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Efficiently build and run workloads, keeping applications performant and available. Cloud Logging is a fully managed service that performs at scale and can ingest application and system log data, as well as custom log data from thousands of VMs. Cloud Logging allows you to analyze and export selected logs to long-term storage in real time.
Cloud Monitoring provides visibility into the performance, uptime, and overall health of cloud-powered applications. Collect metrics, events, and metadata from Google Cloud services, hosted uptime probes, application instrumentation, and a variety of common application components. Application Performance Management APM includes tools to help you reduce latency and cost, so you can run more efficient applications.
With Cloud TraceCloud Debuggerand Cloud Profileryou gain insight into how your code and services are functioning and troubleshoot if needed. View all features. Centralize your logging and operations. Build observability into applications and infrastructure. Reduce latency and inefficiency with Application Performance Management.
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Reduced time spent on infrastructure management using Cloud Monitoring. Increased productivity by managing all operations products in one centralized platform. Guides and set-up docs to help you get up and running with Cloud Logging. Learn how Cloud Audit Logs maintains three audit logs: admin activity, data access, and system event. Learn about Workspaces, monitoring agent, uptime checks, and other features.
How to collect Google Compute Engine metrics
All operations products documentation. Learn the ins and outs of application monitoring, report logging, and diagnoses. Build observability into your platform through the use of integrated logging, monitoring, and application performance management tools. Ingest logs from Google Cloud services and external sources for short-term operations and long-term log analysis. Use integrated audit logging to perform detailed forensic analysis. Integrate with your third-party logging systems using real-time log exports.
Cloud Logging provides an operational datastore for logs and rich export capabilities. Export logs for long-term storage, to meet compliance requirements, or to run data analytics. Cloud Logging and Cloud Monitoring provide advanced logging and monitoring services for Google Cloud.
Cloud Monitoring provides centralized dashboards and alerting to efficiently operate services. Use integrated logging to power vulnerability detection and bring proactive intelligent monitoring to your security and operations team.Strackdriver Monitoring provides dashboards and alerts for your cloud-powered applications. Review performance metrics for cloud services, virtual machines, and common open source servers such as MongoDB, Apache, Nginx, Elasticsearch, and more.
In this codelab, you'll learn the basics of Stackdriver Monitoring, how to navigate the Monitoring console, and where to look for basic monitoring events and statistics.
Sign-in to Google Cloud Platform console console. Remember the project ID, a unique name across all Google Cloud projects the name above has already been taken and will not work for you, sorry! Next, you'll need to enable billing in the Cloud Console in order to use Google Cloud resources. Running through this codelab shouldn't cost you more than a few dollars, but it could be more if you decide to use more resources or if you leave them running see "cleanup" section at the end of this document.
Before we can enable monitoring, we will need some kind of infrastructure within this Google Cloud Platform project to actually monitor, so let us create that now. You are now looking at the Stackdriver Monitoring Console. The information shown will vary depending on the Google and AWS services you are using and the monitoring features you have set up, but it will look something like the following:.
Let's take an initial look at the monitoring dashboard, because there is a lot of good information here, right from the outset. In the top left corner, you can see a list of current Uptime checks.
This is likely to be empty at this stage, as we have yet to set any up. We will do that in a separate code lab. Uptime checks let you quickly verify the health of any web page, instance, or group of resources. Each configured check is regularly contacted from a variety of locations around the world. Uptime checks can be used as conditions in alerting policy definitions.
Within Stackdriver Monitoring you can set up alerting policies to define conditions that determine whether or not your cloud services and platforms are operating normally. Stackdriver Monitoring provides many different kinds of metrics and health checks that you can use in the policies. When an alerting policy's conditions are violated, an incident is created and displayed on in the Incident section that can be found on the dashboard.
Responders can acknowledge receipt of the notification and can close the incident when it has been taken care of. The Event Log is a list of events that have occurred in your project. This includes system events such as servers being created or being restarted, but you can also add your own message as an event, to keep a record not captured by the monitoring software.
You will also find a variety of graphs and charts on the right hand side that will show you metrics, depending on what resources are currently utilised in your project.
