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Asked 1 month ago by SaturnianNomad880

From Stack Overflow

How Can I Cache Azure CLI Tokens in Pandas When Reading from Azure Storage?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I'm using AzureCliCredential to read files from Azure Storage with Pandas, as shown below:

PYTHON
import pandas as pd
from azure.identity import AzureCliCredential

credential = AzureCliCredential()
pd.read_csv(
    "abfs://my_container/my_file.csv",
    storage_options={'account_name': 'my_account', 'credential': credential}
)

The token retrieval from AzureCliCredential is slow, and it happens each time I open a new file. Is there a way to have Pandas/fsspec cache the token so that the expensive token retrieval process isn't repeated for every file read?

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Answers 2

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2 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is some background on the issue along with what you can and cannot do within the current Pandas/fsspec framework.

Background

When you pass the Azure credential (in your case from AzureCliCredential) in the storage options, Pandas hands it off to the underlying fsspec implementation for Azure. fsspec does not include built‑in caching for credentials or tokens. This means that each time you use an API (for example, when reading a new file) the token retrieval step is performed as part of the connection process.

What You Can Do

Implement Your Own Caching:
Since Pandas/fsspec does not cache tokens, you could manage this yourself. For example, you could call the credential’s token retrieval method (like get_token) once, cache the token until it is near expiry, and then pass the cached token (or a wrapper that reuses it) in your storage options for subsequent calls.
Use a Credential with Built‑in Caching:
Consider if another Azure identity credential (e.g., DefaultAzureCredential) offers caching or a faster token retrieval based on how it’s set up in your environment. Note, however, that the caching behavior (if any) depends on the credential type and its implementation.

Example Approach

Below is a pattern where you generate and cache a token before using it in your Pandas calls. (Be sure to account for token expiry in a production scenario.)

PYTHON
import time
import pandas as pd
from azure.identity import AzureCliCredential

class CachedAzureCredential:
    def __init__(self, credential, scope):
        self.credential = credential
        self.scope = scope
        self.token = None
        self.expiry = 0  # Unix time

    def get_token(self):
        # Only refresh token if it's near expiry
        # (Here we refresh if token expires within the next 60 seconds)
        if not self.token or time.time() >= self.expiry - 60:
            access_token = self.credential.get_token(self.scope)
            self.token = access_token.token
            self.expiry = access_token.expires_on
        return self.token

# Instantiate your credential and wrap it with caching
scope = "https://storage.azure.com/.default"
azure_cli_credential = AzureCliCredential()
cached_credential = CachedAzureCredential(azure_cli_credential, scope)

# Retrieve the token once (or let your caching mechanism handle retrieval)
token = cached_credential.get_token()

# Pass the cached token to storage options
storage_options = {
    'account_name': 'my_account',
    # Instead of passing the credential object, you pass the token. Note that the way
    # you pass a pre-fetched token might depend on the Azure fsspec implementation.
    'credential': token
}

# Read your CSV using the token from cache
df = pd.read_csv("abfs://my_container/my_file.csv", storage_options=storage_options)

Important Considerations

Token Lifetime:
Tokens generally expire (often in one hour). When caching, ensure that you refresh the token before it expires.
Backend Expectations:
Verify that the underlying Azure storage/fsspec implementation accepts a pre-fetched token. In some cases, the API might expect the full credential object and perform token retrieval internally. In such cases, you may need to explore if the credential can be wrapped or if another credential type fits better.

Conclusion

Currently, Pandas/fsspec does not offer an out‑of‑the‑box mechanism to cache tokens from AzureCliCredential. You can work around this by caching the token retrieval yourself or by choosing an alternative credential method if caching improves performance in your setup.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by QuasarResearcher024 • 1 month ago

Getting the token from AzureCliCredential is slow. Is there a way to make pandas/fsspec cache the token so that the slow token retrieval process is not repeated over and over again when I open many files?

