SparkSession

java.lang.Object
- org.apache.spark.sql.SparkSession

All Implemented Interfaces:: java.io.Serializable

public class SparkSession
extends java.lang.Object
implements scala.Serializable

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

To create a SparkSession, use the following builder pattern:


   SparkSession.builder()
     .master("local")
     .appName("Word Count")
     .config("spark.some.config.option", "some-value").
     .getOrCreate()

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

Methods
Modifier and Type	Method and Description
`protected Dataset<Row>`	`applySchemaToPythonRDD(RDD<java.lang.Object[]> rdd, java.lang.String schemaString)` Apply a schema defined by the schemaString to an RDD.
`protected Dataset<Row>`	`applySchemaToPythonRDD(RDD<java.lang.Object[]> rdd, StructType schema)` Apply a schema defined by the schema to an RDD.
`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`.
`protected org.apache.spark.sql.execution.CacheManager`	`cacheManager()`
`Catalog`	`catalog()` Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc.
`RuntimeConfig`	`conf()` Runtime configuration interface for Spark.
`Dataset<Row>`	`createDataFrame(JavaRDD<?> rdd, java.lang.Class<?> beanClass)` Applies a schema to an RDD of Java Beans.
`Dataset<Row>`	`createDataFrame(JavaRDD<Row> rowRDD, StructType schema)` :: DeveloperApi :: Creates a `DataFrame` from an `JavaRDD` containing `Row`s using the given schema.
`Dataset<Row>`	`createDataFrame(java.util.List<?> data, java.lang.Class<?> beanClass)` Applies a schema to an List of Java Beans.
`Dataset<Row>`	`createDataFrame(java.util.List<Row> rows, StructType schema)` :: DeveloperApi :: Creates a `DataFrame` from an `List` containing `Row`s using the given schema.
`Dataset<Row>`	`createDataFrame(RDD<?> rdd, java.lang.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$1)` :: 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.
`protected Dataset<Row>`	`createDataFrame(RDD<Row> rowRDD, StructType schema, boolean needsConversion)` Creates a `DataFrame` from an RDD[Row].
`<A extends scala.Product> Dataset<Row>`	`createDataFrame(scala.collection.Seq<A> data, scala.reflect.api.TypeTags.TypeTag<A> evidence$2)` :: Experimental :: Creates a `DataFrame` from a local Seq of Product.
`<T> Dataset<T>`	`createDataset(java.util.List<T> data, Encoder<T> evidence$5)`
`<T> Dataset<T>`	`createDataset(RDD<T> data, Encoder<T> evidence$4)`
`<T> Dataset<T>`	`createDataset(scala.collection.Seq<T> data, Encoder<T> evidence$3)`
`protected void`	`createTempView(java.lang.String viewName, Dataset<Row> df, boolean replaceIfExists)` Creates a temporary view with a DataFrame.
`Dataset<Row>`	`emptyDataFrame()` :: Experimental :: Returns a `DataFrame` with no rows or columns.
`protected org.apache.spark.sql.execution.QueryExecution`	`executePlan(org.apache.spark.sql.catalyst.plans.logical.LogicalPlan plan)`
`protected org.apache.spark.sql.execution.QueryExecution`	`executeSql(java.lang.String sql)`
`ExperimentalMethods`	`experimental()` :: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
`protected org.apache.spark.sql.catalyst.catalog.ExternalCatalog`	`externalCatalog()`
`SparkSession.implicits$`	`implicits()` Accessor for nested Scala object
`protected static void`	`initializeLogIfNecessary(boolean isInterpreter)`
`protected Dataset<Row>`	`internalCreateDataFrame(RDD<org.apache.spark.sql.catalyst.InternalRow> catalystRows, StructType schema)` Creates a `DataFrame` from an RDD[Row].
`protected static boolean`	`isTraceEnabled()`
`protected org.apache.spark.sql.execution.ui.SQLListener`	`listener()`
`ExecutionListenerManager`	`listenerManager()` :: Experimental :: An interface to register custom `QueryExecutionListener`s that listen for execution metrics.
`protected static org.slf4j.Logger`	`log()`
`protected static void`	`logDebug(scala.Function0<java.lang.String> msg)`
`protected static void`	`logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)`
`protected static void`	`logError(scala.Function0<java.lang.String> msg)`
`protected static void`	`logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)`
`protected static void`	`logInfo(scala.Function0<java.lang.String> msg)`
`protected static void`	`logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)`
`protected static java.lang.String`	`logName()`
`protected static void`	`logTrace(scala.Function0<java.lang.String> msg)`
`protected static void`	`logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)`
`protected static void`	`logWarning(scala.Function0<java.lang.String> msg)`
`protected static void`	`logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)`
`SparkSession`	`newSession()` Start a new session with isolated SQL configurations, temporary tables, registered functions are isolated, but sharing the underlying `SparkContext` and cached data.
`protected DataType`	`parseDataType(java.lang.String dataTypeString)` Parses the data type in our internal string representation.
`protected org.apache.spark.sql.catalyst.plans.logical.LogicalPlan`	`parseSql(java.lang.String sql)`
`Dataset<java.lang.Long>`	`range(long end)` :: Experimental :: Creates a `Dataset` with a single `LongType` column named `id`, containing elements in an range from 0 to `end` (exclusive) with step value 1.
`Dataset<java.lang.Long>`	`range(long start, long end)` :: Experimental :: Creates a `Dataset` with a single `LongType` column named `id`, containing elements in an range from `start` to `end` (exclusive) with step value 1.
`Dataset<java.lang.Long>`	`range(long start, long end, long step)` :: Experimental :: Creates a `Dataset` with a single `LongType` column named `id`, containing elements in an range from `start` to `end` (exclusive) with an step value.
`Dataset<java.lang.Long>`	`range(long start, long end, long step, int numPartitions)` :: Experimental :: Creates a `Dataset` 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.
`DataFrameReader`	`read()` :: Experimental :: Returns a `DataFrameReader` that can be used to read data and streams in as a `DataFrame`.
`protected 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`.
`protected void`	`setWrappedContext(SQLContext sqlContext)`
`protected 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(java.lang.String sqlText)` Executes a SQL query using Spark, returning the result as a `DataFrame`.
`void`	`stop()` Stop the underlying `SparkContext`.
`ContinuousQueryManager`	`streams()` Returns a `ContinuousQueryManager` that allows managing all the `ContinuousQueries` active on `this`.
`Dataset<Row>`	`table(java.lang.String tableName)` Returns the specified table as a `DataFrame`.
`protected Dataset<Row>`	`table(org.apache.spark.sql.catalyst.TableIdentifier tableIdent)`
`UDFRegistration`	`udf()` A collection of methods for registering user-defined functions (UDF).
`protected SQLContext`	`wrapped()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - builder
```
public static SparkSession.Builder builder()
```
    Creates a SparkSession.Builder for constructing a SparkSession.
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - logName
```
protected static java.lang.String logName()
```
  - log
```
protected static org.slf4j.Logger log()
```
  - logInfo
```
protected static void logInfo(scala.Function0<java.lang.String> msg)
```
  - logDebug
```
protected static void logDebug(scala.Function0<java.lang.String> msg)
```
  - logTrace
```
protected static void logTrace(scala.Function0<java.lang.String> msg)
```
  - logWarning
```
protected static void logWarning(scala.Function0<java.lang.String> msg)
```
  - logError
```
protected static void logError(scala.Function0<java.lang.String> msg)
```
  - logInfo
```
protected static void logInfo(scala.Function0<java.lang.String> msg,
           java.lang.Throwable throwable)
```
  - logDebug
```
protected static void logDebug(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)
```
  - logTrace
```
protected static void logTrace(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)
```
  - logWarning
```
protected static void logWarning(scala.Function0<java.lang.String> msg,
              java.lang.Throwable throwable)
```
  - logError
```
protected static void logError(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)
```
  - isTraceEnabled
```
protected static boolean isTraceEnabled()
```
  - initializeLogIfNecessary
```
protected static void initializeLogIfNecessary(boolean isInterpreter)
```
  - sparkContext
```
public SparkContext sparkContext()
```
  - sharedState
```
protected 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.
    
    Returns:
    (undocumented)
  - sessionState
```
protected 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.
    
