Specifies the input data source format.

Loads input in as a DataFrame, for data streams that read from some path.

Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g. external key-value stores).

Adds an input option for the underlying data source.

(Scala-specific) Adds input options for the underlying data 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.

Define a Streaming DataFrame on a Table. The DataSource corresponding to the table should support streaming mode.

Loads a CSV file stream and returns the result as a 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.

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

You can find the CSV-specific options for reading CSV file stream in <a href="https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option"> Data Source Option in the version you use.

Loads a JSON file stream and returns the results as a DataFrame.

JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine option to true.

This function goes through the input once to determine the input schema. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan.

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

You can find the JSON-specific options for reading JSON file stream in <a href="https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option"> Data Source Option in the version you use.

Adds an input option for the underlying data source.

Adds an input option for the underlying data source.

Adds an input option for the underlying data source.

(Java-specific) Adds input options for the underlying data source.

Loads a ORC file stream, returning the result as a DataFrame.

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

ORC-specific option(s) for reading ORC file stream can be found in Data Source Option in the version you use.

Loads a Parquet file stream, returning the result as a DataFrame.

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

Parquet-specific option(s) for reading Parquet file stream can be found in Data Source Option in the version you use.

Specifies the schema by using the input DDL-formatted string. 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.

Loads text files and returns a 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 files is a new row in the resulting DataFrame. For example:

// Scala:
spark.readStream.text("/path/to/directory/")

// Java:
spark.readStream().text("/path/to/directory/")

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

You can find the text-specific options for reading text files in <a href="https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option"> Data Source Option in the version you use.

Loads text file(s) and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value". The text files must be encoded as UTF-8.

If the directory structure of the text files contains partitioning information, those are ignored in the resulting Dataset. To include partitioning information as columns, use text.

By default, each line in the text file is a new element in the resulting Dataset. For example:

// Scala:
spark.readStream.textFile("/path/to/spark/README.md")

// Java:
spark.readStream().textFile("/path/to/spark/README.md")

You can set the text-specific options as specified in DataStreamReader.text.

Loads a XML file stream and returns the result as a 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.

You can set the following option(s):

• maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
• maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

You can find the XML-specific options for reading XML file stream in <a href="https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option"> Data Source Option in the version you use.