I agree with mdurant's comment, that if you read multiple files within the same session, fsspec should reuse the original filesystem instance and its credentials.

When using AzureCliCredential, the token lifetime depends on the Azure AD configuration, and it typically lasts for 1 hour before expiring.

You can use the below code that explains to persist and reuse tokens across sessions by caching them to disk.

Code:

PYTHON
import json
import os
from datetime import datetime, timezone
import pandas as pd

from azure.core.credentials import AccessToken, TokenCredential
from azure.identity import AzureCliCredential

TOKEN_CACHE_FILE = "azure_token_cache.json"

class CachedCredential(TokenCredential):
    def __init__(self, underlying_credential):
        self.underlying_credential = underlying_credential
        self._token = None
        self._expires_on = None
        self.load_cached_token()

    def load_cached_token(self):
        if os.path.exists(TOKEN_CACHE_FILE):
            try:
                with open(TOKEN_CACHE_FILE, "r") as f:
                    cache = json.load(f)
                expiry_datetime = datetime.fromtimestamp(cache["expires_on"], timezone.utc)
                if expiry_datetime > datetime.now(timezone.utc):
                    self._token = cache["token"]
                    self._expires_on = cache["expires_on"]
                    print("Loaded cached token, expires at:", expiry_datetime)
            except Exception as e:
                print("Failed to load cached token:", e)

    def save_token(self):
        cache = {"token": self._token, "expires_on": self._expires_on}
        with open(TOKEN_CACHE_FILE, "w") as f:
            json.dump(cache, f)

    def get_token(self, *scopes, **kwargs):
        now_ts = datetime.now(timezone.utc).timestamp()
        if self._token is None or now_ts >= self._expires_on:
            token_obj = self.underlying_credential.get_token(*scopes, **kwargs)
            self._token = token_obj.token
            self._expires_on = token_obj.expires_on
            self.save_token()
            expiry_datetime = datetime.fromtimestamp(self._expires_on, timezone.utc)
            print("Fetched new token, expires at:", expiry_datetime)
        return AccessToken(self._token, self._expires_on)

def main():
    underlying_credential = AzureCliCredential()
    cached_credential = CachedCredential(underlying_credential)
    
    token_obj = cached_credential.get_token("https://storage.azure.com/.default")
    token_str = token_obj.token
    expiry_datetime = datetime.fromtimestamp(token_obj.expires_on, tz=timezone.utc)
    
    print("\nAccess Token:")
    print(token_str)
    print("\nExpires On:")
    print(expiry_datetime)
    storage_options = {
        "account_name": "xxxxx",  # Replace with your actual storage account name.
        "credential": cached_credential  # Pass the credential object.
    }
    try:
        df = pd.read_csv("abfs://sample/001.csv", storage_options=storage_options)
        print("\nDataFrame Head:")
        print(df.head())
    except Exception as e:
        print("\nError reading file:", e)

if __name__ == "__main__":
    main()

Output:

BASH
Fetched new token, expires at: 2025-02-03 09:48:59+00:00

Access Token:
xxxxx

Expires On:
2025-02-03 xxxxx9+00:00

DataFrame Head:
   PassengerId  Survived  Pclass  
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   
4                           Allen, Mr. William Henry    male  35.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   NaN        S  
3      0            113803  53.1000  C123        S  
4      0            373450   8.0500   NaN        S

But I will suggest you use SAS token as an alternative to using credentials like AzureCliCredential

Code:

PYTHON
import pandas as pd

storage_options = {
    "account_name": "your_account_name",
    "sas_token": "your_sas_token"
}

df = pd.read_csv("abfs://your_container/your_file.csv", storage_options=storage_options)
print(df.head())

You can generate sas token for long time expiration to read the csv files.

Reference:

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How Can I Cache Azure CLI Tokens in Pandas When Reading from Azure Storage?

2 Answers

Background

What You Can Do

Example Approach

Important Considerations

Conclusion

Discussion

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