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https://github.com/scopely-devops/skew


https://github.com/scopely-devops/skew

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README 

        
# skew



![PyPI](https://img.shields.io/pypi/v/skew) https://pypi.org/project/skew/



`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](http://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html) (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](http://docs.aws.amazon.com/general/latest/gr/acct-identifiers.html).

To allow `skew` to find your account number, you need to create a `skew`

YAML config file.  By default, `skew` will look for your config file in

`~/.skew` but you can use the `SKEW_CONFIG` environment variable to tell `skew`

where to find your config file if you choose to put it somewhere else.  The

basic format of the `skew` config file is:



```yaml

---

  accounts:

    "123456789012":

      profile: dev

    "234567890123":

      profile: prod

```



Within the `accounts` section, you create keys named after your 12-digit

account ID (as a string).  Within that, you must have an entry called *profile*

that lists the profile name this account maps to within your AWS credential

file.



The main purpose of skew is to identify resources or sets of resources

across services, regions, and accounts 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:



```python

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](https://docs.python.org/2/library/stdtypes.html#iterator-types)

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:



```python

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:



```python

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

```



CloudWatch Metrics

------------------



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:



```python

>>> 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:



```python

>>> instance.get_metric_data('CPUUtilization')

[{'Average': 0.134, 'Timestamp': '2014-09-29T14:04:00Z', 'Unit': 'Percent'},

 {'Average': 0.066, 'Timestamp': '2014-09-29T13:54:00Z', 'Unit': 'Percent'},

 {'Average': 0.066, 'Timestamp': '2014-09-29T14:09:00Z', 'Unit': 'Percent'},

 {'Average': 0.134, 'Timestamp': '2014-09-29T13:34:00Z', 'Unit': 'Percent'},

 {'Average': 0.066, 'Timestamp': '2014-09-29T14:19:00Z', 'Unit': 'Percent'},

 {'Average': 0.068, 'Timestamp': '2014-09-29T13:44:00Z', 'Unit': 'Percent'},

 {'Average': 0.134, 'Timestamp': '2014-09-29T14:14:00Z', 'Unit': 'Percent'},

 {'Average': 0.066, 'Timestamp': '2014-09-29T13:29:00Z', 'Unit': 'Percent'},

 {'Average': 0.132, 'Timestamp': '2014-09-29T13:59:00Z', 'Unit': 'Percent'},

 {'Average': 0.134, 'Timestamp': '2014-09-29T13:49:00Z', 'Unit': 'Percent'},

 {'Average': 0.134, 'Timestamp': '2014-09-29T13:39:00Z', 'Unit': 'Percent'}]

>>>

```



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



```python

>>> instance.get_metric_data('CPUUtilization', hours=8, statistics=['Average', 'Minimum', 'Maximum'])

[{'Average': 0.132,

  'Maximum': 0.33,

  'Minimum': 0.0,

  'Timestamp': '2014-09-29T10:54:00Z',

  'Unit': 'Percent'},

 {'Average': 0.134,

  'Maximum': 0.34,

  'Minimum': 0.0,

  'Timestamp': '2014-09-29T14:04:00Z',

  'Unit': 'Percent'},

  ...,

 {'Average': 0.066,

  'Maximum': 0.33,

  'Minimum': 0.0,

  'Timestamp': '2014-09-29T08:34:00Z',

  'Unit': 'Percent'},

 {'Average': 0.134,

  'Maximum': 0.34,

  'Minimum': 0.0,

  'Timestamp': '2014-09-29T08:04:00Z',

  'Unit': 'Percent'}]

>>>

```



Filtering Data

--------------



Each resource that is retrieved is a Python dictionary.  Some of these (e.g.

an EC2 Instance) can be quite large and complex.  Skew allows you to filter

the data returned by applying a [jmespath](http://jmespath.org) query to

the resulting data.  If you aren't familiar with jmespath, check it out.

Its a very powerful query language for JSON data and has full support in

Python as well as a number of other languages such as Ruby, PHP, and

Javascript.  It is also the query language used in the

[AWSCLI](https://aws.amazon.com/cli/) so if you are familiar with the

`--query` option there, you can use the same thing with skew.



To specify a query to be applied to results of a scan, simply append

the query to the end of the ARN, separated by a `|` (pipe) character.

For example:



```

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

```



Would retrieve the data for this particular EC2 instance and would then

filter the returned data through the (very) simple jmespath query to which

retrieves the value of the attribute `InstanceType` within the data.  The

filtered data is available as the `filtered_data` attribute of the

Resource object.  The full, unfiltered data is still available as the

`data` attribute.



Multithreaded Usage

-------------------



Skew is single-threaded by default, like most Python libraries. In order to

speed up the enumeration of matching resources, you can use multiple threads:



```python

import skew



class Worker(Thread):

   def __init__(self, arn):

       Thread.__init__(self)

       self.arn = arn

       self.name = arn



   def run(self):

       for i in skew.scan(self.arn):

           # now do something with the stuff



arn = skew.ARN()



for service in arn.service.choices():

    uri = 'arn:aws:' + service + ':*:*:*/*'

    worker = Worker(uri);

    worker.start()

```



(thanks to @alFReD-NSH for the snippet)



More Examples

-------------



[Find Unattached Volumes](https://gist.github.com/garnaat/73804a6b0bd506ee6075)



[Audit Security Groups](https://gist.github.com/garnaat/4123f1aefe7d65df9b48)



[Find Untagged Instances](https://gist.github.com/garnaat/11004f5661b4798d27c7)