Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql
import java.util.Properties
import scala.collection.immutable
import scala.reflect.runtime.universe.TypeTag
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.annotation.{DeveloperApi, Experimental, InterfaceStability}
import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config.ConfigEntry
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.execution.command.ShowTablesCommand
import org.apache.spark.sql.internal.{SessionState, SharedState, SQLConf}
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.streaming.{DataStreamReader, StreamingQueryManager}
import org.apache.spark.sql.types._
import org.apache.spark.sql.util.ExecutionListenerManager
/**
* The entry point for working with structured data (rows and columns) in Spark 1.x.
*
* As of Spark 2.0, this is replaced by [[SparkSession]]. However, we are keeping the class
* here for backward compatibility.
*
* @groupname basic Basic Operations
* @groupname ddl_ops Persistent Catalog DDL
* @groupname cachemgmt Cached Table Management
* @groupname genericdata Generic Data Sources
* @groupname specificdata Specific Data Sources
* @groupname config Configuration
* @groupname dataframes Custom DataFrame Creation
* @groupname dataset Custom Dataset Creation
* @groupname Ungrouped Support functions for language integrated queries
* @since 1.0.0
*/
@InterfaceStability.Stable
class SQLContext private[sql](val sparkSession: SparkSession)
extends Logging with Serializable {
self =>
sparkSession.sparkContext.assertNotStopped()
// Note: Since Spark 2.0 this class has become a wrapper of SparkSession, where the
// real functionality resides. This class remains mainly for backward compatibility.
@deprecated("Use SparkSession.builder instead", "2.0.0")
def this(sc: SparkContext) = {
this(SparkSession.builder().sparkContext(sc).getOrCreate())
}
@deprecated("Use SparkSession.builder instead", "2.0.0")
def this(sparkContext: JavaSparkContext) = this(sparkContext.sc)
// TODO: move this logic into SparkSession
private[sql] def sessionState: SessionState = sparkSession.sessionState
private[sql] def sharedState: SharedState = sparkSession.sharedState
private[sql] def conf: SQLConf = sessionState.conf
def sparkContext: SparkContext = sparkSession.sparkContext
/**
* Returns a [[SQLContext]] as new session, with separated SQL configurations, temporary
* tables, registered functions, but sharing the same `SparkContext`, cached data and
* other things.
*
* @since 1.6.0
*/
def newSession(): SQLContext = sparkSession.newSession().sqlContext
/**
* An interface to register custom [[org.apache.spark.sql.util.QueryExecutionListener]]s
* that listen for execution metrics.
*/
@Experimental
@InterfaceStability.Evolving
def listenerManager: ExecutionListenerManager = sparkSession.listenerManager
/**
* Set Spark SQL configuration properties.
*
* @group config
* @since 1.0.0
*/
def setConf(props: Properties): Unit = {
sessionState.conf.setConf(props)
}
/**
* Set the given Spark SQL configuration property.
*/
private[sql] def setConf[T](entry: ConfigEntry[T], value: T): Unit = {
sessionState.conf.setConf(entry, value)
}
/**
* Set the given Spark SQL configuration property.
*
* @group config
* @since 1.0.0
*/
def setConf(key: String, value: String): Unit = {
sparkSession.conf.set(key, value)
}
/**
* Return the value of Spark SQL configuration property for the given key.
*
* @group config
* @since 1.0.0
*/
def getConf(key: String): String = {
sparkSession.conf.get(key)
}
/**
* Return the value of Spark SQL configuration property for the given key. If the key is not set
* yet, return `defaultValue`.
*
* @group config
* @since 1.0.0
*/
def getConf(key: String, defaultValue: String): String = {
sparkSession.conf.get(key, defaultValue)
}
/**
* Return all the configuration properties that have been set (i.e. not the default).
* This creates a new copy of the config properties in the form of a Map.
*
* @group config
* @since 1.0.0
*/
def getAllConfs: immutable.Map[String, String] = {
sparkSession.conf.getAll
}
/**
* :: Experimental ::
* A collection of methods that are considered experimental, but can be used to hook into
* the query planner for advanced functionality.
*
* @group basic
* @since 1.3.0
*/
@Experimental
@transient
@InterfaceStability.Unstable
def experimental: ExperimentalMethods = sparkSession.experimental
/**
* Returns a `DataFrame` with no rows or columns.
