#
# 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 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
from pyspark.errors import (
PySparkTypeError,
PySparkValueError,
PySparkAttributeError,
PySparkRuntimeError,
)
if TYPE_CHECKING:
from py4j.java_gateway import JavaObject
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Examples
--------
>>> spark.readStream
<...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 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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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)]))
<...streaming.readwriter.DataStreamReader object ...>
>>> spark.readStream.schema("col0 INT, col1 DOUBLE")
<...streaming.readwriter.DataStreamReader object ...>
The example below specifies a different schema to CSV file.
>>> import tempfile
>>> import time
>>> with tempfile.TemporaryDirectory(prefix="schema") 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 PySparkTypeError(
errorClass="NOT_STR_OR_STRUCT",
messageParameters={"arg_name": "schema", "arg_type": type(schema).__name__},
)
return self
[docs] def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamReader":
"""Adds an input option for the underlying data source.
.. versionadded:: 2.0.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Examples
--------
>>> spark.readStream.option("x", 1)
<...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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Examples
--------
>>> spark.readStream.options(x="1", y=2)
<...streaming.readwriter.DataStreamReader object ...>
Specify options in a dictionary.
>>> spark.readStream.options(**{"k1": "v1", "k2": "v2"})
<...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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="load") 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 PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={"arg_name": "path", "arg_value": str(path)},
)
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,
useUnsafeRow: 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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="json") 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,
useUnsafeRow=useUnsafeRow,
)
if isinstance(path, str):
return self._df(self._jreader.json(path))
else:
raise PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
[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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="orc") 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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
[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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="parquet") 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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
[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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Parameters
----------
path : str
string for input path.
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(prefix="text") 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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
[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
string for input path.
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="csv") 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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
def xml(
self,
path: str,
rowTag: Optional[str] = None,
schema: Optional[Union[StructType, str]] = None,
excludeAttribute: Optional[Union[bool, str]] = None,
attributePrefix: Optional[str] = None,
valueTag: Optional[str] = None,
ignoreSurroundingSpaces: Optional[Union[bool, str]] = None,
rowValidationXSDPath: Optional[str] = None,
ignoreNamespace: Optional[Union[bool, str]] = None,
wildcardColName: Optional[str] = None,
encoding: Optional[str] = None,
inferSchema: Optional[Union[bool, str]] = None,
nullValue: Optional[str] = None,
dateFormat: Optional[str] = None,
timestampFormat: Optional[str] = None,
mode: Optional[str] = None,
columnNameOfCorruptRecord: Optional[str] = None,
multiLine: Optional[Union[bool, str]] = None,
samplingRatio: Optional[Union[float, str]] = None,
locale: Optional[str] = None,
) -> "DataFrame":
r"""Loads a XML file stream and returns the result as a :class:`DataFrame`.
If the ``schema`` parameter is not specified, this function goes
through the input once to determine the input schema.
Parameters
----------
path : str
string for input path.
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:: 4.0.0
Other Parameters
----------------
Extra options
For the extra options, refer to
`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
in the version you use.
.. # noqa
Notes
-----
This API is evolving.
Examples
--------
Write a DataFrame into a XML file and read it back.
>>> import tempfile
>>> import time
>>> with tempfile.TemporaryDirectory(prefix="xml") as d:
... # Write a DataFrame into a XML file
... spark.createDataFrame(
... [{"age": 100, "name": "Hyukjin Kwon"}]
... ).write.mode("overwrite").option("rowTag", "person").xml(d)
...
... # Start a streaming query to read the XML file.
... q = spark.readStream.schema(
... "age INT, name STRING"
... ).xml(d, rowTag="person").writeStream.format("console").start()
... time.sleep(3)
... q.stop()
"""
self._set_opts(
rowTag=rowTag,
schema=schema,
excludeAttribute=excludeAttribute,
attributePrefix=attributePrefix,
valueTag=valueTag,
ignoreSurroundingSpaces=ignoreSurroundingSpaces,
rowValidationXSDPath=rowValidationXSDPath,
ignoreNamespace=ignoreNamespace,
wildcardColName=wildcardColName,
encoding=encoding,
inferSchema=inferSchema,
nullValue=nullValue,
dateFormat=dateFormat,
timestampFormat=timestampFormat,
mode=mode,
columnNameOfCorruptRecord=columnNameOfCorruptRecord,
multiLine=multiLine,
samplingRatio=samplingRatio,
locale=locale,
)
if isinstance(path, str):
return self._df(self._jreader.xml(path))
else:
raise PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "path", "arg_type": type(path).__name__},
)
[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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="table") 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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "tableName", "arg_type": type(tableName).__name__},
)
[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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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')
<...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 PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={"arg_name": "outputMode", "arg_value": str(outputMode)},
)
self._jwrite = self._jwrite.outputMode(outputMode)
return self
[docs] def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamWriter":
"""Adds an output option for the underlying data source.
.. versionadded:: 2.0.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Examples
--------
>>> df = spark.readStream.format("rate").load()
>>> df.writeStream.option("x", 1)
<...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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Examples
--------
>>> df = spark.readStream.format("rate").load()
>>> df.writeStream.option("x", 1)
<...streaming.readwriter.DataStreamWriter object ...>
Specify options in a dictionary.
>>> df.writeStream.options(**{"k1": "v1", "k2": "v2"})
<...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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Parameters
----------
cols : str or list
name of columns
Notes
-----
This API is evolving.
Examples
--------
>>> df = spark.readStream.format("rate").load()
>>> df.writeStream.partitionBy("value")
<...streaming.readwriter.DataStreamWriter object ...>
Partition-by timestamp column from Rate source.
