Class/Object

org.apache.spark.sql

SparkSession

Related Docs: object SparkSession | package sql

Permalink

class SparkSession extends Serializable with Logging

The entry point to programming Spark with the Dataset and DataFrame API.

In environments that this has been created upfront (e.g. REPL, notebooks), use the builder to get an existing session:

SparkSession.builder().getOrCreate()

The builder can also be used to create a new session:

SparkSession.builder()
  .master("local")
  .appName("Word Count")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()
Self Type
SparkSession
Source
SparkSession.scala
Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. SparkSession
  2. Logging
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

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

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

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

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

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

    Permalink

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

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

    Since

    2.0.0

  6. lazy val catalog: Catalog

    Permalink

    Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc.

    Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc.

    Since

    2.0.0

  7. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. lazy val conf: RuntimeConfig

    Permalink

    Runtime configuration interface for Spark.

    Runtime configuration interface for Spark.

    This is the interface through which the user can get and set all Spark and Hadoop configurations that are relevant to Spark SQL. When getting the value of a config, this defaults to the value set in the underlying SparkContext, if any.

    Since

    2.0.0

  9. def createDataFrame(data: List[_], beanClass: Class[_]): DataFrame

    Permalink

    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.

    Since

    1.6.0

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

    Permalink

    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.

    Since

    2.0.0

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

    Permalink

    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.

    Since

    2.0.0

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

    Permalink

    :: 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.

    Annotations
    @DeveloperApi()
    Since

    2.0.0

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

    Permalink

    :: 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.

    Annotations
    @DeveloperApi()
    Since

    2.0.0

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

    Permalink

    :: 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 sparkSession = new org.apache.spark.sql.SparkSession(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 = sparkSession.createDataFrame(people, schema)
    dataFrame.printSchema
    // root
    // |-- name: string (nullable = false)
    // |-- age: integer (nullable = true)
    
    dataFrame.createOrReplaceTempView("people")
    sparkSession.sql("select name from people").collect.foreach(println)
    Annotations
    @DeveloperApi()
    Since

    2.0.0

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

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

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

    Permalink

    :: 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).

    Annotations
    @Experimental()
    Since

    2.0.0

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

    Permalink

    :: 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());
    Annotations
    @Experimental()
    Since

    2.0.0

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

    Permalink

    :: 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.

    Annotations
    @Experimental()
    Since

    2.0.0

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

    Permalink

    :: 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|
    // +-------+---+
    Annotations
    @Experimental()
    Since

    2.0.0

  20. lazy val emptyDataFrame: DataFrame

    Permalink

    Returns a DataFrame with no rows or columns.

    Returns a DataFrame with no rows or columns.

    Since

    2.0.0

  21. def emptyDataset[T](implicit arg0: Encoder[T]): Dataset[T]

    Permalink

    :: Experimental :: Creates a new Dataset of type T containing zero elements.

    :: Experimental :: Creates a new Dataset of type T containing zero elements.

    returns

    2.0.0

    Annotations
    @Experimental()
  22. final def eq(arg0: AnyRef): Boolean

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

    Permalink
    Definition Classes
    AnyRef → Any
  24. def experimental: ExperimentalMethods

    Permalink

    :: 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.

    Annotations
    @Experimental()
    Since

    2.0.0

  25. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  27. def hashCode(): Int

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

    Permalink

    :: 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 sparkSession = SparkSession.builder.getOrCreate()
    import sparkSession.implicits._
    Annotations
    @Experimental()
    Since

    2.0.0

  29. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  30. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  31. def isTraceEnabled(): Boolean

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  32. def listenerManager: ExecutionListenerManager

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

  33. def log: Logger

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

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

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

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

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  40. def logName: String

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

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

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

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

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

    Permalink
    Definition Classes
    AnyRef
  46. def newSession(): SparkSession

    Permalink

    Start a new session with isolated SQL configurations, temporary tables, registered functions are isolated, but sharing the underlying SparkContext and cached data.

    Start a new session with isolated SQL configurations, temporary tables, registered functions are isolated, but sharing the underlying SparkContext and cached data.

    Note: Other than the SparkContext, all shared state is initialized lazily. This method will force the initialization of the shared state to ensure that parent and child sessions are set up with the same shared state. If the underlying catalog implementation is Hive, this will initialize the metastore, which may take some time.

    Since

    2.0.0

  47. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  48. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  49. def parseDataType(dataTypeString: String): DataType

    Permalink

    Parses the data type in our internal string representation.

    Parses the data type in our internal string representation. The data type string should have the same format as the one generated by toString in scala. It is only used by PySpark.

    Attributes
    protected[org.apache.spark.sql]
  50. def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset[Long]

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

  51. def range(start: Long, end: Long, step: Long): Dataset[Long]

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

  52. def range(start: Long, end: Long): Dataset[Long]

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

  53. def range(end: Long): Dataset[Long]

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

  54. def read: DataFrameReader

    Permalink

    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.

    sparkSession.read.parquet("/path/to/file.parquet")
    sparkSession.read.schema(schema).json("/path/to/file.json")
    Since

    2.0.0

  55. def readStream: DataStreamReader

    Permalink

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

    :: Experimental :: 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")
    Annotations
    @Experimental()
    Since

    2.0.0

  56. val sparkContext: SparkContext

    Permalink
  57. def sql(sqlText: String): DataFrame

    Permalink

    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'.

    Since

    2.0.0

  58. val sqlContext: SQLContext

    Permalink

    A wrapped version of this session in the form of a SQLContext, for backward compatibility.

    A wrapped version of this session in the form of a SQLContext, for backward compatibility.

    Since

    2.0.0

  59. def stop(): Unit

    Permalink

    Stop the underlying SparkContext.

    Stop the underlying SparkContext.

    Since

    2.0.0

  60. def streams: StreamingQueryManager

    Permalink

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

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

    Annotations
    @Experimental()
    Since

    2.0.0

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

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

    Permalink

    Returns the specified table as a DataFrame.

    Returns the specified table as a DataFrame.

    Since

    2.0.0

  63. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  64. def udf: UDFRegistration

    Permalink

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

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

    The following example registers a Scala closure as UDF:

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

    The following example registers a UDF in Java:

    sparkSession.udf().register("myUDF",
        new UDF2<Integer, String, String>() {
            @Override
            public String call(Integer arg1, String arg2) {
                return arg2 + arg1;
            }
       }, DataTypes.StringType);

    Or, to use Java 8 lambda syntax:

    sparkSession.udf().register("myUDF",
        (Integer arg1, String arg2) -> arg2 + arg1,
        DataTypes.StringType);
    Since

    2.0.0

  65. def version: String

    Permalink

    The version of Spark on which this application is running.

    The version of Spark on which this application is running.

    Since

    2.0.0

  66. final def wait(): Unit

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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped