Datera Python SDK

Introduction

This is Python SDK version v1.2 for the Datera Fabric Services API. Download and use of this package implicitly accepts the terms in COPYING

Users of this package are assumed to have familiarity with the Datera API. Details around the API itself are not necessarily covered through this SDK.

Features

• Automatic session management and login
• Automatic and configuable request retries
• Object to REST request translation
• Standard Logging Format (compatible with Datera SREQ log parsing)
• Endpoint validation (toggleable)
• Dot-notation access to response attributes
• UDC compliance

Installation

From Source

    apt-get install python-virtualenv (or yum install python-virtualenv for CentOS)
    virtualenv sdk
    source sdk/bin/activate
    git clone https://github.com/Datera/python-sdk.git
    cd python-sdk
    pip install -r requirements.txt
    python setup.py install

From PYPI

    pip install -U dfs_sdk

Universal Datera Config

The Universal Datera Config (UDC) is a config that can be specified in a number of ways:

• JSON file with any of the following names:
  • .datera-config
  • datera-config
  • .datera-config.json
  • datera-config.json
• The JSON file has the following configuration:

     {"mgmt_ip": "1.1.1.1",
      "username": "admin",
      "password": "password",
      "tenant": "/root",
      "api_version": "2.3",
      "ldap": ""}

• The file can be in any of the following places. This is also the lookup order for config files:
  • current directory
  • home directory
  • home/config directory
  • /etc/datera
• If no datera config file is found and a cinder.conf file is present, the config parser will try and pull connection credentials from the cinder.conf
• Tenant and API version and LDAP are always optional, but it's generally suggested to include them in your UDC file for easy reference.
• Instead of a JSON file, environment variables can be used.
  • DAT_MGMT
  • DAT_USER
  • DAT_PASS
  • DAT_TENANT
  • DAT_API
  • DAT_LDAP
• Most tools built to use the Universal Datera Config will also allow for providing/overriding any of the config values via command line flags.
  • --hostname
  • --username
  • --password
  • --tenant
  • --api-version
  • --ldap

Developing with Universal Datera Config

To use UDC in a new python tool is very simple just add the following to your python script:

from dfs_sdk import scaffold

parser = scaffold.get_argparser()
parser.add_argument('my-new-arg')
args = parser.parse_args()

If you want to use subparsers, or customize the help outptu of your parser then use the following

import argparse
from dfs_sdk import scaffold

top_parser = scaffold.get_argparser(add_help=False)
new_parser = argparse.ArgumentParser(parents=[top_parser])
new_parser.add_argument('my-new-arg')
args = new_parser.parse_args()

Inside a script the config can be recieved by calling

from dfs_sdk import scaffold

scaffold.get_argparser()
config = scaffold.get_config()

NOTE: It is no longer required to call scaffold.get_argparser() before calling scaffold.get_config(). This is only necessary if building a CLI tool that needs the cli parser.

Logging

To set custom logging.json file

    export DSDK_LOG_CFG=your/log/location.json

Or the value can be set to a debug, info or error

    export DSDK_LOG_CFG=info

To set logging to stdout. The value can be any logging level supported by the python logging module (eg: debug, info, etc)

    export DSDK_LOG_STDOUT=debug

The debug logs generated by the python-sdk are quite large, and are on a rotating file handler (provided that a custom logging.json file is not provided)

Managed Objects

Datera provides an application-driven storage management model, whose goal is to closely align storage with a corresponding application's requirements.

The main storage objects are defined and differentiated as follows:

Application Instance (AppInstance)

-    Corresponds to an application, service, etc.
-    Contains Zero or more Storage Instances

Storage Instance

-    Corresponds to one set of storage requirements for a given AppInstance
-    ACL Policies, including IQN Initiators
-    Target IQN
-    Contains Zero or more Volumes

Volumes

-    Corresponds to a single allocated storage object
-    Size (default unit is GB)
-    Replication Factor
-    Performance Policies (QoS for Bandwidth and IOPS)
-    Protection Policies (Snapshot scheduling)

Another way of viewing the managed object hierarchy is as follows:

app_instances:
    - storage_instances:                 (1 or more per app_instance)
        + acl_policy                     (1 or more host initiators )
        + iqn                            (target IQN)
        + ips                            (target IPs)
        + volumes:                       (1 or more per storage_instance)
            * name
            * size
            * replication
            * performance_policy         (i.e. QoS)
            * protection_policy          (i.e. Snapshot schedules)

Endpoints

HTTP operations on URL endpoints is the only way to interact with the set of managed objects. URL's have the format:

      http://192.168.42.13:7717/v2.3/<object_class>/[<instance>]/...

where 7717 is the port used to access the API, and "v2.3" corresponds to an API version control.

Briefly, the REST API supports 4 operations/methods create (POST), modify (PUT), list (GET), delete (DELETE). Any input payload is in JSON format; any return payload is in JSON format. Login session keys are required within the "header" of any HTTP request. Sessions keys have a 15 minute lifetime.

For a full reference documentation of the REST API, please review the Datera REST API Guide.

This Python SDK serves as a wrapper around the raw HTTP layer.

Using this SDK

The Datera module is named dfs_sdk, and the main entry point is called DateraApi. Obtaining an object handle can be done as follows:

    from dfs_sdk import get_api
    [...]
    api = get_api(mgmt_ip, username, password, "v2.3" **kwargs)

You can also initialize the SDK using a Datera UDC file. The following will read any valid UDC file on the system or from the current environment variables.

    from dfs_sdk.scaffold import get_api
    [...]
    api = get_api()

Configurable Options

These options can be set on instantiation via the get_api constructor

Common Objects, Examples and Use Cases

Please see the utils directory for programming examples that cover the following:

Common methods for all objects include create(), set(), delete(), list()

• To create an app_instance with name FOO:

        ai = api.app_instances.create(name="FOO")

• Looping through objects can be done via list():

        for ai in api.app_instances.list():
            print "AppInstance: ", ai

• To set a given app_instance into an offline state:

        ai.set(admin_state="offline")

• To delete a given app_instance:

        ai.delete()

Building the PyPI package

Run the following to build the packages (if uploading, ensure the version is incremented in constants.py)

        python setup.py sdist bdist_wheel

Then to upload the package to PyPI (this step requires valid PyPI credentials)

        twine upload dist/*

You can perform a test upload by running. This requires credentials on the test PyPI server

        twine upload --repository-url https://test.pypi.org/legacy/ dist/*

Reporting Problems

For problems and feedback, please open an github issue. This project is community supported.