# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys from collections.abc import Iterator from typing import cast, overload, Any, Callable, List, Optional, TYPE_CHECKING, Union from py4j.java_gateway import java_import, JavaObject from pyspark.sql.column import _to_seq from pyspark.sql.readwriter import OptionUtils, to_str from pyspark.sql.streaming.query import StreamingQuery from pyspark.sql.types import Row, StructType from pyspark.sql.utils import ForeachBatchFunction if TYPE_CHECKING: from pyspark.sql.session import SparkSession from pyspark.sql._typing import SupportsProcess, OptionalPrimitiveType from pyspark.sql.dataframe import DataFrame __all__ = ["DataStreamReader", "DataStreamWriter"] [docs]class DataStreamReader(OptionUtils): """ Interface used to load a streaming :class:`DataFrame <pyspark.sql.DataFrame>` from external storage systems (e.g. file systems, key-value stores, etc). Use :attr:`SparkSession.readStream <pyspark.sql.SparkSession.readStream>` to access this. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- >>> spark.readStream <pyspark.sql.streaming.readwriter.DataStreamReader object ...> The example below uses Rate source that generates rows continuously. After that, we operate a modulo by 3, and then writes the stream out to the console. The streaming query stops in 3 seconds. >>> import time >>> df = spark.readStream.format("rate").load() >>> df = df.selectExpr("value % 3 as v") >>> q = df.writeStream.format("console").start() >>> time.sleep(3) >>> q.stop() """ def __init__(self, spark: "SparkSession") -> None: self._jreader = spark._jsparkSession.readStream() self._spark = spark def _df(self, jdf: JavaObject) -> "DataFrame": from pyspark.sql.dataframe import DataFrame return DataFrame(jdf, self._spark) [docs] def format(self, source: str) -> "DataStreamReader": """Specifies the input data source format. .. versionadded:: 2.0.0 Parameters ---------- source : str name of the data source, e.g. 'json', 'parquet'. Notes ----- This API is evolving. Examples -------- >>> spark.readStream.format("text") <pyspark.sql.streaming.readwriter.DataStreamReader object ...> This API allows to configure other sources to read. The example below writes a small text file, and reads it back via Text source. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary text file to read it. ... spark.createDataFrame( ... [("hello",), ("this",)]).write.mode("overwrite").format("text").save(d) ... ... # Start a streaming query to read the text file. ... q = spark.readStream.format("text").load(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._jreader = self._jreader.format(source) return self [docs] def schema(self, schema: Union[StructType, str]) -> "DataStreamReader": """Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading. .. versionadded:: 2.0.0 Parameters ---------- schema : :class:`pyspark.sql.types.StructType` or str a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``). Notes ----- This API is evolving. Examples -------- >>> from pyspark.sql.types import StructField, StructType, StringType >>> spark.readStream.schema(StructType([StructField("data", StringType(), True)])) <pyspark.sql.streaming.readwriter.DataStreamReader object ...> >>> spark.readStream.schema("col0 INT, col1 DOUBLE") <pyspark.sql.streaming.readwriter.DataStreamReader object ...> The example below specifies a different schema to CSV file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Start a streaming query to read the CSV file. ... spark.readStream.schema("col0 INT, col1 STRING").format("csv").load(d).printSchema() root |-- col0: integer (nullable = true) |-- col1: string (nullable = true) """ from pyspark.sql import SparkSession spark = SparkSession._getActiveSessionOrCreate() if isinstance(schema, StructType): jschema = spark._jsparkSession.parseDataType(schema.json()) self._jreader = self._jreader.schema(jschema) elif isinstance(schema, str): self._jreader = self._jreader.schema(schema) else: raise TypeError("schema should be StructType or string") return self [docs] def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamReader": """Adds an input option for the underlying data source. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- >>> spark.readStream.option("x", 1) <pyspark.sql.streaming.readwriter.DataStreamReader object ...> The example below specifies 'rowsPerSecond' option to Rate source in order to generate 10 rows every second. >>> import time >>> q = spark.readStream.format( ... "rate").option("rowsPerSecond", 10).load().writeStream.format("console").start() >>> time.sleep(3) >>> q.stop() """ self._jreader = self._jreader.option(key, to_str(value)) return self [docs] def options(self, **options: "OptionalPrimitiveType") -> "DataStreamReader": """Adds input options for the underlying data source. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- >>> spark.readStream.options(x="1", y=2) <pyspark.sql.streaming.readwriter.DataStreamReader object ...