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SparkSession (Spark 4.2.0 JavaDoc)

Method Details

  • builder

    Inheritdoc:

  • setActiveSession

    public static void setActiveSession(SparkSession session)

    Inheritdoc:

  • setDefaultSession

    public static void setDefaultSession(SparkSession session)

    Inheritdoc:

  • getActiveSession

    public static scala.Option<SparkSession> getActiveSession()

    Inheritdoc:

  • getDefaultSession

    public static scala.Option<SparkSession> getDefaultSession()

    Inheritdoc:

  • clearActiveSession

    public static void clearActiveSession()

  • clearDefaultSession

    public static void clearDefaultSession()

  • active

    public static org.apache.spark.sql.SparkSessionCompanion active()

  • addArtifacts

    public void addArtifacts(URI... uri)

    Add one or more artifacts to the session.

    Currently it supports local files with extensions .jar and .class and Apache Ivy URIs

    Parameters:
    uri - (undocumented)
    Since:
    4.0.0

  • sparkContext

    The Spark context associated with this Spark session.

    Returns:
    (undocumented)
    Note:
    this is only supported in Classic.

  • version

    public abstract String version()

    The version of Spark on which this application is running.

    Returns:
    (undocumented)
    Since:
    2.0.0

  • sharedState

    public abstract 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
    Note:
    this is only supported in Classic.

  • sessionState

    public abstract org.apache.spark.sql.internal.SessionState sessionState()

    State isolated across sessions, including SQL configurations, temporary tables, registered functions, and everything else that accepts a org.apache.spark.sql.internal.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
    Note:
    this is only supported in Classic.

  • sqlContext

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

    Returns:
    (undocumented)
    Since:
    2.0.0

  • 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

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

    Returns:
    (undocumented)
    Since:
    2.0.0
    Note:
    this is only supported in Classic.

  • 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
    Note:
    this is only supported in Classic.

  • 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

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

    Returns:
    (undocumented)
    Since:
    2.0.0

  • 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 abstract Dataset<Row> emptyDataFrame()

    Returns a DataFrame with no rows or columns.

    Returns:
    (undocumented)
    Since:
    2.0.0

  • emptyDataFrame

    Returns a DataFrame with schema schema and no rows.

    Parameters:
    schema - (undocumented)
    Returns:
    (undocumented)
    Since:
    4.2.0

  • createDataFrame

    public abstract <A extends scala.Product> Dataset<Row> createDataFrame(scala.collection.immutable.Seq<A> data, scala.reflect.api.TypeTags.TypeTag<A> evidence$1)

    Creates a DataFrame from a local Seq of Product.

    Parameters:
    data - (undocumented)
    evidence$1 - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0

  • createDataFrame

    :: 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 abstract Dataset<Row> createDataFrame(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

  • createDataFrame

    public abstract <A extends scala.Product> Dataset<Row> createDataFrame(RDD<A> rdd, scala.reflect.api.TypeTags.TypeTag<A> evidence$2)

    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
    Note:
    this is only supported in Classic.

  • createDataFrame

    :: 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
    Note:
    this is only supported in Classic.

  • createDataFrame

    :: 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
    Note:
    this is only supported in Classic.

  • createDataFrame

    public abstract 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
    Note:
    this is only supported in Classic.

  • createDataFrame

    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
    Note:
    this is only supported in Classic.

  • baseRelationToDataFrame

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

    Parameters:
    baseRelation - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0
    Note:
    this is only supported in Classic.

  • emptyDataset

    public abstract <T> Dataset<T> emptyDataset(Encoder<T> evidence$3)

    Creates a new Dataset of type T containing zero elements.

    Parameters:
    evidence$3 - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0

  • createDataset

    public abstract <T> Dataset<T> createDataset(scala.collection.immutable.Seq<T> data, Encoder<T> evidence$4)

    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 abstract <T> Dataset<T> createDataset(List<T> data, Encoder<T> evidence$5)

    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$5 - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0

  • createDataset

    public abstract <T> Dataset<T> createDataset(RDD<T> data, Encoder<T> evidence$6)

    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$6 - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0
    Note:
    this method is not supported in Spark Connect.

