| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| from pyspark.sql.connect.utils import check_dependencies |
| |
| check_dependencies(__name__) |
| |
| import threading |
| import os |
| import warnings |
| from collections.abc import Sized |
| from functools import reduce |
| from threading import RLock |
| from typing import ( |
| Optional, |
| Any, |
| Union, |
| Dict, |
| List, |
| Tuple, |
| Set, |
| cast, |
| overload, |
| Iterable, |
| TYPE_CHECKING, |
| ClassVar, |
| ) |
| |
| import numpy as np |
| import pandas as pd |
| import pyarrow as pa |
| from pandas.api.types import ( # type: ignore[attr-defined] |
| is_datetime64_dtype, |
| is_timedelta64_dtype, |
| ) |
| import urllib |
| |
| from pyspark import SparkContext, SparkConf, __version__ |
| from pyspark.loose_version import LooseVersion |
| from pyspark.sql.connect.client import SparkConnectClient, ChannelBuilder |
| from pyspark.sql.connect.conf import RuntimeConf |
| from pyspark.sql.connect.dataframe import DataFrame |
| from pyspark.sql.connect.plan import ( |
| SQL, |
| Range, |
| LocalRelation, |
| LogicalPlan, |
| CachedLocalRelation, |
| CachedRelation, |
| CachedRemoteRelation, |
| ) |
| from pyspark.sql.connect.readwriter import DataFrameReader |
| from pyspark.sql.connect.streaming.readwriter import DataStreamReader |
| from pyspark.sql.connect.streaming.query import StreamingQueryManager |
| from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer |
| from pyspark.sql.pandas.types import to_arrow_schema, to_arrow_type, _deduplicate_field_names |
| from pyspark.sql.session import classproperty, SparkSession as PySparkSession |
| from pyspark.sql.types import ( |
| _infer_schema, |
| _has_nulltype, |
| _merge_type, |
| Row, |
| DataType, |
| DayTimeIntervalType, |
| StructType, |
| AtomicType, |
| TimestampType, |
| ) |
| from pyspark.sql.utils import to_str |
| from pyspark.errors import ( |
| PySparkAttributeError, |
| PySparkNotImplementedError, |
| PySparkRuntimeError, |
| PySparkValueError, |
| PySparkTypeError, |
| ) |
| |
| if TYPE_CHECKING: |
| from pyspark.sql.connect._typing import OptionalPrimitiveType |
| from pyspark.sql.connect.catalog import Catalog |
| from pyspark.sql.connect.udf import UDFRegistration |
| from pyspark.sql.connect.udtf import UDTFRegistration |
| |
| |
| class SparkSession: |
| # The active SparkSession for the current thread |
| _active_session: ClassVar[threading.local] = threading.local() |
| # Reference to the root SparkSession |
| _default_session: ClassVar[Optional["SparkSession"]] = None |
| _lock: ClassVar[RLock] = RLock() |
| |
| class Builder: |
| """Builder for :class:`SparkSession`.""" |
| |
| _lock = RLock() |
| |
| def __init__(self) -> None: |
| self._options: Dict[str, Any] = {} |
| self._channel_builder: Optional[ChannelBuilder] = None |
| |
| @overload |
| def config(self, key: str, value: Any) -> "SparkSession.Builder": |
| ... |
| |
| @overload |
| def config(self, *, map: Dict[str, "OptionalPrimitiveType"]) -> "SparkSession.Builder": |
| ... |
| |
| def config( |
| self, |
| key: Optional[str] = None, |
| value: Optional[Any] = None, |
| *, |
| map: Optional[Dict[str, "OptionalPrimitiveType"]] = None, |
| ) -> "SparkSession.Builder": |
| with self._lock: |
| if map is not None: |
| for k, v in map.items(): |
| self._options[k] = to_str(v) |
| else: |
| self._options[cast(str, key)] = to_str(value) |
| return self |
| |
| def master(self, master: str) -> "SparkSession.Builder": |
| return self |
| |
| def appName(self, name: str) -> "SparkSession.Builder": |
| return self.config("spark.app.name", name) |
| |
| def remote(self, location: str = "sc://localhost") -> "SparkSession.Builder": |
| return self.config("spark.remote", location) |
| |
| def channelBuilder(self, channelBuilder: ChannelBuilder) -> "SparkSession.Builder": |
| """Uses custom :class:`ChannelBuilder` implementation, when there is a need |