> The example below specifies 'rowsPerSecond' and 'numPartitions' options to Rate source in order to generate 10 rows with 10 partitions every second. >>> import time >>> q = spark.readStream.format("rate").options( ... rowsPerSecond=10, numPartitions=10 ... ).load().writeStream.format("console").start() >>> time.sleep(3) >>> q.stop() """ for k in options: self._jreader = self._jreader.option(k, to_str(options[k])) return self [docs] def load( self, path: Optional[str] = None, format: Optional[str] = None, schema: Optional[Union[StructType, str]] = None, **options: "OptionalPrimitiveType", ) -> "DataFrame": """Loads a data stream from a data source and returns it as a :class:`DataFrame <pyspark.sql.DataFrame>`. .. versionadded:: 2.0.0 Parameters ---------- path : str, optional optional string for file-system backed data sources. format : str, optional optional string for format of the data source. Default to 'parquet'. schema : :class:`pyspark.sql.types.StructType` or str, optional optional :class:`pyspark.sql.types.StructType` for the input schema or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``). **options : dict all other string options Notes ----- This API is evolving. Examples -------- Load a data stream from a temporary JSON file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary JSON file to read it. ... spark.createDataFrame( ... [(100, "Hyukjin Kwon"),], ["age", "name"] ... ).write.mode("overwrite").format("json").save(d) ... ... # Start a streaming query to read the JSON file. ... q = spark.readStream.schema( ... "age INT, name STRING" ... ).format("json").load(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ if format is not None: self.format(format) if schema is not None: self.schema(schema) self.options(**options) if path is not None: if type(path) != str or len(path.strip()) == 0: raise ValueError( "If the path is provided for stream, it needs to be a " + "non-empty string. List of paths are not supported." ) return self._df(self._jreader.load(path)) else: return self._df(self._jreader.load()) [docs] def json( self, path: str, schema: Optional[Union[StructType, str]] = None, primitivesAsString: Optional[Union[bool, str]] = None, prefersDecimal: Optional[Union[bool, str]] = None, allowComments: Optional[Union[bool, str]] = None, allowUnquotedFieldNames: Optional[Union[bool, str]] = None, allowSingleQuotes: Optional[Union[bool, str]] = None, allowNumericLeadingZero: Optional[Union[bool, str]] = None, allowBackslashEscapingAnyCharacter: Optional[Union[bool, str]] = None, mode: Optional[str] = None, columnNameOfCorruptRecord: Optional[str] = None, dateFormat: Optional[str] = None, timestampFormat: Optional[str] = None, multiLine: Optional[Union[bool, str]] = None, allowUnquotedControlChars: Optional[Union[bool, str]] = None, lineSep: Optional[str] = None, locale: Optional[str] = None, dropFieldIfAllNull: Optional[Union[bool, str]] = None, encoding: Optional[str] = None, pathGlobFilter: Optional[Union[bool, str]] = None, recursiveFileLookup: Optional[Union[bool, str]] = None, allowNonNumericNumbers: Optional[Union[bool, str]] = None, ) -> "DataFrame": """ Loads a JSON file stream and returns the results as a :class:`DataFrame`. `JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default. For JSON (one record per file), set the ``multiLine`` parameter to ``true``. If the ``schema`` parameter is not specified, this function goes through the input once to determine the input schema. .. versionadded:: 2.0.0 Parameters ---------- path : str string represents path to the JSON dataset, or RDD of Strings storing JSON objects. schema : :class:`pyspark.sql.types.StructType` or str, optional an optional :class:`pyspark.sql.types.StructType` for the input schema or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``). Other Parameters ---------------- Extra options For the extra options, refer to `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_ in the version you use. .. # noqa Notes ----- This API is evolving. Examples -------- Load a data stream from a temporary JSON file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary JSON file to read it. ... spark.createDataFrame( ... [(100, "Hyukjin Kwon"),], ["age", "name"] ... ).write.mode("overwrite").format("json").save(d) ... ... # Start a streaming query to read the JSON file. ... q = spark.readStream.schema( ... "age INT, name STRING" ... ).json(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._set_opts( schema=schema, primitivesAsString=primitivesAsString, prefersDecimal=prefersDecimal, allowComments=allowComments, allowUnquotedFieldNames=allowUnquotedFieldNames, allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero, allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter, mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat, timestampFormat=timestampFormat, multiLine=multiLine, allowUnquotedControlChars=allowUnquotedControlChars, lineSep=lineSep, locale=locale, dropFieldIfAllNull=dropFieldIfAllNull, encoding=encoding, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup, allowNonNumericNumbers=allowNonNumericNumbers, ) if isinstance(path, str): return self._df(self._jreader.json(path)) else: raise TypeError("path can be only a single string") [docs] def orc( self, path: str, mergeSchema: Optional[bool] = None, pathGlobFilter: Optional[Union[bool, str]] = None, recursiveFileLookup: Optional[Union[bool, str]] = None, ) -> "DataFrame": """Loads a ORC file stream, returning the result as a :class:`DataFrame`. .. versionadded:: 2.3.0 Other Parameters ---------------- Extra options For the extra options, refer to `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option>`_ in the version you use. .. # noqa Examples -------- Load a data stream from a temporary ORC file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary ORC file to read it. ... spark.range(10).write.mode("overwrite").format("orc").save(d) ... ... # Start a streaming query to read the ORC file. ... q = spark.readStream.schema("id LONG").orc(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._set_opts( mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup, ) if isinstance(path, str): return self._df(self._jreader.orc(path)) else: raise TypeError("path can be only a single string") [docs] def parquet( self, path: str, mergeSchema: Optional[bool] = None, pathGlobFilter: Optional[Union[bool, str]] = None, recursiveFileLookup: Optional[Union[bool, str]] = None, datetimeRebaseMode: Optional[Union[bool, str]] = None, int96RebaseMode: Optional[Union[bool, str]] = None, ) -> "DataFrame": """ Loads a Parquet file stream, returning the result as a :class:`DataFrame`. .. versionadded:: 2.0.0 Parameters ---------- path : str the path in any Hadoop supported file system Other Parameters ---------------- Extra options For the extra options, refer to `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option>`_. in the version you use. .. # noqa Examples -------- Load a data stream from a temporary Parquet file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary Parquet file to read it. ... spark.range(10).write.mode("overwrite").format("parquet").save(d) ... ... # Start a streaming query to read the Parquet file. ... q = spark.readStream.schema( ... "id LONG").parquet(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._set_opts( mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup, datetimeRebaseMode=datetimeRebaseMode, int96RebaseMode=int96RebaseMode, ) if isinstance(path, str): return self._df(self._jreader.parquet(path)) else: raise TypeError("path can be only a single string") [docs] def text( self, path: str, wholetext: bool = False, lineSep: Optional[str] = None, pathGlobFilter: Optional[Union[bool, str]] = None, recursiveFileLookup: Optional[Union[bool, str]] = None, ) -> "DataFrame": """ Loads a text file stream and returns a :class:`DataFrame` whose schema starts with a string column named "value", and followed by partitioned columns if there are any. The text files must be encoded as UTF-8. By default, each line in the text file is a new row in the resulting DataFrame. .. versionadded:: 2.0.0 Parameters ---------- path : str or list string, or list of strings, for input path(s). Other Parameters ---------------- Extra options For the extra options, refer to `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option>`_ in the version you use. .. # noqa Notes ----- This API is evolving. Examples -------- Load a data stream from a temporary text file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary text file to read it. ... spark.createDataFrame( ... [("hello",), ("this",)]).write.mode("overwrite").format("text").save(d) ... ... # Start a streaming query to read the text file. ... q = spark.readStream.text(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._set_opts( wholetext=wholetext, lineSep=lineSep, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup, ) if isinstance(path, str): return self._df(self._jreader.text(path)) else: raise TypeError("path can be only a single string") [docs] def csv( self, path: str, schema: Optional[Union[StructType, str]] = None, sep: Optional[str] = None, encoding: Optional[str] = None, quote: Optional[str] = None, escape: Optional[str] = None, comment: Optional[str] = None, header: Optional[Union[bool, str]] = None, inferSchema: Optional[Union[bool, str]] = None, ignoreLeadingWhiteSpace: Optional[Union[bool, str]] = None, ignoreTrailingWhiteSpace: Optional[Union[bool, str]] = None, nullValue: Optional[str] = None, nanValue: Optional[str] = None, positiveInf: Optional[str] = None, negativeInf: Optional[str] = None, dateFormat: Optional[str] = None, timestampFormat: Optional[str] = None, maxColumns: Optional[Union[int, str]] = None, maxCharsPerColumn: Optional[Union[int, str]] = None, maxMalformedLogPerPartition: Optional[Union[int, str]] = None, mode: Optional[str] = None, columnNameOfCorruptRecord: Optional[str] = None, multiLine: Optional[Union[bool, str]] = None, charToEscapeQuoteEscaping: Optional[Union[bool, str]] = None, enforceSchema: Optional[Union[bool, str]] = None, emptyValue: Optional[str] = None, locale: Optional[str] = None, lineSep: Optional[str] = None, pathGlobFilter: Optional[Union[bool, str]] = None, recursiveFileLookup: Optional[Union[bool, str]] = None, unescapedQuoteHandling: Optional[str] = None, ) -> "DataFrame": r"""Loads a CSV file stream and returns the result as a :class:`DataFrame`. This function will go through the input once to determine the input schema if ``inferSchema`` is enabled. To avoid going through the entire data once, disable ``inferSchema`` option or specify the schema explicitly using ``schema``. Parameters ---------- path : str or list string, or list of strings, for input path(s). schema : :class:`pyspark.sql.types.StructType` or str, optional an optional :class:`pyspark.sql.types.StructType` for the input schema or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``). .. versionadded:: 2.0.0 Other Parameters ---------------- Extra options For the extra options, refer to `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_ in the version you use. .. # noqa Notes ----- This API is evolving. Examples -------- Load a data stream from a temporary CSV file. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d: ... # Write a temporary text file to read it. ... spark.createDataFrame([(1, "2"),]).write.mode("overwrite").format("csv").save(d) ... ... # Start a streaming query to read the CSV file. ... q = spark.readStream.schema( ... "col0 INT, col1 STRING" ... ).format("csv").load(d).writeStream.format("console").start() ... time.sleep(3) ... q.stop() """ self._set_opts( schema=schema, sep=sep, encoding=encoding, quote=quote, escape=escape, comment=comment, header=header, inferSchema=inferSchema, ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace, nullValue=nullValue, nanValue=nanValue, positiveInf=positiveInf, negativeInf=negativeInf, dateFormat=dateFormat, timestampFormat=timestampFormat, maxColumns=maxColumns, maxCharsPerColumn=maxCharsPerColumn, maxMalformedLogPerPartition=maxMalformedLogPerPartition, mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, multiLine=multiLine, charToEscapeQuoteEscaping=charToEscapeQuoteEscaping, enforceSchema=enforceSchema, emptyValue=emptyValue, locale=locale, lineSep=lineSep, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup, unescapedQuoteHandling=unescapedQuoteHandling, ) if isinstance(path, str): return self._df(self._jreader.csv(path)) else: raise TypeError("path can be only a single string") [docs] def table(self, tableName: str) -> "DataFrame": """Define a Streaming DataFrame on a Table. The DataSource corresponding to the table should support streaming mode. .. versionadded:: 3.1.0 Parameters ---------- tableName : str string, for the name of the table. Returns ------- :class:`DataFrame` Notes ----- This API is evolving. Examples -------- Load a data stream from a table. >>> import tempfile >>> import time >>> _ = spark.sql("DROP TABLE IF EXISTS my_table") >>> with tempfile.TemporaryDirectory() as d: ... # Create a table with Rate source. ... q1 = spark.readStream.format("rate").load().writeStream.toTable( ... "my_table", checkpointLocation=d) ... ... # Read the table back and print out in the console. ... q2 = spark.readStream.table("my_table").writeStream.format("console").start() ... time.sleep(3) ... q1.stop() ... q2.stop() ... _ = spark.sql("DROP TABLE my_table") """ if isinstance(tableName, str): return self._df(self._jreader.table(tableName)) else: raise TypeError("tableName can be only a single string") [docs]class DataStreamWriter: """ Interface used to write a streaming :class:`DataFrame <pyspark.sql.DataFrame>` to external storage systems (e.g. file systems, key-value stores, etc). Use :attr:`DataFrame.writeStream <pyspark.sql.DataFrame.writeStream>` to access this. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- The example below uses Rate source that generates rows continuously. After that, we operate a modulo by 3, and then writes the stream out to the console. The streaming query stops in 3 seconds. >>> import time >>> df = spark.readStream.format("rate").load() >>> df = df.selectExpr("value % 3 as v") >>> q = df.writeStream.format("console").start() >>> time.sleep(3) >>> q.stop() """ def __init__(self, df: "DataFrame") -> None: self._df = df self._spark = df.sparkSession self._jwrite = df._jdf.writeStream() def _sq(self, jsq: JavaObject) -> StreamingQuery: return StreamingQuery(jsq) [docs] def outputMode(self, outputMode: str) -> "DataStreamWriter": """Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. .. versionadded:: 2.0.0 Options include: * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to the sink * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the sink every time these are some updates * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to `append` mode. Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() >>> df.writeStream.outputMode('append') <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> The example below uses Complete mode that the entire aggregated counts are printed out. >>> import time >>> df = spark.readStream.format("rate").option("rowsPerSecond", 10).load() >>> df = df.groupby().count() >>> q = df.writeStream.outputMode("complete").format("console").start() >>> time.sleep(3) >>> q.stop() """ if not outputMode or type(outputMode) != str or len(outputMode.strip()) == 0: raise ValueError("The output mode must be a non-empty string. Got: %s" % outputMode) self._jwrite = self._jwrite.outputMode(outputMode) return self [docs] def format(self, source: str) -> "DataStreamWriter": """Specifies the underlying output data source. .. versionadded:: 2.0.0 Parameters ---------- source : str string, name of the data source, which for now can be 'parquet'. Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() >>> df.writeStream.format("text") <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> This API allows to configure the source to write. The example below writes a CSV file from Rate source in a streaming manner. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d, tempfile.TemporaryDirectory() as cp: ... df = spark.readStream.format("rate").load() ... q = df.writeStream.format("csv").option("checkpointLocation", cp).start(d) ... time.sleep(5) ... q.stop() ... spark.read.schema("timestamp TIMESTAMP, value STRING").csv(d).show() +...---------+-----+ |...timestamp|value| +...---------+-----+ ... """ self._jwrite = self._jwrite.format(source) return self [docs] def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamWriter": """Adds an output option for the underlying data source. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() >>> df.writeStream.option("x", 1) <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> The example below specifies 'numRows' option to Console source in order to print 3 rows for every batch. >>> import time >>> q = spark.readStream.format( ... "rate").option("rowsPerSecond", 10).load().writeStream.format( ... "console").option("numRows", 3).start() >>> time.sleep(3) >>> q.stop() """ self._jwrite = self._jwrite.option(key, to_str(value)) return self [docs] def options(self, **options: "OptionalPrimitiveType") -> "DataStreamWriter": """Adds output options for the underlying data source. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() >>> df.writeStream.option("x", 1) <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> The example below specifies 'numRows' and 'truncate' options to Console source in order to print 3 rows for every batch without truncating the results. >>> import time >>> q = spark.readStream.format( ... "rate").option("rowsPerSecond", 10).load().writeStream.format( ... "console").options(numRows=3, truncate=False).start() >>> time.sleep(3) >>> q.stop() """ for k in options: self._jwrite = self._jwrite.option(k, to_str(options[k])) return self @overload def partitionBy(self, *cols: str) -> "DataStreamWriter": ... @overload def partitionBy(self, __cols: List[str]) -> "DataStreamWriter": ... [docs] def partitionBy(self, *cols: str) -> "DataStreamWriter": # type: ignore[misc] """Partitions the output by the given columns on the file system. If specified, the output is laid out on the file system similar to Hive's partitioning scheme. .. versionadded:: 2.0.0 Parameters ---------- cols : str or list name of columns Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() >>> df.writeStream.partitionBy("value") <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> Partition-by timestamp column from Rate source. >>> import tempfile >>> import time >>> with tempfile.TemporaryDirectory() as d, tempfile.TemporaryDirectory() as cp: ... df = spark.readStream.format("rate").option("rowsPerSecond", 10).load() ... q = df.writeStream.partitionBy( ... "timestamp").format("parquet").option("checkpointLocation", cp).start(d) ... time.sleep(5) ... q.stop() ... spark.read.schema(df.schema).parquet(d).show() +...---------+-----+ |...timestamp|value| +...---------+-----+ ... """ if len(cols) == 1 and isinstance(cols[0], (list, tuple)): cols = cols[0] self._jwrite = self._jwrite.partitionBy(_to_seq(self._spark._sc, cols)) return self [docs] def queryName(self, queryName: str) -> "DataStreamWriter": """Specifies the name of the :class:`StreamingQuery` that can be started with :func:`start`. This name must be unique among all the currently active queries in the associated SparkSession. .. versionadded:: 2.0.0 Parameters ---------- queryName : str unique name for the query Notes ----- This API is evolving. Examples -------- >>> import time >>> df = spark.readStream.format("rate").load() >>> q = df.writeStream.queryName("streaming_query").format("console").start() >>> q.stop() >>> q.name 'streaming_query' """ if not queryName or type(queryName) != str or len(queryName.strip()) == 0: raise ValueError("The queryName must be a non-empty string. Got: %s" % queryName) self._jwrite = self._jwrite.queryName(queryName) return self @overload def trigger(self, *, processingTime: str) -> "DataStreamWriter": ... @overload def trigger(self, *, once: bool) -> "DataStreamWriter": ... @overload def trigger(self, *, continuous: str) -> "DataStreamWriter": ... @overload def trigger(self, *, availableNow: bool) -> "DataStreamWriter": ... [docs] def trigger( self, *, processingTime: Optional[str] = None, once: Optional[bool] = None, continuous: Optional[str] = None, availableNow: Optional[bool] = None, ) -> "DataStreamWriter": """Set the trigger for the stream query. If this is not set it will run the query as fast as possible, which is equivalent to setting the trigger to ``processingTime='0 