Class

org.apache.spark.sql.hive

HiveContext

Related Doc: package hive

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class HiveContext extends SQLContext with Logging

An instance of the Spark SQL execution engine that integrates with data stored in Hive. Configuration for Hive is read from hive-site.xml on the classpath.

Self Type
HiveContext
Annotations
@deprecated
Deprecated

(Since version 2.0.0) Use SparkSession.builder.enableHiveSupport instead

Source
HiveContext.scala
Linear Supertypes
SQLContext, Serializable, Serializable, Logging, AnyRef, Any
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Inherited
  1. HiveContext
  2. SQLContext
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new HiveContext(sc: JavaSparkContext)

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  2. new HiveContext(sc: SparkContext)

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Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  5. def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame

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    Convert a BaseRelation created for external data sources into a DataFrame.

    Convert a BaseRelation created for external data sources into a DataFrame.

    Definition Classes
    SQLContext
    Since

    1.3.0

  6. def cacheTable(tableName: String): Unit

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    Caches the specified table in-memory.

    Caches the specified table in-memory.

    Definition Classes
    SQLContext
    Since

    1.3.0

  7. def clearCache(): Unit

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    Removes all cached tables from the in-memory cache.

    Removes all cached tables from the in-memory cache.

    Definition Classes
    SQLContext
    Since

    1.3.0

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def createDataFrame(data: List[_], beanClass: Class[_]): DataFrame

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    Applies a schema to a List of Java Beans.

    Applies a schema to a List of Java Beans.

    WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.

    Definition Classes
    SQLContext
    Since

    1.6.0

  10. def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

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    Applies a schema to an RDD of Java Beans.

    Applies a schema to an RDD of Java Beans.

    WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.

    Definition Classes
    SQLContext
    Since

    1.3.0

  11. def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame

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    Applies a schema to an RDD of Java Beans.

    Applies a schema to an RDD of Java Beans.

    WARNING: Since there is no guaranteed ordering for fields in a Java Bean, SELECT * queries will return the columns in an undefined order.

    Definition Classes
    SQLContext
    Since

    1.3.0

  12. def createDataFrame(rows: List[Row], schema: StructType): DataFrame

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    :: DeveloperApi :: Creates a DataFrame from a java.util.List containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from a java.util.List containing Rows using the given schema. It is important to make sure that the structure of every Row of the provided List matches the provided schema. Otherwise, there will be runtime exception.

    Definition Classes
    SQLContext
    Annotations
    @DeveloperApi() @Evolving()
    Since

    1.6.0

  13. def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

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    :: DeveloperApi :: Creates a DataFrame from a JavaRDD containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from a JavaRDD containing Rows using the given schema. It is important to make sure that the structure of every Row of the provided RDD matches the provided schema. Otherwise, there will be runtime exception.

    Definition Classes
    SQLContext
    Annotations
    @DeveloperApi() @Evolving()
    Since

    1.3.0

  14. def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame

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    :: DeveloperApi :: Creates a DataFrame from an RDD containing Rows using the given schema.

    :: DeveloperApi :: Creates a DataFrame from an RDD containing Rows using the given schema. It is important to make sure that the structure of every Row of the provided RDD matches the provided schema. Otherwise, there will be runtime exception. Example:

    import org.apache.spark.sql._
    import org.apache.spark.sql.types._
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    
    val schema =
      StructType(
        StructField("name", StringType, false) ::
        StructField("age", IntegerType, true) :: Nil)
    
    val people =
      sc.textFile("examples/src/main/resources/people.txt").map(
        _.split(",")).map(p => Row(p(0), p(1).trim.toInt))
    val dataFrame = sqlContext.createDataFrame(people, schema)
    dataFrame.printSchema
    // root
    // |-- name: string (nullable = false)
    // |-- age: integer (nullable = true)
    
    dataFrame.createOrReplaceTempView("people")
    sqlContext.sql("select name from people").collect.foreach(println)
    Definition Classes
    SQLContext
    Annotations
    @DeveloperApi() @Evolving()
    Since

    1.3.0

  15. def createDataFrame[A <: Product](data: Seq[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

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    :: Experimental :: Creates a DataFrame from a local Seq of Product.