  • range

    public abstract Dataset<Long> range(long end)

    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 abstract Dataset<Long> range(long start, long end)

    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 abstract Dataset<Long> range(long start, long end, long step)

    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 abstract Dataset<Long> range(long start, long end, long step, int numPartitions)

    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 abstract 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

    Returns the specified table/view as a DataFrame. If it's a table, it must support batch reading and the returned DataFrame is the batch scan query plan of this table. If it's a view, the returned DataFrame is simply the query plan of the view, which can either be a batch or streaming query plan.

    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

    Executes a SQL query substituting positional parameters by the given arguments, returning the result as a DataFrame. This API eagerly runs DDL/DML commands, but not for SELECT queries.

    Parameters:
    sqlText - A SQL statement with positional parameters to execute.
    args - An array of Java/Scala objects that can be converted to SQL literal expressions. See Supported Data Types for supported value types in Scala/Java. For example, 1, "Steven", LocalDate.of(2023, 4, 2). A value can be also a Column of a literal or collection constructor functions such as map(), array(), struct(), in that case it is taken as is.
    Returns:
    (undocumented)
    Since:
    3.5.0

  • sql

    Executes a SQL query substituting named parameters by the given arguments, returning the result as a DataFrame. This API eagerly runs DDL/DML commands, but not for SELECT queries.

    Parameters:
    sqlText - A SQL statement with named parameters to execute.
    args - A map of parameter names to Java/Scala objects that can be converted to SQL literal expressions. See Supported Data Types for supported value types in Scala/Java. For example, map keys: "rank", "name", "birthdate"; map values: 1, "Steven", LocalDate.of(2023, 4, 2). Map value can be also a Column of a literal or collection constructor functions such as map(), array(), struct(), in that case it is taken as is.
    Returns:
    (undocumented)
    Since:
    3.4.0

  • sql

    Executes a SQL query substituting named parameters by the given arguments, returning the result as a DataFrame. This API eagerly runs DDL/DML commands, but not for SELECT queries.

    Parameters:
    sqlText - A SQL statement with named parameters to execute.
    args - A map of parameter names to Java/Scala objects that can be converted to SQL literal expressions. See Supported Data Types for supported value types in Scala/Java. For example, map keys: "rank", "name", "birthdate"; map values: 1, "Steven", LocalDate.of(2023, 4, 2). Map value can be also a Column of a literal or collection constructor functions such as map(), array(), struct(), in that case it is taken as is.
    Returns:
    (undocumented)
    Since:
    3.4.0

  • sql

    Executes a SQL query using Spark, returning the result as a DataFrame. This API eagerly runs DDL/DML commands, but not for SELECT queries.

    Parameters:
    sqlText - (undocumented)
    Returns:
    (undocumented)
    Since:
    2.0.0

  • executeCommand

    Execute an arbitrary string command inside an external execution engine rather than Spark. This could be useful when user wants to execute some commands out of Spark. For example, executing custom DDL/DML command for JDBC, creating index for ElasticSearch, creating cores for Solr and so on.

    The command will be eagerly executed after this method is called and the returned DataFrame will contain the output of the command(if any).

    Parameters:
    runner - The class name of the runner that implements ExternalCommandRunner.
    command - The target command to be executed
    options - The options for the runner.
    Returns:
    (undocumented)
    Since:
    3.0.0

  • addArtifact

    public abstract void addArtifact(String path)

    Add a single artifact to the current session.

    Currently only local files with extensions .jar and .class are supported.

    Parameters:
    path - (undocumented)
    Since:
    4.0.0

  • addArtifact

    public abstract void addArtifact(URI uri)

    Add a single artifact to the current session.

    Currently it supports local files with extensions .jar and .class and Apache Ivy URIs.

    Parameters:
    uri - (undocumented)
    Since:
    4.0.0

  • addArtifact

    public abstract void addArtifact(byte[] bytes, String target)

    Add a single in-memory artifact to the session while preserving the directory structure specified by target under the session's working directory of that particular file extension.

    Supported target file extensions are .jar and .class.

    ==Example== 

    
  addArtifact(bytesBar, "foo/bar.class")
  addArtifact(bytesFlat, "flat.class")
  // Directory structure of the session's working directory for class files would look like:
  // ${WORKING_DIR_FOR_CLASS_FILES}/flat.class
  // ${WORKING_DIR_FOR_CLASS_FILES}/foo/bar.class
    Parameters:
    bytes - (undocumented)
    target - (undocumented)
    Since:
    4.0.0

  • addArtifact

    public abstract void addArtifact(String source, String target)

    Add a single artifact to the session while preserving the directory structure specified by target under the session's working directory of that particular file extension.

