SparkSession (Spark 2.3.4 JavaDoc)

Object
- org.apache.spark.sql.SparkSession

All Implemented Interfaces:

java.io.Closeable, java.io.Serializable, AutoCloseable, Logging
```
@InterfaceStability.Stable
public class SparkSession
extends Object
implements scala.Serializable, java.io.Closeable, 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()
 
```
param: sparkContext The Spark context associated with this Spark session. param: existingSharedState If supplied, use the existing shared state instead of creating a new one. param: parentSessionState If supplied, inherit all session state (i.e. temporary views, SQL config, UDFs etc) from parent.
See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`SparkSession.Builder` Builder for `SparkSession`.
`class`	`SparkSession.implicits$` :: Experimental :: (Scala-specific) Implicit methods available in Scala for converting common Scala objects into `DataFrame`s.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Dataset<Row>`	`baseRelationToDataFrame(BaseRelation baseRelation)` Convert a `BaseRelation` created for external data sources into a `DataFrame`.
`static SparkSession.Builder`	`builder()` Creates a `SparkSession.Builder` for constructing a `SparkSession`.
`Catalog`	`catalog()` Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc.
`static void`	`clearActiveSession()` Clears the active SparkSession for current thread.
`static void`	`clearDefaultSession()` Clears the default SparkSession that is returned by the builder.
`void`	`close()` Synonym for `stop()`.
`RuntimeConfig`	`conf()` Runtime configuration interface for Spark.
`Dataset<Row>`	`createDataFrame(JavaRDD<?> rdd, Class<?> beanClass)` Applies a schema to an RDD of Java Beans.
`Dataset<Row>`	`createDataFrame(JavaRDD<Row> rowRDD, StructType schema)` :: DeveloperApi :: Creates a `DataFrame` from a `JavaRDD` containing `Row`s using the given schema.
`Dataset<Row>`	`createDataFrame(java.util.List<?> data, Class<?> beanClass)` Applies a schema to a List of Java Beans.
`Dataset<Row>`	`createDataFrame(java.util.List<Row> rows, StructType schema)` :: DeveloperApi :: Creates a `DataFrame` from a `java.util.List` containing `Row`s using the given schema.
`Dataset<Row>`	`createDataFrame(RDD<?> rdd, Class<?> beanClass)` Applies a schema to an RDD of Java Beans.
`<A extends scala.Product> Dataset<Row>`	`createDataFrame(RDD<A> rdd, scala.reflect.api.TypeTags.TypeTag<A> evidence$2)` :: Experimental :: Creates a `DataFrame` from an RDD of Product (e.g.
`Dataset<Row>`	`createDataFrame(RDD<Row> rowRDD, StructType schema)` :: DeveloperApi :: Creates a `DataFrame` from an `RDD` containing `Row`s using the given schema.
`<A extends scala.Product> Dataset<Row>`	`createDataFrame(scala.collection.Seq<A> data, scala.reflect.api.TypeTags.TypeTag<A> evidence$3)` :: Experimental :: Creates a `DataFrame` from a local Seq of Product.
`<T> Dataset<T>`	`createDataset(java.util.List<T> data, Encoder<T> evidence$6)` :: Experimental :: Creates a `Dataset` from a `java.util.List` of a given type.
`<T> Dataset<T>`	`createDataset(RDD<T> data, Encoder<T> evidence$5)` :: Experimental :: Creates a `Dataset` from an RDD of a given type.
`<T> Dataset<T>`	`createDataset(scala.collection.Seq<T> data, Encoder<T> evidence$4)` :: Experimental :: Creates a `Dataset` from a local Seq of data of a given type.
