Datadog monitor tags

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Getting started with tags

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Datadog is a monitoring service for IT, Operations and Development teams who write and run applications at scale and then turn the massive amounts of data produced by their apps, tools and services into actionable insight.

Learn more. Questions tagged [datadog]. Ask Question. Learn more… Top users Synonyms. Filter by. Sorted by. Tagged with. Apply filter. How to get the metrics collected on datadog monitor using Java code?

I need to monitor fifty application. As apart of which I need to perform healthcheck on datadog dashboard to all the application everyday. So, Is it possible to collect the metrics collected in Spring boot micro meter datadog socket connection error I am working on to create some custom metrics for my spring boot 2 rest api. I have added the required micro meter and datadog dependency.

My office machine works behind a proxy. I have setup proxy GAK 6 6 silver badges 21 21 bronze badges. How to get the number of different values of a metric's tag in Datadog I have a metric which has a tag with lots of different values the value is a file name.

How can I create a query that determines the number of different values of that tag exist on a metric?

datadog monitor tags

Bowen Jin 1. However, Datadog integration requires custom fields such as service, dd Resetting error budget in Datadog SLO widget When an application frequently goes beyond SLO threshold respective error budget keeps decreasing drastically in a Datadog widget.

Is there a way to reset Datadog SLO error budget? Datadog regex to find a text that has double quote I have logs that contain this kind of line I tried many AmazingDayToday 1, 2 2 gold badges 18 18 silver badges 44 44 bronze badges.

Is it possible to use tags for excluding instances in DataDog while creating a graph? Is it possible to create a graph and use a tag to exclude some hosts from the result. I have let's say hosts with tag environment:live Datadog Query for current time difference I have a datadog metrics thats represent a time value of an event that happened as epoch milli second. Kumar Sambhav 6, 11 11 gold badges 55 55 silver badges 79 79 bronze badges. Datadog spans lost in python thread pool I have a function that runs in a thread pool, but it only shows up in the Datadog tracing UI when I run it outside of my threadpool.

Timeseries for Specific Windows I have some data that tends to have a specific pattern: the rate always has a distribution around a high point at midday. I'd like to average that window of time so I can compare multiple days in aGitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. For even greater visibility into your Amazon EBS volumes and your entire infrastructure, you can install the Datadog Agent on your instances.

This enables you to gather system-level metrics from your volumes, including disk usage, at second resolution. And with Datadog APM and the addition of logginginstalling the Datadog Agent provides a fully unified monitoring platform.

There are two ways to start using Datadog to monitor your EBS volumes. These approaches can be used in a complementary fashion.

The AWS integration allows you to pull the full suite of AWS metrics into Datadog immediately, whereas the Agent allows you to monitor your applications and infrastructure with greater detail and depth. You can then visualize and monitor them on your dashboards.

You can create fully customized dashboards that meet your specific monitoring needs. You can also bring in application performance metrics to correlate throughput, errors, and latency with key resource metrics from the volumes those applications rely on. The Datadog Agent is open source software that can collect and forward metrics, logs, and request traces from your instances.

Once the Agent is installed on an instance, it will automatically report system-level metrics for that instance and any EBS volumes that are mounted to it.

You can also enable integrations for any supported applications and services that are running on your instances to begin collecting metrics specific to those technologies.

The Agent is installed on the root volume of an instance.

Getting started with tags

On most platforms this can be done with just a one-line command. For example, to install the Agent on an instance running Amazon Linux, simply use the following:.

You should then see your instance reporting metrics in your Datadog account. You can also quickly and easily automate deployment of the Agent across your entire infrastructure with popular configuration management tools like ChefPuppetand Ansibleor to your container fleet via Docker or Kubernetes. See the Datadog Agent documentation for more information. The screenshot below shows a default host dashboard for an EC2 instance with the Agent installed.

Compared to monitoring only the metrics that CloudWatch reports, installing the Agent provides a number of benefits. Besides the difference in granularity, note that the volume or device name is different. In this case, the device name sdf reported by CloudWatch is labeled as xvdf by the system check. See more information about device naming here. In Datadog, tags make it easy to see that each device name comes from the same source.

Here, both are identified by the same host name. Installing the Agent also enables you to begin tracing requests with Datadog APM after instrumenting your applications. With Datadog Agent versions 6 and later, you can take advantage of Datadog log management to collect logs from the applications and technologies running on your EC2 instances and attached volumes.

With combined aggregation of metrics, distributed request traces, and logs, Datadog provides a unified platform for full visibility into your infrastructure. If you are running containers on your instances, Datadog's Live Container view gives you complete coverage of your fleet, with metrics reported at two-second resolution.Tags are a way of adding dimensions to metrics, so they can be filtered, aggregated, and compared in Datadog visualizations.