Let's take a look at some of the deeper information we can find out about Compute Engine instances via Stackdriver Monitoring. Let's also take a look at some of the deeper information we can find out about the Cloud SQL instance we created earlier. You are well on your way to having your Google Cloud Platform project monitored with Stackdriver Monitoring.Explore key steps for implementing a successful cloud-scale monitoring strategy.
This post is part 2 of a 3-part series on monitoring the health and performance of virtual machines in Google Compute Engine GCE. Google provides a few methods by which you can surface and collect most of the metrics mentioned in part 1 of this series. In this post we will explore how you can access Google Compute Engine metrics, using standard tools that Google provides:. You can query the API using common tools typically found on most operating systems, including wget and curl.
User account authorization is great for interactive experimentation, but service account is the recommended auth type if you plan to use the REST API in a production environment. This service account has the authorization scopes you need to query the Stackdriver API and much more. To follow along, you can use the default service account, or you can easily create a new service account and use it instead, so long as you create it with a Project OwnerProject Editoror Project Viewer role.
Once you select a service account, you will need to create a service account key to use in obtaining the OAuth token needed to interact with the API. Navigate to the service accounts page in the console and select your project.
Then, download and save the key. Creating a JWT is a multistep process which is easily automated. You can either manually construct the JWTor download this helper script Python to automatically request an API access token using your downloaded credentials. Next, click the blue Create credentials button, and from the dropdown that follows, select Oauth client ID. The request below has been split into distinct parts for readability; you must concatenate all the parts together before executing in a browser:.
The token string in the resulting URL is our request token. The Stackdriver monitoring API allows you to query all of the performance metrics mentioned in part one of this series. The minimum required parameters are interval. The token should be passed as an Authorization header of type Bearer ; with curl such a header looks like this:. Note : If you receive a MissingSecurityHeader response when calling the API, verify that you are not behind a proxy that is stripping your request headers.
As detailed in the first part of this series, all GCE instance metrics begin with compute. We can compose a query to return the list of all CPU utilization metrics from any instance, collected between pm and pm on January 30, As you can see in the output above, without any other parameters, GCE will return a list of all timeseries that satisfy the filter rules.The recommended method to connect to GCP is to use a Project Viewer role for the service account you create.
Choosing this role ensures that any future functionality updates implemented in SignalFx will not require any changes to your GCP setup. If you want to create a role with more restrictive permissions than those available to the Project Viewer role, you can create a new role to use for the service account you create.
If you see this error, review the permissions assigned to the role and add any permissions that have not been enabled or, alternately, change the role for the service account to Project Viewer.
The service account ID autofills; enter a Service account description. Select a role to grant this Service account access to the selected project optional. This will cause a new service account key file. SignalFx uses this API to validate permissions on the service account keys.
For each project you want to monitor using this integration, repeat the above steps so that you have downloaded a service account key and enabled the Cloud Resource Manager API for each project. Open SignalFx and click Integrations to open the Integrations page. Click the Google Cloud Platform tile.
Display the Setup tab, then click Create New Integration to display the configuration options. Select all the keys you want to add, then click Open. Project IDs corresponding to the service account keys you imported will be displayed. By default, all available services are monitored, and any new services that are added later will also be monitored.
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If you want to import metrics from only some of the available services, click All Services to display a list of the services you can monitor. Select the services you want to monitor and click Apply. If you selected Compute Engine as one of the services to monitor, you can enter a comma-separated list of Compute Engine Instance metadata keys to send as properties.
If you installed this integration while going through the Quick Start guide, continue by installing the Smart Agent, which monitors host infrastructure metrics. SignalFx provides a robust integration with StackDriver, has a StackDriver-powered mode for the Infrastructure Navigatorand includes many built-in-dashboards to help you get started monitoring Google Cloud Platform services.
The SignalFx collectd agent offers a much higher degree of customization than is possible with StackDriver, and may be preferable for instances where you want to see metrics at a finer resolution, or where detailed control over the metrics sent matters.
The option to use collectd applies only to the cases where you have direct control over the software installed on an instance, as you do with Container Engine.
