Chilkat Tools

GetLambdaFunctionRecommendations 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("filters[0].name","string")
json.UpdateString("filters[0].values[0]","string")
json.UpdateString("functionArns[0]","string")
json.UpdateInt("maxResults",123)
json.UpdateString("nextToken","string")

# The JSON request body created by the above code:

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

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

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("lambdaFunctionRecommendations")
while i < count_i :
    jResp.put_I(i)
    accountId = jResp.stringOf("lambdaFunctionRecommendations[i].accountId")
    currentMemorySize = jResp.IntOf("lambdaFunctionRecommendations[i].currentMemorySize")
    currentPerformanceRisk = jResp.stringOf("lambdaFunctionRecommendations[i].currentPerformanceRisk")
    finding = jResp.stringOf("lambdaFunctionRecommendations[i].finding")
    functionArn = jResp.stringOf("lambdaFunctionRecommendations[i].functionArn")
    functionVersion = jResp.stringOf("lambdaFunctionRecommendations[i].functionVersion")
    lastRefreshTimestamp = jResp.IntOf("lambdaFunctionRecommendations[i].lastRefreshTimestamp")
    lookbackPeriodInDays = jResp.IntOf("lambdaFunctionRecommendations[i].lookbackPeriodInDays")
    numberOfInvocations = jResp.IntOf("lambdaFunctionRecommendations[i].numberOfInvocations")
    j = 0
    count_j = jResp.SizeOfArray("lambdaFunctionRecommendations[i].findingReasonCodes")
    while j < count_j :
        jResp.put_J(j)
        strVal = jResp.stringOf("lambdaFunctionRecommendations[i].findingReasonCodes[j]")
        j = j + 1

    j = 0
    count_j = jResp.SizeOfArray("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions")
    while j < count_j :
        jResp.put_J(j)
        memorySize = jResp.IntOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].memorySize")
        rank = jResp.IntOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].rank")
        v_Currency = jResp.stringOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.currency")
        Value = jResp.IntOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.value")
        SavingsOpportunityPercentage = jResp.IntOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].savingsOpportunity.savingsOpportunityPercentage")
        k = 0
        count_k = jResp.SizeOfArray("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].projectedUtilizationMetrics")
        while k < count_k :
            jResp.put_K(k)
            name = jResp.stringOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].projectedUtilizationMetrics[k].name")
            statistic = jResp.stringOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].projectedUtilizationMetrics[k].statistic")
            value = jResp.IntOf("lambdaFunctionRecommendations[i].memorySizeRecommendationOptions[j].projectedUtilizationMetrics[k].value")
            k = k + 1

        j = j + 1

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

    i = i + 1

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

# {
#   "lambdaFunctionRecommendations": [
#     {
#       "accountId": "string",
#       "currentMemorySize": number,
#       "currentPerformanceRisk": "string",
#       "finding": "string",
#       "findingReasonCodes": [
#         "string"
#       ],
#       "functionArn": "string",
#       "functionVersion": "string",
#       "lastRefreshTimestamp": number,
#       "lookbackPeriodInDays": number,
#       "memorySizeRecommendationOptions": [
#         {
#           "memorySize": number,
#           "projectedUtilizationMetrics": [
#             {
#               "name": "string",
#               "statistic": "string",
#               "value": number
#             }
#           ],
#           "rank": number,
#           "savingsOpportunity": {
#             "estimatedMonthlySavings": {
#               "currency": "string",
#               "value": number
#             },
#             "savingsOpportunityPercentage": number
#           }
#         }
#       ],
#       "numberOfInvocations": number,
#       "utilizationMetrics": [
#         {
#           "name": "string",
#           "statistic": "string",
#           "value": number
#         }
#       ]
#     }
#   ],
#   "nextToken": "string"
# }
Chilkat Online Tools

GetLambdaFunctionRecommendations Python Example

AWS Compute Optimizer