`Dataset<Row>`	`emptyDataFrame()` Returns a `DataFrame` with no rows or columns.
`<T> Dataset<T>`	`emptyDataset(Encoder<T> evidence$1)` :: Experimental :: Creates a new `Dataset` of type T containing zero elements.
`ExperimentalMethods`	`experimental()` :: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
`static scala.Option<SparkSession>`	`getActiveSession()` Returns the active SparkSession for the current thread, returned by the builder.
`static scala.Option<SparkSession>`	`getDefaultSession()` Returns the default SparkSession that is returned by the builder.
`SparkSession.implicits$`	`implicits()` Accessor for nested Scala object
`ExecutionListenerManager`	`listenerManager()` :: Experimental :: An interface to register custom `QueryExecutionListener`s that listen for execution metrics.
`SparkSession`	`newSession()` Start a new session with isolated SQL configurations, temporary tables, registered functions are isolated, but sharing the underlying `SparkContext` and cached data.
`Dataset<Long>`	`range(long end)` :: 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.
`Dataset<Long>`	`range(long start, long end)` :: 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.
`Dataset<Long>`	`range(long start, long end, long step)` :: 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.
`Dataset<Long>`	`range(long start, long end, long step, int numPartitions)` :: 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.
`DataFrameReader`	`read()` Returns a `DataFrameReader` that can be used to read non-streaming data in as a `DataFrame`.
`DataStreamReader`	`readStream()` Returns a `DataStreamReader` that can be used to read streaming data in as a `DataFrame`.
`org.apache.spark.sql.internal.SessionState`	`sessionState()` State isolated across sessions, including SQL configurations, temporary tables, registered functions, and everything else that accepts a `SQLConf`.
`static void`	`setActiveSession(SparkSession session)` Changes the SparkSession that will be returned in this thread and its children when SparkSession.getOrCreate() is called.
`static void`	`setDefaultSession(SparkSession session)` Sets the default SparkSession that is returned by the builder.
`org.apache.spark.sql.internal.SharedState`	`sharedState()` State shared across sessions, including the `SparkContext`, cached data, listener, and a catalog that interacts with external systems.
`SparkContext`	`sparkContext()`
`Dataset<Row>`	`sql(String sqlText)` Executes a SQL query using Spark, returning the result as a `DataFrame`.
`SQLContext`	`sqlContext()` A wrapped version of this session in the form of a `SQLContext`, for backward compatibility.
`void`	`stop()` Stop the underlying `SparkContext`.
`StreamingQueryManager`	`streams()` :: Experimental :: Returns a `StreamingQueryManager` that allows managing all the `StreamingQuery`s active on `this`.
`Dataset<Row>`	`table(String tableName)` Returns the specified table/view as a `DataFrame`.
`<T> T`	`time(scala.Function0<T> f)` Executes some code block and prints to stdout the time taken to execute the block.
`UDFRegistration`	`udf()` A collection of methods for registering user-defined functions (UDF).
`String`	`version()` The version of Spark on which this application is running.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