| to customize the behavior for creation of GRPC connections. |
| |
| .. versionadded:: 3.5.0 |
| |
| An example to use this class looks like this: |
| |
| .. code-block:: python |
| |
| from pyspark.sql.connect import SparkSession, ChannelBuilder |
| |
| class CustomChannelBuilder(ChannelBuilder): |
| ... |
| |
| custom_channel_builder = CustomChannelBuilder(...) |
| spark = SparkSession.builder().channelBuilder(custom_channel_builder).getOrCreate() |
| |
| Returns |
| ------- |
| :class:`SparkSession.Builder` |
| """ |
| with self._lock: |
| # self._channel_builder is a separate field, because it may hold the state |
| # and cannot be serialized with to_str() |
| self._channel_builder = channelBuilder |
| return self |
| |
| def enableHiveSupport(self) -> "SparkSession.Builder": |
| raise PySparkNotImplementedError( |
| error_class="NOT_IMPLEMENTED", message_parameters={"feature": "enableHiveSupport"} |
| ) |
| |
| def _apply_options(self, session: "SparkSession") -> None: |
| with self._lock: |
| for k, v in self._options.items(): |
| # the options are applied after session creation, |
| # so following options always take no effect |
| if k not in [ |
| "spark.remote", |
| "spark.master", |
| ]: |
| try: |
| session.conf.set(k, v) |
| except Exception as e: |
| warnings.warn(str(e)) |
| |
| def create(self) -> "SparkSession": |
| has_channel_builder = self._channel_builder is not None |
| has_spark_remote = "spark.remote" in self._options |
| |
| if has_channel_builder and has_spark_remote: |
| raise ValueError( |
| "Only one of connection string or channelBuilder " |
| "can be used to create a new SparkSession." |
| ) |
| |
| if not has_channel_builder and not has_spark_remote: |
| raise ValueError( |
| "Needs either connection string or channelBuilder to create a new SparkSession." |
| ) |
| |
| if has_channel_builder: |
| assert self._channel_builder is not None |
| session = SparkSession(connection=self._channel_builder) |
| else: |
| spark_remote = to_str(self._options.get("spark.remote")) |
| assert spark_remote is not None |
| session = SparkSession(connection=spark_remote) |
| |
| SparkSession._set_default_and_active_session(session) |
| self._apply_options(session) |
| return session |
| |
| def getOrCreate(self) -> "SparkSession": |
| with SparkSession._lock: |
| session = SparkSession.getActiveSession() |
| if session is None: |
| session = SparkSession._default_session |
| if session is None: |
| session = self.create() |
| self._apply_options(session) |
| return session |
| |
| _client: SparkConnectClient |
| |
| @classproperty |
| def builder(cls) -> Builder: |
| return cls.Builder() |
| |
| builder.__doc__ = PySparkSession.builder.__doc__ |
| |
| def __init__(self, connection: Union[str, ChannelBuilder], userId: Optional[str] = None): |
| """ |
| Creates a new SparkSession for the Spark Connect interface. |
| |
| Parameters |
| ---------- |
| connection: Union[str,ChannelBuilder] |
| Connection string that is used to extract the connection parameters and configure |
| the GRPC connection. Or instance of ChannelBuilder that creates GRPC connection. |
| Defaults to `sc://localhost`. |
| userId : str, optional |
| Optional unique user ID that is used to differentiate multiple users and |
| isolate their Spark Sessions. If the `user_id` is not set, will default to |
| the $USER environment. Defining the user ID as part of the connection string |
| takes precedence. |
| """ |
| self._client = SparkConnectClient(connection=connection, user_id=userId) |
| self._session_id = self._client._session_id |
| |
| # Set to false to prevent client.release_session on close() (testing only) |
| self.release_session_on_close = True |
| |
| @classmethod |
| def _set_default_and_active_session(cls, session: "SparkSession") -> None: |
| """ |
| Set the (global) default :class:`SparkSession`, and (thread-local) |
| active :class:`SparkSession` when they are not set yet. |