*
* @group basic
* @since 1.3.0
*/
def emptyDataFrame: DataFrame = sparkSession.emptyDataFrame
/**
* A collection of methods for registering user-defined functions (UDF).
*
* The following example registers a Scala closure as UDF:
* {{{
* sqlContext.udf.register("myUDF", (arg1: Int, arg2: String) => arg2 + arg1)
* }}}
*
* The following example registers a UDF in Java:
* {{{
* sqlContext.udf().register("myUDF",
* (Integer arg1, String arg2) -> arg2 + arg1,
* DataTypes.StringType);
* }}}
*
* @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.
*
* @group basic
* @since 1.3.0
*/
def udf: UDFRegistration = sparkSession.udf
/**
* Returns true if the table is currently cached in-memory.
* @group cachemgmt
* @since 1.3.0
*/
def isCached(tableName: String): Boolean = {
sparkSession.catalog.isCached(tableName)
}
/**
* Caches the specified table in-memory.
* @group cachemgmt
* @since 1.3.0
*/
def cacheTable(tableName: String): Unit = {
sparkSession.catalog.cacheTable(tableName)
}
/**
* Removes the specified table from the in-memory cache.
* @group cachemgmt
* @since 1.3.0
*/
def uncacheTable(tableName: String): Unit = {
sparkSession.catalog.uncacheTable(tableName)
}
/**
* Removes all cached tables from the in-memory cache.
* @since 1.3.0
*/
def clearCache(): Unit = {
sparkSession.catalog.clearCache()
}
// scalastyle:off
// Disable style checker so "implicits" object can start with lowercase i
/**
* :: Experimental ::
* (Scala-specific) Implicit methods available in Scala for converting
* common Scala objects into `DataFrame`s.
*
* {{{
* val sqlContext = new SQLContext(sc)
* import sqlContext.implicits._
* }}}
*
* @group basic
* @since 1.3.0
*/
@Experimental
@InterfaceStability.Evolving
object implicits extends SQLImplicits with Serializable {
protected override def _sqlContext: SQLContext = self
}
// scalastyle:on
/**
* :: Experimental ::
* Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
*
* @group dataframes
* @since 1.3.0
*/
@Experimental
@InterfaceStability.Evolving
def createDataFrame[A <: Product : TypeTag](rdd: RDD[A]): DataFrame = {
sparkSession.createDataFrame(rdd)
}
/**
* :: Experimental ::
* Creates a DataFrame from a local Seq of Product.
*
* @group dataframes
* @since 1.3.0
*/
@Experimental
@InterfaceStability.Evolving
def createDataFrame[A <: Product : TypeTag](data: Seq[A]): DataFrame = {
sparkSession.createDataFrame(data)
}
/**
* Convert a `BaseRelation` created for external data sources into a `DataFrame`.
*
* @group dataframes
* @since 1.3.0
*/
def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame = {
sparkSession.baseRelationToDataFrame(baseRelation)
}
/**
* :: DeveloperApi ::
* Creates a `DataFrame` from an `RDD` containing [[Row]]s 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 sqlContext = new org.apache.spark.sql.SQLContext(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 = sqlContext.createDataFrame(people, schema)
* dataFrame.printSchema
* // root
* // |-- name: string (nullable = false)
* // |-- age: integer (nullable = true)
*
* dataFrame.createOrReplaceTempView("people")
* sqlContext.sql("select name from people").collect.foreach(println)
* }}}
*
* @group dataframes
* @since 1.3.0
*/
@DeveloperApi
@InterfaceStability.Evolving
def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame = {
sparkSession.createDataFrame(rowRDD, schema)
}
/**
* Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be
* converted to Catalyst rows.
*/
private[sql]
def createDataFrame(rowRDD: RDD[Row], schema: StructType, needsConversion: Boolean) = {
sparkSession.createDataFrame(rowRDD, schema, needsConversion)
}
/**
* :: 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|
* // +-------+---+
* }}}
*
* @since 2.0.0
* @group dataset
*/
@Experimental
@InterfaceStability.Evolving
def createDataset[T : Encoder](data: Seq[T]): Dataset[T] = {
sparkSession.createDataset(data)
}
/**
* :: 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]].