    :: Experimental :: Creates a DataFrame from a local Seq of Product.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.3.0

  16. def createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

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    :: Experimental :: Creates a DataFrame from an RDD of Product (e.g.

    :: Experimental :: Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.3.0

  17. def createDataset[T](data: List[T])(implicit arg0: Encoder[T]): Dataset[T]

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    :: Experimental :: Creates a Dataset from a java.util.List of a given type.

    :: Experimental :: Creates a Dataset from a java.util.List of a given type. This method requires an encoder (to convert a JVM object of type T to and from the internal Spark SQL representation) that is generally created automatically through implicits from a SparkSession, or can be created explicitly by calling static methods on Encoders.

    Java Example

    List<String> data = Arrays.asList("hello", "world");
    Dataset<String> ds = spark.createDataset(data, Encoders.STRING());
    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    2.0.0

  18. def createDataset[T](data: RDD[T])(implicit arg0: Encoder[T]): Dataset[T]

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    :: Experimental :: Creates a Dataset from an RDD of a given type.

    :: Experimental :: Creates a Dataset from an RDD of a given type. This method requires an encoder (to convert a JVM object of type T to and from the internal Spark SQL representation) that is generally created automatically through implicits from a SparkSession, or can be created explicitly by calling static methods on Encoders.

    Definition Classes
    SQLContext
    Annotations
    @Experimental()
    Since

    2.0.0

  19. def createDataset[T](data: Seq[T])(implicit arg0: Encoder[T]): Dataset[T]

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    :: Experimental :: Creates a Dataset from a local Seq of data of a given type.

    :: Experimental :: Creates a Dataset from a local Seq of data of a given type. This method requires an encoder (to convert a JVM object of type T to and from the internal Spark SQL representation) that is generally created automatically through implicits from a SparkSession, or can be created explicitly by calling static methods on Encoders.

    Example

    import spark.implicits._
    case class Person(name: String, age: Long)
    val data = Seq(Person("Michael", 29), Person("Andy", 30), Person("Justin", 19))
    val ds = spark.createDataset(data)
    
    ds.show()
    // +-------+---+
    // |   name|age|
    // +-------+---+
    // |Michael| 29|
    // |   Andy| 30|
    // | Justin| 19|
    // +-------+---+
    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    2.0.0

  20. def dropTempTable(tableName: String): Unit

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    Drops the temporary table with the given table name in the catalog.

    Drops the temporary table with the given table name in the catalog. If the table has been cached/persisted before, it's also unpersisted.

    tableName

    the name of the table to be unregistered.

    Definition Classes
    SQLContext
    Since

    1.3.0

  21. def emptyDataFrame: DataFrame

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    Returns a DataFrame with no rows or columns.

    Returns a DataFrame with no rows or columns.

    Definition Classes
    SQLContext
    Since

    1.3.0

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

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    Definition Classes
    AnyRef
  23. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  24. def experimental: ExperimentalMethods

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    :: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.

    :: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @transient() @Unstable()
    Since

    1.3.0

  25. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. def getAllConfs: Map[String, String]

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    Return all the configuration properties that have been set (i.e.

    Return all the configuration properties that have been set (i.e. not the default). This creates a new copy of the config properties in the form of a Map.

    Definition Classes
    SQLContext
    Since

    1.0.0

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

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    Definition Classes
    AnyRef → Any
  28. def getConf(key: String, defaultValue: String): String

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    Return the value of Spark SQL configuration property for the given key.

    Return the value of Spark SQL configuration property for the given key. If the key is not set yet, return defaultValue.

    Definition Classes
    SQLContext
    Since

    1.0.0

  29. def getConf(key: String): String

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    Return the value of Spark SQL configuration property for the given key.

    Return the value of Spark SQL configuration property for the given key.