    Returns:
    (undocumented)
  - wrapped
```
protected SQLContext wrapped()
```
  - setWrappedContext
```
protected void setWrappedContext(SQLContext sqlContext)
```
  - cacheManager
```
protected org.apache.spark.sql.execution.CacheManager cacheManager()
```
  - listener
```
protected org.apache.spark.sql.execution.ui.SQLListener listener()
```
  - externalCatalog
```
protected org.apache.spark.sql.catalyst.catalog.ExternalCatalog externalCatalog()
```
  - 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",
       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);
 
```
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - streams
```
public ContinuousQueryManager streams()
```
    Returns a ContinuousQueryManager that allows managing all the ContinuousQueries 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.
    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.
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - emptyDataFrame
```
public Dataset<Row> emptyDataFrame()
```
    :: Experimental :: Returns a DataFrame with no rows or columns.
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - createDataFrame
```
public <A extends scala.Product> Dataset<Row> createDataFrame(RDD<A> rdd,
                                                     scala.reflect.api.TypeTags.TypeTag<A> evidence$1)
```
    :: Experimental :: Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    
    Parameters:
    rdd - (undocumented)
    evidence$1 - (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$2)
```
    :: Experimental :: Creates a DataFrame from a local Seq of Product.
    
    Parameters:
    data - (undocumented)
    evidence$2 - (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 an 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 an 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,
                           java.lang.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,
                           java.lang.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,
                           java.lang.Class<?> beanClass)
```
    Applies a schema to an 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$3)
```
  - createDataset
```
public <T> Dataset<T> createDataset(RDD<T> data,
                           Encoder<T> evidence$4)
```
  - createDataset
```
public <T> Dataset<T> createDataset(java.util.List<T> data,
                           Encoder<T> evidence$5)
```
  - range
```
public Dataset<java.lang.Long> range(long end)
```
    :: Experimental :: Creates a Dataset with a single LongType column named id, containing elements in an range from 0 to end (exclusive) with step value 1.
    