As a result, it is a common practice to use both the StackDriver integration and the SignalFx collectd agent. The metadata allows you to slice and dice by custom tags, zones, host names, and other properties or dimensions. The names of these metrics all start with sf. For more information, see SignalFx organization metrics. If you install collectd on a Compute Engine instance using the standard install script this dimension will automatically be added. If you wish to manually send metrics with this dimension, perhaps the simplest way to discover the unique ID value is to find a timeseries that contains this dimension using the Metadata Catalog in SignalFx.
The timeseries should contain other dimensions that give a more friendly identification to the underlying Google Cloud Platform resource. The metric time series associated with Google Cloud Platform metrics have the following generic dimensions. These dimensions are common to all services. Apart from the above dimensions, each service also has a dimension that identifies the resource to which the metric belongs.
Our Google Cloud Platform integration also queries the GCP API for metadata about the resources it is monitoring, so you can filter and group metrics by this metadata in charts and in the Infrastructure Navigator. For Google Cloud Platform Compute Engine instances, SignalFx gets a subset of metadata about the instance, as well as custom metadata specified by the user on an instance level. Support Training File a Support Ticket. Open the GCP web console and select a project you want to monitor.
Enter a name for this integration. Click Save.Learn about Grafana the monitoring solution for every database. Open Source is at the heart of what we do at Grafana Labs. Grafana ships with built-in support for Google Stackdriver. Just add it as a data source and you are ready to build dashboards for your Stackdriver metrics.
There are two ways to authenticate the Stackdriver plugin - either by uploading a Google JWT file, or by automatically retrieving credentials from Google metadata server. The latter option is only available when running Grafana on GCE virtual machine. If Grafana is running on a Google Compute Engine GCE virtual machine, it is possible for Grafana to automatically retrieve default credentials from the metadata server.
This has the advantage of not needing to generate a private key file for the service account and also not having to upload the file to Grafana. However for this to work, there are a few preconditions that need to be met. Both types return time series data. The Grafana query editor shows the list of available aggregation methods for a selected metric and sets a default reducer and aligner when you select the metric.
Units for the Y-axis are also automatically selected by the query editor. To add a filter, click the plus icon and choose a field to filter by and enter a filter value e.
You can remove the filter by clicking on the filter name and select --remove filter Simple wildcards are less expensive than regular expressions. Leading and trailing slashes are not needed when creating regular expressions. The aggregation field lets you combine time series based on common statistics. Read more about this option here. The Aligner field allows you to align multiple time series after the same group by time interval.
Read more about how it works here. The Alignment Period groups a metric by time if an aggregation is chosen. The option is called Stackdriver auto and the defaults are:. The other automatic option is Grafana auto. This will automatically set the group by time depending on the time range chosen and the width of the graph panel.
Read more about the details here. It is also possible to choose fixed time intervals to group by, like 1h or 1d.
Group by resource or metric labels to reduce the number of time series and to aggregate the results by a group by. Resource metadata labels contain information to uniquely identify a resource in Google Cloud. However, the Group By field dropdown comes with a pre-defined list of common system labels. If a metadata label, user label or system label is included in the Group By segment, then you can create filters based on it and expand its value on the Alias field.
The Alias By field allows you to control the format of the legend keys. The default is to show the metric name and labels. This can be long and hard to read. Using the following patterns in the alias field, you can format the legend key the way you want it. In the Group By dropdown, you can see a list of metric and resource labels for a metric.
These can be included in the legend key using alias patterns.Metric types in Cloud Monitoring are classified into general groups, based on the type of service that collects the data.Welcome to Google Cloud Platform - the Essentials of GCP (Google Cloud Essentials)
This page provides links to reference lists for each of these groups. Istio metricsfor Istio on Google Kubernetes Engine. Agent metricsfor VM instances running the Monitoring and Logging agents. External metricsfor open-source and third-party applications. The metric-type lists are rebuilt frequently and time-stamped, so you know how current they are. The information listed for each metric type comes from the Monitoring API MetricDescriptor object for each metric type.
For more information on how metric types are described, see Metrics, time series, and resources. Each metric type has a launch stage that indicates its maturity.
If no superscript appears, then the launch stage is unspecified. For more information, see Product launch stages. Metric types in the Alpha or Early Access launch stages might not appear in the public lists of metrics. To get information about those metric types, explicitly retrieve the set of metric descriptors from a Google Cloud project that has been given permission to use the restricted metric types.