    Definition Classes
    SQLContext
    Since

    1.0.0

  30. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  31. object implicits extends SQLImplicits with Serializable

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    :: Experimental :: (Scala-specific) Implicit methods available in Scala for converting common Scala objects into DataFrames.

    :: Experimental :: (Scala-specific) Implicit methods available in Scala for converting common Scala objects into DataFrames.

    val sqlContext = new SQLContext(sc)
    import sqlContext.implicits._
    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.3.0

  32. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  33. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  34. def isCached(tableName: String): Boolean

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    Returns true if the table is currently cached in-memory.

    Returns true if the table is currently cached in-memory.

    Definition Classes
    SQLContext
    Since

    1.3.0

  35. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  36. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  37. def listenerManager: ExecutionListenerManager

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    An interface to register custom org.apache.spark.sql.util.QueryExecutionListeners that listen for execution metrics.

    An interface to register custom org.apache.spark.sql.util.QueryExecutionListeners that listen for execution metrics.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
  38. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  39. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  40. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  41. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  42. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  43. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  44. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  45. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  46. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  47. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  48. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  49. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  50. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  51. def newSession(): HiveContext

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    Returns a new HiveContext as new session, which will have separated SQLConf, UDF/UDAF, temporary tables and SessionState, but sharing the same CacheManager, IsolatedClientLoader and Hive client (both of execution and metadata) with existing HiveContext.

    Returns a new HiveContext as new session, which will have separated SQLConf, UDF/UDAF, temporary tables and SessionState, but sharing the same CacheManager, IsolatedClientLoader and Hive client (both of execution and metadata) with existing HiveContext.

    Definition Classes
    HiveContextSQLContext
  52. final def notify(): Unit

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    Definition Classes
    AnyRef
  53. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  54. def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame

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    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with an step value, with partition number specified.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in an range from start to end (exclusive) with an step value, with partition number specified.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.4.0

  55. def range(start: Long, end: Long, step: Long): DataFrame

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    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from start to end (exclusive) with a step value.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from start to end (exclusive) with a step value.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    2.0.0

  56. def range(start: Long, end: Long): DataFrame

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    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from start to end (exclusive) with step value 1.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from start to end (exclusive) with step value 1.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.4.0

  57. def range(end: Long): DataFrame

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    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from 0 to end (exclusive) with step value 1.

    :: Experimental :: Creates a DataFrame with a single LongType column named id, containing elements in a range from 0 to end (exclusive) with step value 1.

    Definition Classes
    SQLContext
    Annotations
    @Experimental() @Evolving()
    Since

    1.4.1

  58. def read: DataFrameReader

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    Returns a DataFrameReader that can be used to read non-streaming data in as a DataFrame.

    Returns a DataFrameReader that can be used to read non-streaming data in as a DataFrame.

    sqlContext.read.parquet("/path/to/file.parquet")
    sqlContext.read.schema(schema).json("/path/to/file.json")
    Definition Classes
    SQLContext
    Since

    1.4.0

  59. def readStream: DataStreamReader

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    Returns a DataStreamReader that can be used to read streaming data in as a DataFrame.

    Returns a DataStreamReader that can be used to read streaming data in as a DataFrame.

    sparkSession.readStream.parquet("/path/to/directory/of/parquet/files")
    sparkSession.readStream.schema(schema).json("/path/to/directory/of/json/files")
    Definition Classes
    SQLContext
    Annotations
    @Evolving()
    Since

    2.0.0

  60. def refreshTable(tableName: String): Unit

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    Invalidate and refresh all the cached the metadata of the given table.

    Invalidate and refresh all the cached the metadata of the given table. For performance reasons, Spark SQL or the external data source library it uses might cache certain metadata about a table, such as the location of blocks. When those change outside of Spark SQL, users should call this function to invalidate the cache.

    Since

    1.3.0

  61. def setConf(key: String, value: String): Unit

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    Set the given Spark SQL configuration property.

    Set the given Spark SQL configuration property.

    Definition Classes
    SQLContext
    Since

    1.0.0

  62. def setConf(props: Properties): Unit

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    Set Spark SQL configuration properties.

