Snowpark Scala API Reference1.19.1 < Back
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# Packages

-  package root
  Definition Classes
    root
-  package com
  Definition Classes
    root
-  package snowflake
  Definition Classes
    com
-  package snowpark
  Definition Classes
    snowflake
-  package types

  This package contains all Snowpark logical types.

  This package contains all Snowpark logical types.

  Definition Classes
    snowpark
  Since

    0.1.0

-  package udaf
  Definition Classes
    snowpark
-  package udtf
  Definition Classes
    snowpark
- AggregateFunction
- AsyncJob
- CaseExpr
- Column
- CopyableDataFrame
- CopyableDataFrameAsyncActor
- DataFrame
- DataFrameAsyncActor
- DataFrameNaFunctions
- DataFrameReader
- DataFrameStatFunctions
- DataFrameWriter
- DataFrameWriterAsyncActor
- DeleteResult
- FileOperation
- GetResult
- GroupingSets
- HasCachedResult
- MatchedClauseBuilder
- MergeBuilder
- MergeBuilderAsyncActor
- MergeResult
- MergeTypedAsyncJob
- NotMatchedClauseBuilder
- PublicPreview
- PutResult
- RelationalGroupedDataFrame
- Row
- SProcRegistration
- SaveMode
- Session
- SnowparkClientException
- StoredProcedure
- TableFunction
- TypedAsyncJob
- UDAFRegistration
- UDFRegistration
- UDTFRegistration
- Updatable
- UpdatableAsyncActor
- UpdateResult
- UserDefinedFunction
- Window
- WindowSpec
- WriteFileResult
- functions
- tableFunctions

c

com.snowflake.snowpark

# Session

###
Companion object Session

#### class Session extends Logging

Establishes a connection with a Snowflake database and provides methods for creating DataFrames and accessing objects for working with files in stages.

When you create a `Session` object, you provide configuration settings to establish a connection with a Snowflake database (e.g. the URL for the account, a user name, etc.). You can specify these settings in a configuration file or in a Map that associates configuration setting names with values.

To create a Session from a file:

    val session = Session.builder.configFile("/path/to/file.properties").create

To create a Session from a map of configuration properties:

    val configMap = Map(
    "URL" -> "demo.snowflakecomputing.com",
    "USER" -> "testUser",
    "PASSWORD" -> "******",
    "ROLE" -> "myrole",
    "WAREHOUSE" -> "warehouse1",
    "DB" -> "db1",
    "SCHEMA" -> "schema1"
    )
    Session.builder.configs(configMap).create

Session contains functions to construct DataFrames like Session.table, Session.sql, and Session.read

Since

  0.1.0

Linear Supertypes
Logging, AnyRef, Any

Ordering

1. Alphabetic
2. By Inheritance

Inherited

1. Session
2. Logging
3. AnyRef
4. Any

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Visibility

1. Public
2. Protected

### Value Members

1.  final def !=(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
2.  final def ##: Int
   Definition Classes
     AnyRef → Any
3.  final def ==(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
4.  def addDependency(path: String): Unit

   Registers a file in stage or a local file as a dependency of a user-defined function (UDF).

   Registers a file in stage or a local file as a dependency of a user-defined function (UDF).

   The local file can be a JAR file, a directory, or any other file resource. If you pass the path to a local file to `addDependency` , the Snowpark library uploads the file to a temporary stage and imports the file when executing a UDF.

   If you pass the path to a file in a stage to `addDependency` , the file is included in the imports when executing a UDF.

   Note that in most cases, you don't need to add the Snowpark JAR file and the JAR file (or directory) of the currently running application as dependencies. The Snowpark library automatically attempts to detect and upload these JAR files. However, if this automatic detection fails, the Snowpark library reports this in an error message, and you must add these JAR files explicitly by calling `addDependency` .

   The following example demonstrates how to add dependencies on local files and files in a stage:

       session.addDependency("@my_stage/http-commons.jar")
       session.addDependency("/home/username/lib/language-detector.jar")
       session.addDependency("./resource-dir/")
       session.addDependency("./resource.xml")

   path

     Path to a local directory, local file, or file in a stage.