| """ |
| with cls._lock: |
| if cls._default_session is None: |
| cls._default_session = session |
| if getattr(cls._active_session, "session", None) is None: |
| cls._active_session.session = session |
| |
| @classmethod |
| def getActiveSession(cls) -> Optional["SparkSession"]: |
| return getattr(cls._active_session, "session", None) |
| |
| getActiveSession.__doc__ = PySparkSession.getActiveSession.__doc__ |
| |
| @classmethod |
| def active(cls) -> "SparkSession": |
| session = cls.getActiveSession() |
| if session is None: |
| session = cls._default_session |
| if session is None: |
| raise PySparkRuntimeError( |
| error_class="NO_ACTIVE_OR_DEFAULT_SESSION", |
| message_parameters={}, |
| ) |
| return session |
| |
| active.__doc__ = PySparkSession.active.__doc__ |
| |
| def table(self, tableName: str) -> DataFrame: |
| return self.read.table(tableName) |
| |
| table.__doc__ = PySparkSession.table.__doc__ |
| |
| @property |
| def read(self) -> "DataFrameReader": |
| return DataFrameReader(self) |
| |
| read.__doc__ = PySparkSession.read.__doc__ |
| |
| @property |
| def readStream(self) -> "DataStreamReader": |
| return DataStreamReader(self) |
| |
| readStream.__doc__ = PySparkSession.readStream.__doc__ |
| |
| def _inferSchemaFromList( |
| self, data: Iterable[Any], names: Optional[List[str]] = None |
| ) -> StructType: |
| """ |
| Infer schema from list of Row, dict, or tuple. |
| """ |
| if not data: |
| raise PySparkValueError( |
| error_class="CANNOT_INFER_EMPTY_SCHEMA", |
| message_parameters={}, |
| ) |
| |
| ( |
| infer_dict_as_struct, |
| infer_array_from_first_element, |
| prefer_timestamp_ntz, |
| ) = self._client.get_configs( |
| "spark.sql.pyspark.inferNestedDictAsStruct.enabled", |
| "spark.sql.pyspark.legacy.inferArrayTypeFromFirstElement.enabled", |
| "spark.sql.timestampType", |
| ) |
| return reduce( |
| _merge_type, |
| ( |
| _infer_schema( |
| row, |
| names, |
| infer_dict_as_struct=(infer_dict_as_struct == "true"), |
| infer_array_from_first_element=(infer_array_from_first_element == "true"), |
| prefer_timestamp_ntz=(prefer_timestamp_ntz == "TIMESTAMP_NTZ"), |
| ) |
| for row in data |
| ), |
| ) |
| |
| def createDataFrame( |
| self, |
| data: Union["pd.DataFrame", "np.ndarray", Iterable[Any]], |
| schema: Optional[Union[AtomicType, StructType, str, List[str], Tuple[str, ...]]] = None, |
| ) -> "DataFrame": |
| assert data is not None |
| if isinstance(data, DataFrame): |
| raise PySparkTypeError( |
| error_class="INVALID_TYPE", |
| message_parameters={"arg_name": "data", "data_type": "DataFrame"}, |
| ) |
| |
| _schema: Optional[Union[AtomicType, StructType]] = None |
| _cols: Optional[List[str]] = None |
| _num_cols: Optional[int] = None |
| |
| if isinstance(schema, str): |
| schema = self.client._analyze( # type: ignore[assignment] |
| method="ddl_parse", ddl_string=schema |
| ).parsed |
| |
| if isinstance(schema, (AtomicType, StructType)): |
| _schema = schema |
| if isinstance(schema, StructType): |
| _num_cols = len(schema.fields) |
| else: |
| _num_cols = 1 |
| |
| elif isinstance(schema, (list, tuple)): |
| # Must re-encode any unicode strings to be consistent with StructField names |
| _cols = [x.encode("utf-8") if not isinstance(x, str) else x for x in schema] |
| _num_cols = len(_cols) |
| |
| if isinstance(data, np.ndarray) and data.ndim not in [1, 2]: |
| raise PySparkValueError( |
| error_class="INVALID_NDARRAY_DIMENSION", |
| message_parameters={"dimensions": "1 or 2"}, |
| ) |
| elif isinstance(data, Sized) and len(data) == 0: |
| if _schema is not None: |
| return DataFrame.withPlan(LocalRelation(table=None, schema=_schema.json()), self) |
| else: |
| raise PySparkValueError( |
| error_class="CANNOT_INFER_EMPTY_SCHEMA", |
| message_parameters={}, |
| ) |
| |
| _table: Optional[pa.Table] = None |
| |
| if isinstance(data, pd.DataFrame): |
| # Logic was borrowed from `_create_from_pandas_with_arrow` in |
