DataFrameReader

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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   @throws( ... )

6. def csv(paths: String*): DataFrame

   

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

   Loads a CSV file 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:

   • 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 from 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. Since 2.0.1, this applies to all supported types including the string type.
   • 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 yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
   • timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.
   • maxColumns (default 20480): defines a hard limit of how many columns a record can have.
   • maxCharsPerColumn (default -1): defines the maximum number of characters allowed for any given value being read. By default, it is -1 meaning unlimited length
   • maxMalformedLogPerPartition (default 10): sets the maximum number of malformed rows Spark will log for each partition. Malformed records beyond this number will be ignored.
   • 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

   @varargs()

   Since

   2.0.0

7. def csv(path: String): DataFrame

   

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

   Loads a CSV file and returns the result as a DataFrame. See the documentation on the other overloaded csv() method for more details.

   Since

   2.0.0

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

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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    protected[java.lang]

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

11. def format(source: String): DataFrameReader

    

    Specifies the input data source format.

    Specifies the input data source format.

    Since

    1.4.0

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

    

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13. def hashCode(): Int

    

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14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. def isTraceEnabled(): Boolean

    

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17. def jdbc(url: String, table: String, predicates: Array[String], connectionProperties: Properties): DataFrame

    

    Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties.

    Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties. The predicates parameter gives a list expressions suitable for inclusion in WHERE clauses; each one defines one partition of the DataFrame.

    Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

    url

    JDBC database url of the form jdbc:subprotocol:subname

    table

    Name of the table in the external database.

    predicates

    Condition in the where clause for each partition.

    connectionProperties

    JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "fetchsize" can be used to control the number of rows per fetch.

    Since

    1.4.0

18. def jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int, connectionProperties: Properties): DataFrame

    

    Construct a DataFrame representing the database table accessible via JDBC URL url named table.

    Construct a DataFrame representing the database table accessible via JDBC URL url named table. Partitions of the table will be retrieved in parallel based on the parameters passed to this function.

    Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

    url

    JDBC database url of the form jdbc:subprotocol:subname.

    table

    Name of the table in the external database.

    columnName

    the name of a column of integral type that will be used for partitioning.

    lowerBound

    the minimum value of columnName used to decide partition stride.

    upperBound

    the maximum value of columnName used to decide partition stride.

    numPartitions

    the number of partitions. This, along with lowerBound (inclusive), upperBound (exclusive), form partition strides for generated WHERE clause expressions used to split the column columnName evenly. When the input is less than 1, the number is set to 1.

    connectionProperties

    JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "fetchsize" can be used to control the number of rows per fetch.

    Since

    1.4.0

19. def jdbc(url: String, table: String, properties: Properties): DataFrame

    

    Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.

    Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.

    Since

    1.4.0

20. def json(jsonRDD: RDD[String]): DataFrame

    

    Loads an RDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Loads an RDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.

    jsonRDD

    input RDD with one JSON object per record

    Since

    1.4.0

21. def json(jsonRDD: JavaRDD[String]): DataFrame

    

    Loads a JavaRDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Loads a JavaRDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.

    jsonRDD

    input RDD with one JSON object per record

    Since

    1.4.0

22. def json(paths: String*): DataFrame

    

    Loads a JSON file (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Loads a JSON file (JSON Lines text format or newline-delimited JSON) 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:

    • 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.
    • dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
    • timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSZZ): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.

    Annotations

    @varargs()

    Since

    2.0.0

23. def json(path: String): DataFrame

    

    Loads a JSON file (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame.

    Loads a JSON file (JSON Lines text format or newline-delimited JSON) and returns the result as a DataFrame. See the documentation on the overloaded json() method with varargs for more details.

    Since

    1.4.0

24. def load(paths: String*): DataFrame

    

    Loads input in as a DataFrame, for data sources that support multiple paths.

    Loads input in as a DataFrame, for data sources that support multiple paths. Only works if the source is a HadoopFsRelationProvider.

    Annotations

    @varargs()

    Since

    1.6.0

25. def load(path: String): DataFrame

    

    Loads input in as a DataFrame, for data sources that require a path (e.g.

    Loads input in as a DataFrame, for data sources that require a path (e.g. data backed by a local or distributed file system).

    Since

    1.4.0

26. def load(): DataFrame

    

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

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

    Since

    1.4.0

27. def log: Logger

    

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

    

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29. def logDebug(msg: ⇒ String): Unit

    

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

    

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31. def logError(msg: ⇒ String): Unit

    

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

    

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33. def logInfo(msg: ⇒ String): Unit

    

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34. def logName: String

    

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

    

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36. def logTrace(msg: ⇒ String): Unit

    

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

    

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

    

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

    

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40. final def notify(): Unit

    

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41. final def notifyAll(): Unit

    

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42. def option(key: String, value: Double): DataFrameReader

    

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

43. def option(key: String, value: Long): DataFrameReader

    

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

44. def option(key: String, value: Boolean): DataFrameReader

    

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    2.0.0

45. def option(key: String, value: String): DataFrameReader

    

    Adds an input option for the underlying data source.

    Adds an input option for the underlying data source.

    Since

    1.4.0

46. def options(options: Map[String, String]): DataFrameReader

    

    Adds input options for the underlying data source.

    Adds input options for the underlying data source.

    Since

    1.4.0

47. def options(options: Map[String, String]): DataFrameReader

    

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

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

    Since

    1.4.0

48. def orc(paths: String*): DataFrame

    

    Loads an ORC file and returns the result as a DataFrame.

    Loads an ORC file and returns the result as a DataFrame.

    paths

    input paths

    Annotations

    @varargs()

    Since

    2.0.0

    Note

    Currently, this method can only be used after enabling Hive support.

49. def orc(path: String): DataFrame

    

    Loads an ORC file and returns the result as a DataFrame.

    Loads an ORC file and returns the result as a DataFrame.

    path

    input path

    Since

    1.5.0

    Note

    Currently, this method can only be used after enabling Hive support.

50. def parquet(paths: String*): DataFrame

    

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

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

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

    • 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

    @varargs()

    Since

    1.4.0

51. def parquet(path: String): DataFrame

    

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

    Loads a Parquet file, returning the result as a DataFrame. See the documentation on the other overloaded parquet() method for more details.

    Since

    2.0.0

52. def schema(schema: StructType): DataFrameReader

    

    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

    1.4.0

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

    

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54. def table(tableName: String): DataFrame

    

    Returns the specified table as a DataFrame.

    Returns the specified table as a DataFrame.

    Since

    1.4.0

55. def text(paths: 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.

    Each line in the text files is a new row in the resulting DataFrame. For example:

    // Scala:
    spark.read.text("/path/to/spark/README.md")
    
    // Java:
    spark.read().text("/path/to/spark/README.md")

    paths

    input paths

    Annotations

    @varargs()

    Since

    1.6.0

56. 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. See the documentation on the other overloaded text() method for more details.

    Since

    2.0.0

57. def textFile(paths: String*): Dataset[String]

    

    Loads text files and returns a Dataset of String.

    Loads text files and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value".

    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.

    Each line in the text files is a new element in the resulting Dataset. For example:

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

    paths

    input path

    Annotations

    @varargs()

    Since

    2.0.0

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

    

    Loads text files and returns a Dataset of String.

    Loads text files and returns a Dataset of String. See the documentation on the other overloaded textFile() method for more details.

    Since

    2.0.0

59. def toString(): String

    

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60. final def wait(): Unit

    

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

    

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

    

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