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org.apache.spark.sql.streaming

DataStreamReader

abstract class DataStreamReader extends AnyRef

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

Annotations
@Evolving()
Source
DataStreamReader.scala
Since

2.0.0

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Instance Constructors

  1. new DataStreamReader()

Abstract Value Members

  1. abstract def assertNoSpecifiedSchema(operation: String): Unit
    Attributes
    protected
  2. abstract def format(source: String): DataStreamReader.this.type

    Specifies the input data source format.

    Specifies the input data source format.

    Since

    2.0.0

  3. abstract def load(path: String): DataFrame

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

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

    Since

    2.0.0

  4. abstract def load(): DataFrame

    Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g.

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

    Since

    2.0.0

  5. abstract def option(key: String, value: String): DataStreamReader.this.type

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

  6. abstract def options(options: Map[String, String]): DataStreamReader.this.type

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

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

    Since

    2.0.0

  7. abstract def schema(schema: StructType): DataStreamReader.this.type

    Specifies the input schema.

    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.

    Since

    2.0.0

  8. abstract def table(tableName: String): DataFrame

    Define a Streaming DataFrame on a Table.

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

    tableName

    The name of the table

    Since

    3.1.0

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  6. def csv(path: String): DataFrame

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

    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.

    Since

    2.0.0

  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
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    Annotations
    @IntrinsicCandidate() @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. def json(path: String): DataFrame

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

    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.

    Since

    2.0.0

  13. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  15. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  16. def option(key: String, value: Double): DataStreamReader.this.type

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

  17. def option(key: String, value: Long): DataStreamReader.this.type

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

  18. def option(key: String, value: Boolean): DataStreamReader.this.type

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

  19. def options(options: Map[String, String]): DataStreamReader.this.type

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

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

    Since

    2.0.0

  20. def orc(path: String): DataFrame

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

    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.

    Since

    2.3.0

  21. def parquet(path: String): DataFrame

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

    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.

    Since

    2.0.0

  22. def schema(schemaString: String): DataStreamReader.this.type

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

    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.

    Since

    2.3.0

  23. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  24. def text(path: String): DataFrame

    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.

    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.

    Since

    2.0.0

  25. def textFile(path: String): Dataset[String]

    Loads text file(s) and returns a Dataset of String.

    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.

    path

    input path

    Since

    2.1.0

  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. def validateJsonSchema(): Unit
    Attributes
    protected
  28. def validateXmlSchema(): Unit
    Attributes
    protected
  29. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  30. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  31. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  32. def xml(path: String): DataFrame

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

    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.

    Since

    4.0.0

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

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