Added gsutil to depot tools

third_party/gsutil/boto/docs/source/autoscale_tut.rst

+.. _autoscale_tut:
+
+=============================================
+An Introduction to boto's Autoscale interface
+=============================================
+
+This tutorial focuses on the boto interface to the Autoscale service. This
+assumes you are familiar with boto's EC2 interface and concepts.
+
+Autoscale Concepts
+------------------
+
+The AWS Autoscale service is comprised of three core concepts:
+
+ #. *Autoscale Group (AG):* An AG can be viewed as a collection of criteria for
+    maintaining or scaling a set of EC2 instances over one or more availability
+    zones. An AG is limited to a single region.
+ #. *Launch Configuration (LC):* An LC is the set of information needed by the
+    AG to launch new instances - this can encompass image ids, startup data,
+    security groups and keys. Only one LC is attached to an AG.
+ #. *Triggers*: A trigger is essentially a set of rules for determining when to
+    scale an AG up or down. These rules can encompass a set of metrics such as
+    average CPU usage across instances, or incoming requests, a threshold for
+    when an action will take place, as well as parameters to control how long
+    to wait after a threshold is crossed.
+
+Creating a Connection
+---------------------
+The first step in accessing autoscaling is to create a connection to the service.
+There are two ways to do this in boto.  The first is:
+
+>>> from boto.ec2.autoscale import AutoScaleConnection
+>>> conn = AutoScaleConnection('<aws access key>', '<aws secret key>')
+
+Alternatively, you can use the shortcut:
+
+>>> conn = boto.connect_autoscale()
+
+A Note About Regions and Endpoints
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Like EC2 the Autoscale service has a different endpoint for each region. By
+default the US endpoint is used. To choose a specific region, instantiate the
+AutoScaleConnection object with that region's endpoint.
+
+>>> import boto.ec2.autoscale
+>>> ec2 = boto.ec2.autoscale.connect_to_region('eu-west-1')
+
+Alternatively, edit your boto.cfg with the default Autoscale endpoint to use::
+
+    [Boto]
+    autoscale_endpoint = autoscaling.eu-west-1.amazonaws.com
+
+Getting Existing AutoScale Groups
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To retrieve existing autoscale groups:
+
+>>> conn.get_all_groups()
+
+You will get back a list of AutoScale group objects, one for each AG you have.
+
+Creating Autoscaling Groups
+---------------------------
+An Autoscaling group has a number of parameters associated with it.
+
+ #. *Name*: The name of the AG.
+ #. *Availability Zones*: The list of availability zones it is defined over.
+ #. *Minimum Size*: Minimum number of instances running at one time.
+ #. *Maximum Size*: Maximum number of instances running at one time.
+ #. *Launch Configuration (LC)*: A set of instructions on how to launch an instance.
+ #. *Load Balancer*: An optional ELB load balancer to use. See the ELB tutorial
+    for information on how to create a load balancer.
+
+For the purposes of this tutorial, let's assume we want to create one autoscale
+group over the us-east-1a and us-east-1b availability zones. We want to have
+two instances in each availability zone, thus a minimum size of 4. For now we
+won't worry about scaling up or down - we'll introduce that later when we talk
+about triggers. Thus we'll set a maximum size of 4 as well. We'll also associate
+the AG with a load balancer which we assume we've already created, called 'my_lb'.
+
+Our LC tells us how to start an instance. This will at least include the image
+id to use, security_group, and key information. We assume the image id, key
+name and security groups have already been defined elsewhere - see the EC2
+tutorial for information on how to create these.
+
+>>> from boto.ec2.autoscale import LaunchConfiguration
+>>> from boto.ec2.autoscale import AutoScalingGroup
+>>> lc = LaunchConfiguration(name='my-launch_config', image_id='my-ami',
+                             key_name='my_key_name',
+                             security_groups=['my_security_groups'])
+>>> conn.create_launch_configuration(lc)
+
+We now have created a launch configuration called 'my-launch-config'. We are now
+ready to associate it with our new autoscale group.
+
+>>> ag = AutoScalingGroup(group_name='my_group', load_balancers=['my-lb'],
+                          availability_zones=['us-east-1a', 'us-east-1b'],
+                          launch_config=lc, min_size=4, max_size=8,
+                          connection=conn)
+>>> conn.create_auto_scaling_group(ag)
+
+We now have a new autoscaling group defined! At this point instances should be
+starting to launch. To view activity on an autoscale group:
+
+>>> ag.get_activities()
+ [Activity:Launching a new EC2 instance status:Successful progress:100,
+  ...]
