GetEC2InstanceRecommendations Python Example
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("filters[0].name","string")
json.UpdateString("filters[0].values[0]","string")
json.UpdateString("instanceArns[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"
# ],
# "filters": [
# {
# "name": "string",
# "values": [
# "string"
# ]
# }
# ],
# "instanceArns": [
# "string"
# ],
# "maxResults": number,
# "nextToken": "string",
# "recommendationPreferences": {
# "cpuVendorArchitectures": [
# "string"
# ]
# }
# }
rest.AddHeader("Content-Type","application/x-amz-json-1.0")
rest.AddHeader("X-Amz-Target","ComputeOptimizerService.GetEC2InstanceRecommendations")
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("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
i = 0
count_i = jResp.SizeOfArray("instanceRecommendations")
while i < count_i :
jResp.put_I(i)
accountId = jResp.stringOf("instanceRecommendations[i].accountId")
currentInstanceType = jResp.stringOf("instanceRecommendations[i].currentInstanceType")
currentPerformanceRisk = jResp.stringOf("instanceRecommendations[i].currentPerformanceRisk")
EnhancedInfrastructureMetrics = jResp.stringOf("instanceRecommendations[i].effectiveRecommendationPreferences.enhancedInfrastructureMetrics")
InferredWorkloadTypes = jResp.stringOf("instanceRecommendations[i].effectiveRecommendationPreferences.inferredWorkloadTypes")
finding = jResp.stringOf("instanceRecommendations[i].finding")
instanceArn = jResp.stringOf("instanceRecommendations[i].instanceArn")
instanceName = jResp.stringOf("instanceRecommendations[i].instanceName")
lastRefreshTimestamp = jResp.IntOf("instanceRecommendations[i].lastRefreshTimestamp")
lookBackPeriodInDays = jResp.IntOf("instanceRecommendations[i].lookBackPeriodInDays")
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures")
while j < count_j :
jResp.put_J(j)
strVal = jResp.stringOf("instanceRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures[j]")
j = j + 1
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].findingReasonCodes")
while j < count_j :
jResp.put_J(j)
strVal = jResp.stringOf("instanceRecommendations[i].findingReasonCodes[j]")
j = j + 1
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].inferredWorkloadTypes")
while j < count_j :
jResp.put_J(j)
strVal = jResp.stringOf("instanceRecommendations[i].inferredWorkloadTypes[j]")
j = j + 1
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].recommendationOptions")
while j < count_j :
jResp.put_J(j)
instanceType = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].instanceType")
migrationEffort = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].migrationEffort")
performanceRisk = jResp.IntOf("instanceRecommendations[i].recommendationOptions[j].performanceRisk")
rank = jResp.IntOf("instanceRecommendations[i].recommendationOptions[j].rank")
v_Currency = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.currency")
Value = jResp.IntOf("instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.value")
SavingsOpportunityPercentage = jResp.IntOf("instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.savingsOpportunityPercentage")
k = 0
count_k = jResp.SizeOfArray("instanceRecommendations[i].recommendationOptions[j].platformDifferences")
while k < count_k :
jResp.put_K(k)
strVal = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].platformDifferences[k]")
k = k + 1
k = 0
count_k = jResp.SizeOfArray("instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics")
while k < count_k :
jResp.put_K(k)
name = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].name")
statistic = jResp.stringOf("instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].statistic")
value = jResp.IntOf("instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].value")
k = k + 1
j = j + 1
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].recommendationSources")
while j < count_j :
jResp.put_J(j)
recommendationSourceArn = jResp.stringOf("instanceRecommendations[i].recommendationSources[j].recommendationSourceArn")
recommendationSourceType = jResp.stringOf("instanceRecommendations[i].recommendationSources[j].recommendationSourceType")
j = j + 1
j = 0
count_j = jResp.SizeOfArray("instanceRecommendations[i].utilizationMetrics")
while j < count_j :
jResp.put_J(j)
name = jResp.stringOf("instanceRecommendations[i].utilizationMetrics[j].name")
statistic = jResp.stringOf("instanceRecommendations[i].utilizationMetrics[j].statistic")
value = jResp.IntOf("instanceRecommendations[i].utilizationMetrics[j].value")
j = j + 1
i = i + 1
# A sample JSON response body parsed by the above code:
# {
# "errors": [
# {
# "code": "string",
# "identifier": "string",
# "message": "string"
# }
# ],
# "instanceRecommendations": [
# {
# "accountId": "string",
# "currentInstanceType": "string",
# "currentPerformanceRisk": "string",
# "effectiveRecommendationPreferences": {
# "cpuVendorArchitectures": [
# "string"
# ],
# "enhancedInfrastructureMetrics": "string",
# "inferredWorkloadTypes": "string"
# },
# "finding": "string",
# "findingReasonCodes": [
# "string"
# ],
# "inferredWorkloadTypes": [
# "string"
# ],
# "instanceArn": "string",
# "instanceName": "string",
# "lastRefreshTimestamp": number,
# "lookBackPeriodInDays": number,
# "recommendationOptions": [
# {
# "instanceType": "string",
# "migrationEffort": "string",
# "performanceRisk": number,
# "platformDifferences": [
# "string"
# ],
# "projectedUtilizationMetrics": [
# {
# "name": "string",
# "statistic": "string",
# "value": number
# }
# ],
# "rank": number,
# "savingsOpportunity": {
# "estimatedMonthlySavings": {
# "currency": "string",
# "value": number
# },
# "savingsOpportunityPercentage": number
# }
# }
# ],
# "recommendationSources": [
# {
# "recommendationSourceArn": "string",
# "recommendationSourceType": "string"
# }
# ],
# "utilizationMetrics": [
# {
# "name": "string",
# "statistic": "string",
# "value": number
# }
# ]
# }
# ],
# "nextToken": "string"
# }
