Skew is a package for identifying and enumerating cloud resources. The name is a homonym for SKU (Stock Keeping Unit). Skew allows you to define different SKU schemes which are a particular encoding of a SKU. Skew then allows you to use this scheme pattern and regular expressions based on the scheme pattern to identify and enumerate a resource or set of resources.

At the moment, the the only available scheme is the ARN scheme. The ARN scheme uses the basic structure of Amazon Resource Names (ARNs) to assign a unique identifier to every AWS resource.

An example ARN pattern would be:

arn:aws:ec2:us-west-2:123456789012:instance/i-12345678

This pattern identifies a specific EC2 instance running in the us-west-2 region under the account ID 123456789012. The account ID is the 12-digit unique identifier for a specific AWS account as described here. To allow skew to find your account number, you need to add it to your botocore/AWSCLI config file. For example:

[profile prod]
aws_access_key_id = <my access key>
aws_secret_access_key = <my secret key>
region = us-west-2
account_id = 123456789012

Skew will look through all of the profiles defined in your config file and keep track of all of the ones that have an account_id associated with them.

The main point of skew is to identify AWS resources or sets of resources and to quickly and easily return the data associated with those resources. For example, if you wanted to return the data associated with the example ARN above:

from skew import scan

arn = scan('arn:aws:ec2:us-west-2:123456789012:instance/i-12345678')
for resource in arn:
    print(resource.data)

The call to scan returns an ARN object which implements the iterator pattern and returns a Resource object for each AWS resource that matches the ARN pattern provided. The Resource object contains all of the data associated with the AWS resource in dictionary under the data attribute.

Any of the elements of the ARN can be replaced with a regular expression. The simplest regular expression is * which means all available choices. So, for example:

arn = scan('arn:aws:ec2:us-east-1:*:instance/*')

would return an iterator for all EC2 instances in the us-east-1 region found in all accounts defined in the config file.

To find all DynamoDB tables in all US regions for the account ID 234567890123 you would use:

arn = scan('arn:aws:dynamodb:us-.*:234567890123:table/*')

In addition to making the metadata about a particular AWS resource available to you, skew also tries to make it easy to access the available CloudWatch metrics for a given resource.

For example, assume that you had did a scan on the original ARN above and had the resource associated with that instance available as the variable instance. You could do the following:

>>> instance.metric_names
['CPUUtilization',
 'NetworkOut',
 'StatusCheckFailed',
 'StatusCheckFailed_System',
 'NetworkIn',
 'DiskWriteOps',
 'DiskReadBytes',
 'DiskReadOps',
 'StatusCheckFailed_Instance',
 'DiskWriteBytes']
 >>>

The metric_names attribute returns the list of available CloudWatch metrics for this resource. The retrieve the metric data for one of these:

>>> instance.get_metric_data('CPUUtilization')
[{u'Average': 0.134, u'Timestamp': '2014-09-29T14:04:00Z', u'Unit': 'Percent'},
 {u'Average': 0.066, u'Timestamp': '2014-09-29T13:54:00Z', u'Unit': 'Percent'},
 {u'Average': 0.066, u'Timestamp': '2014-09-29T14:09:00Z', u'Unit': 'Percent'},
 {u'Average': 0.134, u'Timestamp': '2014-09-29T13:34:00Z', u'Unit': 'Percent'},
 {u'Average': 0.066, u'Timestamp': '2014-09-29T14:19:00Z', u'Unit': 'Percent'},
 {u'Average': 0.068, u'Timestamp': '2014-09-29T13:44:00Z', u'Unit': 'Percent'},
 {u'Average': 0.134, u'Timestamp': '2014-09-29T14:14:00Z', u'Unit': 'Percent'},
 {u'Average': 0.066, u'Timestamp': '2014-09-29T13:29:00Z', u'Unit': 'Percent'},
 {u'Average': 0.132, u'Timestamp': '2014-09-29T13:59:00Z', u'Unit': 'Percent'},
 {u'Average': 0.134, u'Timestamp': '2014-09-29T13:49:00Z', u'Unit': 'Percent'},
 {u'Average': 0.134, u'Timestamp': '2014-09-29T13:39:00Z', u'Unit': 'Percent'}]
>>>

You can also customize the data returned rather than using the default settings:

>>> instance.get_metric_data('CPUUtilization', hours=8, statistics=['Average', 'Minimum', 'Maximum'])
[{u'Average': 0.132,
  u'Maximum': 0.33,
  u'Minimum': 0.0,
  u'Timestamp': '2014-09-29T10:54:00Z',
  u'Unit': 'Percent'},
 {u'Average': 0.134,
  u'Maximum': 0.34,
  u'Minimum': 0.0,
  u'Timestamp': '2014-09-29T14:04:00Z',
  u'Unit': 'Percent'},
  ...,
 {u'Average': 0.066,
  u'Maximum': 0.33,
  u'Minimum': 0.0,
  u'Timestamp': '2014-09-29T08:34:00Z',
  u'Unit': 'Percent'},
 {u'Average': 0.134,
  u'Maximum': 0.34,
  u'Minimum': 0.0,
  u'Timestamp': '2014-09-29T08:04:00Z',
  u'Unit': 'Percent'}]
>>>