            >>> table_env.sql(source_ddl)
            >>> table_env.sql(sink_ddl)
            >>> table_env.sql(query)
            >>> table_env.execute("MyJob")

        :param stmt: The SQL statement to evaluate.

        .. note:: Deprecated in 1.11. Use :func:`execute_sql` for single statement,
                  use :func:`create_statement_set` for multiple DML statements.
        """
        warnings.warn("Deprecated in 1.11. Use execute_sql for single statement, "
                      "use create_statement_set for multiple DML statements.", DeprecationWarning)
        self._j_tenv.sqlUpdate(stmt)

    def get_current_catalog(self) -> str:
        """
        Gets the current default catalog name of the current session.

        :return: The current default catalog name that is used for the path resolution.

        .. seealso:: :func:`~pyflink.table.TableEnvironment.use_catalog`
        """
        return self._j_tenv.getCurrentCatalog()

    def use_catalog(self, catalog_name: str):
        """
        Sets the current catalog to the given value. It also sets the default
        database to the catalog's default one.
        See also :func:`~TableEnvironment.use_database`.

        This is used during the resolution of object paths. Both the catalog and database are
        optional when referencing catalog objects such as tables, views etc. The algorithm looks for
        requested objects in following paths in that order:

        * ``[current-catalog].[current-database].[requested-path]``
        * ``[current-catalog].[requested-path]``
        * ``[requested-path]``

        Example:

        Given structure with default catalog set to ``default_catalog`` and default database set to
        ``default_database``. ::

            root:
              |- default_catalog
                  |- default_database
                      |- tab1
                  |- db1
                      |- tab1
              |- cat1
                  |- db1
                      |- tab1

        The following table describes resolved paths:

        +----------------+-----------------------------------------+
        | Requested path |             Resolved path               |
        +================+=========================================+
        | tab1           | default_catalog.default_database.tab1   |
        +----------------+-----------------------------------------+
        | db1.tab1       | default_catalog.db1.tab1                |
        +----------------+-----------------------------------------+
        | cat1.db1.tab1  | cat1.db1.tab1                           |
        +----------------+-----------------------------------------+

        :param catalog_name: The name of the catalog to set as the current default catalog.
        :throws: :class:`~pyflink.util.exceptions.CatalogException` thrown if a catalog with given
                 name could not be set as the default one.

        .. seealso:: :func:`~pyflink.table.TableEnvironment.use_database`
        """
        self._j_tenv.useCatalog(catalog_name)

    def get_current_database(self) -> str:
        """
        Gets the current default database name of the running session.

        :return: The name of the current database of the current catalog.

        .. seealso:: :func:`~pyflink.table.TableEnvironment.use_database`
        """
        return self._j_tenv.getCurrentDatabase()

    def use_database(self, database_name: str):
        """
        Sets the current default database. It has to exist in the current catalog. That path will
        be used as the default one when looking for unqualified object names.

        This is used during the resolution of object paths. Both the catalog and database are
        optional when referencing catalog objects such as tables, views etc. The algorithm looks for
        requested objects in following paths in that order:

        * ``[current-catalog].[current-database].[requested-path]``
        * ``[current-catalog].[requested-path]``
        * ``[requested-path]``

        Example:

        Given structure with default catalog set to ``default_catalog`` and default database set to
        ``default_database``. ::

            root:
              |- default_catalog
                  |- default_database
                      |- tab1
                  |- db1
                      |- tab1
              |- cat1
                  |- db1
                      |- tab1

        The following table describes resolved paths:

        +----------------+-----------------------------------------+
        | Requested path |             Resolved path               |
        +================+=========================================+
        | tab1           | default_catalog.default_database.tab1   |
        +----------------+-----------------------------------------+
        | db1.tab1       | default_catalog.db1.tab1                |
        +----------------+-----------------------------------------+
        | cat1.db1.tab1  | cat1.db1.tab1                           |
        +----------------+-----------------------------------------+

        :throws: :class:`~pyflink.util.exceptions.CatalogException` thrown if the given catalog and
                 database could not be set as the default ones.

        .. seealso:: :func:`~pyflink.table.TableEnvironment.use_catalog`

        :param database_name: The name of the database to set as the current database.
        """
        self._j_tenv.useDatabase(database_name)

    def get_config(self) -> TableConfig:
        """
        Returns the table config to define the runtime behavior of the Table API.

        :return: Current table config.
        """
        if not hasattr(self, "table_config"):
            table_config = TableConfig()
            table_config._j_table_config = self._j_tenv.getConfig()
            setattr(self, "table_config", table_config)
        return getattr(self, "table_config")

    @abstractmethod
    def connect(self, connector_descriptor: ConnectorDescriptor) -> ConnectTableDescriptor:
        """
        Creates a temporary table from a descriptor.

        Descriptors allow for declaring the communication to external systems in an
        implementation-agnostic way. The classpath is scanned for suitable table factories that
        match the desired configuration.

        The following example shows how to read from a connector using a JSON format and
        registering a temporary table as "MyTable":

        Example:
        ::

            >>> table_env \\
            ...     .connect(ExternalSystemXYZ()
            ...              .version("0.11")) \\
            ...     .with_format(Json()
            ...                  .json_schema("{...}")
            ...                  .fail_on_missing_field(False)) \\
            ...     .with_schema(Schema()
            ...                  .field("user-name", "VARCHAR")
            ...                  .from_origin_field("u_name")
            ...                  .field("count", "DECIMAL")) \\
            ...     .create_temporary_table("MyTable")

        :param connector_descriptor: Connector descriptor describing the external system.
        :return: A :class:`~pyflink.table.descriptors.ConnectTableDescriptor` used to build the
                 temporary table.

