GetEC2InstanceRecommendations Swift3 Example
func chilkatTest() {
// This example requires the Chilkat API to have been previously unlocked.
// See Global Unlock Sample for sample code.
let rest = CkoRest()!
var success: Bool
let authAws = CkoAuthAws()!
authAws.accessKey = "AWS_ACCESS_KEY"
authAws.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.region = "us-west-2"
authAws.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", port: 443, tls: true, autoReconnect: true)
if success != true {
print("ConnectFailReason: \(rest.connectFailReason.intValue)")
print("\(rest.lastErrorText!)")
return
}
// 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
let json = CkoJsonObject()!
json.update("accountIds[0]", value: "string")
json.update("filters[0].name", value: "string")
json.update("filters[0].values[0]", value: "string")
json.update("instanceArns[0]", value: "string")
json.updateInt("maxResults", value: 123)
json.update("nextToken", value: "string")
json.update("recommendationPreferences.cpuVendorArchitectures[0]", value: "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", value: "application/x-amz-json-1.0")
rest.addHeader("X-Amz-Target", value: "ComputeOptimizerService.GetEC2InstanceRecommendations")
let sbRequestBody = CkoStringBuilder()!
json.emitSb(sbRequestBody)
let sbResponseBody = CkoStringBuilder()!
success = rest.fullRequestSb("POST", uriPath: "/", requestBody: sbRequestBody, responseBody: sbResponseBody)
if success != true {
print("\(rest.lastErrorText!)")
return
}
var respStatusCode: Int = rest.responseStatusCode.intValue
print("response status code = \(respStatusCode)")
if respStatusCode != 200 {
print("Response Header:")
print("\(rest.responseHeader!)")
print("Response Body:")
print("\(sbResponseBody.getAsString()!)")
return
}
let jResp = CkoJsonObject()!
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
var code: String?
var identifier: String?
var message: String?
var accountId: String?
var currentInstanceType: String?
var currentPerformanceRisk: String?
var EnhancedInfrastructureMetrics: String?
var InferredWorkloadTypes: String?
var finding: String?
var instanceArn: String?
var instanceName: String?
var lastRefreshTimestamp: Int
var lookBackPeriodInDays: Int
var j: Int
var count_j: Int
var strVal: String?
var instanceType: String?
var migrationEffort: String?
var performanceRisk: Int
var rank: Int
var v_Currency: String?
var Value: Int
var SavingsOpportunityPercentage: Int
var k: Int
var count_k: Int
var name: String?
var statistic: String?
var value: Int
var recommendationSourceArn: String?
var recommendationSourceType: String?
var nextToken: String? = jResp.string(of: "nextToken")
var i: Int = 0
var count_i: Int = jResp.size(ofArray: "errors").intValue
while i < count_i {
jResp.i = i
code = jResp.string(of: "errors[i].code")
identifier = jResp.string(of: "errors[i].identifier")
message = jResp.string(of: "errors[i].message")
i = i + 1
}
i = 0
count_i = jResp.size(ofArray: "instanceRecommendations").intValue
while i < count_i {
jResp.i = i
accountId = jResp.string(of: "instanceRecommendations[i].accountId")
currentInstanceType = jResp.string(of: "instanceRecommendations[i].currentInstanceType")
currentPerformanceRisk = jResp.string(of: "instanceRecommendations[i].currentPerformanceRisk")
EnhancedInfrastructureMetrics = jResp.string(of: "instanceRecommendations[i].effectiveRecommendationPreferences.enhancedInfrastructureMetrics")
InferredWorkloadTypes = jResp.string(of: "instanceRecommendations[i].effectiveRecommendationPreferences.inferredWorkloadTypes")
finding = jResp.string(of: "instanceRecommendations[i].finding")
instanceArn = jResp.string(of: "instanceRecommendations[i].instanceArn")
instanceName = jResp.string(of: "instanceRecommendations[i].instanceName")
lastRefreshTimestamp = jResp.int(of: "instanceRecommendations[i].lastRefreshTimestamp").intValue
lookBackPeriodInDays = jResp.int(of: "instanceRecommendations[i].lookBackPeriodInDays").intValue
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures").intValue
while j < count_j {
jResp.j = j
strVal = jResp.string(of: "instanceRecommendations[i].effectiveRecommendationPreferences.cpuVendorArchitectures[j]")
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].findingReasonCodes").intValue
while j < count_j {
jResp.j = j
strVal = jResp.string(of: "instanceRecommendations[i].findingReasonCodes[j]")
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].inferredWorkloadTypes").intValue
while j < count_j {
jResp.j = j
strVal = jResp.string(of: "instanceRecommendations[i].inferredWorkloadTypes[j]")
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].recommendationOptions").intValue
while j < count_j {
jResp.j = j
instanceType = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].instanceType")
migrationEffort = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].migrationEffort")
performanceRisk = jResp.int(of: "instanceRecommendations[i].recommendationOptions[j].performanceRisk").intValue
rank = jResp.int(of: "instanceRecommendations[i].recommendationOptions[j].rank").intValue
v_Currency = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.currency")
Value = jResp.int(of: "instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.estimatedMonthlySavings.value").intValue
SavingsOpportunityPercentage = jResp.int(of: "instanceRecommendations[i].recommendationOptions[j].savingsOpportunity.savingsOpportunityPercentage").intValue
k = 0
count_k = jResp.size(ofArray: "instanceRecommendations[i].recommendationOptions[j].platformDifferences").intValue
while k < count_k {
jResp.k = k
strVal = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].platformDifferences[k]")
k = k + 1
}
k = 0
count_k = jResp.size(ofArray: "instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics").intValue
while k < count_k {
jResp.k = k
name = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].name")
statistic = jResp.string(of: "instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].statistic")
value = jResp.int(of: "instanceRecommendations[i].recommendationOptions[j].projectedUtilizationMetrics[k].value").intValue
k = k + 1
}
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].recommendationSources").intValue
while j < count_j {
jResp.j = j
recommendationSourceArn = jResp.string(of: "instanceRecommendations[i].recommendationSources[j].recommendationSourceArn")
recommendationSourceType = jResp.string(of: "instanceRecommendations[i].recommendationSources[j].recommendationSourceType")
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "instanceRecommendations[i].utilizationMetrics").intValue
while j < count_j {
jResp.j = j
name = jResp.string(of: "instanceRecommendations[i].utilizationMetrics[j].name")
statistic = jResp.string(of: "instanceRecommendations[i].utilizationMetrics[j].statistic")
value = jResp.int(of: "instanceRecommendations[i].utilizationMetrics[j].value").intValue
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"
// }
}