>>> import tempfile
>>> import time
>>> with tempfile.TemporaryDirectory(prefix="partitionBy1") as d:
... with tempfile.TemporaryDirectory(prefix="partitionBy2") 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|
+...---------+-----+
...
"""
from pyspark.sql.classic.column import _to_seq
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
@overload
def clusterBy(self, *cols: str) -> "DataStreamWriter":
...
@overload
def clusterBy(self, __cols: List[str]) -> "DataStreamWriter":
...
def clusterBy(self, *cols: str) -> "DataStreamWriter": # type: ignore[misc]
"""Clusters the output by the given columns.
If specified, the output is laid out such that records with similar values on the clustering
column(s) are grouped together in the same file.
Clustering improves query efficiency by allowing queries with predicates on the clustering
columns to skip unnecessary data. Unlike partitioning, clustering can be used on very high
cardinality columns.
.. versionadded:: 4.0.0
Parameters
----------
cols : str or list
name of columns
Notes
-----
This API is evolving.
Examples
--------
>>> df = spark.readStream.format("rate").load()
>>> df.writeStream.clusterBy("value")
<...streaming.readwriter.DataStreamWriter object ...>
Cluster-by timestamp column from Rate source.
>>> import tempfile
>>> import time
>>> with tempfile.TemporaryDirectory(prefix="partitionBy1") as d:
... with tempfile.TemporaryDirectory(prefix="partitionBy2") as cp:
... df = spark.readStream.format("rate").option("rowsPerSecond", 10).load()
... q = df.writeStream.clusterBy(
... "timestamp").format("parquet").option("checkpointLocation", cp).start(d)
... time.sleep(5)
... q.stop()
... spark.read.schema(df.schema).parquet(d).show()
+...---------+-----+
|...timestamp|value|
+...---------+-----+
...
"""
from pyspark.sql.classic.column import _to_seq
if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
cols = cols[0]
self._jwrite = self._jwrite.clusterBy(_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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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 PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={"arg_name": "queryName", "arg_value": str(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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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')
<...streaming.readwriter.DataStreamWriter object ...>
Trigger the query for execution every 5 seconds
>>> df.writeStream.trigger(continuous='5 seconds')
<...streaming.readwriter.DataStreamWriter object ...>
Trigger the query for reading all available data with multiple batches
>>> df.writeStream.trigger(availableNow=True)
<...streaming.readwriter.DataStreamWriter object ...>
"""
params = [processingTime, once, continuous, availableNow]
if params.count(None) == 4:
raise PySparkValueError(
errorClass="ONLY_ALLOW_SINGLE_TRIGGER",
messageParameters={},
)
elif params.count(None) < 3:
raise PySparkValueError(
errorClass="ONLY_ALLOW_SINGLE_TRIGGER",
messageParameters={},
)
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 PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={
"arg_name": "processingTime",
"arg_value": str(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 PySparkValueError(
errorClass="VALUE_NOT_TRUE",
messageParameters={"arg_name": "once", "arg_value": str(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 PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={"arg_name": "continuous", "arg_value": str(continuous)},
)
interval = continuous.strip()
jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.Continuous(
interval
)
else:
if availableNow is not True:
raise PySparkValueError(
errorClass="VALUE_NOT_TRUE",
messageParameters={"arg_name": "availableNow", "arg_value": str(availableNow)},
)
jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.AvailableNow()
self._jwrite = self._jwrite.trigger(jTrigger)
return self
@staticmethod
def _construct_foreach_function(
f: Union[Callable[[Row], None], "SupportsProcess"]
) -> Callable[[Any, Iterator], Iterator]:
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)
return iter([])
return 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 PySparkAttributeError(
errorClass="ATTRIBUTE_NOT_CALLABLE",
messageParameters={"attr_name": "process", "obj_name": "f"},
)
if not callable(getattr(f, "process")):
raise PySparkAttributeError(
errorClass="ATTRIBUTE_NOT_CALLABLE",
messageParameters={"attr_name": "process", "obj_name": "f"},
)
def doesMethodExist(method_name: str) -> bool:
exists = hasattr(f, method_name)
if exists and not callable(getattr(f, method_name)):
raise PySparkAttributeError(
errorClass="ATTRIBUTE_NOT_CALLABLE",
messageParameters={"attr_name": method_name, "obj_name": "f"},
)
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 PySparkRuntimeError(
errorClass="CANNOT_GET_BATCH_ID",
messageParameters={"obj_name": "TaskContext"},
)
# Check if the data should be processed
should_process = True
if open_exists:
should_process = f.open( # type: ignore[attr-defined]
partition_id, int_epoch_id
)
error = None
try:
if should_process:
for x in iterator:
f.process(x)
except Exception as ex:
error = ex
finally:
if close_exists:
f.close(error) # type: ignore[attr-defined]
if error:
raise error
return iter([])
return func_with_open_process_close
@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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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.core.rdd import _wrap_function
from pyspark.serializers import CPickleSerializer, AutoBatchedSerializer
func = self._construct_foreach_function(f)
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
This function behaves differently in Spark Connect mode. See examples.
In Connect, the provided function doesn't have access to variables defined outside of it.
Examples
--------
>>> import time
>>> df = spark.readStream.format("rate").load()
>>> my_value = -1
>>> def func(batch_df, batch_id):
... global my_value
... my_value = 100
... batch_df.collect()
...
>>> q = df.writeStream.foreachBatch(func).start()
>>> time.sleep(3)
>>> q.stop()
>>> # if in Spark Connect, my_value = -1, else my_value = 100
"""
from py4j.java_gateway import java_import
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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
.. versionchanged:: 3.5.0
Supports Spark Connect.
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(prefix="toTable") 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()