1 |
¿Cómo se resuelven problemas de disponibilidad en AWS? |
Para resolver problemas de disponibilidad en AWS, se pueden usar servicios como Amazon CloudWatch para monitoreo y alertas, y AWS Elastic Load Balancing (ELB) para distribuir el tráfico. También se pueden utilizar Auto Scaling Groups para ajustar automáticamente la capacidad de instancias. |
// Java example of setting up CloudWatch alarm for EC2 instance
import com.amazonaws.services.cloudwatch.AmazonCloudWatch;
import com.amazonaws.services.cloudwatch.AmazonCloudWatchClientBuilder;
import com.amazonaws.services.cloudwatch.model.PutMetricAlarmRequest;
public class CloudWatchSetup {
public static void main(String[] args) {
AmazonCloudWatch cloudWatch = AmazonCloudWatchClientBuilder.defaultClient();
PutMetricAlarmRequest request = new PutMetricAlarmRequest()
.withAlarmName("HighCPUUsage")
.withMetricName("CPUUtilization")
.withNamespace("AWS/EC2")
.withStatistic("Average")
.withThreshold(80.0)
.withComparisonOperator("GreaterThanThreshold")
.withPeriod(300)
.withEvaluationPeriods(2);
cloudWatch.putMetricAlarm(request);
}
}
|
# Python example of setting up CloudWatch alarm for EC2 instance
import boto3
client = boto3.client('cloudwatch')
response = client.put_metric_alarm(
AlarmName='HighCPUUsage',
MetricName='CPUUtilization',
Namespace='AWS/EC2',
Statistic='Average',
Threshold=80.0,
ComparisonOperator='GreaterThanThreshold',
Period=300,
EvaluationPeriods=2
)
print(response)
|
// C# example of setting up CloudWatch alarm for EC2 instance
using Amazon.CloudWatch;
using Amazon.CloudWatch.Model;
using System.Threading.Tasks;
public class CloudWatchSetup
{
public static async Task Main(string[] args)
{
var client = new AmazonCloudWatchClient();
var request = new PutMetricAlarmRequest
{
AlarmName = "HighCPUUsage",
MetricName = "CPUUtilization",
Namespace = "AWS/EC2",
Statistic = "Average",
Threshold = 80.0,
ComparisonOperator = "GreaterThanThreshold",
Period = 300,
EvaluationPeriods = 2
};
var response = await client.PutMetricAlarmAsync(request);
Console.WriteLine(response);
}
}
|
// TypeScript example of setting up CloudWatch alarm for EC2 instance
import * as AWS from 'aws-sdk';
const cloudwatch = new AWS.CloudWatch();
const params = {
AlarmName: 'HighCPUUsage',
MetricName: 'CPUUtilization',
Namespace: 'AWS/EC2',
Statistic: 'Average',
Threshold: 80.0,
ComparisonOperator: 'GreaterThanThreshold',
Period: 300,
EvaluationPeriods: 2
};
cloudwatch.putMetricAlarm(params, (err, data) => {
if (err) console.error(err);
else console.log(data);
});
|
2 |
¿Cómo se manejan problemas de rendimiento en Azure? |
En Azure, los problemas de rendimiento se pueden manejar usando Azure Monitor para supervisar el rendimiento y configurar alertas. Azure Application Insights proporciona información detallada sobre la aplicación y su rendimiento. También se pueden ajustar los recursos de la aplicación mediante el escalado vertical u horizontal. |
// Java example of setting up Application Insights for performance monitoring
import com.microsoft.applicationinsights.TelemetryClient;
public class AppInsightsSetup {
public static void main(String[] args) {
TelemetryClient telemetryClient = new TelemetryClient();
telemetryClient.trackEvent("PerformanceMonitoring");
}
}
|
# Python example of setting up Application Insights for performance monitoring
from applicationinsights import TelemetryClient
client = TelemetryClient('')
client.track_event('PerformanceMonitoring')
client.flush()
|
// C# example of setting up Application Insights for performance monitoring
using Microsoft.ApplicationInsights;
using Microsoft.ApplicationInsights.Extensibility;
public class AppInsightsSetup
{
public static void Main(string[] args)
{
var telemetryClient = new TelemetryClient(new TelemetryConfiguration(""));
telemetryClient.TrackEvent("PerformanceMonitoring");
}
}
|
