从 azure databricks 将 Dataframe 写入/保存到 azure 文件共享

Question

如何从 azure databricks spark 作业写入 azure 文件共享。

我配置了 Hadoop 存储键和值。

spark.sparkContext.hadoopConfiguration.set(
  "fs.azure.account.key.STORAGEKEY.file.core.windows.net",
  "SECRETVALUE"
)


val wasbFileShare =
    s"wasbs://testfileshare@STORAGEKEY.file.core.windows.net/testPath"

df.coalesce(1).write.mode("overwrite").csv(wasbBlob)

当尝试将数据帧保存到 azure 文件共享时，尽管存在 URI，但我看到以下资源未找到错误。

 Exception in thread "main" org.apache.hadoop.fs.azure.AzureException: com.microsoft.azure.storage.StorageException: The requested URI does not represent any resource on the server.

Answer 1

从数据块连接到 azure 文件共享的步骤

首先使用 Databricks 中的 pip install 安装适用于 Python 的 Microsoft Azure 存储文件共享客户端库。 https://pypi.org/project/azure-storage-file-share/

安装后，创建一个存储帐户。然后您可以从数据块创建文件共享

from azure.storage.fileshare import ShareClient

share = ShareClient.from_connection_string(conn_str="<connection_string consists of FileEndpoint=myFileEndpoint(https://storageaccountname.file.core.windows.net/);SharedAccessSignature=sasToken>", share_name="<file share name that you want to create>")

share.create_share()

此代码是通过databricks将文件上传到文件共享中

from azure.storage.fileshare import ShareFileClient
 
file_client = ShareFileClient.from_connection_string(conn_str="<connection_string consists of FileEndpoint=myFileEndpoint(https://storageaccountname.file.core.windows.net/);SharedAccessSignature=sasToken>", share_name="<your_fileshare_name>", file_path="my_file")
 
with open("./SampleSource.txt", "rb") as source_file:
    file_client.upload_file(source_file)

更多信息请参考此链接https://pypi.org/project/azure-storage-file-share/

Answer 2

很遗憾，Azure 数据块不支持读取和写入 Azure 文件共享。

Azure Databricks 支持的数据源：https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/

我建议您提供相同的反馈：

https://feedback.azure.com/forums/909463-azure-databricks

您在这些论坛中分享的所有反馈都将由负责构建 Azure 的 Microsoft 工程团队进行监控和审查。

您可以查看解决类似问题的 SO 线程：Databricks and Azure Files

下面是用于将 CSV 数据直接写入 Azure Databricks Notebook 中的 Azure blob 存储容器的代码 sn-p。

# Configure blob storage account access key globally
spark.conf.set("fs.azure.account.key.chepra.blob.core.windows.net", "gv7nVIXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXdlOiA==")
output_container_path = "wasbs://sampledata@chepra.blob.core.windows.net"
output_blob_folder = "%s/wrangled_data_folder" % output_container_path

# write the dataframe as a single file to blob storage
(dataframe
 .coalesce(1)
 .write
 .mode("overwrite")
 .option("header", "true")
 .format("com.databricks.spark.csv")
 .save(output_blob_folder))

# Get the name of the wrangled-data CSV file that was just saved to Azure blob storage (it starts with 'part-')
files = dbutils.fs.ls(output_blob_folder)
output_file = [x for x in files if x.name.startswith("part-")]

# Move the wrangled-data CSV file from a sub-folder (wrangled_data_folder) to the root of the blob container
# While simultaneously changing the file name
dbutils.fs.mv(output_file[0].path, "%s/predict-transform-output.csv" % output_container_path)