   Since

     0.1.0

5.  final def asInstanceOf[T0]: T0
   Definition Classes
     Any
6.  def cancelAll(): Unit

   Cancel all action methods that are running currently.

   Cancel all action methods that are running currently. This does not affect on any action methods called in the future.

   Since

     0.5.0

7.  def clone(): AnyRef
   Attributes
     protected[lang]
   Definition Classes
     AnyRef
   Annotations
     @throws(classOf[java.lang.CloneNotSupportedException]) @HotSpotIntrinsicCandidate() @native()
8.  def close(): Unit

   Close this session.

   Close this session.

   Since

     0.7.0

9.  def createAsyncJob(queryID: String): AsyncJob

   Returns an AsyncJob object that you can use to track the status and get the results of the asynchronous query specified by the query ID.

   Returns an AsyncJob object that you can use to track the status and get the results of the asynchronous query specified by the query ID.

   For example, create an AsyncJob by specifying a valid `<query_id>`, check whether the query is running or not, and get the result rows.

       val asyncJob = session.createAsyncJob(<query_id>)
       println(s"Is query ${asyncJob.getQueryId} running? ${asyncJob.isRunning()}")
       val rows = asyncJob.getRows()

   queryID

     A valid query ID

   returns

     An AsyncJob object

   Since

     0.11.0

10.  def createDataFrame(data: Array[Row], schema: StructType): DataFrame

    Creates a new DataFrame that uses the specified schema and contains the specified Row objects.

    Creates a new DataFrame that uses the specified schema and contains the specified Row objects.

    For example, the following code creates a DataFrame containing two columns of the types `int` and `string` with two rows of data:

    For example

        import com.snowflake.snowpark.types._
        ...
        // Create an array of Row objects containing data.
        val data = Array(Row(1, "a"), Row(2, "b"))
        // Define the schema for the columns in the DataFrame.
        val schema = StructType(Seq(StructField("num", IntegerType),
          StructField("str", StringType)))
        // Create the DataFrame.
        val df = session.createDataFrame(data, schema)

    data

      An array of Row objects representing rows of data.

    schema

      StructType representing the schema for the DataFrame.

    returns

      A DataFrame

    Since

      0.7.0

11.  def createDataFrame(data: Seq[Row], schema: StructType): DataFrame

    Creates a new DataFrame that uses the specified schema and contains the specified Row objects.

    Creates a new DataFrame that uses the specified schema and contains the specified Row objects.

    For example, the following code creates a DataFrame containing three columns of the types `int`, `string`, and `variant` with a single row of data:

        import com.snowflake.snowpark.types._
        ...
        // Create a sequence of a single Row object containing data.
        val data = Seq(Row(1, "a", new Variant(1)))
        // Define the schema for the columns in the DataFrame.
        val schema = StructType(Seq(StructField("int", IntegerType),
          StructField("string", StringType),
          StructField("variant", VariantType)))
        // Create the DataFrame.
        val df = session.createDataFrame(data, schema)

    data

      A sequence of Row objects representing rows of data.

    schema

      StructType representing the schema for the DataFrame.

    returns

      A DataFrame

    Since

      0.2.0

12.  def createDataFrame[T](data: Seq[T])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[T]): DataFrame

    Creates a new DataFrame containing the specified values.

    Creates a new DataFrame containing the specified values. Currently, you can use values of the following types:

    - Base types (Int, Short, String etc.). The resulting DataFrame has the column name "VALUE".
    - Tuples consisting of base types. The resulting DataFrame has the column names "_1", "_2", etc.
    - Case classes consisting of base types. The resulting DataFrame has column names that correspond to the case class constituents.

