pyspark.sql.streaming.DataStreamReader

class pyspark.sql.streaming.DataStreamReader(spark)

Interface used to load a streaming DataFrame from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.readStream to access this.

New in version 2.0.0.

Changed in version 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()

Methods

csv(path[, schema, sep, encoding, quote, ...])	Loads a CSV file stream and returns the result as a DataFrame.
format(source)	Specifies the input data source format.
json(path[, schema, primitivesAsString, ...])	Loads a JSON file stream and returns the results as a DataFrame.
load([path, format, schema])	Loads a data stream from a data source and returns it as a DataFrame.
option(key, value)	Adds an input option for the underlying data source.
options(**options)	Adds input options for the underlying data source.
orc(path[, mergeSchema, pathGlobFilter, ...])	Loads a ORC file stream, returning the result as a DataFrame.
parquet(path[, mergeSchema, pathGlobFilter, ...])	Loads a Parquet file stream, returning the result as a DataFrame.
schema(schema)	Specifies the input schema.
table(tableName)	Define a Streaming DataFrame on a Table.
text(path[, wholetext, lineSep, ...])	Loads a text file stream and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any.
xml(path[, rowTag, schema, ...])	Loads a XML file stream and returns the result as a DataFrame.