Pycharm通过SFTP远程连接Spark_pycharm连接spark集群

参考：https://daniel.blog.csdn.net/article/details/107415130

1.添加SFTP连接

选择 Tools =>  Deploment => Configuration。

 2. 在Deployment界面中，设置Connection、Mapping的配置。

Connection 配置：

 Mapping配置：

 2.添加SSH Interpreter

 3.Project Structure

 4.启动设置

点击右上角的三角形，然后删除working directory里面的路径，并更改Environment variables。

 5.WordCount.py代码

# coding=UTF-8
import sys
# 设置服务器上py4j库所在的路径
sys.path.append('/export/servers/spark/python/lib/py4j-0.10.4-src.zip')
from pyspark.sql import SparkSession
 
if __name__ == "__main__":
 
    # 如果spark配置了yarn集群，这里的master可以修改为yarn
    spark = SparkSession.builder \
        .master('local') \
        .appName('Pycharm Connection') \
        .getOrCreate()
    # wordcount操作，这里文件为hdfs的路径
    words = spark.sparkContext \
        .textFile("hdfs:/data/words") \
        .flatMap(lambda line: line.split(" ")) \
        .map(lambda word: (word, 1)) \
        .reduceByKey(lambda a, b: a + b) \
        .collect()
    for word in words:
        print(word)
    spark.stop()
 
    # spark = SparkSession.builder\
    #     .master('local[6]')\
    #     .appName('Course_Test') \
    #     .config("hive.metastore.uris", "thrift://node03:9083")\
    #     .enableHiveSupport()\
    #     .getOrCreate()
    # # 方式一:
    # sql = "select * from course.SCORE"
    # spark.sql("use course")
    # queryResult = spark.sql(sql)
    # spark.sql("drop table if exists course.score_test")
    # queryResult.write.format("hive").mode("overwrite").saveAsTable('course.score_test')
    # spark.stop()
 
    # 方式二:
    # sql = "select * from course.SCORE"
    # queryResult = spark.sql(sql)
    # queryResult.registerTempTable('temp_table')
    # spark.sql("truncate table course.score_test")
    # spark.sql("insert into course.score_test select * from temp_table")
    # spark.stop()

6.右键 => Run "WordCount"