DataStreamReader

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. def csv(path: String): DataFrame

   

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

   :: Experimental :: 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 CSV-specific options to deal with CSV files:

   • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
   • sep (default ,): sets the single character as a separator for each field and value.
   • encoding (default UTF-8): decodes the CSV files by the given encoding type.
   • quote (default "): sets the single character used for escaping quoted values where the separator can be part of the value. If you would like to turn off quotations, you need to set not null but an empty string. This behaviour is different form com.databricks.spark.csv.
   • escape (default \): sets the single character used for escaping quotes inside an already quoted value.
   • comment (default empty string): sets the single character used for skipping lines beginning with this character. By default, it is disabled.
   • header (default false): uses the first line as names of columns.
   • inferSchema (default false): infers the input schema automatically from data. It requires one extra pass over the data.
   • ignoreLeadingWhiteSpace (default false): defines whether or not leading whitespaces from values being read should be skipped.
   • ignoreTrailingWhiteSpace (default false): defines whether or not trailing whitespaces from values being read should be skipped.
   • nullValue (default empty string): sets the string representation of a null value.
   • nanValue (default NaN): sets the string representation of a non-number" value.
   • positiveInf (default Inf): sets the string representation of a positive infinity value.
   • negativeInf (default -Inf): sets the string representation of a negative infinity value.
   • dateFormat (default null): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to both date type and timestamp type. By default, it is null which means trying to parse times and date by java.sql.Timestamp.valueOf() and java.sql.Date.valueOf().
   • maxColumns (default 20480): defines a hard limit of how many columns a record can have.
   • maxCharsPerColumn (default 1000000): defines the maximum number of characters allowed for any given value being read.
   • mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
   • PERMISSIVE : sets other fields to null when it meets a corrupted record. When a schema is set by user, it sets null for extra fields.
   • DROPMALFORMED : ignores the whole corrupted records.
   • FAILFAST : throws an exception when it meets corrupted records.

   Annotations

   @Experimental()

   Since

   2.0.0

7. final def eq(arg0: AnyRef): Boolean

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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   @throws( classOf[java.lang.Throwable] )

10. def format(source: String): DataStreamReader

    

    :: Experimental :: Specifies the input data source format.

    :: Experimental :: Specifies the input data source format.

    Annotations

    @Experimental()

    Since

    2.0.0

11. final def getClass(): Class[_]

    

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12. def hashCode(): Int

    

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13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def isTraceEnabled(): Boolean

    

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16. def json(path: String): DataFrame

    

    :: Experimental :: Loads a JSON file stream (one object per line) and returns the result as a DataFrame.

    :: Experimental :: Loads a JSON file stream (one object per line) and returns the result as a DataFrame.

    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 JSON-specific options to deal with non-standard JSON files:

    • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
    • primitivesAsString (default false): infers all primitive values as a string type
    • prefersDecimal (default false): infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles.
    • allowComments (default false): ignores Java/C++ style comment in JSON records
    • allowUnquotedFieldNames (default false): allows unquoted JSON field names
    • allowSingleQuotes (default true): allows single quotes in addition to double quotes
    • allowNumericLeadingZeros (default false): allows leading zeros in numbers (e.g. 00012)
    • allowBackslashEscapingAnyCharacter (default false): allows accepting quoting of all character using backslash quoting mechanism
    • mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
    • PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts the malformed string into a new field configured by columnNameOfCorruptRecord. When a schema is set by user, it sets null for extra fields.
    • DROPMALFORMED : ignores the whole corrupted records.
    • FAILFAST : throws an exception when it meets corrupted records.
    • columnNameOfCorruptRecord (default is the value specified in spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.

    Annotations

    @Experimental()

    Since

    2.0.0

17. def load(path: String): DataFrame

    

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

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

    Annotations

    @Experimental()

    Since

    2.0.0

18. def load(): DataFrame

    

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

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

    Annotations

    @Experimental()

    Since

    2.0.0

19. def log: Logger

    

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20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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21. def logDebug(msg: ⇒ String): Unit

    

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22. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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23. def logError(msg: ⇒ String): Unit

    

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24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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25. def logInfo(msg: ⇒ String): Unit

    

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26. def logName: String

    

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27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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28. def logTrace(msg: ⇒ String): Unit

    

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29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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30. def logWarning(msg: ⇒ String): Unit

    

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31. final def ne(arg0: AnyRef): Boolean

    

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32. final def notify(): Unit

    

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33. final def notifyAll(): Unit

    

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34. def option(key: String, value: Double): DataStreamReader

    

    :: Experimental :: Adds an input option for the underlying data source.

    :: Experimental :: Adds an input option for the underlying data source.

    Annotations

    @Experimental()

    Since

    2.0.0

35. def option(key: String, value: Long): DataStreamReader

    

    :: Experimental :: Adds an input option for the underlying data source.

    :: Experimental :: Adds an input option for the underlying data source.

    Annotations

    @Experimental()

    Since

    2.0.0

36. def option(key: String, value: Boolean): DataStreamReader

    

    :: Experimental :: Adds an input option for the underlying data source.

    :: Experimental :: Adds an input option for the underlying data source.

    Annotations

    @Experimental()

    Since

    2.0.0

37. def option(key: String, value: String): DataStreamReader

    

    :: Experimental :: Adds an input option for the underlying data source.

    :: Experimental :: Adds an input option for the underlying data source.

    Annotations

    @Experimental()

    Since

    2.0.0

38. def options(options: Map[String, String]): DataStreamReader

    

    :: Experimental :: Adds input options for the underlying data source.

    :: Experimental :: Adds input options for the underlying data source.

    Annotations

    @Experimental()

    Since

    2.0.0

39. def options(options: Map[String, String]): DataStreamReader

    

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

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

    Annotations

    @Experimental()

    Since

    2.0.0

40. def parquet(path: String): DataFrame

    

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

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

    You can set the following Parquet-specific option(s) for reading Parquet files:

    • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
    • mergeSchema (default is the value specified in spark.sql.parquet.mergeSchema): sets whether we should merge schemas collected from all Parquet part-files. This will override spark.sql.parquet.mergeSchema.

    Annotations

    @Experimental()

    Since

    2.0.0

41. def schema(schema: StructType): DataStreamReader

    

    :: Experimental :: Specifies the input schema.

    :: Experimental :: 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.

    Annotations

    @Experimental()

    Since

    2.0.0

42. final def synchronized[T0](arg0: ⇒ T0): T0

    

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43. def text(path: String): DataFrame

    

    :: Experimental :: 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.

    :: Experimental :: 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.

    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 text-specific options to deal with text files:

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

    Annotations

    @Experimental()

    Since

    2.0.0

44. def toString(): String

    

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45. final def wait(): Unit

    

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46. final def wait(arg0: Long, arg1: Int): Unit

    

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47. final def wait(arg0: Long): Unit

    

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