    If you want to create a DataFrame by calling the `toDF` method of a `Seq` object, import `session.implicits._`, where `session` is an object of the `Session` class that you created to connect to the Snowflake database. For example:

        val session = Session.builder.configFile(..).create
        // Importing this allows you to call the toDF method on a Seq object.
        import session.implicits._
        // Create a DataFrame from a Seq object.
        val df = Seq((1, "x"), (2, "y"), (3, "z")).toDF("numCol", "varcharCol")
        df.show()

    T

      DataType

    data

      A sequence in which each element represents a row of values in the DataFrame.

    returns

      A DataFrame

    Since

      0.1.0

13.  final def eq(arg0: AnyRef): Boolean
    Definition Classes
      AnyRef
14.  def equals(arg0: AnyRef): Boolean
    Definition Classes
      AnyRef → Any
15.  lazy val file: FileOperation

    Returns a FileOperation object that you can use to perform file operations on stages.

    Returns a FileOperation object that you can use to perform file operations on stages. For example:

        session.file.put("file:///tmp/file1.csv", "@myStage/prefix1")
        session.file.get("@myStage/prefix1", "file:///tmp")

    Since

      0.4.0

16.  def flatten(input: Column, path: String, outer: Boolean, recursive: Boolean, mode: String): DataFrame

    Creates a new DataFrame by flattening compound values into multiple rows.

    Creates a new DataFrame by flattening compound values into multiple rows.

    for example:

        import com.snowflake.snowpark.functions._
        val df = session.flatten(parse_json(lit("""{"a":[1,2]}""")), "a", false, false, "BOTH")

    input

      The expression that will be unseated into rows. The expression must be of data type VARIANT, OBJECT, or ARRAY.

    path

      The path to the element within a VARIANT data structure which needs to be flattened. Can be a zero-length string (i.e. empty path) if the outermost element is to be flattened.

    outer

      If `false` , any input rows that cannot be expanded, either because they cannot be accessed in the path or because they have zero fields or entries, are completely omitted from the output. Otherwise, exactly one row is generated for zero-row expansions (with NULL in the KEY, INDEX, and VALUE columns).

    recursive

      If `false` , only the element referenced by PATH is expanded. Otherwise, the expansion is performed for all sub-elements recursively.

    mode

      Specifies which types should be flattened (`"OBJECT"`, `"ARRAY"` , or `"BOTH"` ).

    Since

      0.2.0

17.  def flatten(input: Column): DataFrame

    Creates a new DataFrame by flattening compound values into multiple rows.

    Creates a new DataFrame by flattening compound values into multiple rows.

    For example:

        import com.snowflake.snowpark.functions._
        val df = session.flatten(parse_json(lit("""{"a":[1,2]}""")))

    input

      The expression that will be unseated into rows. The expression must be of data type VARIANT, OBJECT, or ARRAY.

    returns

      A DataFrame.

    Since

      0.2.0

18.  def generator(rowCount: Long, col: Column, cols: Column*): DataFrame

    Creates a new DataFrame via Generator function.

    Creates a new DataFrame via Generator function.

    For example:

        import com.snowflake.snowpark.functions._
        session.generator(10, seq4(), uniform(lit(1), lit(5), random())).show()

    rowCount

      The row count of the result DataFrame.

    col

      the column of the result DataFrame

    cols

      A list of columns excepts the first column

    returns

      A DataFrame

    Since

      0.11.0

19.  def generator(rowCount: Long, columns: Seq[Column]): DataFrame

    Creates a new DataFrame via Generator function.

    Creates a new DataFrame via Generator function.

    For example:

        import com.snowflake.snowpark.functions._
        session.generator(10, Seq(seq4(), uniform(lit(1), lit(5), random()))).show()

    rowCount

      The row count of the result DataFrame.

    columns

      the column list of the result DataFrame

    returns

      A DataFrame

    Since

      0.11.0

20.  final def getClass(): Class[_ <: AnyRef]
    Definition Classes
      AnyRef → Any
    Annotations
      @HotSpotIntrinsicCandidate() @native()
21.  def getCurrentDatabase: Option[String]

    Returns the name of the current database for the JDBC session attached to this session.

    Returns the name of the current database for the JDBC session attached to this session.