        .. note:: Deprecated in 1.11. Use :func:`execute_sql` to register a table instead.
        """
        pass

    def register_java_function(self, name: str, function_class_name: str):
        """
        Registers a java user defined function under a unique name. Replaces already existing
        user-defined functions under this name. The acceptable function type contains
        **ScalarFunction**, **TableFunction** and **AggregateFunction**.

        Example:
        ::

            >>> table_env.register_java_function("func1", "java.user.defined.function.class.name")

        :param name: The name under which the function is registered.
        :param function_class_name: The java full qualified class name of the function to register.
                                    The function must have a public no-argument constructor and can
                                    be founded in current Java classloader.

        .. note:: Deprecated in 1.12. Use :func:`create_java_temporary_system_function` instead.
        """
        warnings.warn("Deprecated in 1.12. Use :func:`create_java_temporary_system_function` "
                      "instead.", DeprecationWarning)
        gateway = get_gateway()
        java_function = gateway.jvm.Thread.currentThread().getContextClassLoader()\
            .loadClass(function_class_name).newInstance()
        # this is a temporary solution and will be unified later when we use the new type
        # system(DataType) to replace the old type system(TypeInformation).
        if self._is_blink_planner and isinstance(self, BatchTableEnvironment):
            if self._is_table_function(java_function):
                self._register_table_function(name, java_function)
            elif self._is_aggregate_function(java_function):
                self._register_aggregate_function(name, java_function)
            else:
                self._j_tenv.registerFunction(name, java_function)
        else:
            self._j_tenv.registerFunction(name, java_function)

    def register_function(self, name: str, function: UserDefinedFunctionWrapper):
        """
        Registers a python user-defined function under a unique name. Replaces already existing
        user-defined function under this name.

        Example:
        ::

            >>> table_env.register_function(
            ...     "add_one", udf(lambda i: i + 1, result_type=DataTypes.BIGINT()))

            >>> @udf(result_type=DataTypes.BIGINT())
            ... def add(i, j):
            ...     return i + j
            >>> table_env.register_function("add", add)

            >>> class SubtractOne(ScalarFunction):
            ...     def eval(self, i):
            ...         return i - 1
            >>> table_env.register_function(
            ...     "subtract_one", udf(SubtractOne(), result_type=DataTypes.BIGINT()))

        :param name: The name under which the function is registered.
        :param function: The python user-defined function to register.

        .. versionadded:: 1.10.0

        .. note:: Deprecated in 1.12. Use :func:`create_temporary_system_function` instead.
        """
        warnings.warn("Deprecated in 1.12. Use :func:`create_temporary_system_function` "
                      "instead.", DeprecationWarning)
        function = self._wrap_aggregate_function_if_needed(function)
        java_function = function.java_user_defined_function()
        # this is a temporary solution and will be unified later when we use the new type
        # system(DataType) to replace the old type system(TypeInformation).
        if self._is_blink_planner and isinstance(self, BatchTableEnvironment):
            if self._is_table_function(java_function):
                self._register_table_function(name, java_function)
            elif self._is_aggregate_function(java_function):
                self._register_aggregate_function(name, java_function)
            else:
                self._j_tenv.registerFunction(name, java_function)
        else:
            self._j_tenv.registerFunction(name, java_function)

    def create_temporary_view(self, view_path: str, table: Table):
        """
        Registers a :class:`~pyflink.table.Table` API object as a temporary view similar to SQL
        temporary views.

        Temporary objects can shadow permanent ones. If a permanent object in a given path exists,
        it will be inaccessible in the current session. To make the permanent object available
        again you can drop the corresponding temporary object.

        :param view_path: The path under which the view will be registered. See also the
                          :class:`~pyflink.table.TableEnvironment` class description for the format
                          of the path.
        :param table: The view to register.

        .. versionadded:: 1.10.0
        """
        self._j_tenv.createTemporaryView(view_path, table._j_table)

    def add_python_file(self, file_path: str):
        """
        Adds a python dependency which could be python files, python packages or
        local directories. They will be added to the PYTHONPATH of the python UDF worker.
        Please make sure that these dependencies can be imported.

        :param file_path: The path of the python dependency.

        .. versionadded:: 1.10.0
        """
        jvm = get_gateway().jvm
        python_files = self.get_config().get_configuration().get_string(
            jvm.PythonOptions.PYTHON_FILES.key(), None)
        if python_files is not None:
            python_files = jvm.PythonDependencyUtils.FILE_DELIMITER.join([python_files, file_path])
        else:
            python_files = file_path
        self.get_config().get_configuration().set_string(
            jvm.PythonOptions.PYTHON_FILES.key(), python_files)

    def set_python_requirements(self,
                                requirements_file_path: str,
                                requirements_cache_dir: str = None):
        """
        Specifies a requirements.txt file which defines the third-party dependencies.
        These dependencies will be installed to a temporary directory and added to the
        PYTHONPATH of the python UDF worker.

