object SparkSession is not a member of package org.apache.spark.sql

这个问题通常是由于缺少Spark SQL依赖导致的。请确保在项目中添加以下依赖：

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>${spark.version}</version>
</dependency>

其中`${spark.version}`应该被替换为你正在使用的Spark版本号。如果你使用的是Scala 2.12，则需要将`spark-sql_2.11`替换为`spark-sql_2.12`。

相关问题

object spark is not a member of package org.apache import org.apache.spark.{SparkConf, SparkContext}

这个错误通常是由于缺少Spark依赖或者版本不兼容导致的。你需要确保你的项目中已经正确引入了Spark依赖，并且版本与你的代码兼容。另外，你也可以尝试使用SparkSession来代替SparkContext，因为SparkSession是Spark 2.0之后的推荐使用方式。你可以使用以下代码来创建一个SparkSession：

import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
.appName("YourAppName")
.master("local[*]") // 这里的[*]表示使用所有可用的CPU核心
.getOrCreate()

SparkSession object has no attribute sqlContext

Starting from Spark 2.0, the entry point for Spark SQL is the SparkSession object instead of the SQLContext object. Therefore, if you try to access sqlContext attribute of SparkSession object, you will get the error "SparkSession object has no attribute sqlContext".

To use Spark SQL in Spark 2.0 or later versions, you should use the SparkSession object to create DataFrames, execute SQL queries, etc. Here is an example of how to create a SparkSession object and use it to create a DataFrame:

from pyspark.sql import SparkSession

# create a SparkSession object
spark = SparkSession.builder \
    .appName("MyApp") \
    .getOrCreate()

# create a DataFrame from a CSV file
df = spark.read.csv("my_file.csv", header=True, inferSchema=True)

# execute a SQL query
df.createOrReplaceTempView("my_table")
result = spark.sql("SELECT * FROM my_table WHERE age > 30")

# show the result
result.show()

Note that you should replace "MyApp" and "my_file.csv" with your own application name and file path, respectively.