- Method Detail
  - builder
```
public static SparkSession.Builder builder()
```
    Creates a SparkSession.Builder for constructing a SparkSession.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - setActiveSession
```
public static void setActiveSession(SparkSession session)
```
    Changes the SparkSession that will be returned in this thread and its children when SparkSession.getOrCreate() is called. This can be used to ensure that a given thread receives a SparkSession with an isolated session, instead of the global (first created) context.
    
    Parameters:
    
    session - (undocumented)
    
    Since:
    
    2.0.0
  - clearActiveSession
```
public static void clearActiveSession()
```
    Clears the active SparkSession for current thread. Subsequent calls to getOrCreate will return the first created context instead of a thread-local override.
    
    Since:
    
    2.0.0
  - setDefaultSession
```
public static void setDefaultSession(SparkSession session)
```
    Sets the default SparkSession that is returned by the builder.
    
    Parameters:
    
    session - (undocumented)
    
    Since:
    
    2.0.0
  - clearDefaultSession
```
public static void clearDefaultSession()
```
    Clears the default SparkSession that is returned by the builder.
    
    Since:
    
    2.0.0
  - getActiveSession
```
public static scala.Option<SparkSession> getActiveSession()
```
    Returns the active SparkSession for the current thread, returned by the builder.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
  - getDefaultSession
```
public static scala.Option<SparkSession> getDefaultSession()
```
    Returns the default SparkSession that is returned by the builder.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
  - sparkContext
```
public SparkContext sparkContext()
```
  - version
```
public String version()
```
    The version of Spark on which this application is running.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - sharedState
```
public org.apache.spark.sql.internal.SharedState sharedState()
```
    State shared across sessions, including the SparkContext, cached data, listener, and a catalog that interacts with external systems.
    This is internal to Spark and there is no guarantee on interface stability.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
  - sessionState
```
public org.apache.spark.sql.internal.SessionState sessionState()
```
    State isolated across sessions, including SQL configurations, temporary tables, registered functions, and everything else that accepts a SQLConf. If parentSessionState is not null, the SessionState will be a copy of the parent.
    This is internal to Spark and there is no guarantee on interface stability.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
  - sqlContext
```
public SQLContext sqlContext()
```
    A wrapped version of this session in the form of a SQLContext, for backward compatibility.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - conf
```
public RuntimeConfig conf()
```
    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.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - listenerManager
```
public ExecutionListenerManager listenerManager()
```
    :: Experimental :: An interface to register custom QueryExecutionListeners that listen for execution metrics.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - experimental
```
public ExperimentalMethods experimental()
```
    :: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - udf
```
public UDFRegistration udf()
```
    A collection of methods for registering user-defined functions (UDF).
    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",
       (Integer arg1, String arg2) -> arg2 + arg1,
       DataTypes.StringType);
 
```
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
    
    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.
  - streams
```
public StreamingQueryManager streams()
```
    :: Experimental :: Returns a StreamingQueryManager that allows managing all the StreamingQuerys active on this.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - newSession
```
public SparkSession newSession()
```
    Start a new session with isolated SQL configurations, temporary tables, registered functions are isolated, but sharing the underlying SparkContext and cached data.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
    
    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.
  - emptyDataFrame
```
public Dataset<Row> emptyDataFrame()
```
    Returns a DataFrame with no rows or columns.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - emptyDataset
```
public <T> Dataset<T> emptyDataset(Encoder<T> evidence$1)
```
    :: Experimental :: Creates a new Dataset of type T containing zero elements.
    
    Parameters:
    
    evidence$1 - (undocumented)
    
    Returns:
    
    2.0.0
  - createDataFrame
```
public <A extends scala.Product> Dataset<Row> createDataFrame(RDD<A> rdd,
                                                              scala.reflect.api.TypeTags.TypeTag<A> evidence$2)
```
    :: Experimental :: Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    
    Parameters:
    
    rdd - (undocumented)
    
    evidence$2 - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public <A extends scala.Product> Dataset<Row> createDataFrame(scala.collection.Seq<A> data,
                                                              scala.reflect.api.TypeTags.TypeTag<A> evidence$3)
```
    :: Experimental :: Creates a DataFrame from a local Seq of Product.
    
    Parameters:
    
    data - (undocumented)
    
    evidence$3 - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(RDD<Row> rowRDD,
                                    StructType 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)
 
```
    Parameters:
    
    rowRDD - (undocumented)
    
    schema - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(JavaRDD<Row> rowRDD,
                                    StructType 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.
    
    Parameters:
    
    rowRDD - (undocumented)
    
    schema - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(java.util.List<Row> rows,
                                    StructType 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.
    
    Parameters:
    
    rows - (undocumented)
    
    schema - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(RDD<?> rdd,
                                    Class<?> beanClass)
```
    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.
    