seconds'``. .. versionadded:: 2.0.0 Parameters ---------- processingTime : str, optional a processing time interval as a string, e.g. '5 seconds', '1 minute'. Set a trigger that runs a microbatch query periodically based on the processing time. Only one trigger can be set. once : bool, optional if set to True, set a trigger that processes only one batch of data in a streaming query then terminates the query. Only one trigger can be set. continuous : str, optional a time interval as a string, e.g. '5 seconds', '1 minute'. Set a trigger that runs a continuous query with a given checkpoint interval. Only one trigger can be set. availableNow : bool, optional if set to True, set a trigger that processes all available data in multiple batches then terminates the query. Only one trigger can be set. Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() Trigger the query for execution every 5 seconds >>> df.writeStream.trigger(processingTime='5 seconds') <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> Trigger the query for execution every 5 seconds >>> df.writeStream.trigger(continuous='5 seconds') <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> Trigger the query for reading all available data with multiple batches >>> df.writeStream.trigger(availableNow=True) <pyspark.sql.streaming.readwriter.DataStreamWriter object ...> """ params = [processingTime, once, continuous, availableNow] if params.count(None) == 4: raise ValueError("No trigger provided") elif params.count(None) < 3: raise ValueError("Multiple triggers not allowed.") jTrigger = None assert self._spark._sc._jvm is not None if processingTime is not None: if type(processingTime) != str or len(processingTime.strip()) == 0: raise ValueError( "Value for processingTime must be a non empty string. Got: %s" % processingTime ) interval = processingTime.strip() jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.ProcessingTime( interval ) elif once is not None: if once is not True: raise ValueError("Value for once must be True. Got: %s" % once) jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.Once() elif continuous is not None: if type(continuous) != str or len(continuous.strip()) == 0: raise ValueError( "Value for continuous must be a non empty string. Got: %s" % continuous ) interval = continuous.strip() jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.Continuous( interval ) else: if availableNow is not True: raise ValueError("Value for availableNow must be True. Got: %s" % availableNow) jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.AvailableNow() self._jwrite = self._jwrite.trigger(jTrigger) return self @overload def foreach(self, f: Callable[[Row], None]) -> "DataStreamWriter": ... @overload def foreach(self, f: "SupportsProcess") -> "DataStreamWriter": ... [docs] def foreach(self, f: Union[Callable[[Row], None], "SupportsProcess"]) -> "DataStreamWriter": """ Sets the output of the streaming query to be processed using the provided writer ``f``. This is often used to write the output of a streaming query to arbitrary storage systems. The processing logic can be specified in two ways. #. A **function** that takes a row as input. This is a simple way to express your processing logic. Note that this does not allow you to deduplicate generated data when failures cause reprocessing of some input data. That would require you to specify the processing logic in the next way. #. An **object** with a ``process`` method and optional ``open`` and ``close`` methods. The object can have the following methods. * ``open(partition_id, epoch_id)``: *Optional* method that initializes the processing (for example, open a connection, start a transaction, etc). Additionally, you can use the `partition_id` and `epoch_id` to deduplicate regenerated data (discussed later). * ``process(row)``: *Non-optional* method that processes each :class:`Row`. * ``close(error)``: *Optional* method that finalizes and cleans up (for example, close connection, commit transaction, etc.) after all rows have been processed. The object will be used by Spark in the following way. * A single copy of this object is responsible of all the data generated by a single task in a query. In other words, one instance is responsible for processing one partition of the data generated in a distributed manner. * This object must be serializable because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (e.g. opening a connection or starting a transaction) is done after the `open(...)` method has been called, which signifies that the task is ready to generate data. * The lifecycle of the methods are as follows. For each partition with ``partition_id``: ... For each batch/epoch of streaming data with ``epoch_id``: ....... Method ``open(partitionId, epochId)`` is called. ....... If ``open(...)