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我有两个 ADLSv2 存储帐户，都启用了分层命名空间。在我的 Python Notebook 中，我正在从一个存储帐户读取一个 CSV 文件，并在经过一些扩充后将其作为 parquet 文件写入另一个存储。

编写镶木地板文件时出现以下错误...

StatusCode=400, An HTTP header that's mandatory for this request is not

任何帮助是极大的赞赏。

下面是我的笔记本代码片段...

# Databricks notebook source
# MAGIC %python
# MAGIC 
# MAGIC STAGING_MOUNTPOINT = "/mnt/inputfiles"
# MAGIC if STAGING_MOUNTPOINT in [mnt.mountPoint for mnt in dbutils.fs.mounts()]:
# MAGIC   dbutils.fs.unmount(STAGING_MOUNTPOINT)
# MAGIC 
# MAGIC PERM_MOUNTPOINT = "/mnt/outputfiles"
# MAGIC if PERM_MOUNTPOINT in [mnt.mountPoint for mnt in dbutils.fs.mounts()]:
# MAGIC   dbutils.fs.unmount(PERM_MOUNTPOINT)

STAGING_STORAGE_ACCOUNT = "--------"
STAGING_CONTAINER = "--------"
STAGING_FOLDER = --------"
PERM_STORAGE_ACCOUNT = "--------"
PERM_CONTAINER = "--------"

configs = {
 "fs.azure.account.auth.type": "OAuth",
 "fs.azure.account.oauth.provider.type": 
 "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
 "fs.azure.account.oauth2.client.id": "#####################",
 "fs.azure.account.oauth2.client.secret": dbutils.secrets.get(scope="DemoScope",key="DemoSecret"),
 "fs.azure.account.oauth2.client.endpoint": 
 "https://login.microsoftonline.com/**********************/oauth2/token"}

STAGING_SOURCE = 
"abfss://{container}@{storage_acct}.blob.core.windows.net/".format(container=STAGING_CONTAINER, 
storage_acct=STAGING_STORAGE_ACCOUNT)

try:
 dbutils.fs.mount(
  source=STAGING_SOURCE,
  mount_point=STAGING_MOUNTPOINT,
  extra_configs=configs)
except Exception as e:
 if "Directory already mounted" in str(e):
 pass # Ignore error if already mounted.
else:
 raise e

print("Staging Storage mount Success.")

inputDemoFile = "{}/{}/demo.csv".format(STAGING_MOUNTPOINT, STAGING_FOLDER)
readDF = (spark
          .read.option("header", True)
          .schema(inputSchema)
          .option("inferSchema", True)
          .csv(inputDemoFile))

LANDING_SOURCE = 
 "abfss://{container}@{storage_acct}.blob.core.windows.net/".format(container=LANDING_CONTAINER, 
 storage_acct=PERM_STORAGE_ACCOUNT)

try:
 dbutils.fs.mount(
 source=PERM_SOURCE,
 mount_point=PERM_MOUNTPOINT,
 extra_configs=configs)
except Exception as e:
 if "Directory already mounted" in str(e):
  pass # Ignore error if already mounted.
 else:
  raise e

print("Landing Storage mount Success.")

outPatientsFile = "{}/patients.parquet".format(outPatientsFilePath)
print("Writing to parquet file: " + outPatientsFile)

***Below call is failing…error is 
StatusCode=400
StatusDescription=An HTTP header that's mandatory for this request is not specified.
ErrorCode=
ErrorMessage=***

(readDF
 .coalesce(1)
 .write
 .mode("overwrite")
 .option("header", "true")
 .option("compression", "snappy")
 .parquet(outPatientsFile)
)
4

2 回答 2

1

我将解决方案总结如下。

如果要将 Azure 数据湖存储 gen2 挂载为 Azure databricks 文件系统，则 URL 应类似于abfss://<file-system-name>@<storage-account-name>.dfs.core.windows.net/. 更多详情，请参考这里

例如

  1. 创建 Azure Data Lake Storage Gen2 帐户。
az login
az storage account create \
    --name <account-name> \
    --resource-group <group name> \
    --location westus \
    --sku Standard_RAGRS \
    --kind StorageV2 \
    --enable-hierarchical-namespace true
  1. 创建服务主体并将 Storage Blob Data Contributor 分配给 Data Lake Storage Gen2 存储帐户范围内的 sp
az login

az ad sp create-for-rbac -n "MyApp" --role "Storage Blob Data Contributor" \
    --scopes /subscriptions/<subscription>/resourceGroups/<resource-group>/providers/Microsoft.Storage/storageAccounts/<storage-account>
  1. 在 Azure databricks(python) 中装载 Azure 数据湖 gen2
configs = {"fs.azure.account.auth.type": "OAuth",
       "fs.azure.account.oauth.provider.type": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
       "fs.azure.account.oauth2.client.id": "<appId>",
       "fs.azure.account.oauth2.client.secret": "<clientSecret>",
       "fs.azure.account.oauth2.client.endpoint": "https://login.microsoftonline.com/<tenant>/oauth2/token",
       "fs.azure.createRemoteFileSystemDuringInitialization": "true"}

dbutils.fs.mount(
source = "abfss://<container-name>@<storage-account-name>.dfs.core.windows.net/folder1",
mount_point = "/mnt/flightdata",
extra_configs = configs)
于 2020-10-13T01:30:11.373 回答
1

在 Azure Databricks 中装载存储帐户时需要注意的几个要点。

对于Azure Blob 存储：source = "wasbs://<container-name>@<storage-account-name>.blob.core.windows.net/<directory-name>"

对于Azure Data Lake Storage gen2：source = "abfss://<file-system-name>@<storage-account-name>.dfs.core.windows.net/"

要将 Azure Data Lake Storage Gen2 文件系统或其中的文件夹挂载为 Azure Databricks 文件系统，URL 应类似于abfss://<file-system-name>@<storage-account-name>.dfs.core.windows.net/

在此处输入图像描述

参考： Azure Databricks - Azure Data Lake Storage Gen2

于 2020-10-13T05:52:34.130 回答