Chilkat Online Tools

GetEC2InstanceRecommendations Swift3 Example

AWS Compute Optimizer

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"
    // }

}