spark HiveContext 源码
spark HiveContext 代码
文件路径：/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
/*
* 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.hive
import org.apache.spark.SparkContext
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{SparkSession, SQLContext}
/**
* An instance of the Spark SQL execution engine that integrates with data stored in Hive.
* Configuration for Hive is read from hive-site.xml on the classpath.
*/
@deprecated("Use SparkSession.builder.enableHiveSupport instead", "2.0.0")
class HiveContext private[hive](_sparkSession: SparkSession)
extends SQLContext(_sparkSession) with Logging {
self =>
def this(sc: SparkContext) = {
this(SparkSession.builder().enableHiveSupport().sparkContext(sc).getOrCreate())
}
def this(sc: JavaSparkContext) = this(sc.sc)
/**
* Returns a new HiveContext as new session, which will have separated SQLConf, UDF/UDAF,
* temporary tables and SessionState, but sharing the same CacheManager, IsolatedClientLoader
* and Hive client (both of execution and metadata) with existing HiveContext.
*/
override def newSession(): HiveContext = {
new HiveContext(sparkSession.newSession())
}
/**
* Invalidate and refresh all the cached the metadata of the given table. For performance reasons,
* Spark SQL or the external data source library it uses might cache certain metadata about a
* table, such as the location of blocks. When those change outside of Spark SQL, users should
* call this function to invalidate the cache.
*
* @since 1.3.0
*/
def refreshTable(tableName: String): Unit = {
sparkSession.catalog.refreshTable(tableName)
}
}
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