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import os
import sys
import tempfile
import warnings
from abc import ABCMeta, abstractmethod
from typing import Union, List, Tuple
from py4j.java_gateway import get_java_class, get_method
from pyflink.common import JobExecutionResult
from pyflink.dataset import ExecutionEnvironment
from pyflink.serializers import BatchedSerializer, PickleSerializer
from pyflink.table.catalog import Catalog
from pyflink.table.serializers import ArrowSerializer
from pyflink.table.statement_set import StatementSet
from pyflink.table.table_config import TableConfig
from pyflink.table.descriptors import StreamTableDescriptor, BatchTableDescriptor
from pyflink.java_gateway import get_gateway
from pyflink.table import Table, EnvironmentSettings
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
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'
]
[docs]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)
[docs] def from_table_source(self, table_source):
"""
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.
:type table_source: pyflink.table.TableSource
:return: The result table.
:rtype: pyflink.table.Table
"""
return Table(self._j_tenv.fromTableSource(table_source._j_table_source), self)
[docs] def register_catalog(self, catalog_name, 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.
:type catalog_name: str
:param catalog: The catalog to register.
:type catalog: pyflink.table.catalog.Catalog
"""
self._j_tenv.registerCatalog(catalog_name, catalog._j_catalog)
[docs] def get_catalog(self, catalog_name):
"""
Gets a registered :class:`~pyflink.table.catalog.Catalog` by name.
:param catalog_name: The name to look up the :class:`~pyflink.table.catalog.Catalog`.
:type catalog_name: str
:return: The requested catalog, None if there is no
registered catalog with given name.
:rtype: pyflink.table.catalog.Catalog
"""
catalog = self._j_tenv.getCatalog(catalog_name)
if catalog.isPresent():
return Catalog._get(catalog.get())
else:
return None
[docs] def register_table(self, name, 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.
:type name: str
:param table: The table to register.
:type table: pyflink.table.Table
.. 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)
[docs] def register_table_source(self, name, table_source):
"""
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.
:type name: str
:param table_source: The table source to register.
:type table_source: pyflink.table.TableSource
.. 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)
[docs] def register_table_sink(self, name, table_sink):
"""
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.
:type name: str
:param table_sink: The table sink to register.
:type table_sink: pyflink.table.TableSink
.. 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)
[docs] def scan(self, *table_path):
"""
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.
:type table_path: str
:throws: Exception if no table is found using the given table path.
:return: The resulting table.
:rtype: pyflink.table.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)
[docs] def from_path(self, path):
"""
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.
:type path: str
:return: Either a table or virtual table (=view).
:rtype: pyflink.table.Table
.. seealso:: :func:`use_catalog`
.. seealso:: :func:`use_database`
.. versionadded:: 1.10.0
"""
return Table(get_method(self._j_tenv, "from")(path), self)
[docs] def insert_into(self, target_path, 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.
:type target_path: str
:param table: The Table to write to the sink.
:type table: pyflink.table.Table
.. 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 execute_insert for single sink,"
"use create_statement_set for multiple sinks.", DeprecationWarning)
self._j_tenv.insertInto(target_path, table._j_table)
[docs] def list_catalogs(self):
"""
Gets the names of all catalogs registered in this environment.
:return: List of catalog names.
:rtype: list[str]
"""
j_catalog_name_array = self._j_tenv.listCatalogs()
return [item for item in j_catalog_name_array]
[docs] def list_modules(self):
"""
Gets the names of all modules registered in this environment.
:return: List of module names.
:rtype: list[str]
.. versionadded:: 1.10.0
"""
j_module_name_array = self._j_tenv.listModules()
return [item for item in j_module_name_array]
[docs] def list_databases(self):
"""
Gets the names of all databases in the current catalog.
:return: List of database names in the current catalog.
:rtype: list[str]
"""
j_database_name_array = self._j_tenv.listDatabases()
return [item for item in j_database_name_array]
[docs] def list_tables(self):
"""
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.
:rtype: list[str]
"""
j_table_name_array = self._j_tenv.listTables()
return [item for item in j_table_name_array]
[docs] def list_views(self):
"""
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.
:rtype: list[str]
.. versionadded:: 1.11.0
"""
j_view_name_array = self._j_tenv.listViews()
return [item for item in j_view_name_array]
[docs] def list_user_defined_functions(self):
"""
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.
:rtype: list[str]
"""
j_udf_name_array = self._j_tenv.listUserDefinedFunctions()
return [item for item in j_udf_name_array]
[docs] def list_functions(self):
"""
Gets the names of all functions in this environment.
