event_handler.register(

'building-argument-table.cloudwatch.put-metric-data', _promote_args)

event_handler.register(

'operation-args-parsed.cloudwatch.put-metric-data',

validate_mutually_exclusive_handler(

['metric_data'], ['metric_name', 'timestamp', 'unit', 'value',

# We're providing top level params for metric-data. This means

# that metric-data is now longer a required arg. We do need

# to check that either metric-data or the complex args we've added

# have been provided.

argument_table['metric-data'].required = False

argument_table['metric-name'] = PutMetricArgument(

'metric-name', help_text='The name of the metric.')

argument_table['timestamp'] = PutMetricArgument(

'timestamp', help_text='The time stamp used for the metric. '

'If not specified, the default value is '

'set to the time the metric data was '

'received.')

argument_table['unit'] = PutMetricArgument(

'unit', help_text='The unit of metric.')

argument_table['value'] = PutMetricArgument(

'value', help_text='The value for the metric. Although the --value '

'parameter accepts numbers of type Double, '

'Amazon CloudWatch truncates values with very '

'large exponents. Values with base-10 exponents '

'greater than 126 (1 x 10^126) are truncated. '

'Likewise, values with base-10 exponents less '

'than -130 (1 x 10^-130) are also truncated.')

argument_table['dimensions'] = PutMetricArgument(

'dimensions', help_text=(

'The --dimensions argument further expands '

'on the identity of a metric using a Name=Value '

'pair, separated by commas, for example: '

'<code>--dimensions InstanceID=1-23456789,InstanceType=m1.small'

'</code>. Note that the <code>--dimensions</code> argument has a '

'different format when used in <code>get-metric-data</code>, '

'where for the same example you would use the format '

'<code>--dimensions Name=InstanceID,Value=i-aaba32d4 '

'Name=InstanceType,value=m1.small </code>.'

)

)

argument_table['statistic-values'] = PutMetricArgument(

'statistic-values', help_text='A set of statistical values describing '

'the metric.')

metric_data = operation_model.input_shape.members['MetricData'].member

storage_resolution = metric_data.members['StorageResolution']

argument_table['storage-resolution'] = PutMetricArgument(

'storage-resolution', help_text=storage_resolution.documentation

def _wrap_add_to_params(func):

def _add_to_params(self, parameters, value):

if value is None:

return

if name not in parameters:

# We're taking a shortcut here and assuming that the first

# element is a struct type, hence the default value of

# a dict. If this was going to be more general we'd need

# to have this parameterized, i.e. you pass in some sort of

# factory function that creates the initial starting value.

parameters[name] = [{}]

first_element = parameters[name][0]

return func(self, first_element, value)

return _add_to_params

def add_to_params(self, parameters, value):

method_name = '_add_param_%s' % self.name.replace('-', '_')

return getattr(self, method_name)(parameters, value)

@insert_first_element('MetricData')

def _add_param_metric_name(self, first_element, value):

first_element['MetricName'] = value

@insert_first_element('MetricData')

def _add_param_unit(self, first_element, value):

first_element['Unit'] = value

@insert_first_element('MetricData')

def _add_param_timestamp(self, first_element, value):

first_element['Timestamp'] = value

@insert_first_element('MetricData')

def _add_param_value(self, first_element, value):

# Use a Decimal to avoid loss in precision.

first_element['Value'] = decimal.Decimal(value)

@insert_first_element('MetricData')

def _add_param_dimensions(self, first_element, value):

# Dimensions needs a little more processing. We support

# the key=value,key2=value syntax so we need to parse

# that.

dimensions = []

for pair in split_on_commas(value):

key, value = pair.split('=')

dimensions.append({'Name': key, 'Value': value})

first_element['Dimensions'] = dimensions

@insert_first_element('MetricData')

def _add_param_statistic_values(self, first_element, value):

# StatisticValues is a struct type so we are parsing

# a csv keyval list into a dict.

statistics = {}

for pair in split_on_commas(value):

key, value = pair.split('=')

# There are four supported values: Maximum, Minimum, SampleCount,

# and Sum. All of them are documented as a type double so we can

# convert these to a decimal value to preserve precision.

statistics[key] = decimal.Decimal(value)

first_element['StatisticValues'] = statistics

@insert_first_element('MetricData')

def _add_param_storage_resolution(self, first_element, value):