        For the dependencies which could not be accessed in the cluster, a directory which contains
        the installation packages of these dependencies could be specified using the parameter
        "requirements_cached_dir". It will be uploaded to the cluster to support offline
        installation.

        Example:
        ::

            # commands executed in shell
            $ echo numpy==1.16.5 > requirements.txt
            $ pip download -d cached_dir -r requirements.txt --no-binary :all:

            # python code
            >>> table_env.set_python_requirements("requirements.txt", "cached_dir")

        .. note::

            Please make sure the installation packages matches the platform of the cluster
            and the python version used. These packages will be installed using pip,
            so also make sure the version of Pip (version >= 7.1.0) and the version of
            SetupTools (version >= 37.0.0).

        :param requirements_file_path: The path of "requirements.txt" file.
        :param requirements_cache_dir: The path of the local directory which contains the
                                       installation packages.

        .. versionadded:: 1.10.0
        """
        jvm = get_gateway().jvm
        python_requirements = requirements_file_path
        if requirements_cache_dir is not None:
            python_requirements = jvm.PythonDependencyUtils.PARAM_DELIMITER.join(
                [python_requirements, requirements_cache_dir])
        self.get_config().get_configuration().set_string(
            jvm.PythonOptions.PYTHON_REQUIREMENTS.key(), python_requirements)

    def add_python_archive(self, archive_path: str, target_dir: str = None):
        """
        Adds a python archive file. The file will be extracted to the working directory of
        python UDF worker.

        If the parameter "target_dir" is specified, the archive file will be extracted to a
        directory named ${target_dir}. Otherwise, the archive file will be extracted to a
        directory with the same name of the archive file.

        If python UDF depends on a specific python version which does not exist in the cluster,
        this method can be used to upload the virtual environment.
        Note that the path of the python interpreter contained in the uploaded environment
        should be specified via the method :func:`pyflink.table.TableConfig.set_python_executable`.

        The files uploaded via this method are also accessible in UDFs via relative path.

        Example:
        ::

            # command executed in shell
            # assert the relative path of python interpreter is py_env/bin/python
            $ zip -r py_env.zip py_env

            # python code
            >>> table_env.add_python_archive("py_env.zip")
            >>> table_env.get_config().set_python_executable("py_env.zip/py_env/bin/python")

            # or
            >>> table_env.add_python_archive("py_env.zip", "myenv")
            >>> table_env.get_config().set_python_executable("myenv/py_env/bin/python")

            # the files contained in the archive file can be accessed in UDF
            >>> def my_udf():
            ...     with open("myenv/py_env/data/data.txt") as f:
            ...         ...

        .. note::

            Please make sure the uploaded python environment matches the platform that the cluster
            is running on and that the python version must be 3.5 or higher.

        .. note::

            Currently only zip-format is supported. i.e. zip, jar, whl, egg, etc.
            The other archive formats such as tar, tar.gz, 7z, rar, etc are not supported.

        :param archive_path: The archive file path.
        :param target_dir: Optional, the target dir name that the archive file extracted to.

        .. versionadded:: 1.10.0
        """
        jvm = get_gateway().jvm
        if target_dir is not None:
            archive_path = jvm.PythonDependencyUtils.PARAM_DELIMITER.join(
                [archive_path, target_dir])
        python_archives = self.get_config().get_configuration().get_string(
            jvm.PythonOptions.PYTHON_ARCHIVES.key(), None)
        if python_archives is not None:
            python_files = jvm.PythonDependencyUtils.FILE_DELIMITER.join(
                [python_archives, archive_path])
        else:
            python_files = archive_path
        self.get_config().get_configuration().set_string(
            jvm.PythonOptions.PYTHON_ARCHIVES.key(), python_files)

    def execute(self, job_name: str) -> JobExecutionResult:
        """
        Triggers the program execution. The environment will execute all parts of
        the program.

        The program execution will be logged and displayed with the provided name.

        .. note::

            It is highly advised to set all parameters in the :class:`~pyflink.table.TableConfig`
            on the very beginning of the program. It is undefined what configurations values will
            be used for the execution if queries are mixed with config changes. It depends on
            the characteristic of the particular parameter. For some of them the value from the
            point in time of query construction (e.g. the current catalog) will be used. On the
            other hand some values might be evaluated according to the state from the time when
            this method is called (e.g. timezone).

        :param job_name: Desired name of the job.
        :return: The result of the job execution, containing elapsed time and accumulators.

        .. note:: Deprecated in 1.11. Use :func:`execute_sql` for single sink,
                  use :func:`create_statement_set` for multiple sinks.
        """
        warnings.warn("Deprecated in 1.11. Use execute_sql for single sink, "
                      "use create_statement_set for multiple sinks.", DeprecationWarning)
        self._before_execute()
        return JobExecutionResult(self._j_tenv.execute(job_name))

    def from_elements(self, elements: Iterable, schema: Union[DataType, List[str]] = None,
                      verify_schema: bool = True) -> Table:
        """
        Creates a table from a collection of elements.
        The elements types must be acceptable atomic types or acceptable composite types.
        All elements must be of the same type.
        If the elements types are composite types, the composite types must be strictly equal,
        and its subtypes must also be acceptable types.
        e.g. if the elements are tuples, the length of the tuples must be equal, the element types
        of the tuples must be equal in order.

        The built-in acceptable atomic element types contains:

        **int**, **long**, **str**, **unicode**, **bool**,
        **float**, **bytearray**, **datetime.date**, **datetime.time**, **datetime.datetime**,
        **datetime.timedelta**, **decimal.Decimal**

        The built-in acceptable composite element types contains:

        **list**, **tuple**, **dict**, **array**, :class:`~pyflink.table.Row`

        If the element type is a composite type, it will be unboxed.
        e.g. table_env.from_elements([(1, 'Hi'), (2, 'Hello')]) will return a table like:

        +----+-------+
        | _1 |  _2   |
        +====+=======+
        | 1  |  Hi   |
        +----+-------+
        | 2  | Hello |
        +----+-------+

        "_1" and "_2" are generated field names.

        Example:
        ::

            # use the second parameter to specify custom field names
            >>> table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['a', 'b'])
            # use the second parameter to specify custom table schema
            >>> table_env.from_elements([(1, 'Hi'), (2, 'Hello')],
            ...                         DataTypes.ROW([DataTypes.FIELD("a", DataTypes.INT()),
            ...                                        DataTypes.FIELD("b", DataTypes.STRING())]))
            # use the thrid parameter to switch whether to verify the elements against the schema
            >>> table_env.from_elements([(1, 'Hi'), (2, 'Hello')],
            ...                         DataTypes.ROW([DataTypes.FIELD("a", DataTypes.INT()),
            ...                                        DataTypes.FIELD("b", DataTypes.STRING())]),
            ...                         False)
            # create Table from expressions
            >>> table_env.from_elements([row(1, 'abc', 2.0), row(2, 'def', 3.0)],
            ...                         DataTypes.ROW([DataTypes.FIELD("a", DataTypes.INT()),
            ...                                        DataTypes.FIELD("b", DataTypes.STRING()),
            ...                                        DataTypes.FIELD("c", DataTypes.FLOAT())]))

        :param elements: The elements to create a table from.
        :param schema: The schema of the table.
        :param verify_schema: Whether to verify the elements against the schema.
        :return: The result table.
        """

        # verifies the elements against the specified schema
        if isinstance(schema, RowType):
            verify_func = _create_type_verifier(schema) if verify_schema else lambda _: True

            def verify_obj(obj):
                verify_func(obj)
                return obj
        elif isinstance(schema, DataType):
            data_type = schema
            schema = RowType().add("value", schema)

            verify_func = _create_type_verifier(
                data_type, name="field value") if verify_schema else lambda _: True

            def verify_obj(obj):
                verify_func(obj)
                return obj
        else:
            def verify_obj(obj):
                return obj

        # infers the schema if not specified
        if schema is None or isinstance(schema, (list, tuple)):
            schema = _infer_schema_from_data(elements, names=schema)
            converter = _create_converter(schema)
            elements = map(converter, elements)

        elif not isinstance(schema, RowType):
            raise TypeError(
                "schema should be RowType, list, tuple or None, but got: %s" % schema)

        elements = list(elements)

        # in case all the elements are expressions
        if len(elements) > 0 and all(isinstance(elem, Expression) for elem in elements):
            if schema is None:
                return Table(self._j_tenv.fromValues(to_expression_jarray(elements)), self)
            else:
                return Table(self._j_tenv.fromValues(_to_java_data_type(schema),
                                                     to_expression_jarray(elements)),
                             self)
        elif any(isinstance(elem, Expression) for elem in elements):
            raise ValueError("It doesn't support part of the elements are Expression, while the "
                             "others are not.")

        # verifies the elements against the specified schema
        elements = map(verify_obj, elements)
        # converts python data to sql data
        elements = [schema.to_sql_type(element) for element in elements]
        return self._from_elements(elements, schema)

    def _from_elements(self, elements: List, schema: Union[DataType, List[str]]) -> Table:
        """
        Creates a table from a collection of elements.

        :param elements: The elements to create a table from.
        :return: The result :class:`~pyflink.table.Table`.
        """
        # serializes to a file, and we read the file in java
        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=tempfile.mkdtemp())
        serializer = BatchedSerializer(self._serializer)
        try:
            with temp_file:
                serializer.dump_to_stream(elements, temp_file)
            row_type_info = _to_java_type(schema)
            execution_config = self._get_j_env().getConfig()
            gateway = get_gateway()
            j_objs = gateway.jvm.PythonBridgeUtils.readPythonObjects(temp_file.name, True)
            if self._is_blink_planner:
                PythonTableUtils = gateway.jvm \
                    .org.apache.flink.table.planner.utils.python.PythonTableUtils
                PythonInputFormatTableSource = gateway.jvm \
                    .org.apache.flink.table.planner.utils.python.PythonInputFormatTableSource
            else:
                PythonTableUtils = gateway.jvm.PythonTableUtils
                PythonInputFormatTableSource = gateway.jvm.PythonInputFormatTableSource
            j_input_format = PythonTableUtils.getInputFormat(
                j_objs, row_type_info, execution_config)
            j_table_source = PythonInputFormatTableSource(
                j_input_format, row_type_info)

            return Table(self._j_tenv.fromTableSource(j_table_source), self)
        finally:
            os.unlink(temp_file.name)

    def from_pandas(self, pdf,
                    schema: Union[RowType, List[str], Tuple[str], List[DataType],
                                  Tuple[DataType]] = None,
                    splits_num: int = 1) -> Table:
        """
        Creates a table from a pandas DataFrame.