| # `pyspark.sql.pandas.conversion.py`. Should ideally deduplicate the logics. |
| |
| # If no schema supplied by user then get the names of columns only |
| if schema is None: |
| _cols = [str(x) if not isinstance(x, str) else x for x in data.columns] |
| elif isinstance(schema, (list, tuple)) and cast(int, _num_cols) < len(data.columns): |
| assert isinstance(_cols, list) |
| _cols.extend([f"_{i + 1}" for i in range(cast(int, _num_cols), len(data.columns))]) |
| _num_cols = len(_cols) |
| |
| # Determine arrow types to coerce data when creating batches |
| arrow_schema: Optional[pa.Schema] = None |
| spark_types: List[Optional[DataType]] |
| arrow_types: List[Optional[pa.DataType]] |
| if isinstance(schema, StructType): |
| deduped_schema = cast(StructType, _deduplicate_field_names(schema)) |
| spark_types = [field.dataType for field in deduped_schema.fields] |
| arrow_schema = to_arrow_schema(deduped_schema) |
| arrow_types = [field.type for field in arrow_schema] |
| _cols = [str(x) if not isinstance(x, str) else x for x in schema.fieldNames()] |
| elif isinstance(schema, DataType): |
| raise PySparkTypeError( |
| error_class="UNSUPPORTED_DATA_TYPE_FOR_ARROW", |
| message_parameters={"data_type": str(schema)}, |
| ) |
| else: |
| # Any timestamps must be coerced to be compatible with Spark |
| spark_types = [ |
| TimestampType() |
| if is_datetime64_dtype(t) or isinstance(t, pd.DatetimeTZDtype) |
| else DayTimeIntervalType() |
| if is_timedelta64_dtype(t) |
| else None |
| for t in data.dtypes |
| ] |
| arrow_types = [to_arrow_type(dt) if dt is not None else None for dt in spark_types] |
| |
| timezone, safecheck = self._client.get_configs( |
| "spark.sql.session.timeZone", "spark.sql.execution.pandas.convertToArrowArraySafely" |
| ) |
| |
| ser = ArrowStreamPandasSerializer(cast(str, timezone), safecheck == "true") |
| |
| _table = pa.Table.from_batches( |
| [ |
| ser._create_batch( |
| [ |
| (c, at, st) |
| for (_, c), at, st in zip(data.items(), arrow_types, spark_types) |
| ] |
| ) |
| ] |
| ) |
| |
| if isinstance(schema, StructType): |
| assert arrow_schema is not None |
| _table = _table.rename_columns( |
| cast(StructType, _deduplicate_field_names(schema)).names |
| ).cast(arrow_schema) |
| |
| elif isinstance(data, np.ndarray): |
| if _cols is None: |
| if data.ndim == 1 or data.shape[1] == 1: |
| _cols = ["value"] |
| else: |
| _cols = ["_%s" % i for i in range(1, data.shape[1] + 1)] |
| |
| if data.ndim == 1: |
| if 1 != len(_cols): |
| raise PySparkValueError( |
| error_class="AXIS_LENGTH_MISMATCH", |
| message_parameters={ |
| "expected_length": str(len(_cols)), |
| "actual_length": "1", |
| }, |
| ) |
| |
| _table = pa.Table.from_arrays([pa.array(data)], _cols) |
| else: |
| if data.shape[1] != len(_cols): |
| raise PySparkValueError( |
| error_class="AXIS_LENGTH_MISMATCH", |
| message_parameters={ |
| "expected_length": str(len(_cols)), |
| "actual_length": str(data.shape[1]), |
| }, |
| ) |
| |
| _table = pa.Table.from_arrays( |
| [pa.array(data[::, i]) for i in range(0, data.shape[1])], _cols |
| ) |
| |
| # The _table should already have the proper column names. |
| _cols = None |
| |
| else: |
| _data = list(data) |
| |
| if isinstance(_data[0], dict): |
| # Sort the data to respect inferred schema. |
| # For dictionaries, we sort the schema in alphabetical order. |
| _data = [dict(sorted(d.items())) if d is not None else None for d in _data] |
| |
| elif not isinstance(_data[0], (Row, tuple, list, dict)) and not hasattr( |
| _data[0], "__dict__" |
| ): |
| # input data can be [1, 2, 3] |
| # we need to convert it to [[1], [2], [3]] to be able to infer schema. |
| _data = [[d] for d in _data] |
| |
| if _schema is not None: |
| if not isinstance(_schema, StructType): |
| _schema = StructType().add("value", _schema) |
| else: |
| _schema = self._inferSchemaFromList(_data, _cols) |
| |
| if _cols is not None and cast(int, _num_cols) < len(_cols): |