`` returns true, for each row in the partition and batch/epoch, method ``process(row)`` is called. ....... Method ``close(errorOrNull)`` is called with error (if any) seen while processing rows. Important points to note: * The `partitionId` and `epochId` can be used to deduplicate generated data when failures cause reprocessing of some input data. This depends on the execution mode of the query. If the streaming query is being executed in the micro-batch mode, then every partition represented by a unique tuple (partition_id, epoch_id) is guaranteed to have the same data. Hence, (partition_id, epoch_id) can be used to deduplicate and/or transactionally commit data and achieve exactly-once guarantees. However, if the streaming query is being executed in the continuous mode, then this guarantee does not hold and therefore should not be used for deduplication. * The ``close()`` method (if exists) will be called if `open()` method exists and returns successfully (irrespective of the return value), except if the Python crashes in the middle. .. versionadded:: 2.4.0 Notes ----- This API is evolving. Examples -------- >>> import time >>> df = spark.readStream.format("rate").load() Print every row using a function >>> def print_row(row): ... print(row) ... >>> q = df.writeStream.foreach(print_row).start() >>> time.sleep(3) >>> q.stop() Print every row using a object with process() method >>> class RowPrinter: ... def open(self, partition_id, epoch_id): ... print("Opened %d, %d" % (partition_id, epoch_id)) ... return True ... ... def process(self, row): ... print(row) ... ... def close(self, error): ... print("Closed with error: %s" % str(error)) ... >>> q = df.writeStream.foreach(print_row).start() >>> time.sleep(3) >>> q.stop() """ from pyspark.rdd import _wrap_function from pyspark.serializers import CPickleSerializer, AutoBatchedSerializer from pyspark.taskcontext import TaskContext if callable(f): # The provided object is a callable function that is supposed to be called on each row. # Construct a function that takes an iterator and calls the provided function on each # row. def func_without_process(_: Any, iterator: Iterator) -> Iterator: for x in iterator: f(x) # type: ignore[operator] return iter([]) func = func_without_process else: # The provided object is not a callable function. Then it is expected to have a # 'process(row)' method, and optional 'open(partition_id, epoch_id)' and # 'close(error)' methods. if not hasattr(f, "process"): raise AttributeError("Provided object does not have a 'process' method") if not callable(getattr(f, "process")): raise TypeError("Attribute 'process' in provided object is not callable") def doesMethodExist(method_name: str) -> bool: exists = hasattr(f, method_name) if exists and not callable(getattr(f, method_name)): raise TypeError( "Attribute '%s' in provided object is not callable" % method_name ) return exists open_exists = doesMethodExist("open") close_exists = doesMethodExist("close") def func_with_open_process_close(partition_id: Any, iterator: Iterator) -> Iterator: epoch_id = cast(TaskContext, TaskContext.get()).getLocalProperty( "streaming.sql.batchId" ) if epoch_id: int_epoch_id = int(epoch_id) else: raise RuntimeError("Could not get batch id from TaskContext") # Check if the data should be processed should_process = True if open_exists: should_process = f.open(partition_id, int_epoch_id) # type: ignore[union-attr] error = None try: if should_process: for x in iterator: cast("SupportsProcess", f).process(x) except Exception as ex: error = ex finally: if close_exists: f.close(error) # type: ignore[union-attr] if error: raise error return iter([]) func = func_with_open_process_close # type: ignore[assignment] serializer = AutoBatchedSerializer(CPickleSerializer()) wrapped_func = _wrap_function(self._spark._sc, func, serializer, serializer) assert self._spark._sc._jvm is not None jForeachWriter = ( self._spark._sc._jvm.org.apache.spark.sql.execution.python.PythonForeachWriter( wrapped_func, self._df._jdf.schema() ) ) self._jwrite.foreach(jForeachWriter) return self [docs] def foreachBatch(self, func: Callable[["DataFrame", int], None]) -> "DataStreamWriter": """ Sets the output of the streaming query to be processed using the provided function. This is supported only the in the micro-batch execution modes (that is, when the trigger is not continuous). In every micro-batch, the provided function will be called in every micro-batch with (i) the output rows as a DataFrame and (ii) the batch identifier. The batchId can be used deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output DataFrame is guaranteed to exactly same for the same batchId (assuming all operations are deterministic in the query). .. versionadded:: 2.4.0 Notes ----- This API is evolving. Examples -------- >>> import time >>> df = spark.readStream.format("rate").load() >>> def func(batch_df, batch_id): ... batch_df.collect() ... >>> q = df.writeStream.foreachBatch(func).start() >>> time.sleep(3) >>> q.stop() """ from pyspark.java_gateway import ensure_callback_server_started gw = self._spark._sc._gateway assert gw is not None java_import(gw.jvm, "org.apache.spark.sql.execution.streaming.sources.