Chilkat Tools

GetAutoScalingGroupRecommendations Python Example

AWS Compute Optimizer

import sys
import chilkat

# This example requires the Chilkat API to have been previously unlocked.
# See Global Unlock Sample for sample code.

rest = chilkat.CkRest()

authAws = chilkat.CkAuthAws()
authAws.put_AccessKey("AWS_ACCESS_KEY")
authAws.put_SecretKey("AWS_SECRET_KEY")

# Don't forget to change the region to your particular region. (Also make the same change in the call to Connect below.)
authAws.put_Region("us-west-2")
authAws.put_ServiceName("compute-optimizer")
# SetAuthAws causes Chilkat to automatically add the following headers: Authorization, X-Amz-Date
rest.SetAuthAws(authAws)

# URL: https://compute-optimizer.us-west-2.amazonaws.com/
# Use the same region as specified above.
success = rest.Connect("compute-optimizer.us-west-2.amazonaws.com",443,True,True)
if (success != True):
    print("ConnectFailReason: " + str(rest.get_ConnectFailReason()))
    print(rest.lastErrorText())
    sys.exit()

# The following code creates the JSON request body.
# The JSON created by this code is shown below.

# Use this online tool to generate code from sample JSON:
# Generate Code to Create JSON

json = chilkat.CkJsonObject()
json.UpdateString("accountIds[0]","string")
json.UpdateString("autoScalingGroupArns[0]","string")
json.UpdateString("filters[0].name","string")
json.UpdateString("filters[0].values[0]","string")
json.UpdateInt("maxResults",123)
json.UpdateString("nextToken","string")
json.UpdateString("recommendationPreferences.cpuVendorArchitectures[0]","string")

# The JSON request body created by the above code:

# {
#   "accountIds": [
#     "string"
#   ],
#   "autoScalingGroupArns": [
#     "string"
#   ],
#   "filters": [
#     {
#       "name": "string",
#       "values": [
#         "string"
#       ]
#     }
#   ],
#   "maxResults": number,
#   "nextToken": "string",
#   "recommendationPreferences": {
#     "cpuVendorArchitectures": [
#       "string"
#     ]
#   }
# }

rest.AddHeader("Content-Type","application/x-amz-json-1.0")
rest.AddHeader("X-Amz-Target","ComputeOptimizerService.GetAutoScalingGroupRecommendations")

sbRequestBody = chilkat.CkStringBuilder()
json.EmitSb(sbRequestBody)
sbResponseBody = chilkat.CkStringBuilder()
success = rest.FullRequestSb("POST","/",sbRequestBody,sbResponseBody)
if (success != True):
    print(rest.lastErrorText())
    sys.exit()

respStatusCode = rest.get_ResponseStatusCode()
print("response status code = " + str(respStatusCode))
if (respStatusCode != 200):
    print("Response Header:")
    print(rest.responseHeader())
    print("Response Body:")
    print(sbResponseBody.getAsString())
    sys.exit()

jResp = chilkat.CkJsonObject()
jResp.LoadSb(sbResponseBody)

# The following code parses the JSON response.
# A sample JSON response is shown below the sample code.

# Use this online tool to generate parsing code from sample JSON:
# Generate Parsing Code from JSON

nextToken = jResp.stringOf("nextToken")
i = 0
count_i = jResp.SizeOfArray("autoScalingGroupRecommendations")
while i < count_i :
    jResp.put_I(i)
    accountId = jResp.stringOf("autoScalingGroupRecommendations[i].accountId")
    autoScalingGroupArn = jResp.stringOf("autoScalingGroupRecommendations[i].autoScalingGroupArn")
    autoScalingGroupName = jResp.stringOf("autoScalingGroupRecommendations[i].autoScalingGroupName")
    DesiredCapacity = jResp.IntOf("autoScalingGroupRecommendations[i].currentConfiguration.desiredCapacity")
    InstanceType = jResp.stringOf("autoScalingGroupRecommendations[i].currentConfiguration.instanceType")
    MaxSize = jResp.IntOf("autoScalingGroupRecommendations[i].currentConfiguration.maxSize")
    MinSize = jResp.IntOf("autoScalingGroupRecommendations[i].currentConfiguration.minSize")
    currentPerformanceRisk = jResp.stringOf("autoScalingGroupRecommendations[i].currentPerformanceRisk")
    EnhancedInfrastructureMetrics = jResp.stringOf("autoScalingGroupRecommendations[i].effectiveRecommendationPreferences.enhancedInfrastructureMetrics")
    InferredWorkloadTypes = jResp.stringOf("autoScalingGroupRecommendations[i].effectiveRecommendationPreferences.inferredWorkloadTypes")
    finding = jResp.stringOf("autoScalingGroupRecommendations[i].finding")
    lastRefreshTimestamp = jResp.IntOf("autoScalingGroupRecommendations[i].lastRefreshTimestamp")
    lookBackPeriodInDays = jResp.IntOf("autoScalingGroupRecommendations[i].lookBackPeriodInDays")
    j = 0
    count_j = jResp.SizeOfArray("autoScalingGroupRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures")
    while j < count_j :
        jResp.put_J(j)
        strVal = jResp.stringOf("autoScalingGroupRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures[j]")
        j = j + 1