    Set Spark SQL configuration properties.

    Definition Classes
    SQLContext
    Since

    1.0.0

  63. def sparkContext: SparkContext

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    Definition Classes
    SQLContext
  64. val sparkSession: SparkSession

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    Definition Classes
    SQLContext
  65. def sql(sqlText: String): DataFrame

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    Executes a SQL query using Spark, returning the result as a DataFrame.

    Executes a SQL query using Spark, returning the result as a DataFrame. The dialect that is used for SQL parsing can be configured with 'spark.sql.dialect'.

    Definition Classes
    SQLContext
    Since

    1.3.0

  66. def streams: StreamingQueryManager

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    Returns a StreamingQueryManager that allows managing all the StreamingQueries active on this context.

    Returns a StreamingQueryManager that allows managing all the StreamingQueries active on this context.

    Definition Classes
    SQLContext
    Since

    2.0.0

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

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    Definition Classes
    AnyRef
  68. def table(tableName: String): DataFrame

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    Returns the specified table as a DataFrame.

    Returns the specified table as a DataFrame.

    Definition Classes
    SQLContext
    Since

    1.3.0

  69. def tableNames(databaseName: String): Array[String]

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    Returns the names of tables in the given database as an array.

    Returns the names of tables in the given database as an array.

    Definition Classes
    SQLContext
    Since

    1.3.0

  70. def tableNames(): Array[String]

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    Returns the names of tables in the current database as an array.

    Returns the names of tables in the current database as an array.

    Definition Classes
    SQLContext
    Since

    1.3.0

  71. def tables(databaseName: String): DataFrame

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    Returns a DataFrame containing names of existing tables in the given database.

    Returns a DataFrame containing names of existing tables in the given database. The returned DataFrame has three columns, database, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).

    Definition Classes
    SQLContext
    Since

    1.3.0

  72. def tables(): DataFrame

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    Returns a DataFrame containing names of existing tables in the current database.

    Returns a DataFrame containing names of existing tables in the current database. The returned DataFrame has three columns, database, tableName and isTemporary (a Boolean indicating if a table is a temporary one or not).

    Definition Classes
    SQLContext
    Since

    1.3.0

  73. def toString(): String

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    Definition Classes
    AnyRef → Any
  74. def udf: UDFRegistration

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    A collection of methods for registering user-defined functions (UDF).

    A collection of methods for registering user-defined functions (UDF).

    The following example registers a Scala closure as UDF:

    sqlContext.udf.register("myUDF", (arg1: Int, arg2: String) => arg2 + arg1)

    The following example registers a UDF in Java:

    sqlContext.udf().register("myUDF",
        (Integer arg1, String arg2) -> arg2 + arg1,
        DataTypes.StringType);
    Definition Classes
    SQLContext
    Since

    1.3.0

    Note

    The user-defined functions must be deterministic. Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query.

  75. def uncacheTable(tableName: String): Unit

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    Removes the specified table from the in-memory cache.

    Removes the specified table from the in-memory cache.

    Definition Classes
    SQLContext
    Since

    1.3.0

  76. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  77. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  78. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

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    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame instead.

  2. def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame

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    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame instead.

  3. def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

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    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame instead.

  4. def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame

    Permalink

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use createDataFrame instead.

  5. def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

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    (Scala-specific) Create an external table from the given path based on a data source, a schema and a set of options.

    (Scala-specific) Create an external table from the given path based on a data source, a schema and a set of options. Then, returns the corresponding DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  6. def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

    Permalink

    Create an external table from the given path based on a data source, a schema and a set of options.

    Create an external table from the given path based on a data source, a schema and a set of options. Then, returns the corresponding DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  7. def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

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    (Scala-specific) Creates an external table from the given path based on a data source and a set of options.

    (Scala-specific) Creates an external table from the given path based on a data source and a set of options. Then, returns the corresponding DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  8. def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

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    Creates an external table from the given path based on a data source and a set of options.