    Parameters:
    
    rdd - (undocumented)
    
    beanClass - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(JavaRDD<?> rdd,
                                    Class<?> beanClass)
```
    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.
    
    Parameters:
    
    rdd - (undocumented)
    
    beanClass - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataFrame
```
public Dataset<Row> createDataFrame(java.util.List<?> data,
                                    Class<?> beanClass)
```
    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.
    
    Parameters:
    
    data - (undocumented)
    
    beanClass - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.6.0
  - baseRelationToDataFrame
```
public Dataset<Row> baseRelationToDataFrame(BaseRelation baseRelation)
```
    Convert a BaseRelation created for external data sources into a DataFrame.
    
    Parameters:
    
    baseRelation - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataset
```
public <T> Dataset<T> createDataset(scala.collection.Seq<T> data,
                                    Encoder<T> evidence$4)
```
    :: 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|
   // +-------+---+
 
```
    Parameters:
    
    data - (undocumented)
    
    evidence$4 - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataset
```
public <T> Dataset<T> createDataset(RDD<T> data,
                                    Encoder<T> evidence$5)
```
    :: 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.
    
    Parameters:
    
    data - (undocumented)
    
    evidence$5 - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - createDataset
```
public <T> Dataset<T> createDataset(java.util.List<T> data,
                                    Encoder<T> evidence$6)
```
    :: 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());
 
```
    Parameters:
    
    data - (undocumented)
    
    evidence$6 - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - range
```
public Dataset<Long> range(long end)
```
    :: 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.
    
    Parameters:
    
    end - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - range
```
public Dataset<Long> range(long start,
                           long end)
```
    :: 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.
    
    Parameters:
    
    start - (undocumented)
    
    end - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - range
```
public Dataset<Long> range(long start,
                           long end,
                           long step)
```
    :: 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.
    
    Parameters:
    
    start - (undocumented)
    
    end - (undocumented)
    
    step - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - range
```
public Dataset<Long> range(long start,
                           long end,
                           long step,
                           int numPartitions)
```
    :: 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.
    
    Parameters:
    
    start - (undocumented)
    
    end - (undocumented)
    
    step - (undocumented)
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - catalog
```
public Catalog catalog()
```
    Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - table
```
public Dataset<Row> table(String tableName)
```
    Returns the specified table/view as a DataFrame.
    
    Parameters:
    
    tableName - is either a qualified or unqualified name that designates a table or view. If a database is specified, it identifies the table/view from the database. Otherwise, it first attempts to find a temporary view with the given name and then match the table/view from the current database. Note that, the global temporary view database is also valid here.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - sql
```
public Dataset<Row> sql(String sqlText)
```
    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'.
    
    Parameters:
    
    sqlText - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - read
```
public DataFrameReader read()
```
    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")
 
```
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - readStream
```
public DataStreamReader readStream()
```
    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")
 
```
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - time
```
public <T> T time(scala.Function0<T> f)
```
    Executes some code block and prints to stdout the time taken to execute the block. This is available in Scala only and is used primarily for interactive testing and debugging.
    
    Parameters:
    
    f - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.1.0
  - implicits
```
public SparkSession.implicits$ implicits()
```
    Accessor for nested Scala object
    
    Returns:
    
    (undocumented)
  - stop
```
public void stop()
```
    Stop the underlying SparkContext.
    
    Since:
    
    2.0.0
  - close
```
public void close()
```
    Synonym for stop().
    
    Specified by:
    
    close in interface java.io.Closeable
    
    Specified by:
    
    close in interface AutoCloseable
    
    Since:
    
    2.1.0

Class SparkSession

Nested Class Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Method Detail

builder

setActiveSession

clearActiveSession

setDefaultSession

clearDefaultSession

getActiveSession

getDefaultSession

sparkContext

version

sharedState

sessionState

sqlContext

conf

listenerManager

experimental

udf

streams

newSession

emptyDataFrame

emptyDataset

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

baseRelationToDataFrame

createDataset

createDataset

createDataset

range

range

range

range

catalog

table

sql

read

readStream

time

implicits

stop

close