:return: List of the names of all functions in this environment.
:rtype: list[str]
.. versionadded:: 1.10.0
"""
j_function_name_array = self._j_tenv.listFunctions()
return [item for item in j_function_name_array]
[docs] def list_temporary_tables(self):
"""
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.
:rtype: list[str]
.. 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]
[docs] def list_temporary_views(self):
"""
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.
:rtype: list[str]
.. 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]
[docs] def drop_temporary_table(self, table_path):
"""
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.
:type table_path: str
:return: True if a table existed in the given path and was removed.
:rtype: bool
.. versionadded:: 1.10.0
"""
return self._j_tenv.dropTemporaryTable(table_path)
[docs] def drop_temporary_view(self, view_path):
"""
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.
:rtype: bool
.. versionadded:: 1.10.0
"""
return self._j_tenv.dropTemporaryView(view_path)
[docs] def explain(self, table=None, extended=False):
"""
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.
:type table: pyflink.table.Table
:param extended: If the plan should contain additional properties.
e.g. estimated cost, traits
:type extended: bool
:return: The table for which the AST and execution plan will be returned.
:rtype: str
.. 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)
[docs] def explain_sql(self, stmt, *extra_details):
"""
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.
:type stmt: str
:param extra_details: The extra explain details which the explain result should include,
e.g. estimated cost, changelog mode for streaming
:type extra_details: tuple[ExplainDetail] (variable-length arguments of ExplainDetail)
:return: The statement for which the AST and execution plan will be returned.
:rtype: str
.. versionadded:: 1.11.0
"""
j_extra_details = to_j_explain_detail_arr(extra_details)
return self._j_tenv.explainSql(stmt, j_extra_details)
[docs] def sql_query(self, query):
"""
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.
:type query: str
:return: The result table.
:rtype: pyflink.table.Table
"""
j_table = self._j_tenv.sqlQuery(query)
return Table(j_table, self)
[docs] def execute_sql(self, stmt):
"""
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))
[docs] def create_statement_set(self):
"""
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
:rtype: pyflink.table.StatementSet
.. versionadded:: 1.11.0
"""
_j_statement_set = self._j_tenv.createStatementSet()
return StatementSet(_j_statement_set, self)
[docs] def sql_update(self, stmt):
"""
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.
:type stmt: str
.. 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)
[docs] def get_current_catalog(self):
"""
Gets the current default catalog name of the current session.
:return: The current default catalog name that is used for the path resolution.
:rtype: str
.. seealso:: :func:`~pyflink.table.TableEnvironment.use_catalog`
"""
return self._j_tenv.getCurrentCatalog()
[docs] def use_catalog(self, catalog_name):
"""
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.
:type catalog_name: str
: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)
[docs] def get_current_database(self):
"""
Gets the current default database name of the running session.
:return: The name of the current database of the current catalog.
:rtype: str
.. seealso:: :func:`~pyflink.table.TableEnvironment.use_database`
"""
return self._j_tenv.getCurrentDatabase()
[docs] def use_database(self, database_name):
"""
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.
:type database_name: str
"""
self._j_tenv.useDatabase(database_name)
[docs] @abstractmethod
def get_config(self):
"""
Returns the table config to define the runtime behavior of the Table API.
:return: Current table config.
:rtype: pyflink.table.TableConfig
"""
pass
[docs] @abstractmethod
def connect(self, connector_descriptor):
"""
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.
:type connector_descriptor: pyflink.table.descriptors.ConnectorDescriptor
:return: A :class:`~pyflink.table.descriptors.ConnectTableDescriptor` used to build the
temporary table.
:rtype: pyflink.table.descriptors.ConnectTableDescriptor
.. note:: Deprecated in 1.11. Use :func:`execute_sql` to register a table instead.
"""
pass
[docs] def register_java_function(self, name, function_class_name):
"""
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.
:type name: str
: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.
:type function_class_name: str
"""
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)
[docs] def register_function(self, name, function):
"""
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, DataTypes.BIGINT(), DataTypes.BIGINT()))
>>> @udf(input_types=[DataTypes.BIGINT(), DataTypes.BIGINT()],
... 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(), DataTypes.BIGINT(), DataTypes.BIGINT()))
:param name: The name under which the function is registered.
:type name: str
:param function: The python user-defined function to register.
:type function: pyflink.table.udf.UserDefinedFunctionWrapper
.. versionadded:: 1.10.0
"""
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) and \
self._is_table_function(java_function):
self._register_table_function(name, java_function)
else:
self._j_tenv.registerFunction(name, java_function)
[docs] def create_temporary_view(self, view_path, 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.