// TypeScript example of setting up Application Insights for performance monitoring
import * as appInsights from 'applicationinsights';
appInsights.setup('').start();
const client = appInsights.defaultClient;
client.trackEvent({ name: 'PerformanceMonitoring' });
|
3 |
¿Cómo se gestionan errores en Google Cloud? |
En Google Cloud, se pueden gestionar errores utilizando Stackdriver Logging para registrar y analizar los errores. Google Cloud Error Reporting identifica y agrupa los errores para proporcionar una visión clara del estado de la aplicación. También se pueden configurar alertas para monitorear los errores. |
// Java example of logging errors to Stackdriver
import com.google.cloud.logging.Logging;
import com.google.cloud.logging.LoggingOptions;
public class StackdriverLoggingSetup {
public static void main(String[] args) {
try (Logging logging = LoggingOptions.getDefaultInstance().getService()) {
logging.write(com.google.cloud.logging.LogEntry.newBuilder(com.google.cloud.logging.Payload.StringPayload.of("Error message"))
.setSeverity(com.google.cloud.logging.Severity.ERROR)
.build());
}
}
}
|
# Python example of logging errors to Stackdriver
from google.cloud import logging
client = logging.Client()
logger = client.logger('my-log')
logger.log_text('Error message', severity='ERROR')
|
// C# example of logging errors to Stackdriver
using Google.Cloud.Logging.V2;
using Google.Cloud.Logging.Type;
using System;
public class StackdriverLoggingSetup
{
public static void Main(string[] args)
{
var client = LoggingServiceV2Client.Create();
var logEntry = new LogEntry
{
LogName = "projects//logs/my-log",
Severity = LogSeverity.Error,
TextPayload = "Error message"
};
client.WriteLogEntries(logName: "projects//logs/my-log", entries: new[] { logEntry });
}
}
|
// TypeScript example of logging errors to Stackdriver
import { Logging } from '@google-cloud/logging';
const logging = new Logging();
const log = logging.log('my-log');
const metadata = { severity: 'ERROR' };
const entry = log.entry(metadata, 'Error message');
log.write(entry)
.then(() => {
console.log('Logged error message');
})
.catch(err => {
console.error('Error logging message:', err);
});
|
4 |
¿Cómo se gestionan fallos en los despliegues en AWS? |
Para gestionar fallos en despliegues en AWS, se pueden utilizar AWS CodeDeploy para automatizar el proceso de despliegue y manejar errores mediante estrategias de despliegue. También se pueden configurar rollbacks automáticos y utilizar AWS CloudFormation para gestionar los despliegues y evitar errores. |
// Java example of setting up a rollback in AWS CodeDeploy
import com.amazonaws.services.codedeploy.AmazonCodeDeploy;
import com.amazonaws.services.codedeploy.AmazonCodeDeployClientBuilder;
import com.amazonaws.services.codedeploy.model.CreateDeploymentRequest;
public class CodeDeployRollback {
public static void main(String[] args) {
AmazonCodeDeploy client = AmazonCodeDeployClientBuilder.defaultClient();
CreateDeploymentRequest request = new CreateDeploymentRequest()
.withApplicationName("my-application")
.withDeploymentGroupName("my-deployment-group")
.withRevision(new RevisionLocation().withRevisionType("S3"));
client.createDeployment(request);
}
}
|
# Python example of setting up a rollback in AWS CodeDeploy
import boto3
client = boto3.client('codedeploy')
response = client.create_deployment(
applicationName='my-application',
deploymentGroupName='my-deployment-group',
revision={
'revisionType': 'S3'
}
)
print(response)
|
// C# example of setting up a rollback in AWS CodeDeploy
using Amazon.CodeDeploy;
using Amazon.CodeDeploy.Model;
using System.Threading.Tasks;
public class CodeDeployRollback
{
public static async Task Main(string[] args)
{
var client = new AmazonCodeDeployClient();
var request = new CreateDeploymentRequest
{
ApplicationName = "my-application",
DeploymentGroupName = "my-deployment-group",