Using tags enables you to observe aggregate performance across a number of hosts and optionally narrow the set further based on specific elements. In summary, tagging is a method to observe aggregate data points. Tagging binds different data types in Datadog, allowing for correlation and call to action between metrics, traces, and logs.

This is accomplished with reserved tag keys. Here are some examples:. Tags must start with a letter and after that may contain the characters listed below:. Tags are converted to lowercase.

Therefore, CamelCase tags are not recommended. Commonly used tag keys are envinstanceand name. The key always precedes the first colon of the global tag definition, for example:. Reserved tag keys hostdevicesourceand service cannot be used in the standard way. Doing so may infinitely increase the number of metrics for your organization and impact your billing.

Tags may be assigned using any or all of the following methods. Refer to the dedicated Assigning Tags documentation to learn more:. After you have assigned tags at the host and integration level, start using them to filter and group your metrics, traces, and logs.

Tags are used in the following areas of your Datadog platform. Refer to the dedicated Using Tags documentation to learn more:. Home Docs API. Getting started with tags Introduction Tags are a way of adding dimensions to metrics, so they can be filtered, aggregated, and compared in Datadog visualizations. Note : A tag cannot end with a colon, for example tag: Tags can be up to characters long and support Unicode. Assigning Tags Tags may be assigned using any or all of the following methods.Learn the Learn how Terraform fits into the.

Defaults to false. Should be a non negative integer. Defaults to This is useful for AWS CloudWatch and other backfilled metrics to ensure the monitor will always have data during evaluation. Must be at least 2x the monitor timeframe for metric alerts or 2 minutes for service checks. Default: 2x timeframe for metric alerts, 2 minutes for service checks.

Defaults to 10 minutes. It will only re-notify if it's not resolved. Defaults to true. This is only used by log monitors. We highly recommend you set this to False for sparse metrics, otherwise some evaluations will be skipped. Default: True for "on average", "at all times" and "in total" aggregation. False otherwise.

Defaults to False. This can help you categorize and filter monitors in the manage monitors page of the UI. Note: it's not currently possible to filter by these tags when querying via the API. Can only be used for, and are required for, anomaly monitors.

Use -1 if you want to unmute the scope. Deprecated The silenced parameter is being deprecated in favor of the downtime resource. This will be removed in the next major version of the Terraform Provider. Note: due to HCL limitationsit is impossible to use interpolations in keys. To workaround this, you can use the map function of HCL:.

Datadog 101 - 1 - Overview

Both of these actions add a new value to the silenced map. This can be problematic if the silenced attribute doesn't contain them in your Terraform, as they would be removed on next terraform apply invocation. In order to prevent that from happening, you can add following to your monitor:. You can compose monitors of all types in order to define more specific alert conditions see the doc.

Seven elements of the modern Application Lifecycle. Create Account.With turn-key integrations, Datadog seamlessly aggregates metrics and events across the full devops stack. Quickly search, filter, and analyze your logs for troubleshooting and open-ended exploration of your data. More than summary dashboards, Datadog offers all high-resolution metrics and events for manipulation and graphing. System events and metrics are only part of the story.

Datadog is built to give visibility across teams. Datadog notifies you of performance problems, whether they affect a single host or a massive cluster. Toggle navigation. White modal up arrow. Download Media Assets. Log Management. See across systems, apps, and services With turn-key integrations, Datadog seamlessly aggregates metrics and events across the full devops stack. Get full visibility into modern applications Monitor, troubleshoot, and optimize application performance.

Trace requests from end to end across distributed systems Track app performance with auto-generated service overviews Graph and alert on error rates or latency percentiles p95, p99, etc. Analyze and explore log data in context Quickly search, filter, and analyze your logs for troubleshooting and open-ended exploration of your data. Automatically collect logs from all your services, applications, and platforms Navigate seamlessly between logs, metrics, and request traces See log data in context with automated tagging and correlation Visualize and alert on log data LEARN MORE.

Proactively monitor your user experience End-to-end user experience visibility in a single platform. Build real-time interactive dashboards More than summary dashboards, Datadog offers all high-resolution metrics and events for manipulation and graphing. Share what you saw, write what you did System events and metrics are only part of the story. Get alerted on critical issues Datadog notifies you of performance problems, whether they affect a single host or a massive cluster.

Receive alerts on any metric, for a single host or for an entire cluster Get notifications via e-mail, PagerDuty, Slack, and other channels Build complex alerting logic using multiple trigger conditions Mute all alerts with 1 click during upgrades and maintenance LEARN MORE.

Instrument your apps, write new integrations Datadog includes full API access to bring observability to all your apps and infrastructure.Explore key steps for implementing a successful cloud-scale monitoring strategy. Tags provide critical context for troubleshooting issues across any dimension of your environment.