*
* @since 2.0.0
* @group dataset
*/
@Experimental
def createDataset[T : Encoder](data: RDD[T]): Dataset[T] = {
sparkSession.createDataset(data)
}
/**
* :: 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 data = Arrays.asList("hello", "world");
* Dataset ds = spark.createDataset(data, Encoders.STRING());
* }}}
*
* @since 2.0.0
* @group dataset
*/
@Experimental
@InterfaceStability.Evolving
def createDataset[T : Encoder](data: java.util.List[T]): Dataset[T] = {
sparkSession.createDataset(data)
}
/**
* Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be
* converted to Catalyst rows.
*/
private[sql]
def internalCreateDataFrame(catalystRows: RDD[InternalRow], schema: StructType) = {
sparkSession.internalCreateDataFrame(catalystRows, schema)
}
/**
* :: DeveloperApi ::
* Creates a `DataFrame` from a `JavaRDD` containing [[Row]]s 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.
*
* @group dataframes
* @since 1.3.0
*/
@DeveloperApi
@InterfaceStability.Evolving
def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = {
sparkSession.createDataFrame(rowRDD, schema)
}
/**
* :: DeveloperApi ::
* Creates a `DataFrame` from a `java.util.List` containing [[Row]]s 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.
*
* @group dataframes
* @since 1.6.0
*/
@DeveloperApi
@InterfaceStability.Evolving
def createDataFrame(rows: java.util.List[Row], schema: StructType): DataFrame = {
sparkSession.createDataFrame(rows, schema)
}
/**
* 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.
* @group dataframes
* @since 1.3.0
*/
def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame = {
sparkSession.createDataFrame(rdd, 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.
* @group dataframes
* @since 1.3.0
*/
def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = {
sparkSession.createDataFrame(rdd, 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.
* @group dataframes
* @since 1.6.0
*/
def createDataFrame(data: java.util.List[_], beanClass: Class[_]): DataFrame = {
sparkSession.createDataFrame(data, beanClass)
}
/**
* Returns a [[DataFrameReader]] that can be used to read non-streaming data in as a
* `DataFrame`.
* {{{
* sqlContext.read.parquet("/path/to/file.parquet")
* sqlContext.read.schema(schema).json("/path/to/file.json")
* }}}
*
* @group genericdata
* @since 1.4.0
*/
def read: DataFrameReader = sparkSession.read
/**
* 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")
* }}}
*
* @since 2.0.0
*/
@InterfaceStability.Evolving
def readStream: DataStreamReader = sparkSession.readStream
/**
* Creates an external table from the given path and returns the corresponding DataFrame.
* It will use the default data source configured by spark.sql.sources.default.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(tableName: String, path: String): DataFrame = {
sparkSession.catalog.createTable(tableName, path)
}
/**
* Creates an external table from the given path based on a data source
* and returns the corresponding DataFrame.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(
tableName: String,
path: String,
source: String): DataFrame = {
sparkSession.catalog.createTable(tableName, path, source)
}
/**
* Creates an external table from the given path based on a data source and a set of options.
* Then, returns the corresponding DataFrame.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(
tableName: String,
source: String,
options: java.util.Map[String, String]): DataFrame = {
sparkSession.catalog.createTable(tableName, source, options)
}
/**
* (Scala-specific)
* Creates an external table from the given path based on a data source and a set of options.
* Then, returns the corresponding DataFrame.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(
tableName: String,
source: String,
options: Map[String, String]): DataFrame = {
sparkSession.catalog.createTable(tableName, source, options)
}
/**
* Create an external table from the given path based on a data source, a schema and
* a set of options. Then, returns the corresponding DataFrame.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(
tableName: String,
source: String,
schema: StructType,
options: java.util.Map[String, String]): DataFrame = {
sparkSession.catalog.createTable(tableName, source, schema, options)
}
/**
* (Scala-specific)
* Create an external table from the given path based on a data source, a schema and
* a set of options. Then, returns the corresponding DataFrame.
*
* @group ddl_ops
* @since 1.3.0
*/
@deprecated("use sparkSession.catalog.createTable instead.", "2.2.0")
def createExternalTable(
tableName: String,
source: String,
schema: StructType,
options: Map[String, String]): DataFrame = {
sparkSession.catalog.createTable(tableName, source, schema, options)
}
/**
* Registers the given `DataFrame` as a temporary table in the catalog. Temporary tables exist
* only during the lifetime of this instance of SQLContext.