    For example, if you change the current database by executing the following code:

        session.sql("use database newDB").collect()

    the method returns `newDB`.

    returns

      The name of the current database for this session.

    Since

      0.1.0

22.  def getCurrentSchema: Option[String]

    Returns the name of the current schema for the JDBC session attached to this session.

    Returns the name of the current schema for the JDBC session attached to this session.

    For example, if you change the current schema by executing the following code:

        session.sql("use schema newSchema").collect()

    the method returns `newSchema`.

    returns

      Current schema in session.

    Since

      0.1.0

23.  def getDefaultDatabase: Option[String]

    Returns the name of the default database configured for this session in Session.builder.

    Returns the name of the default database configured for this session in Session.builder.

    returns

      The name of the default database

    Since

      0.1.0

24.  def getDefaultSchema: Option[String]

    Returns the name of the default schema configured for this session in Session.builder.

    Returns the name of the default schema configured for this session in Session.builder.

    returns

      The name of the default schema

    Since

      0.1.0

25.  def getDependencies: Set[URI]

    Returns the list of URLs for all the dependencies that were added for user-defined functions (UDFs).

    Returns the list of URLs for all the dependencies that were added for user-defined functions (UDFs). This list includes any JAR files that were added automatically by the library.

    returns

      Set[URI]

    Since

      0.1.0

26.  def getDependenciesAsJavaSet: Set[URI]

    Returns a Java Set of URLs for all the dependencies that were added for user-defined functions (UDFs).

    Returns a Java Set of URLs for all the dependencies that were added for user-defined functions (UDFs). This list includes any JAR files that were added automatically by the library.

    Since

      0.2.0

27.  def getFullyQualifiedCurrentSchema: String

    Returns the fully qualified name of the current schema for the session.

    Returns the fully qualified name of the current schema for the session.

    returns

      The fully qualified name of the schema

    Since

      0.2.0

28.  def getQueryTag(): Option[String]

    Returns the query tag that you set by calling setQueryTag.

    Returns the query tag that you set by calling setQueryTag.

    Since

      0.1.0

29.  def getSessionInfo(): String

    Get the session information.

    Get the session information.

    Since

      0.11.0

30.  def getSessionStage: String

    Returns the name of the temporary stage created by the Snowpark library for uploading and store temporary artifacts for this session.

    Returns the name of the temporary stage created by the Snowpark library for uploading and store temporary artifacts for this session. These artifacts include classes for UDFs that you define in this session and dependencies that you add when calling addDependency.

    returns

      The name of stage.

    Since

      0.1.0

31.  def hashCode(): Int
    Definition Classes
      AnyRef → Any
    Annotations
      @HotSpotIntrinsicCandidate() @native()
32.  final def isInstanceOf[T0]: Boolean
    Definition Classes
      Any
33.  def jdbcConnection: Connection

    Returns the JDBC Connection object used for the connection to the Snowflake database.

    Returns the JDBC Connection object used for the connection to the Snowflake database.

    returns

      JDBC Connection object

34.  def log(): Logger
    Attributes
      protected[internal]
    Definition Classes
      Logging
35.  def logDebug(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
36.  def logDebug(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
37.  def logError(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
38.  def logError(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
39.  def logInfo(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
40.  def logInfo(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
41.  def logTrace(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
42.  def logTrace(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
43.  def logWarning(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
44.  def logWarning(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
45.  final def ne(arg0: AnyRef): Boolean
    Definition Classes
      AnyRef
46.  final def notify(): Unit
    Definition Classes
      AnyRef
    Annotations
      @HotSpotIntrinsicCandidate() @native()
47.  final def notifyAll(): Unit
    Definition Classes
      AnyRef
    Annotations
      @HotSpotIntrinsicCandidate() @native()
48.  def range(start: Long, end: Long): DataFrame

    Creates a new DataFrame from a range of numbers.

