第46课：Spark中的新解析引擎Catalyst源码初探
spark 1.6.0 与spark 2.0.2 中sql的版本迭代更新， 刚看一个开头，SQLContext 就不同了。sparksession闪亮登场
但以后的学习仍基于spark 1.6.0 学习。
spark 1.6.0
/**
* The entry point for working with structured data (rows and columns) in Spark. Allows the
* creation of [[DataFrame]] objects as well as the execution of SQL queries.
*
* @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 Ungrouped Support functions for language integrated queries
*
* @since 1.0.0
*/
class SQLContext private[sql](
@transient val sparkContext: SparkContext,
@transient protected[sql] val cacheManager: CacheManager,
@transient private[sql] val listener: SQLListener,
val isRootContext: Boolean)
extends org.apache.spark.Logging with Serializable {
spark 2.0.2
/**
* 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
*/
class SQLContext private[sql](val sparkSession: SparkSession)
extends Logging with Serializable {