| _num_cols = len(_cols) |
| |
| if _has_nulltype(_schema): |
| # For cases like createDataFrame([("Alice", None, 80.1)], schema) |
| # we can not infer the schema from the data itself. |
| raise ValueError( |
| "Some of types cannot be determined after inferring, " |
| "a StructType Schema is required in this case" |
| ) |
| |
| from pyspark.sql.connect.conversion import LocalDataToArrowConversion |
| |
| # Spark Connect will try its best to build the Arrow table with the |
| # inferred schema in the client side, and then rename the columns and |
| # cast the datatypes in the server side. |
| _table = LocalDataToArrowConversion.convert(_data, _schema) |
| |
| # TODO: Beside the validation on number of columns, we should also check |
| # whether the Arrow Schema is compatible with the user provided Schema. |
| if _num_cols is not None and _num_cols != _table.shape[1]: |
| raise PySparkValueError( |
| error_class="AXIS_LENGTH_MISMATCH", |
| message_parameters={ |
| "expected_length": str(_num_cols), |
| "actual_length": str(_table.shape[1]), |
| }, |
| ) |
| |
| if _schema is not None: |
| local_relation = LocalRelation(_table, schema=_schema.json()) |
| else: |
| local_relation = LocalRelation(_table) |
| |
| cache_threshold = self._client.get_configs("spark.sql.session.localRelationCacheThreshold") |
| plan: LogicalPlan = local_relation |
| if cache_threshold[0] is not None and int(cache_threshold[0]) <= _table.nbytes: |
| plan = CachedLocalRelation(self._cache_local_relation(local_relation)) |
| |
| df = DataFrame.withPlan(plan, self) |
| if _cols is not None and len(_cols) > 0: |
| df = df.toDF(*_cols) |
| return df |
| |
| createDataFrame.__doc__ = PySparkSession.createDataFrame.__doc__ |
| |
| def sql(self, sqlQuery: str, args: Optional[Union[Dict[str, Any], List]] = None) -> "DataFrame": |
| cmd = SQL(sqlQuery, args) |
| data, properties = self.client.execute_command(cmd.command(self._client)) |
| if "sql_command_result" in properties: |
| return DataFrame.withPlan(CachedRelation(properties["sql_command_result"]), self) |
| else: |
| return DataFrame.withPlan(cmd, self) |
| |
| sql.__doc__ = PySparkSession.sql.__doc__ |
| |
| def range( |
| self, |
| start: int, |
| end: Optional[int] = None, |
| step: int = 1, |
| numPartitions: Optional[int] = None, |
| ) -> DataFrame: |
| if end is None: |
| actual_end = start |
| start = 0 |
| else: |
| actual_end = end |
| |
| if numPartitions is not None: |
| numPartitions = int(numPartitions) |
| |
| return DataFrame.withPlan( |
| Range( |
| start=int(start), end=int(actual_end), step=int(step), num_partitions=numPartitions |
| ), |
| self, |
| ) |
| |
| range.__doc__ = PySparkSession.range.__doc__ |
| |
| @property |
| def catalog(self) -> "Catalog": |
| from pyspark.sql.connect.catalog import Catalog |
| |
| if not hasattr(self, "_catalog"): |
| self._catalog = Catalog(self) |
| return self._catalog |
| |
| catalog.__doc__ = PySparkSession.catalog.__doc__ |
| |
| def __del__(self) -> None: |
| try: |
| # Try its best to close. |
| self.client.close() |
| except Exception: |
| pass |
| |
| def interruptAll(self) -> List[str]: |
| op_ids = self.client.interrupt_all() |
| assert op_ids is not None |
| return op_ids |
| |
| interruptAll.__doc__ = PySparkSession.interruptAll.__doc__ |
| |
| def interruptTag(self, tag: str) -> List[str]: |
| op_ids = self.client.interrupt_tag(tag) |
| assert op_ids is not None |
| return op_ids |
| |
| interruptTag.__doc__ = PySparkSession.interruptTag.__doc__ |
| |
| def interruptOperation(self, op_id: str) -> List[str]: |
| op_ids = self.client.interrupt_operation(op_id) |
| assert op_ids is not None |
| return op_ids |
| |
| interruptOperation.__doc__ = PySparkSession.interruptOperation.__doc__ |
| |
| def addTag(self, tag: str) -> None: |
| self.client.add_tag(tag) |
| |
| addTag.__doc__ = PySparkSession.addTag.__doc__ |
| |
| def removeTag(self, tag: str) -> None: |
