Python Spark算子执行报错Connection reset：如何排查及解决？

from pyspark import sparkconf, sparkcontext

conf = sparkconf() \
    .setappname("yourappname") \
    .setmaster("local[*]") \
    .set("spark.executor.memory", "4g") \
    .set("spark.executor.cores", "2") \
    .set("spark.driver.memory", "4g")

sc = sparkcontext(conf=conf)

conf.set("spark.network.timeout", "600s")
conf.set("spark.executor.heartbeatinterval", "100s")

rdd = sc.textfile("hdfs://path/to/your/file").repartition(100)

from pyspark import SparkConf, SparkContext

conf = SparkConf() \
    .setAppName("YourAppName") \
    .setMaster("local[*]") \
    .set("spark.executor.memory", "4g") \
    .set("spark.executor.cores", "2") \
    .set("spark.driver.memory", "4g") \
    .set("spark.network.timeout", "600s") \
    .set("spark.executor.heartbeatInterval", "100s")

sc = SparkContext(conf=conf)

# 你的spark任务代码
rdd = sc.textFile("hdfs://path/to/your/file").repartition(100)
result = rdd.map(lambda x: x).collect()

print(result)