*/
private[sql] def registerDataFrameAsTable(df: DataFrame, tableName: String): Unit = {
df.createOrReplaceTempView(tableName)
}
/**
* Drops the temporary table with the given table name in the catalog. If the table has been
* cached/persisted before, it's also unpersisted.
*
* @param tableName the name of the table to be unregistered.
* @group basic
* @since 1.3.0
*/
def dropTempTable(tableName: String): Unit = {
sparkSession.catalog.dropTempView(tableName)
}
/**
* :: Experimental ::
* Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from 0 to `end` (exclusive) with step value 1.
*
* @since 1.4.1
* @group dataframe
*/
@Experimental
@InterfaceStability.Evolving
def range(end: Long): DataFrame = sparkSession.range(end).toDF()
/**
* :: Experimental ::
* Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from `start` to `end` (exclusive) with step value 1.
*
* @since 1.4.0
* @group dataframe
*/
@Experimental
@InterfaceStability.Evolving
def range(start: Long, end: Long): DataFrame = sparkSession.range(start, end).toDF()
/**
* :: Experimental ::
* Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from `start` to `end` (exclusive) with a step value.
*
* @since 2.0.0
* @group dataframe
*/
@Experimental
@InterfaceStability.Evolving
def range(start: Long, end: Long, step: Long): DataFrame = {
sparkSession.range(start, end, step).toDF()
}
/**
* :: Experimental ::
* Creates a `DataFrame` 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.
*
* @since 1.4.0
* @group dataframe
*/
@Experimental
@InterfaceStability.Evolving
def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame = {
sparkSession.range(start, end, step, numPartitions).toDF()
}
/**
* 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'.
*
* @group basic
* @since 1.3.0
*/
def sql(sqlText: String): DataFrame = sparkSession.sql(sqlText)
/**
* Returns the specified table as a `DataFrame`.
*
* @group ddl_ops
* @since 1.3.0
*/
def table(tableName: String): DataFrame = {
sparkSession.table(tableName)
}
/**
* Returns a `DataFrame` containing names of existing tables in the current database.
* The returned DataFrame has two columns, tableName and isTemporary (a Boolean
* indicating if a table is a temporary one or not).
*
* @group ddl_ops
* @since 1.3.0
*/
def tables(): DataFrame = {
Dataset.ofRows(sparkSession, ShowTablesCommand(None, None))
}
/**
* Returns a `DataFrame` containing names of existing tables in the given database.
* The returned DataFrame has two columns, tableName and isTemporary (a Boolean
* indicating if a table is a temporary one or not).
*
* @group ddl_ops
* @since 1.3.0
*/
def tables(databaseName: String): DataFrame = {
Dataset.ofRows(sparkSession, ShowTablesCommand(Some(databaseName), None))
}
/**
* Returns a `StreamingQueryManager` that allows managing all the
* [[org.apache.spark.sql.streaming.StreamingQuery StreamingQueries]] active on `this` context.
*
* @since 2.0.0
*/
def streams: StreamingQueryManager = sparkSession.streams
/**
* Returns the names of tables in the current database as an array.
*
* @group ddl_ops
* @since 1.3.0
*/
def tableNames(): Array[String] = {
tableNames(sparkSession.catalog.currentDatabase)
}
/**
* Returns the names of tables in the given database as an array.
*
* @group ddl_ops
* @since 1.3.0
*/
def tableNames(databaseName: String): Array[String] = {
sessionState.catalog.listTables(databaseName).map(_.table).toArray
}
////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////
// Deprecated methods
////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////
/**
* @deprecated As of 1.3.0, replaced by `createDataFrame()`.
*/
@deprecated("Use createDataFrame instead.", "1.3.0")
def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame = {
createDataFrame(rowRDD, schema)
}
/**
* @deprecated As of 1.3.0, replaced by `createDataFrame()`.
*/
@deprecated("Use createDataFrame instead.", "1.3.0")
def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = {
createDataFrame(rowRDD, schema)
}
/**
* @deprecated As of 1.3.0, replaced by `createDataFrame()`.