| self.client.remove_tag(tag) |
| |
| removeTag.__doc__ = PySparkSession.removeTag.__doc__ |
| |
| def getTags(self) -> Set[str]: |
| return self.client.get_tags() |
| |
| getTags.__doc__ = PySparkSession.getTags.__doc__ |
| |
| def clearTags(self) -> None: |
| return self.client.clear_tags() |
| |
| clearTags.__doc__ = PySparkSession.clearTags.__doc__ |
| |
| def stop(self) -> None: |
| # Whereas the regular PySpark session immediately terminates the Spark Context |
| # itself, meaning that stopping all Spark sessions, this will only stop this one session |
| # on the server. |
| # It is controversial to follow the existing the regular Spark session's behavior |
| # specifically in Spark Connect the Spark Connect server is designed for |
| # multi-tenancy - the remote client side cannot just stop the server and stop |
| # other remote clients being used from other users. |
| with SparkSession._lock: |
| if not self.is_stopped and self.release_session_on_close: |
| self.client.release_session() |
| self.client.close() |
| if self is SparkSession._default_session: |
| SparkSession._default_session = None |
| if self is getattr(SparkSession._active_session, "session", None): |
| SparkSession._active_session.session = None |
| |
| if "SPARK_LOCAL_REMOTE" in os.environ: |
| # When local mode is in use, follow the regular Spark session's |
| # behavior by terminating the Spark Connect server, |
| # meaning that you can stop local mode, and restart the Spark Connect |
| # client with a different remote address. |
| if PySparkSession._activeSession is not None: |
| PySparkSession._activeSession.stop() |
| del os.environ["SPARK_LOCAL_REMOTE"] |
| del os.environ["SPARK_CONNECT_MODE_ENABLED"] |
| if "SPARK_REMOTE" in os.environ: |
| del os.environ["SPARK_REMOTE"] |
| |
| stop.__doc__ = PySparkSession.stop.__doc__ |
| |
| @property |
| def is_stopped(self) -> bool: |
| """ |
| Returns if this session was stopped |
| """ |
| return self.client.is_closed |
| |
| @property |
| def conf(self) -> RuntimeConf: |
| return RuntimeConf(self.client) |
| |
| conf.__doc__ = PySparkSession.conf.__doc__ |
| |
| @property |
| def streams(self) -> "StreamingQueryManager": |
| return StreamingQueryManager(self) |
| |
| streams.__doc__ = PySparkSession.streams.__doc__ |
| |
| def __getattr__(self, name: str) -> Any: |
| if name in ["_jsc", "_jconf", "_jvm", "_jsparkSession", "sparkContext", "newSession"]: |
| raise PySparkAttributeError( |
| error_class="JVM_ATTRIBUTE_NOT_SUPPORTED", message_parameters={"attr_name": name} |
| ) |
| return object.__getattribute__(self, name) |
| |
| @property |
| def udf(self) -> "UDFRegistration": |
| from pyspark.sql.connect.udf import UDFRegistration |
| |
| return UDFRegistration(self) |
| |
| udf.__doc__ = PySparkSession.udf.__doc__ |
| |
| @property |
| def udtf(self) -> "UDTFRegistration": |
| from pyspark.sql.connect.udtf import UDTFRegistration |
| |
| return UDTFRegistration(self) |
| |
| udtf.__doc__ = PySparkSession.udtf.__doc__ |
| |
| @property |
| def version(self) -> str: |
| result = self._client._analyze(method="spark_version").spark_version |
| assert result is not None |
| return result |
| |
| version.__doc__ = PySparkSession.version.__doc__ |
| |
| @property |
| def client(self) -> "SparkConnectClient": |
| return self._client |
| |
| client.__doc__ = PySparkSession.client.__doc__ |
| |
| def addArtifacts( |
| self, *path: str, pyfile: bool = False, archive: bool = False, file: bool = False |
| ) -> None: |
| if sum([file, pyfile, archive]) > 1: |
| raise ValueError("'pyfile', 'archive' and/or 'file' cannot be True together.") |
| self._client.add_artifacts(*path, pyfile=pyfile, archive=archive, file=file) |
| |
| addArtifacts.__doc__ = PySparkSession.addArtifacts.__doc__ |
| |
| addArtifact = addArtifacts |
| |
| def _cache_local_relation(self, local_relation: LocalRelation) -> str: |
| """ |