    Creates an external table from the given path based on a data source and a set of options. Then, returns the corresponding DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  9. def createExternalTable(tableName: String, path: String, source: String): DataFrame

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    Creates an external table from the given path based on a data source and returns the corresponding DataFrame.

    Creates an external table from the given path based on a data source and returns the corresponding DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  10. def createExternalTable(tableName: String, path: String): DataFrame

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    Creates an external table from the given path and returns the corresponding DataFrame.

    Creates an external table from the given path and returns the corresponding DataFrame. It will use the default data source configured by spark.sql.sources.default.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 2.2.0) use sparkSession.catalog.createTable instead.

    Since

    1.3.0

  11. def jdbc(url: String, table: String, theParts: Array[String]): DataFrame

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    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. The theParts parameter gives a list expressions suitable for inclusion in WHERE clauses; each one defines one partition of the DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.jdbc() instead.

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

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    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.

    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. the range minValue-maxValue will be split evenly into this many partitions

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.jdbc() instead.

  13. def jdbc(url: String, table: String): DataFrame

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    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.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.jdbc() instead.

  14. def jsonFile(path: String, samplingRatio: Double): DataFrame

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    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  15. def jsonFile(path: String, schema: StructType): DataFrame

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    Loads a JSON file (one object per line) and applies the given schema, returning the result as a DataFrame.

    Loads a JSON file (one object per line) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  16. def jsonFile(path: String): DataFrame

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    Loads a JSON file (one object per line), returning the result as a DataFrame.

    Loads a JSON file (one object per line), returning the result as a DataFrame. It goes through the entire dataset once to determine the schema.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  17. def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame

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    Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  18. def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame

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    Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Loads an RDD[String] storing JSON objects (one object per record) inferring the schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  19. def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame

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    Loads an JavaRDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Loads an JavaRDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

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

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    Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema, returning the result as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

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

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    Loads an RDD[String] storing JSON objects (one object per record), returning the result as a DataFrame.

    Loads an RDD[String] storing JSON objects (one object per record), returning the result as a DataFrame. It goes through the entire dataset once to determine the schema.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

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

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    Loads an RDD[String] storing JSON objects (one object per record), returning the result as a DataFrame.

    Loads an RDD[String] storing JSON objects (one object per record), returning the result as a DataFrame. It goes through the entire dataset once to determine the schema.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.json() instead.

  23. def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

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    (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.

    (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.

  24. def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

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    (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.

    (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame, using the given schema as the schema of the DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.

  25. def load(source: String, options: Map[String, String]): DataFrame

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    (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    (Scala-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.format(source).options(options).load() instead.

  26. def load(source: String, options: Map[String, String]): DataFrame

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    (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    (Java-specific) Returns the dataset specified by the given data source and a set of options as a DataFrame.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.format(source).options(options).load() instead.

  27. def load(path: String, source: String): DataFrame

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    Returns the dataset stored at path as a DataFrame, using the given data source.

    Returns the dataset stored at path as a DataFrame, using the given data source.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.format(source).load(path) instead.

  28. def load(path: String): DataFrame

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    Returns the dataset stored at path as a DataFrame, using the default data source configured by spark.sql.sources.default.

    Returns the dataset stored at path as a DataFrame, using the default data source configured by spark.sql.sources.default.

    Definition Classes
    SQLContext
    Annotations
    @deprecated
    Deprecated

    (Since version 1.4.0) Use read.load(path) instead.

  29. def parquetFile(paths: String*): DataFrame

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

    Loads a Parquet file, returning the result as a DataFrame. This function returns an empty DataFrame if no paths are passed in.

    Definition Classes
    SQLContext
    Annotations
    @deprecated @varargs()
    Deprecated

    (Since version 1.4.0) Use read.parquet() instead.

Inherited from SQLContext

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Basic Operations

Cached Table Management

Configuration

dataframe

Custom DataFrame Creation

Custom Dataset Creation

Persistent Catalog DDL

Generic Data Sources

Specific Data Sources

Support functions for language integrated queries