AWS account components
Project description
cs.aws_account
Boto3 provides nice bindings into the AWS API. However, when dealing with a complex environment with many regions and accounts, the default calls can become cumbersome. This package provides some convenience layers to application builders that wish to remove some of the complexities for common tasks such as account coordination, call limiting, and threaded access.
Why might you want to use this package?
- You're worried about call rates to the AWS API
- You're developing a multi-threaded application and you'd like to provide thread-safe global access to boto3.session.Session objects.
- You'd like to allow runtime configuration to determine which AWS accounts and regions are acted on by your application.
Narrative Application Usage
Although individual package components are easy enough to understand, as a whole, things get complex. To help your introduction, this section provides quick narratives to component usage.
Configuration
It all starts with configuration. In these examples, we'll leverage Yaml and Jinga (to allow for anchors/references and config macros) to help iilustrate how a real-world deployment might work.
Given the following YAML file, named config.yaml
:
SessionParameters: &aws_api_creds
aws_access_key_id: {{AWS_ACCESS_KEY_ID}}
aws_secret_access_key: {{AWS_SECRET_ACCESS_KEY}}
Account: &default_account
SessionParameters: *aws_api_creds
AssumeRole:
RoleArn: {{AWS_ASSUME_ROLE}}
RoleSessionName: test_assumed_role_session #arbitrary name
RateLimit: &default_ratelimit
max_count: 10
interval: 1
block: True
RegionalAccounts: &us-east-1
Account: *default_account
RateLimit: *default_ratelimit
Filter:
Partitions:
aws:
IncludeNonRegional: True
Regions:
include: [us-east-1]
RegionalAccounts: &all-execpt-us-east-1
Account: *default_account
RateLimit: *default_ratelimit
Filter:
Partitions:
aws:
Regions:
exclude: [us-east-1]
YourApp:
RegionalAccountSet:
- RegionalAccounts: *us-east-1
- RegionalAccounts: *all-execpt-us-east-1
The following code will parse the YAML into Python data structures and interpolate the named environment variables into the configuration.
>>> from jinja2 import Template
>>> import os
>>> import yaml
>>> with open('config.yaml') as config_file:
... rendered_config = Template(config_file.read()).render(os.environ)
>>> config = yaml.load(rendered_config)
Most of the entries in the config have parameters defined by cs.aws_account
.
The exception is the YourApp entry, which determines how your application
will consume the rest of the configuration parameters. In this example,
the application is choosing to produce a
cs.aws_account.regional_account_set.RegionalAccountSet
instance for consumption.
Here's how the app should initialize this object
>>> from cs.aws_account.regional_account_set import regional_account_set_factory
>>> accounts = None
>>> def my_app_initialization(config):
... global accounts
... accounts = regional_account_set_factory(*config['YourApp']['RegionalAccountSet'])
>>> my_app_initialization(config)
There is now a global accounts
variable that can be leveraged across your
application whose contents was determined via configuration. Some important
notes about this object:
- It is iterable, producing instances of
cs.aws_account.regional_account.RegionalAccount
objects. cs.aws_account.regional_account.RegionalAccount
objects have methods to interface with boto3Session.Client()
andSession.Client().get_paginator()
that have built-in ratelimiting...this is one of the reasons you're leveraging this package.- the usage of
cs.aws_account.regional_account_set.regional_account_set_factory
insured that the objects referenced are cached object singletons. This means that other config-driven factory based objects with common call signatures will also reference these singletons.
Custom endpoints
With AWS and boto3, customizing the endpoints used for the various AWS services
is possible, though doing so can be a bit finicky. To simplify this process with
cs.aws_account
, add a nested object to the SessionParameters
configuration block in your YAML config, like the example below:
ServiceEndpoints: &vpc_service_endpoints
ec2: {{EC2_VPC_ENDPOINT}}
sqs:
us-east-1: {{SQS_VPC_ENDPOINT_US_EAST_1}}
us-west-2: {{SQS_VPC_ENDPOINT_US_WEST_2}}
SessionParameters: &aws_api_creds
aws_access_key_id: {{AWS_ACCESS_KEY_ID}}
aws_secret_access_key: {{AWS_SECRET_ACCESS_KEY}}
ServiceEndpoints: *vpc_service_endpoints
Account: &default_account
SessionParameters: *aws_api_creds
AssumeRole:
RoleArn: {{AWS_ASSUME_ROLE}}
RoleSessionName: test_assumed_role_session #arbitrary name
RateLimit: &default_ratelimit
max_count: 10
interval: 1
block: True
RegionalAccounts: &us-east-1
Account: *default_account
RateLimit: *default_ratelimit
Filter:
Partitions:
aws:
IncludeNonRegional: True
Regions:
include: [us-east-1]
YourApp:
RegionalAccountSet:
- RegionalAccounts: *us-east-1
There are two important points to note here:
- Cross-region custom endpoints are supported by specifying the endpoint as a map of region aliases to endpoints instead of strings for each service.