| Cache the local relation at the server side if it has not been cached yet. |
| """ |
| serialized = local_relation.serialize(self._client) |
| return self._client.cache_artifact(serialized) |
| |
| def copyFromLocalToFs(self, local_path: str, dest_path: str) -> None: |
| if urllib.parse.urlparse(dest_path).scheme: |
| raise ValueError( |
| "`spark_session.copyFromLocalToFs` API only allows `dest_path` to be a path " |
| "without scheme, and spark driver uses the default scheme to " |
| "determine the destination file system." |
| ) |
| self._client.copy_from_local_to_fs(local_path, dest_path) |
| |
| copyFromLocalToFs.__doc__ = PySparkSession.copyFromLocalToFs.__doc__ |
| |
| def _create_remote_dataframe(self, remote_id: str) -> "DataFrame": |
| """ |
| In internal API to reference a runtime DataFrame on the server side. |
| This is used in ForeachBatch() runner, where the remote DataFrame refers to the |
| output of a micro batch. |
| """ |
| return DataFrame.withPlan(CachedRemoteRelation(remote_id), self) |
| |
| @staticmethod |
| def _start_connect_server(master: str, opts: Dict[str, Any]) -> None: |
| """ |
| Starts the Spark Connect server given the master (thread-unsafe). |
| |
| At the high level, there are two cases. The first case is development case, e.g., |
| you locally build Apache Spark, and run ``SparkSession.builder.remote("local")``: |
| |
| 1. This method automatically finds the jars for Spark Connect (because the jars for |
| Spark Connect are not bundled in the regular Apache Spark release). |
| |
| 2. Temporarily remove all states for Spark Connect, for example, ``SPARK_REMOTE`` |
| environment variable. |
| |
| 3. Starts a JVM (without Spark Context) first, and adds the Spark Connect server jars |
| into the current class loader. Otherwise, Spark Context with ``spark.plugins`` |
| cannot be initialized because the JVM is already running without the jars in |
| the classpath before executing this Python process for driver side (in case of |
| PySpark application submission). |
| |
| 4. Starts a regular Spark session that automatically starts a Spark Connect server |
| via ``spark.plugins`` feature. |
| |
| The second case is when you use Apache Spark release: |
| |
| 1. Users must specify either the jars or package, e.g., ``--packages |
| org.apache.spark:spark-connect_2.12:3.4.0``. The jars or packages would be specified |
| in SparkSubmit automatically. This method does not do anything related to this. |
| |
| 2. Temporarily remove all states for Spark Connect, for example, ``SPARK_REMOTE`` |
| environment variable. It does not do anything for PySpark application submission as |
| well because jars or packages were already specified before executing this Python |
| process for driver side. |
| |
| 3. Starts a regular Spark session that automatically starts a Spark Connect server |
| with JVM via ``spark.plugins`` feature. |
| """ |
| session = PySparkSession._instantiatedSession |
| if session is None or session._sc._jsc is None: |
| # Configurations to be overwritten |
| overwrite_conf = opts |
| overwrite_conf["spark.master"] = master |
| overwrite_conf["spark.local.connect"] = "1" |
| |
| # Configurations to be set if unset. |
| default_conf = {"spark.plugins": "org.apache.spark.sql.connect.SparkConnectPlugin"} |
| |
| if "SPARK_TESTING" in os.environ: |
| # For testing, we use 0 to use an ephemeral port to allow parallel testing. |
| # See also SPARK-42272. |
| overwrite_conf["spark.connect.grpc.binding.port"] = "0" |
| |
| def create_conf(**kwargs: Any) -> SparkConf: |
| conf = SparkConf(**kwargs) |
| for k, v in overwrite_conf.items(): |
| conf.set(k, v) |
| for k, v in default_conf.items(): |
| if not conf.contains(k): |
| conf.set(k, v) |
| return conf |
| |
| # Check if we're using unreleased version that is in development. |
| # Also checks SPARK_TESTING for RC versions. |
| is_dev_mode = ( |
| "dev" in LooseVersion(__version__).version or "SPARK_TESTING" in os.environ |