    Creates a new DataFrame from a range of numbers. The resulting DataFrame has the column name "ID" and a row for each number in the sequence.

    start

      Start of the range.

    end

      End of the range.

    returns

      A DataFrame

    Since

      0.1.0

49.  def range(end: Long): DataFrame

    Creates a new DataFrame from a range of numbers starting from 0.

    Creates a new DataFrame from a range of numbers starting from 0. The resulting DataFrame has the column name "ID" and a row for each number in the sequence.

    end

      End of the range.

    returns

      A DataFrame

    Since

      0.1.0

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

    Creates a new DataFrame from a range of numbers.

    Creates a new DataFrame from a range of numbers. The resulting DataFrame has the column name "ID" and a row for each number in the sequence.

    start

      Start of the range.

    end

      End of the range.

    step

      Step function for producing the numbers in the range.

    returns

      A DataFrame

    Since

      0.1.0

51.  def read: DataFrameReader

    Returns a DataFrameReader that you can use to read data from various supported sources (e.g.

    Returns a DataFrameReader that you can use to read data from various supported sources (e.g. a file in a stage) as a DataFrame.

    returns

      A DataFrameReader

    Since

      0.1.0

52.  def removeDependency(path: String): Unit

    Removes a path from the set of dependencies.

    Removes a path from the set of dependencies.

    path

      Path to a local directory, local file, or file in a stage.

    Since

      0.1.0

53.  def setQueryTag(queryTag: String): Unit

    Sets a query tag for this session.

    Sets a query tag for this session. You can use the query tag to find all queries run for this session.

    If not set, the default value of query tag is the Snowpark library call and the class and method in your code that invoked the query (e.g. `com.snowflake.snowpark.DataFrame.collect Main$.main(Main.scala:18)`).

    queryTag

      String to use as the query tag for this session.

    Since

      0.1.0

54.  lazy val sproc: SProcRegistration

    Returns a SProcRegistration object that you can use to register Stored Procedures.

    Returns a SProcRegistration object that you can use to register Stored Procedures. For example:

        val sp = session.sproc.registerTemporary((session: Session, num: Int) => num * 2)
        session.storedProcedure(sp, 100).show()

    Annotations
      @PublicPreview()
    Since

      1.8.0

55.  def sql(query: String, params: Seq[Any]): DataFrame

    Returns a new DataFrame representing the results of a SQL query.

    Returns a new DataFrame representing the results of a SQL query.

    You can use this method to execute an arbitrary SQL statement.

    query

      The SQL statement to execute.

    params

      for bind variables in SQL statement.

    returns

      A DataFrame

    Since

      1.15.0

56.  def sql(query: String): DataFrame

    Returns a new DataFrame representing the results of a SQL query.

    Returns a new DataFrame representing the results of a SQL query.

    You can use this method to execute an arbitrary SQL statement.

    query

      The SQL statement to execute.

    returns

      A DataFrame

    Since

      0.1.0

57.  def storedProcedure(sp: StoredProcedure, args: Any*): DataFrame

    Creates a new DataFrame from the given Stored Procedure and arguments.

    Creates a new DataFrame from the given Stored Procedure and arguments.

        val sp = session.sproc.register(...)
        session.storedProcedure(
          sp, "arg1", "arg2"
        ).show()

    sp

      The stored procedures object, can be created by `Session.sproc.register` methods.

    args

      The arguments of the given stored procedure

    Since

      1.8.0

58.  def storedProcedure(spName: String, args: Any*): DataFrame

    Creates a new DataFrame from the given Stored Procedure and arguments.

    Creates a new DataFrame from the given Stored Procedure and arguments.

        session.storedProcedure(
          "sp_name", "arg1", "arg2"
        ).show()

    spName

      The name of stored procedures.

    args

      The arguments of the given stored procedure

    Since

      1.8.0

59.  final def synchronized[T0](arg0: => T0): T0
    Definition Classes
      AnyRef
60.  def table(multipartIdentifier: Array[String]): Updatable

    Returns an Updatable that points to the specified table.

    Returns an Updatable that points to the specified table.

    multipartIdentifier

      An array of strings that specify the database name, schema name, and table name.