        Example:
        ::

            >>> pdf = pd.DataFrame(np.random.rand(1000, 2))
            # use the second parameter to specify custom field names
            >>> table_env.from_pandas(pdf, ["a", "b"])
            # use the second parameter to specify custom field types
            >>> table_env.from_pandas(pdf, [DataTypes.DOUBLE(), DataTypes.DOUBLE()]))
            # use the second parameter to specify custom table schema
            >>> table_env.from_pandas(pdf,
            ...                       DataTypes.ROW([DataTypes.FIELD("a", DataTypes.DOUBLE()),
            ...                                      DataTypes.FIELD("b", DataTypes.DOUBLE())]))

        :param pdf: The pandas DataFrame.
        :param schema: The schema of the converted table.
        :param splits_num: The number of splits the given Pandas DataFrame will be split into. It
                           determines the number of parallel source tasks.
                           If not specified, the default parallelism will be used.
        :return: The result table.

        .. versionadded:: 1.11.0
        """

        if not self._is_blink_planner and isinstance(self, BatchTableEnvironment):
            raise TypeError("It doesn't support to convert from Pandas DataFrame in the batch "
                            "mode of old planner")

        import pandas as pd
        if not isinstance(pdf, pd.DataFrame):
            raise TypeError("Unsupported type, expected pandas.DataFrame, got %s" % type(pdf))

        import pyarrow as pa
        arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)

        if schema is not None:
            if isinstance(schema, RowType):
                result_type = schema
            elif isinstance(schema, (list, tuple)) and isinstance(schema[0], str):
                result_type = RowType(
                    [RowField(field_name, from_arrow_type(field.type, field.nullable))
                     for field_name, field in zip(schema, arrow_schema)])
            elif isinstance(schema, (list, tuple)) and isinstance(schema[0], DataType):
                result_type = RowType(
                    [RowField(field_name, field_type) for field_name, field_type in zip(
                        arrow_schema.names, schema)])
            else:
                raise TypeError("Unsupported schema type, it could only be of RowType, a "
                                "list of str or a list of DataType, got %s" % schema)
        else:
            result_type = RowType([RowField(field.name, from_arrow_type(field.type, field.nullable))
                                   for field in arrow_schema])

        # serializes to a file, and we read the file in java
        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=tempfile.mkdtemp())
        import pytz
        serializer = ArrowSerializer(
            create_arrow_schema(result_type.field_names(), result_type.field_types()),
            result_type,
            pytz.timezone(self.get_config().get_local_timezone()))
        step = -(-len(pdf) // splits_num)
        pdf_slices = [pdf.iloc[start:start + step] for start in range(0, len(pdf), step)]
        data = [[c for (_, c) in pdf_slice.iteritems()] for pdf_slice in pdf_slices]
        try:
            with temp_file:
                serializer.dump_to_stream(data, temp_file)
            jvm = get_gateway().jvm

            data_type = jvm.org.apache.flink.table.types.utils.TypeConversions\
                .fromLegacyInfoToDataType(_to_java_type(result_type)).notNull()
            if self._is_blink_planner:
                data_type = data_type.bridgedTo(
                    load_java_class('org.apache.flink.table.data.RowData'))

            j_arrow_table_source = \
                jvm.org.apache.flink.table.runtime.arrow.ArrowUtils.createArrowTableSource(
                    data_type, temp_file.name)
            return Table(self._j_tenv.fromTableSource(j_arrow_table_source), self)
        finally:
            os.unlink(temp_file.name)

    def _set_python_executable_for_local_executor(self):
        jvm = get_gateway().jvm
        j_config = get_j_env_configuration(self)
        if not j_config.containsKey(jvm.PythonOptions.PYTHON_EXECUTABLE.key()) \
                and is_local_deployment(j_config):
            j_config.setString(jvm.PythonOptions.PYTHON_EXECUTABLE.key(), sys.executable)

    def _add_jars_to_j_env_config(self, config_key):
        jvm = get_gateway().jvm
        jar_urls = self.get_config().get_configuration().get_string(config_key, None)
        if jar_urls is not None:
            # normalize and remove duplicates
            jar_urls_set = set([jvm.java.net.URL(url).toString() for url in jar_urls.split(";")])
            j_configuration = get_j_env_configuration(self)
            if j_configuration.containsKey(config_key):
                for url in j_configuration.getString(config_key, "").split(";"):
                    jar_urls_set.add(url)
            j_configuration.setString(config_key, ";".join(jar_urls_set))

    @abstractmethod
    def _get_j_env(self):
        pass

    @staticmethod
    def _is_table_function(java_function):
        java_function_class = java_function.getClass()
        j_table_function_class = get_java_class(
            get_gateway().jvm.org.apache.flink.table.functions.TableFunction)
        return j_table_function_class.isAssignableFrom(java_function_class)