*") wrapped_func = ForeachBatchFunction(self._spark, func) gw.jvm.PythonForeachBatchHelper.callForeachBatch(self._jwrite, wrapped_func) ensure_callback_server_started(gw) return self [docs] def start( self, path: Optional[str] = None, format: Optional[str] = None, outputMode: Optional[str] = None, partitionBy: Optional[Union[str, List[str]]] = None, queryName: Optional[str] = None, **options: "OptionalPrimitiveType", ) -> StreamingQuery: """Streams the contents of the :class:`DataFrame` to a data source. The data source is specified by the ``format`` and a set of ``options``. If ``format`` is not specified, the default data source configured by ``spark.sql.sources.default`` will be used. .. versionadded:: 2.0.0 Parameters ---------- path : str, optional the path in a Hadoop supported file system format : str, optional the format used to save outputMode : str, optional specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to the sink * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the sink every time these are some updates * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to `append` mode. partitionBy : str or list, optional names of partitioning columns queryName : str, optional unique name for the query **options : dict All other string options. You may want to provide a `checkpointLocation` for most streams, however it is not required for a `memory` stream. Notes ----- This API is evolving. Examples -------- >>> df = spark.readStream.format("rate").load() Basic example. >>> q = df.writeStream.format('memory').queryName('this_query').start() >>> q.isActive True >>> q.name 'this_query' >>> q.stop() >>> q.isActive False Example with using other parameters with a trigger. >>> q = df.writeStream.trigger(processingTime='5 seconds').start( ... queryName='that_query', outputMode="append", format='memory') >>> q.name 'that_query' >>> q.isActive True >>> q.stop() """ self.options(**options) if outputMode is not None: self.outputMode(outputMode) if partitionBy is not None: self.partitionBy(partitionBy) if format is not None: self.format(format) if queryName is not None: self.queryName(queryName) if path is None: return self._sq(self._jwrite.start()) else: return self._sq(self._jwrite.start(path)) [docs] def toTable( self, tableName: str, format: Optional[str] = None, outputMode: Optional[str] = None, partitionBy: Optional[Union[str, List[str]]] = None, queryName: Optional[str] = None, **options: "OptionalPrimitiveType", ) -> StreamingQuery: """ Starts the execution of the streaming query, which will continually output results to the given table as new data arrives. The returned :class:`StreamingQuery` object can be used to interact with the stream. .. versionadded:: 3.1.0 Parameters ---------- tableName : str string, for the name of the table. format : str, optional the format used to save. outputMode : str, optional specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to the sink * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the sink every time these are some updates * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to `append` mode. partitionBy : str or list, optional names of partitioning columns queryName : str, optional unique name for the query **options : dict All other string options. You may want to provide a `checkpointLocation`. Notes ----- This API is evolving. For v1 table, partitioning columns provided by `partitionBy` will be respected no matter the table exists or not. A new table will be created if the table not exists. For v2 table, `partitionBy` will be ignored if the table already exists. `partitionBy` will be respected only if the v2 table does not exist. Besides, the v2 table created by this API lacks some functionalities (e.g., customized properties, options, and serde info). If you need them, please create the v2 table manually before the execution to avoid creating a table with incomplete information. Examples -------- Save a data stream to a table. >>> import tempfile >>> import time >>> _ = spark.sql("DROP TABLE IF EXISTS my_table2") >>> with tempfile.TemporaryDirectory() as d: ... # Create a table with Rate source. ... q = spark.readStream.format("rate").option( ... "rowsPerSecond", 10).load().writeStream.toTable( ... "my_table2", ... queryName='that_query', ... outputMode="append", ... format='parquet', ... checkpointLocation=d) ... time.sleep(3) ... q.stop() ... spark.read.table("my_table2").show() ... _ = spark.sql("DROP TABLE my_table2") +...---------+-----+ |...timestamp|value| +...---------+-----+ ... """ self.options(**options) if outputMode is not None: self.outputMode(outputMode) if partitionBy is not None: self.partitionBy(partitionBy) if format is not None: self.format(format) if queryName is not None: self.queryName(queryName) return self._sq(self._jwrite.toTable(tableName)) def _test() -> None: import doctest import os from pyspark.sql import SparkSession import pyspark.sql.streaming.readwriter os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.sql.streaming.readwriter.__dict__.copy() globs["spark"] = ( SparkSession.builder.master("local[4]") .appName("sql.streaming.readwriter tests") .getOrCreate() ) (failure_count, test_count) = doctest.testmod( pyspark.sql.streaming.readwriter, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF, ) globs["spark"].stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()