    Supported target file extensions are .jar and .class.

    ==Example== 

    
  addArtifact("/Users/dummyUser/files/foo/bar.class", "foo/bar.class")
  addArtifact("/Users/dummyUser/files/flat.class", "flat.class")
  // Directory structure of the session's working directory for class files would look like:
  // ${WORKING_DIR_FOR_CLASS_FILES}/flat.class
  // ${WORKING_DIR_FOR_CLASS_FILES}/foo/bar.class
    Parameters:
    source - (undocumented)
    target - (undocumented)
    Since:
    4.0.0

  • addArtifacts

    public abstract void addArtifacts(scala.collection.immutable.Seq<URI> uri)

    Add one or more artifacts to the session.

    Currently it supports local files with extensions .jar and .class and Apache Ivy URIs

    Parameters:
    uri - (undocumented)
    Since:
    4.0.0

  • addTag

    public abstract void addTag(String tag)

    Add a tag to be assigned to all the operations started by this thread in this session.

    Often, a unit of execution in an application consists of multiple Spark executions. Application programmers can use this method to group all those jobs together and give a group tag. The application can use org.apache.spark.sql.SparkSession.interruptTag to cancel all running executions with this tag. For example: 

    
 // In the main thread:
 spark.addTag("myjobs")
 spark.range(10).map(i => { Thread.sleep(10); i }).collect()

 // In a separate thread:
 spark.interruptTag("myjobs")

    There may be multiple tags present at the same time, so different parts of application may use different tags to perform cancellation at different levels of granularity.

    Parameters:
    tag - The tag to be added. Cannot contain ',' (comma) character or be an empty string.
    Since:
    4.0.0

  • removeTag

    public abstract void removeTag(String tag)

    Remove a tag previously added to be assigned to all the operations started by this thread in this session. Noop if such a tag was not added earlier.

    Parameters:
    tag - The tag to be removed. Cannot contain ',' (comma) character or be an empty string.
    Since:
    4.0.0

  • getTags

    public abstract scala.collection.immutable.Set<String> getTags()

    Get the operation tags that are currently set to be assigned to all the operations started by this thread in this session.

    Returns:
    (undocumented)
    Since:
    4.0.0

  • clearTags

    public abstract void clearTags()

    Clear the current thread's operation tags.

    Since:
    4.0.0

  • interruptAll

    public abstract scala.collection.immutable.Seq<String> interruptAll()

    Request to interrupt all currently running operations of this session.

    Returns:
    Sequence of operation IDs requested to be interrupted.
    Since:
    4.0.0
    Note:
    This method will wait up to 60 seconds for the interruption request to be issued.

  • interruptTag

    public abstract scala.collection.immutable.Seq<String> interruptTag(String tag)

    Request to interrupt all currently running operations of this session with the given job tag.

    Parameters:
    tag - (undocumented)
    Returns:
    Sequence of operation IDs requested to be interrupted.
    Since:
    4.0.0
    Note:
    This method will wait up to 60 seconds for the interruption request to be issued.

  • interruptOperation

    public abstract scala.collection.immutable.Seq<String> interruptOperation(String operationId)

    Request to interrupt an operation of this session, given its operation ID.

    Parameters:
    operationId - (undocumented)
    Returns:
    The operation ID requested to be interrupted, as a single-element sequence, or an empty sequence if the operation is not started by this session.
    Since:
    4.0.0
    Note:
    This method will wait up to 60 seconds for the interruption request to be issued.

  • 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

    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

  • tvf

    Returns:
    (undocumented)
    Since:
    4.0.0

  • implicits

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

    
   val sparkSession = SparkSession.builder.getOrCreate()
   import sparkSession.implicits._
    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

  • stop

    public void stop()

    Since:
    2.0.0

  • withActive

    public <T> T withActive(scala.Function0<T> block)

    Execute a block of code with this session set as the active session, and restore the previous session on completion.

    Parameters:
    block - (undocumented)
    Returns:
    (undocumented)