:type view_path: str
:param table: The view to register.
:type table: pyflink.table.Table
.. versionadded:: 1.10.0
"""
self._j_tenv.createTemporaryView(view_path, table._j_table)
[docs] def add_python_file(self, file_path):
"""
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.
:type file_path: str
.. 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)
[docs] def set_python_requirements(self, requirements_file_path, requirements_cache_dir=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.
:type requirements_file_path: str
:param requirements_cache_dir: The path of the local directory which contains the
installation packages.
:type requirements_cache_dir: str
.. 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)
[docs] def add_python_archive(self, archive_path, target_dir=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.
:type archive_path: str
:param target_dir: Optional, the target dir name that the archive file extracted to.
:type target_dir: str
.. 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)
[docs] def execute(self, job_name):
"""
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.
:type job_name: str
: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))
[docs] def from_elements(self, elements, schema=None, verify_schema=True):
"""
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)
:param elements: The elements to create a table from.
:type elements: list
:param schema: The schema of the table.
:type schema: pyflink.table.types.DataType or list[str]
:param verify_schema: Whether to verify the elements against the schema.
:type verify_schema: bool
:return: The result table.
:rtype: pyflink.table.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
if "__len__" not in dir(elements):
elements = list(elements)
# 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)
if isinstance(schema, (list, tuple)):
for i, name in enumerate(schema):
schema.fields[i].name = name
schema.names[i] = name
elif not isinstance(schema, RowType):
raise TypeError(
"schema should be RowType, list, tuple or None, but got: %s" % schema)
# 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, schema):
"""
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)
[docs] 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))
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.UserDefinedAggregateFunction)
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)
[docs]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()
[docs] def get_config(self):
"""
Returns the table config to define the runtime behavior of the Table API.
:return: Current table config.
:rtype: pyflink.table.TableConfig
"""
table_config = TableConfig()
table_config._j_table_config = self._j_tenv.getConfig()
return table_config
[docs] def connect(self, connector_descriptor):
"""
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.
:type connector_descriptor: pyflink.table.descriptors.ConnectorDescriptor
:return: A :class:`~pyflink.table.descriptors.StreamTableDescriptor` used to build the
temporary table.
:rtype: pyflink.table.descriptors.StreamTableDescriptor
.. 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))
[docs] @staticmethod
def create(stream_execution_environment=None, table_config=None, environment_settings=None):
"""
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.
:type stream_execution_environment: pyflink.datastream.StreamExecutionEnvironment
:param table_config: The configuration of the TableEnvironment, optional.
:type table_config: pyflink.table.TableConfig
:param environment_settings: The environment settings used to instantiate the
TableEnvironment. It provides the interfaces about planner
selection(flink or blink), optional.
:type environment_settings: pyflink.table.EnvironmentSettings
:return: The StreamTableEnvironment created from given StreamExecutionEnvironment and
configuration.
:rtype: pyflink.table.StreamTableEnvironment
"""
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)
[docs]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()
[docs] def get_config(self):
"""
Returns the table config to define the runtime behavior of the Table API.
:return: Current table config.
:rtype: pyflink.table.TableConfig
"""
table_config = TableConfig()
table_config._j_table_config = self._j_tenv.getConfig()
return table_config
[docs] def connect(self, connector_descriptor):
"""
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.
:type connector_descriptor: pyflink.table.descriptors.ConnectorDescriptor
:return: A :class:`~pyflink.table.descriptors.BatchTableDescriptor` or a
:class:`~pyflink.table.descriptors.StreamTableDescriptor` (for blink planner) used
to build the temporary table.
:rtype: pyflink.table.descriptors.BatchTableDescriptor or
pyflink.table.descriptors.StreamTableDescriptor
.. 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))
[docs] @staticmethod
def create(execution_environment=None, table_config=None, environment_settings=None):
"""
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.
:type execution_environment: pyflink.dataset.ExecutionEnvironment
:param table_config: The configuration of the TableEnvironment, optional.
:type table_config: pyflink.table.TableConfig
:param environment_settings: The environment settings used to instantiate the
TableEnvironment. It provides the interfaces about planner
selection(flink or blink), optional.
:type environment_settings: pyflink.table.EnvironmentSettings
:return: The BatchTableEnvironment created from given ExecutionEnvironment and
configuration.
:rtype: pyflink.table.BatchTableEnvironment
"""
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)