    Since

      0.7.0

61.  def table(multipartIdentifier: List[String]): Updatable

    Returns an Updatable that points to the specified table.

    Returns an Updatable that points to the specified table.

    multipartIdentifier

      A list of strings that specify the database name, schema name, and table name.

    returns

      A Updatable

    Since

      0.2.0

62.  def table(multipartIdentifier: Seq[String]): Updatable

    Returns an Updatable that points to the specified table.

    Returns an Updatable that points to the specified table.

    multipartIdentifier

      A sequence of strings that specify the database name, schema name, and table name (e.g. `Seq("database_name", "schema_name", "table_name")` ).

    returns

      A Updatable

    Since

      0.1.0

63.  def table(name: String): Updatable

    Returns an Updatable that points to the specified table.

    Returns an Updatable that points to the specified table.

    `name` can be a fully qualified identifier and must conform to the rules for a Snowflake identifier.

    name

      Table name that is either a fully qualified name or a name in the current database/schema.

    returns

      A Updatable

    Since

      0.1.0

64.  def tableFunction(func: Column): DataFrame

    Creates a new DataFrame from the given table function.

    Creates a new DataFrame from the given table function.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        session.tableFunction(
          flatten(parse_json(lit("[1,2]")))
        )

    func

      Table function object, can be created from TableFunction class or referred from the built-in list from tableFunctions.

    Since

      1.10.0

65.  def tableFunction(func: TableFunction, args: Map[String, Column]): DataFrame

    Creates a new DataFrame from the given table function and arguments.

    Creates a new DataFrame from the given table function and arguments.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        session.tableFunction(
          flatten,
          Map("input" -> parse_json(lit("[1,2]")))
        )
        // Since 1.8.0, DataFrame columns are accepted as table function arguments:
        df = Seq("[1,2]").toDF("a")
        session.tableFunction((
          flatten,
          Map("input" -> parse_json(df("a")))
        )

    func

      Table function object, can be created from TableFunction class or referred from the built-in list from tableFunctions.

    args

      function arguments map of the given table function. Some functions, like flatten, have named parameters. use this map to assign values to the corresponding parameters.

    Since

      0.4.0

66.  def tableFunction(func: TableFunction, args: Seq[Column]): DataFrame

    Creates a new DataFrame from the given table function and arguments.

    Creates a new DataFrame from the given table function and arguments.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        session.tableFunction(
          split_to_table,
          Seq(lit("split by space"), lit(" "))
        )
        // Since 1.8.0, DataFrame columns are accepted as table function arguments:
        df = Seq(Seq("split by space", " ")).toDF(Seq("a", "b"))
        session.tableFunction((
          split_to_table,
          Seq(df("a"), df("b"))
        )

    func

      Table function object, can be created from TableFunction class or referred from the built-in list from tableFunctions.

    args

      function arguments of the given table function.

    Since

      0.4.0

67.  def tableFunction(func: TableFunction, firstArg: Column, remaining: Column*): DataFrame

    Creates a new DataFrame from the given table function and arguments.

    Creates a new DataFrame from the given table function and arguments.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        session.tableFunction(
          split_to_table,
          lit("split by space"),
          lit(" ")
        )

    func

      Table function object, can be created from TableFunction class or referred from the built-in list from tableFunctions.

    firstArg

      the first function argument of the given table function.

    remaining

      all remaining function arguments.

    Since

      0.4.0

68.  def toString(): String
    Definition Classes
      AnyRef → Any
69.  lazy val udaf: UDAFRegistration

    Returns a UDAFRegistration object that you can use to register UDAFs.

    Returns a UDAFRegistration object that you can use to register UDAFs.

    Since

      1.19.0

70.  lazy val udf: UDFRegistration

    Returns a UDFRegistration object that you can use to register UDFs.

