Python 如何在AWS glue中使用snowflake JDBC连接驱动程序运行pySpark

并且得到了一个错误

I am trying to run the below code in AWS glue:
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from py4j.java_gateway import java_import
SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake"

## @params: [JOB_NAME, URL, ACCOUNT, WAREHOUSE, DB, SCHEMA, USERNAME, PASSWORD]
args = getResolvedOptions(sys.argv, ['JOB_NAME', 'URL', 'ACCOUNT', 'WAREHOUSE', 'DB', 'SCHEMA', 'USERNAME', 'PASSWORD'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
java_import(spark._jvm, "net.snowflake.spark.snowflake")

## uj = sc._jvm.net.snowflake.spark.snowflake
spark._jvm.net.snowflake.spark.snowflake.SnowflakeConnectorUtils.enablePushdownSession(spark._jvm.org.apache.spark.sql.SparkSession.builder().getOrCreate())

options = {
"sfURL" : args['URL'],
"sfAccount" : args['ACCOUNT'],
"sfUser" : args['USERNAME'],
"sfPassword" : args['PASSWORD'],
"sfDatabase" : args['DB'],
"sfSchema" : args['SCHEMA'],
"sfWarehouse" : args['WAREHOUSE'],
}

df = spark.read \
  .format("snowflake") \
  .options(**options) \
  .option("dbtable", "STORE") \
  .load()

display(df)

## Perform any kind of transformations on your data and save as a new Data Frame: “df1”
##df1 = [Insert any filter, transformation, etc]

## Write the Data Frame contents back to Snowflake in a new table
##df1.write.format(SNOWFLAKE_SOURCE_NAME).options(**sfOptions).option("dbtable", "[new_table_name]").mode("overwrite").save()
job.commit()

Traceback（最近一次调用last）：文件“/tmp/spark\u snowflake”，第35行，在
.option（“数据库表”、“存储”）\File
“/opt/amazon/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py”，加载返回中的第172行
self._df（self._jreader.load（））文件“/opt/amazon/spark/python/lib/py4j-0.10.7-

src.zip/py4j/java_gateway.py”，第1257行，在callanswer中，self.gateway_客户端，self.target_id， self.name）文件“/opt/amazon/spark/python/lib/pyspark.zip/pyspark/sql/utils.py”，第63行，deco格式 返回f（*a，**kw）文件“/opt/amazon/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py”，行 328，在get_return_value格式（target_id，“.”，name），value）py4j.protocol.Py4JJavaError：一个错误 调用o78.load时发生：java.lang.ClassNotFoundException:未能找到数据源： 雪花。请在以下地址查找包裹： org.apache.spark.sql.execution.datasources.DataSource$.lookupdateasource（DataSource.scala:657）位于 org.apache.spark.sql.DataFrameReader.load（DataFrameReader.scala:194）位于 org.apache.spark.sql.DataFrameReader.load（DataFrameReader.scala:167）位于 sun.reflect.NativeMethodAccessorImpl.invoke0（本机方法）位于 位于的sun.reflect.NativeMethodAccessorImpl.invoke（NativeMethodAccessorImpl.java:62） sun.reflect.DelegatingMethodAccessorImpl.invoke（DelegatingMethodAccessorImpl.java:43）位于 java.lang.reflect.Method.invoke（Method.java:498）位于 py4j.reflection.MethodInvoker.invoke（MethodInvoker.java:244）位于 位于的py4j.reflection.ReflectionEngine.invoke（ReflectionEngine.java:357） py4j.Gateway.invoke（Gateway.java:282）位于 py4j.commands.AbstractCommand.invokeMethod（AbstractCommand.java:132）位于 py4j.commands.CallCommand.execute（CallCommand.java:79）位于 在java.lang.Thread.run（Thread.java:748）处运行py4j.GatewayConnection.run（GatewayConnection.java:238） 原因：java.lang.ClassNotFoundException:snowflake.DefaultSource位于 java.net.URLClassLoader.findClass（URLClassLoader.java:382）位于 loadClass（ClassLoader.java:418）位于 sun.misc.Launcher$AppClassLoader.loadClass（Launcher.java:352）位于 loadClass（ClassLoader.java:351）位于

org.apache.spark.sql.execution.datasources.DataSource$$anonfun$20$$anonfun$apply$12.apply（DataSource.scal a:634）在

处，错误消息显示“java.lang.ClassNotFoundException:未能找到数据源：snowflake”。创建作业时，是否使用了合适的罐子并将其传递给胶水？这里有一些例子

Traceback (most recent call last): File "/tmp/spark_snowflake", line 35, in <module> 
.option("dbtable", "STORE") \ File 
"/opt/amazon/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 172, in load return 
self._df(self._jreader.load()) File "/opt/amazon/spark/python/lib/py4j-0.10.7-