    Parameters:
    end - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - range
```
public Dataset<java.lang.Long> range(long start,
                            long end)
```
    :: Experimental :: Creates a Dataset with a single LongType column named id, containing elements in an range from start to end (exclusive) with step value 1.
    
    Parameters:
    start - (undocumented)
    end - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - range
```
public Dataset<java.lang.Long> range(long start,
                            long end,
                            long step)
```
    :: Experimental :: Creates a Dataset with a single LongType column named id, containing elements in an range from start to end (exclusive) with an step value.
    
    Parameters:
    start - (undocumented)
    end - (undocumented)
    step - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - range
```
public Dataset<java.lang.Long> range(long start,
                            long end,
                            long step,
                            int numPartitions)
```
    :: Experimental :: Creates a Dataset 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.
    
    Parameters:
    start - (undocumented)
    end - (undocumented)
    step - (undocumented)
    numPartitions - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - internalCreateDataFrame
```
protected Dataset<Row> internalCreateDataFrame(RDD<org.apache.spark.sql.catalyst.InternalRow> catalystRows,
                                   StructType schema)
```
    Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be converted to Catalyst rows.
    
    Parameters:
    catalystRows - (undocumented)
    schema - (undocumented)
    
    Returns:
    (undocumented)
  - createDataFrame
```
protected Dataset<Row> createDataFrame(RDD<Row> rowRDD,
                           StructType schema,
                           boolean needsConversion)
```
    Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be converted to Catalyst rows.
    
    Parameters:
    rowRDD - (undocumented)
    schema - (undocumented)
    needsConversion - (undocumented)
    
    Returns:
    (undocumented)
  - 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(java.lang.String tableName)
```
    Returns the specified table as a DataFrame.
    
    Parameters:
    tableName - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    2.0.0
  - table
```
protected Dataset<Row> table(org.apache.spark.sql.catalyst.TableIdentifier tableIdent)
```
  - createTempView
```
protected void createTempView(java.lang.String viewName,
                  Dataset<Row> df,
                  boolean replaceIfExists)
```
    Creates a temporary view with a DataFrame. The lifetime of this temporary view is tied to this SparkSession.
    
    Parameters:
    viewName - (undocumented)
    df - (undocumented)
    replaceIfExists - (undocumented)
  - sql
```
public Dataset<Row> sql(java.lang.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()
```
    :: Experimental :: Returns a DataFrameReader that can be used to read data and streams 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
  - implicits
```
public SparkSession.implicits$ implicits()
```
    Accessor for nested Scala object
    
    Returns:
    (undocumented)
  - stop
```
public void stop()
```
    Stop the underlying SparkContext.
    
    Since:
    
    2.0.0
  - parseSql
```
protected org.apache.spark.sql.catalyst.plans.logical.LogicalPlan parseSql(java.lang.String sql)
```
  - executeSql
```
protected org.apache.spark.sql.execution.QueryExecution executeSql(java.lang.String sql)
```
  - executePlan
```
protected org.apache.spark.sql.execution.QueryExecution executePlan(org.apache.spark.sql.catalyst.plans.logical.LogicalPlan plan)
```
  - parseDataType
```
protected DataType parseDataType(java.lang.String dataTypeString)
```
    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.
    
    Parameters:
    dataTypeString - (undocumented)
    
    Returns:
    (undocumented)
  - applySchemaToPythonRDD
```
protected Dataset<Row> applySchemaToPythonRDD(RDD<java.lang.Object[]> rdd,
                                  java.lang.String schemaString)
```
    Apply a schema defined by the schemaString to an RDD. It is only used by PySpark.
    
    Parameters:
    rdd - (undocumented)
    schemaString - (undocumented)
    
    Returns:
    (undocumented)
  - applySchemaToPythonRDD
```
protected Dataset<Row> applySchemaToPythonRDD(RDD<java.lang.Object[]> rdd,
                                  StructType schema)
```
    Apply a schema defined by the schema to an RDD. It is only used by PySpark.
    
    Parameters:
    rdd - (undocumented)
    schema - (undocumented)
    
    Returns:
    (undocumented)

Class SparkSession

Nested Class Summary

Method Summary

Methods inherited from class java.lang.Object

Method Detail

builder

logName

log

logInfo

logDebug

logTrace

logWarning

logError

logInfo

logDebug

logTrace

logWarning

logError

isTraceEnabled

initializeLogIfNecessary

sparkContext

sharedState

sessionState

wrapped

setWrappedContext

cacheManager

listener

externalCatalog

conf

listenerManager

experimental

udf

streams

newSession

emptyDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

createDataFrame

baseRelationToDataFrame

createDataset

createDataset

createDataset

range

range

range

range

internalCreateDataFrame

createDataFrame

catalog

table

table

createTempView

sql

read

implicits

stop

parseSql

executeSql

executePlan

parseDataType

applySchemaToPythonRDD

applySchemaToPythonRDD