    @staticmethod
    def _is_aggregate_function(java_function):
        java_function_class = java_function.getClass()
        j_aggregate_function_class = get_java_class(
            get_gateway().jvm.org.apache.flink.table.functions.ImperativeAggregateFunction)
        return j_aggregate_function_class.isAssignableFrom(java_function_class)

    def _register_table_function(self, name, table_function):
        function_catalog = self._get_function_catalog()
        gateway = get_gateway()
        helper = gateway.jvm.org.apache.flink.table.functions.UserDefinedFunctionHelper
        result_type = helper.getReturnTypeOfTableFunction(table_function)
        function_catalog.registerTempSystemTableFunction(name, table_function, result_type)

    def _register_aggregate_function(self, name, aggregate_function):
        function_catalog = self._get_function_catalog()
        gateway = get_gateway()
        helper = gateway.jvm.org.apache.flink.table.functions.UserDefinedFunctionHelper
        result_type = helper.getReturnTypeOfAggregateFunction(aggregate_function)
        acc_type = helper.getAccumulatorTypeOfAggregateFunction(aggregate_function)
        function_catalog.registerTempSystemAggregateFunction(
            name, aggregate_function, result_type, acc_type)

    def _get_function_catalog(self):
        function_catalog_field = self._j_tenv.getClass().getDeclaredField("functionCatalog")
        function_catalog_field.setAccessible(True)
        function_catalog = function_catalog_field.get(self._j_tenv)
        return function_catalog

    def _before_execute(self):
        jvm = get_gateway().jvm
        jars_key = jvm.org.apache.flink.configuration.PipelineOptions.JARS.key()
        classpaths_key = jvm.org.apache.flink.configuration.PipelineOptions.CLASSPATHS.key()
        self._add_jars_to_j_env_config(jars_key)
        self._add_jars_to_j_env_config(classpaths_key)

    def _wrap_aggregate_function_if_needed(self, function) -> UserDefinedFunctionWrapper:
        if isinstance(function, (AggregateFunction, UserDefinedAggregateFunctionWrapper)):
            if not self._is_blink_planner:
                raise Exception("Python UDAF is only supported in blink planner")
        if isinstance(function, AggregateFunction):
            function = udaf(function,
                            result_type=function.get_result_type(),
                            accumulator_type=function.get_accumulator_type(),
                            name=str(function.__class__.__name__))
        return function


class StreamTableEnvironment(TableEnvironment):

    def __init__(self, j_tenv):
        self._j_tenv = j_tenv
        super(StreamTableEnvironment, self).__init__(j_tenv)

    def _get_j_env(self):
        if self._is_blink_planner:
            return self._j_tenv.getPlanner().getExecEnv()
        else:
            return self._j_tenv.getPlanner().getExecutionEnvironment()

    def connect(self, connector_descriptor: ConnectorDescriptor) -> StreamTableDescriptor:
        """
        Creates a temporary table from a descriptor.

        Descriptors allow for declaring the communication to external systems in an
        implementation-agnostic way. The classpath is scanned for suitable table factories that
        match the desired configuration.

        The following example shows how to read from a connector using a JSON format and
        registering a temporary table as "MyTable":
        ::

            >>> table_env \\
            ...     .connect(ExternalSystemXYZ()
            ...              .version("0.11")) \\
            ...     .with_format(Json()
            ...                  .json_schema("{...}")
            ...                  .fail_on_missing_field(False)) \\
            ...     .with_schema(Schema()
            ...                  .field("user-name", "VARCHAR")
            ...                  .from_origin_field("u_name")
            ...                  .field("count", "DECIMAL")) \\
            ...     .create_temporary_table("MyTable")

        :param connector_descriptor: Connector descriptor describing the external system.
        :return: A :class:`~pyflink.table.descriptors.StreamTableDescriptor` used to build the
                 temporary table.

        .. note:: Deprecated in 1.11. Use :func:`execute_sql` to register a table instead.
        """
        warnings.warn("Deprecated in 1.11. Use execute_sql instead.", DeprecationWarning)
        return StreamTableDescriptor(
            self._j_tenv.connect(connector_descriptor._j_connector_descriptor))

    @staticmethod
    def create(stream_execution_environment: StreamExecutionEnvironment = None,
               table_config: TableConfig = None,
               environment_settings: EnvironmentSettings = None) -> 'StreamTableEnvironment':
        """
        Creates a :class:`~pyflink.table.StreamTableEnvironment`.

        Example:
        ::

            # create with StreamExecutionEnvironment.
            >>> env = StreamExecutionEnvironment.get_execution_environment()
            >>> table_env = StreamTableEnvironment.create(env)
            # create with StreamExecutionEnvironment and TableConfig.
            >>> table_config = TableConfig()
            >>> table_config.set_null_check(False)
            >>> table_env = StreamTableEnvironment.create(env, table_config)
            # create with StreamExecutionEnvironment and EnvironmentSettings.
            >>> environment_settings = EnvironmentSettings.new_instance().use_blink_planner() \\
            ...     .build()
            >>> table_env = StreamTableEnvironment.create(
            ...     env, environment_settings=environment_settings)
            # create with EnvironmentSettings.
            >>> table_env = StreamTableEnvironment.create(environment_settings=environment_settings)