By applying best practices for tagging your systems, you can efficiently organize and analyze all your monitoring data, and set up automated multi alerts to streamline alerting workflows.

Similar to any tags you would add to your services and infrastructure, monitor tags —tags that you apply to your monitors—are an essential feature for organizing and simplifying your workflows.

datadog monitor tags

This blog post will highlight recommended best practices for tagging your monitors, and cover the many benefits of using monitor tags extensively to:.

Monitor tags add dimensions to your monitors, allowing you to filter, aggregate, and visualize them just like any other kind of monitoring data i. When used judiciously, monitor tags help you effectively organize your monitors and streamline the way you manage and utilize your monitors —which in turn makes it easier to troubleshoot issues.

If your organization has many teams—all using a wide array of monitors to track their services—monitor tags allow everyone to get essential context around every monitor, and immediately use that information to respond appropriately. When you create a monitor, you should think about how to tag it with information that describes how this monitor relates to your infrastructure, applications, teams, and other monitors.

While there are many ways to use tags to organize your monitors, in general, we recommend:. In Datadog, you also have the option to tag monitors with values but no keys.

For example, if you are creating a monitor as a test, you could simply tag it with test. Below is an example of an APM monitor tagged with all of the above suggestions.

Once your monitors are tagged with useful metadata, you can use those tags to quickly find specific monitors in your Datadog account. You can also use boolean logic operators to search for any specific combination of tags. You can also use the Datadog Monitors API to programmatically search for specific monitors, using the same tag query. Doing so returns the IDs and other details of all the monitors that match your search query, which in turn can be fed as the inputs for other API capabilities, such as muting and resolving monitors.

Whenever monitors trigger or recover from an alerting state, Datadog creates an event that helps you track this change in status. Adding a tags query allows you to use tags to drill down with precision. In this case, we are using monitor tags to filter for events that are associated with a specific team and service. With the Datadog Events APIyou can also use the tags argument to programmatically query the Datadog event stream for monitor-related events.

In certain situations, you may not want your monitors to trigger e. To plan for these situations and reduce potential alert fatigue, you can configure downtime for your monitorswhich will suppress any notifications that would have been sent during the specified period.

This does not impact the status of your monitors i.

datadog monitor tags

You can schedule downtime by searching for the names of the monitors you want to mute. However, if there are a large number of monitors that will be affected by a maintenance window, it will quickly become a very tedious process to manually enter the name of each monitor.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. It can also help provide more context when troubleshooting, by enabling you to compare and correlate RDS PostgreSQL data with metrics from other services throughout your environment.

In this post, we will show you how to set up Datadog to automatically collect all of the key metrics covered in Part 1 in two steps:. We'll also show you how to get even more visibility into your RDS database instances and the applications that rely on them by:.

Questions tagged [datadog]

Integrating Datadog with AWS CloudWatch enables you to aggregate metrics from all of your AWS services so that you can visualize and alert on them from one central platform.

If you're new to Datadog, you can sign up for a day free trial to follow along with the rest of this guide. You'll need to create a role e.

By default, your CloudWatch RDS metrics will automatically be tagged in Datadog with metadata about each database instance, including:. These tags will be structured in key:value format. Tags give you the power to slice and dice your metrics by any dimension. For example, you can filter your RDS dashboard to view metrics from database instances located in a specific region, or limit your view to metrics from just one database instance at a time.

Datadog will also ingest any custom CloudWatch tags you may have added to your RDS database instances. These metrics will need to be accessed directly from the database itself. Instead, you'll need to install the Agent on another server that can access your database, such as an EC2 instance in the same security group as your RDS instance. Consult the documentation for OS-specific installation steps. Exit the psql session and run this command from your EC2 instance to confirm that the datadog user can access your metrics:.

You'll be prompted to enter the password for your datadog user. After you've done so, you should see the following output: Postgres connection - OK. The Agent comes bundled with an example configuration file for PostgreSQL that you can modify to your liking.

The location of this file varies according to your OS and platform—consult the documentation for details on where to locate the file. Create a copy of the example configuration file and edit it with the information that the Datadog Agent needs to access metrics from your RDS instance.

The example below instructs the Agent to access an RDS database instance through the default portusing the datadog user and password we just created. You can also add custom tag s to your PostgreSQL metrics, and limit metric collection to specific schemas, if desired. If you wish to collect and track table-level metrics such as the amount of disk space used per tableadd each table to the relations section of the YAML file. Save your changes as conf. These commands vary according to your OS; consult the documentation to find instructions for your platform.

This enables you to unify metrics from the same database instance, whether they were collected from CloudWatch or directly from PostgreSQL. Normally you'd query the view with something like:.


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