DataStreamReader.schema(schema: Union[pyspark.sql.types.StructType, str]) → pyspark.sql.streaming.readwriter.DataStreamReader[source]

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.

New in version 2.0.0.

schemapyspark.sql.types.StructType or str

a pyspark.sql.types.StructType object or a DDL-formatted string (For example col0 INT, col1 DOUBLE).


This API is evolving.


>>> 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()
 |-- col0: integer (nullable = true)
 |-- col1: string (nullable = true)