Revision = new RevisionLocation { RevisionType = "S3" }
};
var response = await client.CreateDeploymentAsync(request);
Console.WriteLine(response);
}
}
|
// TypeScript example of setting up a rollback in AWS CodeDeploy
import * as AWS from 'aws-sdk';
const codedeploy = new AWS.CodeDeploy();
const params = {
applicationName: 'my-application',
deploymentGroupName: 'my-deployment-group',
revision: {
revisionType: 'S3'
}
};
codedeploy.createDeployment(params, (err, data) => {
if (err) console.error(err);
else console.log(data);
});
|
5 |
¿Cómo se implementa el monitoreo proactivo en Azure? |
En Azure, el monitoreo proactivo se puede implementar usando Azure Monitor para recopilar y analizar métricas y registros. Azure Application Insights proporciona información detallada sobre el rendimiento de la aplicación y los problemas potenciales. Además, se pueden configurar alertas y dashboards personalizados para supervisar el estado de los recursos. |
// Java example of setting up a dashboard in Azure Monitor
import com.microsoft.azure.management.monitor.MonitorManager;
import com.microsoft.azure.management.monitor.Dashboard;
import com.microsoft.azure.management.monitor.DashboardItem;
public class AzureMonitorSetup {
public static void main(String[] args) {
MonitorManager monitorManager = MonitorManager.authenticate(...);
Dashboard dashboard = monitorManager.dashboards().define("my-dashboard")
.withRegion("eastus")
.withNewResourceGroup("my-resource-group")
.withDashboardItem(new DashboardItem("metric", "my-metric"))
.create();
}
}
|
# Python example of setting up a dashboard in Azure Monitor
from azure.mgmt.monitor import MonitorManagementClient
from azure.identity import DefaultAzureCredential
client = MonitorManagementClient(credential=DefaultAzureCredential(), subscription_id='')
dashboard = client.dashboards.begin_create_or_update(
resource_group_name='my-resource-group',
dashboard_name='my-dashboard',
parameters={
'location': 'eastus',
'properties': {
'lenses': {
'0': {
'parts': {
'0': {
'position': { 'x': 0, 'y': 0, 'z': 0 },
'size': { 'height': 6, 'width': 6 },
'type': 'metrics',
'settings': { 'metric': 'my-metric' }
}
}
}
}
}
}
)
print(dashboard.result())
|
// C# example of setting up a dashboard in Azure Monitor
using Azure.Identity;
using Azure.ResourceManager;
using Azure.ResourceManager.Monitor;
public class AzureMonitorSetup
{
public static async Task Main(string[] args)
{
var credential = new DefaultAzureCredential();
var armClient = new ArmClient(credential);
var monitorClient = armClient.GetMonitorManager();
var dashboard = await monitorClient.Dashboards.CreateOrUpdateAsync(
resourceGroupName: "my-resource-group",
dashboardName: "my-dashboard",
parameters: new Dashboard
{
Location = "eastus",
Properties = new DashboardProperties
{
Lenses = new Dictionary
{
{ "0", new Lens
{
Parts = new Dictionary
{
{ "0", new Part
{
Position = new Position { X = 0, Y = 0, Z = 0 },
Size = new Size { Height = 6, Width = 6 },
Type = "metrics",
Settings = new Settings { Metric = "my-metric" }
}
}
}
}
}
}
}
}
);
Console.WriteLine(dashboard);
}
}
|
// TypeScript example of setting up a dashboard in Azure Monitor
import * as azure from '@azure/arm-monitor';
import { DefaultAzureCredential } from '@azure/identity';
const credential = new DefaultAzureCredential();
const client = new azure.MonitorManagementClient(credential, '');
const dashboard = {
location: 'eastus',
properties: {
lenses: {
'0': {
parts: {
'0': {
position: { x: 0, y: 0, z: 0 },
size: { height: 6, width: 6 },
type: 'metrics',
settings: { metric: 'my-metric' }
}
}
}
}
}
};
client.dashboards.createOrUpdate('my-resource-group', 'my-dashboard', dashboard)
.then(result => {
console.log(result);
})
.catch(err => {
console.error('Error creating dashboard:', err);
});
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