        :param stream_execution_environment: The
                                             :class:`~pyflink.datastream.StreamExecutionEnvironment`
                                             of the TableEnvironment.
        :param table_config: The configuration of the TableEnvironment, optional.
        :param environment_settings: The environment settings used to instantiate the
                                     TableEnvironment. It provides the interfaces about planner
                                     selection(flink or blink), optional.
        :return: The StreamTableEnvironment created from given StreamExecutionEnvironment and
                 configuration.
        """
        if stream_execution_environment is None and \
                table_config is None and \
                environment_settings is None:
            raise ValueError("No argument found, the param 'stream_execution_environment' "
                             "or 'environment_settings' is required.")
        elif stream_execution_environment is None and \
                table_config is not None and \
                environment_settings is None:
            raise ValueError("Only the param 'table_config' is found, "
                             "the param 'stream_execution_environment' is also required.")
        if table_config is not None and \
                environment_settings is not None:
            raise ValueError("The param 'table_config' and "
                             "'environment_settings' cannot be used at the same time")

        gateway = get_gateway()
        if environment_settings is not None:
            if not environment_settings.is_streaming_mode():
                raise ValueError("The environment settings for StreamTableEnvironment must be "
                                 "set to streaming mode.")
            if stream_execution_environment is None:
                j_tenv = gateway.jvm.TableEnvironment.create(
                    environment_settings._j_environment_settings)
            else:
                j_tenv = gateway.jvm.StreamTableEnvironment.create(
                    stream_execution_environment._j_stream_execution_environment,
                    environment_settings._j_environment_settings)
        else:
            if table_config is not None:
                j_tenv = gateway.jvm.StreamTableEnvironment.create(
                    stream_execution_environment._j_stream_execution_environment,
                    table_config._j_table_config)
            else:
                j_tenv = gateway.jvm.StreamTableEnvironment.create(
                    stream_execution_environment._j_stream_execution_environment)
        return StreamTableEnvironment(j_tenv)

    def from_data_stream(self, data_stream: DataStream, *fields: Union[str, Expression]) -> Table:
        """
        Converts the given DataStream into a Table with specified field names.

        There are two modes for mapping original fields to the fields of the Table:
            1. Reference input fields by name:
            All fields in the schema definition are referenced by name (and possibly renamed using
            and alias (as). Moreover, we can define proctime and rowtime attributes at arbitrary
            positions using arbitrary names (except those that exist in the result schema). In this
            mode, fields can be reordered and projected out. This mode can be used for any input
            type.
            2. Reference input fields by position:
            In this mode, fields are simply renamed. Event-time attributes can replace the field on
            their position in the input data (if it is of correct type) or be appended at the end.
            Proctime attributes must be appended at the end. This mode can only be used if the input
            type has a defined field order (tuple, case class, Row) and none of the fields
            references a field of the input type.

        :param data_stream: The datastream to be converted.
        :param fields: The fields expressions to map original fields of the DataStream to the fields
                       of the Table
        :return: The converted Table.

        .. versionadded:: 1.12.0
        """
        j_data_stream = data_stream._j_data_stream
        if len(fields) == 0:
            return Table(j_table=self._j_tenv.fromDataStream(j_data_stream), t_env=self)
        elif all(isinstance(f, Expression) for f in fields):
            return Table(j_table=self._j_tenv.fromDataStream(
                j_data_stream, to_expression_jarray(fields)), t_env=self)
        elif len(fields) == 1 and isinstance(fields[0], str):
            warnings.warn(
                "Deprecated in 1.12. Use from_data_stream(DataStream, *Expression) instead.",
                DeprecationWarning)
            return Table(j_table=self._j_tenv.fromDataStream(j_data_stream, fields[0]), t_env=self)
        raise ValueError("Invalid arguments for 'fields': %r" % fields)

    def to_append_stream(self, table: Table, type_info: TypeInformation) -> DataStream:
        """
        Converts the given Table into a DataStream of a specified type. The Table must only have
        insert (append) changes. If the Table is also modified by update or delete changes, the
        conversion will fail.

        The fields of the Table are mapped to DataStream as follows: Row and Tuple types: Fields are
        mapped by position, field types must match.

        :param table: The Table to convert.
        :param type_info: The TypeInformation that specifies the type of the DataStream.
        :return: The converted DataStream.

        .. versionadded:: 1.12.0
        """
        if isinstance(type_info, WrapperTypeInfo):
            j_data_stream = self._j_tenv.toAppendStream(table._j_table,
                                                        type_info.get_java_type_info())
        else:
            raise TypeError('type_info must be WrapperTypeInfo')
        return DataStream(j_data_stream=j_data_stream)

    def to_retract_stream(self, table: Table, type_info: TypeInformation) -> DataStream:
        """
        Converts the given Table into a DataStream of add and retract messages. The message will be
        encoded as Tuple. The first field is a boolean flag, the second field holds the record of
        the specified type.

        A true flag indicates an add message, a false flag indicates a retract message.

        The fields of the Table are mapped to DataStream as follows: Row and Tuple types: Fields are
        mapped by position, field types must match.

        :param table: The Table to convert.
        :param type_info: The TypeInformation of the requested record type.
        :return: The converted DataStream.

        .. versionadded:: 1.12.0
        """
        if isinstance(type_info, WrapperTypeInfo):
            j_data_stream = self._j_tenv.toRetractStream(table._j_table,
                                                         type_info.get_java_type_info())
        else:
            raise TypeError('type_info must be WrapperTypeInfo')
        return DataStream(j_data_stream=j_data_stream)


class BatchTableEnvironment(TableEnvironment):

    def __init__(self, j_tenv):
        self._j_tenv = j_tenv
        super(BatchTableEnvironment, self).__init__(j_tenv)

    def _get_j_env(self):
        if self._is_blink_planner:
            return self._j_tenv.getPlanner().getExecEnv()
        else:
            return self._j_tenv.execEnv()

    def connect(self, connector_descriptor: ConnectorDescriptor) -> \
            Union[BatchTableDescriptor, StreamTableDescriptor]:
        """
        Creates a temporary table from a descriptor.