    Returns a UDFRegistration object that you can use to register UDFs. For example:

        session.udf.registerTemporary("mydoubleudf", (x: Int) => 2 * x)
        session.sql(s"SELECT mydoubleudf(c) FROM table")

    Since

      0.1.0

71.  lazy val udtf: UDTFRegistration

    Returns a UDTFRegistration object that you can use to register UDTFs.

    Returns a UDTFRegistration object that you can use to register UDTFs. For example:

        class MyWordSplitter extends UDTF1[String] {
          override def process(input: String): Iterable[Row] = input.split(" ").map(Row(_))
          override def endPartition(): Iterable[Row] = Array.empty[Row]
          override def outputSchema(): StructType = StructType(StructField("word", StringType))
        }
        val tableFunction = session.udtf.registerTemporary(new MyWordSplitter)
        session.tableFunction(tableFunction, lit("My name is Snow Park")).show()

    Since

      1.2.0

72.  def unsetQueryTag(): Unit

    Unset query_tag parameter for this session.

    Unset query_tag parameter for this session.

    If not set, the default value of query tag is the Snowpark library call and the class and method in your code that invoked the query (e.g. `com.snowflake.snowpark.DataFrame.collect Main$.main(Main.scala:18)`).

    Since

      0.10.0

73.  def updateQueryTag(queryTag: String): Unit

    Updates the query tag that is a JSON encoded string for the current session.

    Updates the query tag that is a JSON encoded string for the current session.

    Keep in mind that assigning a value via setQueryTag will remove any current query tag state.

    Example 1:

        session.setQueryTag("""{"key1":"value1"}""")
        session.updateQueryTag("""{"key2":"value2"}""")
        print(session.getQueryTag().get)
        {"key1":"value1","key2":"value2"}

    Example 2:

        session.sql("""ALTER SESSION SET QUERY_TAG = '{"key1":"value1"}'""").collect()
        session.updateQueryTag("""{"key2":"value2"}""")
        print(session.getQueryTag().get)
        {"key1":"value1","key2":"value2"}

    Example 3:

        session.setQueryTag("")
        session.updateQueryTag("""{"key1":"value1"}""")
        print(session.getQueryTag().get)
        {"key1":"value1"}

    queryTag

      A JSON encoded string that provides updates to the current query tag.

    Since

      1.13.0

    Exceptions thrown

      `SnowparkClientException` If the provided query tag or the query tag of the current session are not valid JSON strings; or if it could not serialize the query tag into a JSON string.

74.  final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
      AnyRef
    Annotations
      @throws(classOf[java.lang.InterruptedException])
75.  final def wait(arg0: Long): Unit
    Definition Classes
      AnyRef
    Annotations
      @throws(classOf[java.lang.InterruptedException]) @native()
76.  final def wait(): Unit
    Definition Classes
      AnyRef
    Annotations
      @throws(classOf[java.lang.InterruptedException])
77.  object implicits extends Implicits with Serializable

    Provides implicit methods for convert Scala objects to Snowpark DataFrame and Column objects.

    Provides implicit methods for convert Scala objects to Snowpark DataFrame and Column objects.

    To use this, import `session.implicits._` :

        val session = Session.builder.configFile(..).create
        import session.implicits._

    After you import this, you can call the `toDF` method of a `Seq` to convert a sequence to a DataFrame:

        // Create a DataFrame from a local sequence of integers.
        val df = (1 to 10).toDF("a")
        val df = Seq((1, "one"), (2, "two")).toDF("a", "b")

    You can also refer to columns in DataFrames by using `$"colName"` and `'colName`:

        // Refer to a column in a DataFrame by using $"colName".
        val df = session.table("T").filter($"a" > 1)
        // Refer to columns by using 'colName.
        val df = session.table("T").filter('a > 1)

    Since

      0.1.0

### Deprecated Value Members

1.  def finalize(): Unit
   Attributes
     protected[lang]
   Definition Classes
     AnyRef
   Annotations
     @throws(classOf[java.lang.Throwable]) @Deprecated
   Deprecated

     (Since version 9)

### Inherited from Logging

### Inherited from AnyRef

### Inherited from Any

### Ungrouped