+
+or alternatively:
+
+>>> conn.get_all_activities(ag)
+
+This autoscale group is fairly useful in that it will maintain the minimum size without
+breaching the maximum size defined. That means if one instance crashes, the autoscale
+group will use the launch configuration to start a new one in an attempt to maintain
+its minimum defined size. It knows instance health using the health check defined on
+its associated load balancer.
+
+Scaling a Group Up or Down
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+It can also be useful to scale a group up or down depending on certain criteria.
+For example, if the average CPU utilization of the group goes above 70%, you may
+want to scale up the number of instances to deal with demand. Likewise, you
+might want to scale down if usage drops again.
+These rules for **how** to scale are defined by *Scaling Policies*, and the rules for
+**when** to scale are defined by CloudWatch *Metric Alarms*.
+
+For example, let's configure scaling for the above group based on CPU utilization.
+We'll say it should scale up if the average CPU usage goes above 70% and scale
+down if it goes below 40%.
+
+Firstly, define some Scaling Policies. These tell Auto Scaling how to scale
+the group (but not when to do it, we'll specify that later).
+
+We need one policy for scaling up and one for scaling down.
+
+>>> from boto.ec2.autoscale import ScalingPolicy
+>>> scale_up_policy = ScalingPolicy(
+            name='scale_up', adjustment_type='ChangeInCapacity',
+            as_name='my_group', scaling_adjustment=1, cooldown=180)
+>>> scale_down_policy = ScalingPolicy(
+            name='scale_down', adjustment_type='ChangeInCapacity',
+            as_name='my_group', scaling_adjustment=-1, cooldown=180)
+
+The policy objects are now defined locally.
+Let's submit them to AWS.
+
+>>> conn.create_scaling_policy(scale_up_policy)
+>>> conn.create_scaling_policy(scale_down_policy)
+
+Now that the polices have been digested by AWS, they have extra properties
+that we aren't aware of locally. We need to refresh them by requesting them
+back again.
+
+>>> scale_up_policy = conn.get_all_policies(
+            as_group='my_group', policy_names=['scale_up'])[0]
+>>> scale_down_policy = conn.get_all_policies(
+            as_group='my_group', policy_names=['scale_down'])[0]
+
+Specifically, we'll need the Amazon Resource Name (ARN) of each policy, which
+will now be a property of our ScalingPolicy objects.
+
+Next we'll create CloudWatch alarms that will define when to run the
+Auto Scaling Policies.
+
+>>> cloudwatch = boto.connect_cloudwatch()
+
+It makes sense to measure the average CPU usage across the whole Auto Scaling
+Group, rather than individual instances. We express that as CloudWatch
+*Dimensions*.
+
+>>> alarm_dimensions = {"AutoScalingGroupName": 'my_group'}
+
+Create an alarm for when to scale up, and one for when to scale down.
+
+>>> from boto.ec2.cloudwatch import MetricAlarm
+>>> scale_up_alarm = MetricAlarm(
+            name='scale_up_on_cpu', namespace='AWS/EC2',
+            metric='CPUUtilization', statistic='Average',
+            comparison='>', threshold='70',
+            period='60', evaluation_periods=2,
+            alarm_actions=[scale_up_policy.policy_arn],
+            dimensions=alarm_dimensions)
+>>> cloudwatch.create_alarm(scale_up_alarm)
+
+>>> scale_down_alarm = MetricAlarm(
+            name='scale_down_on_cpu', namespace='AWS/EC2',
+            metric='CPUUtilization', statistic='Average',
+            comparison='<', threshold='40',
+            period='60', evaluation_periods=2,
+            alarm_actions=[scale_down_policy.policy_arn],
+            dimensions=alarm_dimensions)
+>>> cloudwatch.create_alarm(scale_down_alarm)
+
+Auto Scaling will now create a new instance if the existing cluster averages
+more than 70% CPU for two minutes. Similarly, it will terminate an instance
+when CPU usage sits below 40%. Auto Scaling will not add or remove instances
+beyond the limits of the Scaling Group's 'max_size' and 'min_size' properties.
+
+To retrieve the instances in your autoscale group:
+
+>>> ec2 = boto.connect_ec2()
+>>> conn.get_all_groups(names=['my_group'])[0]
+>>> instance_ids = [i.instance_id for i in group.instances]
+>>> reservations = ec2.get_all_instances(instance_ids)
+>>> instances = [i for i in reservations for i in r.instances]
+
+To delete your autoscale group, we first need to shutdown all the
+instances:
+
+>>> ag.shutdown_instances()
+
+Once the instances have been shutdown, you can delete the autoscale
+group:
+
+>>> ag.delete()
+
+You can also delete your launch configuration:
+
+>>> lc.delete()