Advanced Configuration

Authentication

Single user / interactive

The default and recommended method for authentication for users is using Authenticator. You will be guided to your organizations login webpage, and login as usual. (We will not see or store your credentials!). Once authenticated, you can choose to re-use your (valid) access token (i.e. not be prompted to authenticate next time) or re-authenticate everytime:

import datareservoirio as drio

# Re-use (valid) access token from last sesssion
auth = drio.Authenticator()

# or re-authenticate
auth = drio.Authenticator(auth_force=True)

Caution

Users on shared computers should always re-authenticate since access token from a different user may unintentionally be used.

If you desire to have multiple seperate session, it is advisable to set a session key during authetication. This will keep the sessions (token cache) seperate:

auth_0 = drio.Authenticator(session_key="my_unique_session_0")
auth_1 = drio.Authenticator(session_key="my_unique_session_1")

Service account / non-interactive client

If you require client/backend type of authentication flow where user interaction is not feasible nor desired, you can use the authenticate.ClientAuthenticator:

import datareservoirio as drio

auth = drio.authenticate.ClientAuthenticator("my_client_id", "my_client_secret")

Contact us and we will provide you the specifics.

Caching

The Client class employs a disk cache to speed up repeating series downloads. Beside turning the cache on and off, several aspects of it can be configured during instantiation. The configuration are passed on as a dictionary:

  • format: format used to store series on disk, either 'parquet' or 'csv'. Default is 'parquet'.

  • max_size: size in megabytes that the cache is allowed to use. Default is 1024MB.

  • cache_root: control the cache storage location. Default locations are:

    • Windows: %LOCALAPPDATA%\\datareservoirio\\Cache

    • Linux: ~/.cache/datareservoirio (XDG default)

    • MacOs: ~/Library/Caches/datareservoirio

Example:

import datareservoirio as drio


auth = drio.Authenticator()

# Initiate a client with 32GB cache in the 'c:\project\drio_cache'
client = drio.Client(auth, cache=True,
                     cache_opt={'format': 'parquet', 'max_size': 32*1024,
                                'cache_root': r'c:\project\drio_cache'})

The cache has near disk-bound performance and will benefit greatly from fast low-latency solid state drives.

Warning

The cache is "cleaned up" during instantiation of Client. If it is instantiated with defaults cache options, it will potentially delete the larger cache set up by another instance! Caution is adviced!

Note

If you are working with several "larger" projects at once, it may be a good idea to configure dedicated cache locations for each project.

Logging

To simplify debugging, enable logging for the logger named 'datareservoirio'.

import logging

# Basic configuration of the root logger, including 'datareservoirio'
logging.basicConfig(format='%(asctime)s %(name)-20s %(levelname)-5s %(message)s', level=logging.INFO)
import logging
import datareservoirio

# Configure desired log level specifically for 'datareservoirio'
logger = logging.getLogger('datareservoirio')
logger.setLevel(logging.DEBUG)

# Short-hand for the above
datareservoirio.set_log_level(logging.DEBUG)
import logging

# Advanced configuration allowing control of log level, message format and output handler
logger = logging.getLogger('datareservoirio')
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s %(name)-20s %(levelname)-5s %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)

The following log names can be used to fine-tune the desired log output:

  • datareservoirio: top level module including configuration, authentication and client

  • datareservoirio.storage: storage module, including cache and data download

  • datareservoirio.rest_api: API module with logging of request parameters and responses

If you require even more detailed logging, consider using loggers from requests, oauthlib, requests-oauthlib and azure-storage-blob