内存错误：在python中从BLOB读取大的.txt文件时

Question

我正在尝试从python中的Azure blob读取较大的（〜1.5 GB）.txt文件，这会导致内存错误。 有没有一种方法可以有效地读取此文件？

以下是我尝试运行的代码：

from azure.storage.blob import BlockBlobService
import pandas as pd
from io import StringIO
import time

STORAGEACCOUNTNAME= '*********'
STORAGEACCOUNTKEY= "********"

CONTAINERNAME= '******'
BLOBNAME= 'path/to/blob'

blob_service = BlockBlobService(account_name=STORAGEACCOUNTNAME, account_key=STORAGEACCOUNTKEY)

start = time.time()
blobstring = blob_service.get_blob_to_text(CONTAINERNAME,BLOBNAME).content

df = pd.read_csv(StringIO(blobstring))
end = time.time()

print("Time taken = ",end-start)

以下是错误的最后几行：

---> 16 blobstring = blob_service.get_blob_to_text(CONTAINERNAME,BLOBNAME)
     17 
     18 #df = pd.read_csv(StringIO(blobstring))

~/anaconda3_420/lib/python3.5/site-packages/azure/storage/blob/baseblobservice.py in get_blob_to_text(self, container_name, blob_name, encoding, snapshot, start_range, end_range, validate_content, progress_callback, max_connections, lease_id, if_modified_since, if_unmodified_since, if_match, if_none_match, timeout)
   2378                                       if_none_match,
   2379                                       timeout)
-> 2380         blob.content = blob.content.decode(encoding)
   2381         return blob
   2382 

MemoryError:

如何从Blob容器中读取Python大小约为1.5 GB的文件？ 另外，我想为我的代码提供最佳的运行时。

Answer 1

假设您的计算机中有足够的内存，并且根据下面的pandas.read_csv API参考，您可以通过带有sas令牌的csv blob URL将csv blob内容直接读取到pandas数据帧中。

这是我的示例代码供您参考。

from azure.storage.blob.baseblobservice import BaseBlobService
from azure.storage.blob import BlobPermissions
from datetime import datetime, timedelta

import pandas as pd

account_name = '<your storage account name>'
account_key = '<your storage account key>'
container_name = '<your container name>'
blob_name = '<your csv blob name>'

url = f"https://{account_name}.blob.core.windows.net/{container_name}/{blob_name}"

service = BaseBlobService(account_name=account_name, account_key=account_key)
# Generate the sas token for your csv blob
token = service.generate_blob_shared_access_signature(container_name, blob_name, permission=BlobPermissions.READ, expiry=datetime.utcnow() + timedelta(hours=1),)

# Directly read the csv blob content into dataframe by the url with sas token
df = pd.read_csv(f"{url}?{token}")
print(df)

我认为这样做可以避免在读取文本内容时将内存复制几次，并将其转换为类似file-like对象buffer 。

希望能帮助到你。