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

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

Add a single artifact to the current session.

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

Add a single artifact to the current session.

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

Add one or more artifacts to the session.

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

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

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.

:: DeveloperApi :: Creates a DataFrame from a java.util.List containing org.apache.spark.sql.Rows using the given schema.It is important to make sure that the structure of every org.apache.spark.sql.Row of the provided List matches the provided schema. Otherwise, there will be runtime exception.

Creates a DataFrame from a local Seq of Product.

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());

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|
// +-------+---+

Returns a DataFrame with no rows or columns.

Creates a new Dataset of type T containing zero elements.

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

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.

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

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

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

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")

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.

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.

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.

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);

The version of Spark on which this application is running.

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

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.

Synonym for close().

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.