    j = 0
    count_j = jResp.SizeOfArray("autoScalingGroupRecommendations[i].inferredWorkloadTypes")
    while j < count_j :
        jResp.put_J(j)
        strVal = jResp.stringOf("autoScalingGroupRecommendations[i].inferredWorkloadTypes[j]")
        j = j + 1

    j = 0
    count_j = jResp.SizeOfArray("autoScalingGroupRecommendations[i].recommendationOptions")
    while j < count_j :
        jResp.put_J(j)
        configurationDesiredCapacity = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].configuration.desiredCapacity")
        configurationInstanceType = jResp.stringOf("autoScalingGroupRecommendations[i].recommendationOptions[j].configuration.instanceType")
        configurationMaxSize = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].configuration.maxSize")
        configurationMinSize = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].configuration.minSize")
        migrationEffort = jResp.stringOf("autoScalingGroupRecommendations[i].recommendationOptions[j].migrationEffort")
        performanceRisk = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].performanceRisk")
        rank = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].rank")
        v_Currency = jResp.stringOf("autoScalingGroupRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.currency")
        Value = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.value")
        SavingsOpportunityPercentage = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].savingsOpportunity.savingsOpportunityPercentage")
        k = 0
        count_k = jResp.SizeOfArray("autoScalingGroupRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics")
        while k < count_k :
            jResp.put_K(k)
            name = jResp.stringOf("autoScalingGroupRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].name")
            statistic = jResp.stringOf("autoScalingGroupRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].statistic")
            value = jResp.IntOf("autoScalingGroupRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].value")
            k = k + 1

        j = j + 1

    j = 0
    count_j = jResp.SizeOfArray("autoScalingGroupRecommendations[i].utilizationMetrics")
    while j < count_j :
        jResp.put_J(j)
        name = jResp.stringOf("autoScalingGroupRecommendations[i].utilizationMetrics[j].name")
        statistic = jResp.stringOf("autoScalingGroupRecommendations[i].utilizationMetrics[j].statistic")
        value = jResp.IntOf("autoScalingGroupRecommendations[i].utilizationMetrics[j].value")
        j = j + 1

    i = i + 1

i = 0
count_i = jResp.SizeOfArray("errors")
while i < count_i :
    jResp.put_I(i)
    code = jResp.stringOf("errors[i].code")
    identifier = jResp.stringOf("errors[i].identifier")
    message = jResp.stringOf("errors[i].message")
    i = i + 1

# A sample JSON response body parsed by the above code:

# {
#   "autoScalingGroupRecommendations": [
#     {
#       "accountId": "string",
#       "autoScalingGroupArn": "string",
#       "autoScalingGroupName": "string",
#       "currentConfiguration": {
#         "desiredCapacity": number,
#         "instanceType": "string",
#         "maxSize": number,
#         "minSize": number
#       },
#       "currentPerformanceRisk": "string",
#       "effectiveRecommendationPreferences": {
#         "cpuVendorArchitectures": [
#           "string"
#         ],
#         "enhancedInfrastructureMetrics": "string",
#         "inferredWorkloadTypes": "string"
#       },
#       "finding": "string",
#       "inferredWorkloadTypes": [
#         "string"
#       ],
#       "lastRefreshTimestamp": number,
#       "lookBackPeriodInDays": number,
#       "recommendationOptions": [
#         {
#           "configuration": {
#             "desiredCapacity": number,
#             "instanceType": "string",
#             "maxSize": number,
#             "minSize": number
#           },
#           "migrationEffort": "string",
#           "performanceRisk": number,
#           "projectedUtilizationMetrics": [
#             {
#               "name": "string",
#               "statistic": "string",
#               "value": number
#             }
#           ],
#           "rank": number,
#           "savingsOpportunity": {
#             "estimatedMonthlySavings": {
#               "currency": "string",
#               "value": number
#             },
#             "savingsOpportunityPercentage": number
#           }
#         }
#       ],
#       "utilizationMetrics": [
#         {
#           "name": "string",
#           "statistic": "string",
#           "value": number
#         }
#       ]
#     }
#   ],
#   "errors": [
#     {
#       "code": "string",
#       "identifier": "string",
#       "message": "string"
#     }
#   ],
#   "nextToken": "string"
# }
Chilkat Online Tools

GetAutoScalingGroupRecommendations Python Example

AWS Compute Optimizer