*/
@deprecated("Use createDataFrame instead.", "1.3.0")
def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame = {
createDataFrame(rdd, beanClass)
}
/**
* @deprecated As of 1.3.0, replaced by `createDataFrame()`.
*/
@deprecated("Use createDataFrame instead.", "1.3.0")
def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = {
createDataFrame(rdd, beanClass)
}
/**
* Loads a Parquet file, returning the result as a `DataFrame`. This function returns an empty
* `DataFrame` if no paths are passed in.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().parquet()`.
*/
@deprecated("Use read.parquet() instead.", "1.4.0")
@scala.annotation.varargs
def parquetFile(paths: String*): DataFrame = {
if (paths.isEmpty) {
emptyDataFrame
} else {
read.parquet(paths : _*)
}
}
/**
* Loads a JSON file (one object per line), returning the result as a `DataFrame`.
* It goes through the entire dataset once to determine the schema.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonFile(path: String): DataFrame = {
read.json(path)
}
/**
* Loads a JSON file (one object per line) and applies the given schema,
* returning the result as a `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonFile(path: String, schema: StructType): DataFrame = {
read.schema(schema).json(path)
}
/**
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonFile(path: String, samplingRatio: Double): DataFrame = {
read.option("samplingRatio", samplingRatio.toString).json(path)
}
/**
* Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
* `DataFrame`.
* It goes through the entire dataset once to determine the schema.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: RDD[String]): DataFrame = read.json(json)
/**
* Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
* `DataFrame`.
* It goes through the entire dataset once to determine the schema.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: JavaRDD[String]): DataFrame = read.json(json)
/**
* Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema,
* returning the result as a `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: RDD[String], schema: StructType): DataFrame = {
read.schema(schema).json(json)
}
/**
* Loads an JavaRDD[String] storing JSON objects (one object per record) and applies the given
* schema, returning the result as a `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame = {
read.schema(schema).json(json)
}
/**
* Loads an RDD[String] storing JSON objects (one object per record) inferring the
* schema, returning the result as a `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = {
read.option("samplingRatio", samplingRatio.toString).json(json)
}
/**
* Loads a JavaRDD[String] storing JSON objects (one object per record) inferring the
* schema, returning the result as a `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().json()`.
*/
@deprecated("Use read.json() instead.", "1.4.0")
def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame = {
read.option("samplingRatio", samplingRatio.toString).json(json)
}
/**
* Returns the dataset stored at path as a DataFrame,
* using the default data source configured by spark.sql.sources.default.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by `read().load(path)`.
*/
@deprecated("Use read.load(path) instead.", "1.4.0")
def load(path: String): DataFrame = {
read.load(path)
}
/**
* Returns the dataset stored at path as a DataFrame, using the given data source.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by `read().format(source).load(path)`.
*/
@deprecated("Use read.format(source).load(path) instead.", "1.4.0")
def load(path: String, source: String): DataFrame = {
read.format(source).load(path)
}
/**
* (Java-specific) Returns the dataset specified by the given data source and
* a set of options as a DataFrame.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by `read().format(source).options(options).load()`.
*/
@deprecated("Use read.format(source).options(options).load() instead.", "1.4.0")
def load(source: String, options: java.util.Map[String, String]): DataFrame = {
read.options(options).format(source).load()
}
/**
* (Scala-specific) Returns the dataset specified by the given data source and
* a set of options as a DataFrame.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by `read().format(source).options(options).load()`.
*/
@deprecated("Use read.format(source).options(options).load() instead.", "1.4.0")
def load(source: String, options: Map[String, String]): DataFrame = {
read.options(options).format(source).load()
}
/**
* (Java-specific) Returns the dataset specified by the given data source and
* a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by
* `read().format(source).schema(schema).options(options).load()`.
*/
@deprecated("Use read.format(source).schema(schema).options(options).load() instead.", "1.4.0")
def load(
source: String,
schema: StructType,
options: java.util.Map[String, String]): DataFrame = {
read.format(source).schema(schema).options(options).load()
}
/**
* (Scala-specific) Returns the dataset specified by the given data source and
* a set of options as a DataFrame, using the given schema as the schema of the DataFrame.
*
* @group genericdata
* @deprecated As of 1.4.0, replaced by
* `read().format(source).schema(schema).options(options).load()`.
