如何在AWS glue中用雪花JDBC连接驱动程序运行pySpark

Question

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()

并得到一个错误。

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- 

src.zip/py4j/java_gateway.py“，第1257行，在调用应答中，self.gateway_client，self.target_id,self.name）文件”/opt/amazon/spark/python/lib/pyspark.zip/pyspark/sql/utils.py“，第63行，在deco返回f(*a,**kw)文件”/opt/amazon/spark/python/lib/py4j/protocol.py“，第328行，在get_return_value格式(target_id,：java.lang.ClassNotFoundException：未能找到数据源：Snowflake。请在http://spark.apache.org/third-party-projects.html找到包，地址为org.apache.spark.sql.execution.datasources.datasource$.lookupdatasource(datasource.scala:657)，地址为org.apache.spark.sql.dataframereader.load(dataframereader.scala:194)，地址为sun.reflect.nativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)位于sun.reflect.DelegatingMethodAccessorImpL.invoke(DelegatingMethodAccessorImpl.java:43)，java.lang.reflect.method.invoke(method.java:498)，py4j.reflection.methodInvoker.invoke(methodInvoker.java:244)，py4j.reflection.methodInvoker.java:244)，py4j.reflection.reflection.reflectionEngine.invoke(reflectionEngine.invoke(reflectionEngine..thread.run（thread.java：748）由:java.lang.ClassNotFoundException:snowflake.DefaultSource在java.net.urlClassLoader.FindClass(URLClassLoader.java:382)在java.lang.ClassLoader.LoadClass(ClassLoader.java:418)在sun.misc.launcher$AppClassLoader.LoadClass(Launcher.java:352)在java.lang.ClassLoader.LoadClass(Launcher.java:351)

org.apache.spark.sql.execution.datasources.datasource$$$anonfun$20$$anonfun$apply$12.apply(datasource.scal A:634)at

Answer 1

错误消息表示“java.lang.ClassNotFoundException：未能找到数据源：Snowflake”。当你创建一个工作时，你是否使用了合适的罐子并将其传递给胶水？这里的一些例子

在PySpark中运行自定义Java类