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https://issues.apache.org/jira/browse/SPARK-48805?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yang Jie resolved SPARK-48805.
------------------------------
Fix Version/s: 4.0.0
Resolution: Fixed
Issue resolved by pull request 47210
[https://github.com/apache/spark/pull/47210]
> Replace calls to bridged APIs based on SparkSession#sqlContext with
> SparkSession API
> ------------------------------------------------------------------------------------
>
> Key: SPARK-48805
> URL: https://issues.apache.org/jira/browse/SPARK-48805
> Project: Spark
> Issue Type: Improvement
> Components: Examples, ML, SQL, Structured Streaming
> Affects Versions: 4.0.0
> Reporter: Yang Jie
> Assignee: Yang Jie
> Priority: Major
> Labels: pull-request-available
> Fix For: 4.0.0
>
>
> In the internal code of Spark, there are instances where, despite having a
> SparkSession instance, the bridged APIs based on SparkSession#sqlContext are
> still used. So we can makes some simplifications:
> 1. `SparkSession#sqlContext#read` -> `SparkSession#read`
> ```scala
> /**
> * 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
> ```
> 2. `SparkSession#sqlContext#setConf` -> `SparkSession#conf#set`
> ```scala
> /**
> * 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)
> }
> ```
> 3. `SparkSession#sqlContext#getConf` -> `SparkSession#conf#get`
> ```scala
> /**
> * 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)
> }
> ```
> 4. `SparkSession#sqlContext#createDataFrame` -> `SparkSession#createDataFrame`
> ```scala
> /**
> * Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
> *
> * @group dataframes
> * @since 1.3.0
> */
> def createDataFrame[A <: Product : TypeTag](rdd: RDD[A]): DataFrame = {
> sparkSession.createDataFrame(rdd)
> }
> ```
> 5. `SparkSession#sqlContext#sessionState` -> `SparkSession#sessionState`
> ```scala
> private[sql] def sessionState: SessionState = sparkSession.sessionState
> ```
> 6. `SparkSession#sqlContext#sharedState` -> `SparkSession#sharedState`
> ```scala
> private[sql] def sharedState: SharedState = sparkSession.sharedState
> ```
> 7. `SparkSession#sqlContext#streams` -> `SparkSession#streams`
> ```
> /**
> * 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
> ```
> 8. `SparkSession#sqlContext#uncacheTable` ->
> ``SparkSession#catalog#uncacheTable`
> ```
> /**
> * Removes the specified table from the in-memory cache.
> * @group cachemgmt
> * @since 1.3.0
> */
> def uncacheTable(tableName: String): Unit = {
> sparkSession.catalog.uncacheTable(tableName)
> }
> ```
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