| ) |
| |
| origin_remote = os.environ.get("SPARK_REMOTE", None) |
| try: |
| if origin_remote is not None: |
| # So SparkSubmit thinks no remote is set in order to |
| # start the regular PySpark session. |
| del os.environ["SPARK_REMOTE"] |
| |
| SparkContext._ensure_initialized(conf=create_conf(loadDefaults=False)) |
| |
| if is_dev_mode: |
| # Try and catch for a possibility in production because pyspark.testing |
| # does not exist in the canonical release. |
| try: |
| from pyspark.testing.utils import search_jar |
| |
| # Note that, in production, spark.jars.packages configuration should be |
| # set by users. Here we're automatically searching the jars locally built. |
| connect_jar = search_jar( |
| "connector/connect/server", "spark-connect-assembly-", "spark-connect" |
| ) |
| if connect_jar is None: |
| warnings.warn( |
| "Attempted to automatically find the Spark Connect jars because " |
| "'SPARK_TESTING' environment variable is set, or the current " |
| f"PySpark version is dev version ({__version__}). However, the jar" |
| " was not found. Manually locate the jars and specify them, e.g., " |
| "'spark.jars' configuration." |
| ) |
| else: |
| pyutils = SparkContext._jvm.PythonSQLUtils # type: ignore[union-attr] |
| pyutils.addJarToCurrentClassLoader(connect_jar) |
| |
| # Required for local-cluster testing as their executors need the jars |
| # to load the Spark plugin for Spark Connect. |
| if master.startswith("local-cluster"): |
| if "spark.jars" in overwrite_conf: |
| overwrite_conf[ |
| "spark.jars" |
| ] = f"{overwrite_conf['spark.jars']},{connect_jar}" |
| else: |
| overwrite_conf["spark.jars"] = connect_jar |
| |
| except ImportError: |
| pass |
| |
| # The regular PySpark session is registered as an active session |
| # so would not be garbage-collected. |
| PySparkSession( |
| SparkContext.getOrCreate(create_conf(loadDefaults=True, _jvm=SparkContext._jvm)) |
| ) |
| |
| # Lastly only keep runtime configurations because other configurations are |
| # disallowed to set in the regular Spark Connect session. |
| utl = SparkContext._jvm.PythonSQLUtils # type: ignore[union-attr] |
| runtime_conf_keys = [c._1() for c in utl.listRuntimeSQLConfigs()] |
| new_opts = {k: opts[k] for k in opts if k in runtime_conf_keys} |
| opts.clear() |
| opts.update(new_opts) |
| |
| finally: |
| if origin_remote is not None: |
| os.environ["SPARK_REMOTE"] = origin_remote |
| else: |
| raise PySparkRuntimeError( |
| error_class="SESSION_OR_CONTEXT_EXISTS", |
| message_parameters={}, |
| ) |
| |
| @property |
| def session_id(self) -> str: |
| return self._session_id |
| |
| |
| SparkSession.__doc__ = PySparkSession.__doc__ |
| |
| |
| def _test() -> None: |
| import sys |
| import doctest |
| from pyspark.sql import SparkSession as PySparkSession |
| import pyspark.sql.connect.session |
| |
| globs = pyspark.sql.connect.session.__dict__.copy() |
| globs["spark"] = ( |
| PySparkSession.builder.appName("sql.connect.session tests").remote("local[4]").getOrCreate() |
| ) |
| |
| # Uses PySpark session to test builder. |
| globs["SparkSession"] = PySparkSession |
| # Spark Connect does not support to set master together. |
| pyspark.sql.connect.session.SparkSession.__doc__ = None |
| del pyspark.sql.connect.session.SparkSession.Builder.master.__doc__ |
| # RDD API is not supported in Spark Connect. |
| del pyspark.sql.connect.session.SparkSession.createDataFrame.__doc__ |
| |
| # TODO(SPARK-41811): Implement SparkSession.sql's string formatter |
| del pyspark.sql.connect.session.SparkSession.sql.__doc__ |
| |
| (failure_count, test_count) = doctest.testmod( |
| pyspark.sql.connect.session, |
| globs=globs, |
| optionflags=doctest.ELLIPSIS |
| | doctest.NORMALIZE_WHITESPACE |
| | doctest.IGNORE_EXCEPTION_DETAIL, |
| ) |
| |
| globs["spark"].stop() |
| |
| if failure_count: |
| sys.exit(-1) |
| |
| |
| if __name__ == "__main__": |
| _test() |