- When using custom endpoints, because of limitations in the underlying boto3 library, the
region specified for a given AWS API call must match the region of the custom endpoint, or
cs.aws_account
will fallback to the default endpoint for that service.
Components
The Session
The cs.aws_account.Session
class provides convienent, threadlocal access to
boto3.session.Session
objects. Major features includes:
- a single
cs.aws_account.Session
object can be used across threads safely - memoized methods for some methods for performant re-calls (i.e. for logging)
- built-in support for IAM role-assumption, including automated credential refreshing
Creating a cs.aws_account.Session
is easy...just pass in the same kwargs you
would for boto3.session.Session
(in this example, we get the values from the
current environment).
>>> import os
>>> from cs.aws_account import Session
>>> session_kwargs = {
... 'aws_access_key_id': os.environ.get('AWS_ACCESS_KEY_ID'),
... 'aws_secret_access_key': os.environ.get('AWS_SECRET_ACCESS_KEY')
... }
>>> session = Session(**session_kwargs)
You can now get access to a threadlocal boto3.session.Session
object.
>>> b3 = session.boto3()
>>> type(b3)
<class 'boto3.session.Session'>
>>> b3.get_credentials().access_key == session_kwargs['aws_access_key_id']
True
Multiple calls will return the same object (within the same thread)
>>> session.boto3() is session.boto3()
True
Different threads will get unique threadlocal boto3.session.Session
objects
>>> import threading
>>> thread_b3 = []
>>> def b3():
... thread_b3.append(session.boto3())
>>> t1 = threading.Thread(target=b3)
>>> t1.start(); t1.join()
>>> thread_b3[0] is session.boto3()
False
It's easy to assume new IAM roles, simply call
cs.aws_account.Session.assume_role()
with the same arguments as
boto3.session.Session().client('sts').assume_role()
>>> b4_key = session.access_key()
>>> assume_role_kwargs = {
... 'RoleArn': os.environ.get('AWS_ASSUME_ROLE'),
... 'RoleSessionName': 'testing_assume_role_for_cs_aws_account_package'
... }
>>> session.assume_role(**assume_role_kwargs)
>>> b4_key == session.access_key()
False
Assuming a role is threadsafe and will cascade to other threads. Keep in mind
that other threads will need to get new references to realize this (e.g.
call cs.aws_account.Session.boto3()
again)
>>> thread_key = []
>>> def b3():
... thread_key.append(session.access_key())
... new_b3 = session.boto3() # just illustrating that threads will need to get a new reference
>>> t1 = threading.Thread(target=b3)
>>> t1.start(); t1.join()
>>> session.access_key() == thread_key[0]
True
We can also revert from an assumed role (same as above from a thread standpoint)
>>> assumed_role_key = session.access_key()
>>> assumed_boto3_session = session.revert() #returns the pop'd session
>>> session.access_key() == assumed_role_key
False
>>> session.access_key() == b4_key
True
For environments that desire to leverage singletons for common
cs.aws_account.Session()
initialization parameters, there is a convienence
factory
>>> from cs.aws_account import session_factory
>>> session_factory(SessionParameters=session_kwargs) is \
... session_factory(SessionParameters=session_kwargs)
True
The caching singleton factories are only semi intelligent when it comes to understanding common non-hashable argument values. The internal algorithm simply iterates args, sorts kwargs, and aggregates the
str()
values to determine the results hash...e.g. hash is impacted by things like value ordering.
The Account
The cs.aws_account.Account
class is mostly a small wrapper on top of
cs.aws_account.Session
that provides accessors to cachable account information.