*/
@deprecated("Use read.format(source).schema(schema).options(options).load() instead.", "1.4.0")
def load(source: String, schema: StructType, options: Map[String, String]): DataFrame = {
read.format(source).schema(schema).options(options).load()
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().jdbc()`.
*/
@deprecated("Use read.jdbc() instead.", "1.4.0")
def jdbc(url: String, table: String): DataFrame = {
read.jdbc(url, table, new Properties)
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table. Partitions of the table will be retrieved in parallel based on the parameters
* passed to this function.
*
* @param columnName the name of a column of integral type that will be used for partitioning.
* @param lowerBound the minimum value of `columnName` used to decide partition stride
* @param upperBound the maximum value of `columnName` used to decide partition stride
* @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split
* evenly into this many partitions
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().jdbc()`.
*/
@deprecated("Use read.jdbc() instead.", "1.4.0")
def jdbc(
url: String,
table: String,
columnName: String,
lowerBound: Long,
upperBound: Long,
numPartitions: Int): DataFrame = {
read.jdbc(url, table, columnName, lowerBound, upperBound, numPartitions, new Properties)
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table. The theParts parameter gives a list expressions
* suitable for inclusion in WHERE clauses; each one defines one partition
* of the `DataFrame`.
*
* @group specificdata
* @deprecated As of 1.4.0, replaced by `read().jdbc()`.
*/
@deprecated("Use read.jdbc() instead.", "1.4.0")
def jdbc(url: String, table: String, theParts: Array[String]): DataFrame = {
read.jdbc(url, table, theParts, new Properties)
}
}
/**
* This SQLContext object contains utility functions to create a singleton SQLContext instance,
* or to get the created SQLContext instance.
*
* It also provides utility functions to support preference for threads in multiple sessions
* scenario, setActive could set a SQLContext for current thread, which will be returned by
* getOrCreate instead of the global one.
*/
object SQLContext {
/**
* Get the singleton SQLContext if it exists or create a new one using the given SparkContext.
*
* This function can be used to create a singleton SQLContext object that can be shared across
* the JVM.
*
* If there is an active SQLContext for current thread, it will be returned instead of the global
* one.
*
* @since 1.5.0
*/
@deprecated("Use SparkSession.builder instead", "2.0.0")
def getOrCreate(sparkContext: SparkContext): SQLContext = {
SparkSession.builder().sparkContext(sparkContext).getOrCreate().sqlContext
}
/**
* Changes the SQLContext that will be returned in this thread and its children when
* SQLContext.getOrCreate() is called. This can be used to ensure that a given thread receives
* a SQLContext with an isolated session, instead of the global (first created) context.
*
* @since 1.6.0
*/
@deprecated("Use SparkSession.setActiveSession instead", "2.0.0")
def setActive(sqlContext: SQLContext): Unit = {
SparkSession.setActiveSession(sqlContext.sparkSession)
}
/**
* Clears the active SQLContext for current thread. Subsequent calls to getOrCreate will
* return the first created context instead of a thread-local override.
*
* @since 1.6.0
*/
@deprecated("Use SparkSession.clearActiveSession instead", "2.0.0")
def clearActive(): Unit = {
SparkSession.clearActiveSession()
}
/**
* Converts an iterator of Java Beans to InternalRow using the provided
* bean info & schema. This is not related to the singleton, but is a static
* method for internal use.
*/
private[sql] def beansToRows(
data: Iterator[_],
beanClass: Class[_],
attrs: Seq[AttributeReference]): Iterator[InternalRow] = {
val extractors =
JavaTypeInference.getJavaBeanReadableProperties(beanClass).map(_.getReadMethod)
val methodsToConverts = extractors.zip(attrs).map { case (e, attr) =>
(e, CatalystTypeConverters.createToCatalystConverter(attr.dataType))
}
data.map { element =>
new GenericInternalRow(
methodsToConverts.map { case (e, convert) => convert(e.invoke(element)) }
): InternalRow
}
}
/**
* Extract `spark.sql.*` properties from the conf and return them as a [[Properties]].
*/
private[sql] def getSQLProperties(sparkConf: SparkConf): Properties = {
val properties = new Properties
sparkConf.getAll.foreach { case (key, value) =>
if (key.startsWith("spark.sql")) {
properties.setProperty(key, value)
}
}
properties
}
}