If you have permission to use restricted metric types, you can retrieve the metric descriptors by using the metricDescriptors. See Listing metric descriptors for more information. Cloud Monitoring charges for some metrics. For more information, see Pricing: Monitoring details and Workspaces. The description for each metric type might include additional information, called metadataabout the metric.
The metadata included in the description includes the following:. Sample Period : For metrics that are written periodically, this is the time interval between consecutive data points, excluding data loss due to errors. Ingest Delay : Data points older than this value are guaranteed to be available to be read, excluding data loss due to errors. The delay does not include any time spent waiting until the next sampling period.
For an introduction to the concepts and terminology used in the Cloud Monitoring metric model, see Metrics, time series, and resources. To create your own metrics, see Using custom metricsCustom agent metricsand Logs-based metrics. To quickly see graphs of metric data, use the Metrics Explorer. For information on using this tool, see Metrics Explorer. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies.
Why Google close Groundbreaking solutions. Transformative know-how. Whether your business is early in its journey or well on its way to digital transformation, Google Cloud's solutions and technologies help chart a path to success. Learn more. Keep your data secure and compliant. Scale with open, flexible technology. Build on the same infrastructure Google uses. Customer stories. Learn how businesses use Google Cloud.Do you package your web applications in Docker container images and run those in Google Kubernetes Engine cluster?
Then most likely your application depends on at least a few GCP services. As you might then already know, Google does not currently charge for monitoring data when it comes to GCP metrics.
Besides, if you write applications in Java on top of Spring Bootyou may have heard of the vendor-neutral application metrics facade Micrometer. Micrometer provides built-in support for the top listed full-metrics solution for Kubernetes : Prometheus. Prerequisites : This blog post assumes that you already have access to the Google Cloud Platform.
When you create a GCP project from scratch, you need: a Kubernetes cluster, that can be created from the Google Console a data processing pipeline, checkout Dataflow Word Count Tutorial as an example. Also the Prometheus Operator for Kubernetes should already be running in your Kubernetes cluster. I recommend Prometheus Operator - Quick install how-to guide from Sysdigwhen you are just starting with Prometheus Operator for Kubernetes. The extensive list of what Stackdriver currently supports is documented here.
In the context of the weareblox project, the list looks like below:. You will be able to monitor billing costs for it in Prometheus UI.
For getting the GCP metrics listed above into Prometheus time-series monitoring datastore, we need a few things:. Both the Service and ServiceMonitor Kubernetes resource definitions are accessible here.
Another subtle tweak to the community maintained helm chart, we have used envFrom to define all of the container environment variables as key-value pairs in a Kubernetes ConfigMap. To verify the installation, you can list Kubernetes Pods, Services and ConfigMaps in the monitoring namespace and you should see an output similar to below:.
Also in Prometheus GUI, a new target named stackdriver-exporter-prometheus-metrics should be listed:. The advantage of having all important metrics centralized is that you can see alerts you set up in one overview:. Also the way you write alerts is consistent. As exemplified above, Prometheus alerting rules are used for alerts on Dataflow metrics.
The rule expression is checking whether the alert condition is being met. System lag is the current maximum duration that an item of data has been awaiting processing, in seconds. The offset you need to set is also the value configured in the Helm chart installation for parameter stackdriver. As an example, I like Grafana most when it comes to graphing capabilities. It also integrates out-of-the box with both Prometheus and Stackdriver metrics data sources.
Prometheus however supports templating in the annotations and labels of alerts, giving it an edge when it comes to writing alerting rules. Background Do you package your web applications in Docker container images and run those in Google Kubernetes Engine cluster?
Decide which GCP metric types you want to export The extensive list of what Stackdriver currently supports is documented here. Below some visuals of the deployment scenario: Both the Service and ServiceMonitor Kubernetes resource definitions are accessible here. Create insights and Prometheus alerting rules based on GCP metrics The advantage of having all important metrics centralized is that you can see alerts you set up in one overview: Also the way you write alerts is consistent.
More information from Trifork:. Do or Do Not.