>>> from cs.aws_account import Account
>>> account = Account(session=session)
>>> assert(account.account_id())
>>> account.session() is session
True
>>> account.alias() in account.aliases()
True
>>> account.account_id() == account.session().account_id()
True
As with cs.aws_account.Session
, there is a caching singleton factory
available for common initialization parameters. unlike Account, parameters
are the same as those for cs.aws_account.Session
.
>>> from cs.aws_account import account_factory
>>> account_factory(SessionParameters=session_kwargs) is \
... account_factory(SessionParameters=session_kwargs)
True
The Regional Account
The cs.aws_account.RegionalAccount
class provides a thread-safe, runtime
adjustable, region-specific, rate limited boto3 Client caller and
paginator.
See
cs.ratelimit
for detailed information on how rate limiting operates and the various configuration choices.
>>> from cs.aws_account import RegionalAccount
>>> from cs.ratelimit import RateLimitProperties, RateLimitExceeded
>>> from datetime import timedelta
>>> rl = RateLimitProperties(max_count=1, interval=timedelta(seconds=1), block=False)
>>> raccount = RegionalAccount(rl, account, region_name='us-east-1')
>>> raccount.region() == 'us-east-1'
True
>>> raccount.account() is account
True
Simple boto3 Client call rate limits are now available (these calls are always routed to named region).
>>> _ = raccount.call_client('sts', 'get_caller_identity')
>>> try:
... _ = raccount.call_client('sts', 'get_caller_identity')
... except RateLimitExceeded:
... print('Too fast!')
Too fast!
We can change the rate limit behavior at runtime for the instance
>>> raccount.ratelimit.max_count = 2
>>> _ = raccount.call_client('sts', 'get_caller_identity')
We can also get access to a rate limited paginator. All calls to boto3 are contolled by the same instance rate limiter.
>>> paginator = raccount.get_paginator('ec2', 'describe_instances')
>>> page_iterator = paginator.paginate(PaginationConfig={'MaxItems': 10})
>>> try:
... _ = page_iterator.__iter__().next()
... except RateLimitExceeded:
... print('Too fast!')
Too fast!
>>> raccount.ratelimit.max_count = 3
>>> _ = page_iterator.__iter__().next()
All of this functionality is thread safe
>>> raccount.ratelimit.max_count = 1
>>> raccount.ratelimit.interval = timedelta(seconds=.1)
>>> def parallel_call(raccount):
... raccount.ratelimit.block = True
... _ = raccount.call_client('sts', 'get_caller_identity')
>>> t1 = threading.Thread(target=parallel_call, args=(raccount,))
>>> t1.start()
>>> _ = raccount.call_client('sts', 'get_caller_identity')
>>> t1.join()
As with cs.aws_account.Session
and cs.aws_account.Account
, there is a caching
singleton factory available for common initialization parameters.
>>> from cs.aws_account import regional_account_factory
>>> kwargs_raccount = {
... 'RateLimit': {'max_count':1, 'interval':1, 'block':False},
... 'Account': {'SessionParameters': session_kwargs},
... 'region_name': 'us-east-1'
... }
>>> regional_account_factory(**kwargs_raccount) is \
... regional_account_factory(**kwargs_raccount)
True
Regional Account Containers
Typically, an application has a set of operations that need to be performed
across several AWS accounts and/or regions. cs.aws_account.RegionalAccounts
provides a readonly dict-like interfaces for filtered groups of
cs.aws_account.RegionalAccount
objects. A couple things to note:
- The
RegionalAccounts
is a read only, thread-safe, container whose contents is initialized based a filter specification (see below). - A
RegionalAccounts
object only containsRegionalAccount
objects from a singleAccount
(i.e. from a single AWS account) - You can specify a default rate limit that gets individually applied across contained 'RegionAccount` objects in addition to named-region rate limits.
The Container Filter Specification
The key to understanding RegionalAccounts
is to understand the dict
specification that determines their contents. The full filter spec is listed
below (in Yaml format). All keys are optional.