        Descriptors allow for declaring the communication to external systems in an
        implementation-agnostic way. The classpath is scanned for suitable table factories that
        match the desired configuration.

        The following example shows how to read from a connector using a JSON format and
        registering a temporary table as "MyTable":
        ::

            >>> table_env \\
            ...     .connect(ExternalSystemXYZ()
            ...              .version("0.11")) \\
            ...     .with_format(Json()
            ...                  .json_schema("{...}")
            ...                  .fail_on_missing_field(False)) \\
            ...     .with_schema(Schema()
            ...                  .field("user-name", "VARCHAR")
            ...                  .from_origin_field("u_name")
            ...                  .field("count", "DECIMAL")) \\
            ...     .create_temporary_table("MyTable")

        :param connector_descriptor: Connector descriptor describing the external system.
        :return: A :class:`~pyflink.table.descriptors.BatchTableDescriptor` or a
                 :class:`~pyflink.table.descriptors.StreamTableDescriptor` (for blink planner) used
                 to build the temporary table.

        .. note:: Deprecated in 1.11. Use :func:`execute_sql` to register a table instead.
        """
        warnings.warn("Deprecated in 1.11. Use execute_sql instead.", DeprecationWarning)
        gateway = get_gateway()
        blink_t_env_class = get_java_class(
            gateway.jvm.org.apache.flink.table.api.internal.TableEnvironmentImpl)
        if blink_t_env_class == self._j_tenv.getClass():
            return StreamTableDescriptor(
                self._j_tenv.connect(connector_descriptor._j_connector_descriptor))
        else:
            return BatchTableDescriptor(
                self._j_tenv.connect(connector_descriptor._j_connector_descriptor))

    @staticmethod
    def create(execution_environment: ExecutionEnvironment = None,
               table_config: TableConfig = None,
               environment_settings: EnvironmentSettings = None) -> 'BatchTableEnvironment':
        """
        Creates a :class:`~pyflink.table.BatchTableEnvironment`.

        Example:
        ::

            # create with ExecutionEnvironment.
            >>> env = ExecutionEnvironment.get_execution_environment()
            >>> table_env = BatchTableEnvironment.create(env)
            # create with ExecutionEnvironment and TableConfig.
            >>> table_config = TableConfig()
            >>> table_config.set_null_check(False)
            >>> table_env = BatchTableEnvironment.create(env, table_config)
            # create with EnvironmentSettings.
            >>> environment_settings = EnvironmentSettings.new_instance().in_batch_mode() \\
            ...     .use_blink_planner().build()
            >>> table_env = BatchTableEnvironment.create(environment_settings=environment_settings)

        :param execution_environment: The batch :class:`~pyflink.dataset.ExecutionEnvironment` of
                                      the TableEnvironment.
        :param table_config: The configuration of the TableEnvironment, optional.
        :param environment_settings: The environment settings used to instantiate the
                                     TableEnvironment. It provides the interfaces about planner
                                     selection(flink or blink), optional.
        :return: The BatchTableEnvironment created from given ExecutionEnvironment and
                 configuration.
        """
        if execution_environment is None and \
                table_config is None and \
                environment_settings is None:
            raise ValueError("No argument found, the param 'execution_environment' "
                             "or 'environment_settings' is required.")
        elif execution_environment is None and \
                table_config is not None and \
                environment_settings is None:
            raise ValueError("Only the param 'table_config' is found, "
                             "the param 'execution_environment' is also required.")
        elif execution_environment is not None and \
                environment_settings is not None:
            raise ValueError("The param 'execution_environment' and "
                             "'environment_settings' cannot be used at the same time")
        elif table_config is not None and \
                environment_settings is not None:
            raise ValueError("The param 'table_config' and "
                             "'environment_settings' cannot be used at the same time")

        gateway = get_gateway()
        if environment_settings is not None:
            if environment_settings.is_streaming_mode():
                raise ValueError("The environment settings for BatchTableEnvironment must be "
                                 "set to batch mode.")
            JEnvironmentSettings = get_gateway().jvm.org.apache.flink.table.api.EnvironmentSettings

            old_planner_class_name = EnvironmentSettings.new_instance().in_batch_mode() \
                .use_old_planner().build()._j_environment_settings \
                .toPlannerProperties()[JEnvironmentSettings.CLASS_NAME]
            planner_properties = environment_settings._j_environment_settings.toPlannerProperties()
            if JEnvironmentSettings.CLASS_NAME in planner_properties and \
                    planner_properties[JEnvironmentSettings.CLASS_NAME] == old_planner_class_name:
                # The Java EnvironmentSettings API does not support creating table environment with
                # old planner. Create it from other API.
                j_tenv = gateway.jvm.BatchTableEnvironment.create(
                    ExecutionEnvironment.get_execution_environment()._j_execution_environment)
            else:
                j_tenv = gateway.jvm.TableEnvironment.create(
                    environment_settings._j_environment_settings)
        else:
            if table_config is None:
                j_tenv = gateway.jvm.BatchTableEnvironment.create(
                    execution_environment._j_execution_environment)
            else:
                j_tenv = gateway.jvm.BatchTableEnvironment.create(
                    execution_environment._j_execution_environment,
                    table_config._j_table_config)
        return BatchTableEnvironment(j_tenv)