Partitions:
aws: # valid AWS partition name. If absent, defaults 'aws'
IncludeNonRegional: True|False # include non-regional endpoint names, defaults to False
Regions: #if absent, defaults to all available regions
include: [list, of, regions] #if absent, defaults to all available regions
exclude: [list, of, regions] #takes precedence over include
Here is a sample Python filter (implements above spec):
>>> filter1 = {'Partitions':
... {'aws':
... {'Regions':
... {'include': ['us-east-1']}
... } } }
RegionalAccounts
containers act like read-only dicts
>>> from cs.aws_account import RegionalAccounts
>>> rl_kwargs = {'max_count':1, 'interval':1, 'block':False}
>>> filter1_raccts = RegionalAccounts(rl_kwargs, {'SessionParameters': session_kwargs}, Filter=filter1)
>>> print([r for r in filter1_raccts])
['us-east-1']
>>> 'us-east-1' in filter1_raccts
True
>>> isinstance(filter1_raccts['us-east-1'], RegionalAccount)
True
>>> filter1_raccts['us-east-1'].region() == 'us-east-1'
True
>>> len(filter1_raccts)
1
>>> print(filter1_raccts.get('us-east-2', 'not there'))
not there
>>> print([k for k in filter1_raccts.keys()])
['us-east-1']
>>> isinstance(filter1_raccts.values()[0], RegionalAccount)
True
>>> len(filter1_raccts.items()) == 1
True
We can now compare filtered vs non-filtered containers
>>> all_raccts = RegionalAccounts(rl_kwargs, {'SessionParameters': session_kwargs})
>>> len(all_raccts) > len(filter1_raccts)
True
>>> 'us-east-2' not in filter1_raccts
True
>>> len(filter1_raccts)
1
>>> 'us-east-2' in all_raccts
True
Filters can be mutated, but not replaced. Mutations are not generally thread-safe, but the most typcail operations are atomic (e.g. thread safe) see http://effbot.org/pyfaq/what-kinds-of-global-value-mutation-are-thread-safe.htm
>>> filter1_raccts.filter['Partitions']['aws']['Regions']['include'].append('us-east-2')
>>> filter1_raccts.filter['Partitions']['aws']['Regions']['include'] = ('us-east-1', 'us-east-2',)
>>> 'us-east-2' in filter1_raccts
True
>>> try:
... filter1_raccts.filter = {}
... except ValueError:
... print("not allowed!")
not allowed!
because the RegionalAccounts
implementation leverages the caching
regional_account_factory
factory to produce RegionalAccount
objects, the
referenced objects in the 2 containers above are the same
>>> all_raccts['us-east-1'] is filter1_raccts['us-east-1']
True
The fact that these containers leverage caching factories to populate their contents enables common rate-limits to be applied across containers....that's a feature.
As with the previous types, there is a caching singleton factory available for common initialization parameters.
>>> from cs.aws_account import regional_accounts_factory
>>> regional_accounts_factory(RateLimit=rl_kwargs, Account={'SessionParameters': session_kwargs}) is \
... regional_accounts_factory(RateLimit=rl_kwargs, Account={'SessionParameters': session_kwargs})
True
Regional Account Set
So far we've seen a Session
, Account
, RegionalAccount
, and RegionalAccounts
.
All instances of these types can act against a single AWS account. Real
world usage scenarios extend beyond single AWS accounts. To deal with this,
we provide a new (and final) container type...the
cs.aws_account.RegionalAccountSet
. The purpose of this new container is to
provide easy threadsafe, iterable access to aggregated sets of
RegionalAccounts
values (e.g. RegionalAccount
objects)
Creating a group is straight forward. Just keep in mind that add()
,
discard()
and values()
calls refer to RegionAccounts
containers, whilst
__iter__()
returns RegionAccount
objects
>>> from cs.aws_account import RegionalAccountSet
>>> set1 = RegionalAccountSet(all_raccts)
>>> len(set1.values())
1
>>> isinstance(list(set1.values())[0], RegionalAccounts)
True
>>> len([ra for ra in set1]) > 1
True
>>> isinstance(set1.__iter__().next(), RegionalAccount)
True
New RegionalAccounts
can be added to the group, but the result set of
itered RegionalAccount
objects are checked for uniqueness
>>> set1.add(filter1_raccts)
>>> len(list(set1)) == len(all_raccts.values())
True
We can also remove RegionalAccounts
from the group
>>> set1.discard(all_raccts)
>>> len(list(set1)) == len(filter1_raccts.values())
True
You can check the group to see which RegionalAccounts
containers are available
>>> [filter1_raccts] == list(set1.values())
True
There is also a factory available at
cs.aws_account.regional_account_set.regional_account_set_factory
which accepts
an arbritrary number of dicts providing the call signature of
cs.aws_account.regional_account_set.regional_accounts_factory
.
WE STOP BREACHES
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