Data sources on Grafana Labs

Alertmanager data source

Fri, 03 Apr 2026 19:43:06 +0000

Alertmanager data source

Grafana includes built-in support for Alertmanager implementations in Prometheus and Mimir.

Once you add an Alertmanager as a data source, you can use the Choose Alertmanager drop-down on Grafana Alerting to view and manage Alertmanager resources, such as silences, contact points, and notification policies. Additionally, you can enable the Alertmanager to receive Grafana-managed alerts.

For more details about using other Alertmanagers, refer to Alertmanagers in the Grafana Alerting documentation.

Alertmanager implementations

The data source supports Prometheus and Grafana Mimir (default) implementations of Alertmanager. You can specify the implementation in the data source’s Settings page.

When using Prometheus, you can manage silences in the Grafana Alerting UI. However, other Alertmanager resources such as contact points, notification policies, and templates are read-only because the Prometheus Alertmanager HTTP API does not support updates for these resources.

Configure the data source

To configure basic settings for the data source, complete the following steps:

Click Connections in the left-side menu.
Under Your connections, click Data sources.
Enter Alertmanager in the search bar.
Click Alertmanager.

The Settings tab of the data source is displayed.

Set the data source’s basic configuration options:

Name	Description
Name	Sets the name you use to refer to the data source
Default	Sets whether the data source is pre-selected for new panels and queries
Alertmanager Implementation	Alertmanager implementation. Mimir, Cortex, and Prometheus are supported
Receive Grafana Alerts	When enabled, the Alertmanager can receive Grafana-managed alerts. Important: This works only if receiving alerts is enabled for the Alertmanager in the Grafana Alerting Settings page
HTTP URL	Sets the HTTP protocol, IP, and port of your Alertmanager instance, such as `https://alertmanager.example.org:9093`
Access	Only Server access mode is functional

Provision the Alertmanager data source

You can provision Alertmanager data sources by updating Grafana’s configuration files. For more information on provisioning, and common settings available, refer to the provisioning docs page.

Here is an example for provisioning the Alertmanager data source:

apiVersion: 1

datasources:
  - name: Alertmanager
    type: alertmanager
    url: http://localhost:9093
    access: proxy
    jsonData:
      # Valid options for implementation include mimir, cortex and prometheus
      implementation: prometheus
      # Whether or not Grafana should send alert instances to this Alertmanager
      handleGrafanaManagedAlerts: false
    # optionally
    basicAuth: true
    basicAuthUser: my_user
    secureJsonData:
      basicAuthPassword: test_password

Amazon CloudWatch data source

Fri, 03 Apr 2026 19:43:06 +0000

Amazon CloudWatch data source

Amazon CloudWatch is the AWS native monitoring and observability service that collects, aggregates, and stores metrics, logs, and events from AWS resources, applications, and services. CloudWatch enables you to visualize performance data, track system health, and set up automated alerts based on defined thresholds. The Amazon CloudWatch data source in Grafana extends these capabilities by allowing you to query CloudWatch data and create rich, interactive visualizations that can be correlated with data from other systems within unified dashboards.

Grafana includes native support for the Amazon CloudWatch plugin, so there’s no need to install a plugin.

The following documents will help you get started working with the CloudWatch data source:

Import pre-configured dashboards

The CloudWatch data source includes curated, pre-configured dashboards for five popular AWS services:

Amazon Elastic Compute Cloud: Amazon EC2
Amazon Elastic Block Store: Amazon EBS
AWS Lambda: AWS Lambda
Amazon CloudWatch Logs: Amazon CloudWatch Logs
Amazon Relational Database Service: Amazon RDS

To import curated dashboards:

Navigate to the data source’s configuration page.
Click the Dashboards tab.

This displays the curated selection of importable dashboards.
Click Import for each dashboard you want to import.

To customize one of these dashboards, Grafana recommends saving it under a different name; otherwise, Grafana upgrades will overwrite your customizations with the new version.

Get the most out of the data source

After installing and configuring the Amazon CloudWatch data source, you can:

Create a wide variety of visualizations
Configure and use templates and variables
Add transformations
Add annotations
Set up alerting
Optimize performance with query caching

Control pricing

The Amazon CloudWatch data source for Grafana uses ListMetrics and GetMetricData CloudWatch API calls to list and retrieve metrics. Pricing for CloudWatch Logs is based on the amount of data ingested, archived, and analyzed via CloudWatch Logs Insights queries. Each time you select a dimension in the query editor, Grafana issues a ListMetrics API request. Each time you change queries in the query editor, Grafana issues a new request to the GetMetricData API.

Note
Grafana now uses the GetMetricData API instead of GetMetricStatistics for CloudWatch queries. This change improves support for CloudWatch metric math and allows Grafana to automatically generate search expressions when you use wildcards or disable the Match Exact option.

Unlike GetMetricStatistics requests, GetMetricData requests do not qualify for the CloudWatch API free tier.

For more information, refer to the CloudWatch pricing page.

Manage service quotas

AWS defines quotas, or limits, for resources, actions, and items in your AWS account. Depending on the number of queries in your dashboard and the number of users accessing the dashboard, you might reach the usage limits for various CloudWatch and CloudWatch Logs resources. Quotas are defined per account and per region.

If you use multiple regions or have configured more than one CloudWatch data source to query against multiple accounts, you must request a quota increase for each account and region in which you reach the limit.

To request a quota increase, visit the AWS Service Quotas console. For more information, refer to the AWS documentation for Service Quotas and CloudWatch limits.

Cross-account observability

The CloudWatch plugin enables you to monitor and troubleshoot applications across multiple regional accounts. Using cross-account observability, you can seamlessly search, visualize and analyze metrics and logs without worrying about account boundaries.

To use this feature, configure a monitoring and source account in the AWS console under CloudWatch Settings, and then add the necessary IAM permissions as described above.

Azure Monitor data source

Fri, 03 Apr 2026 19:43:06 +0000

Azure Monitor data source

The Azure Monitor data source plugin allows you to query and visualize data from Azure Monitor, the Azure service to maximize the availability and performance of applications and services in the Azure Cloud.

Supported Azure clouds

The Azure Monitor data source supports the following Azure cloud environments:

Azure - Azure public cloud (default)
Azure US Government - Azure Government cloud
Azure China - Azure China cloud operated by 21Vianet

Supported Azure services

The Azure Monitor data source supports the following Azure services:

Service	Description
Azure Monitor Metrics	Collect numeric data from resources in your Azure account. Supports dimensions, aggregations, and time grain configuration.
Azure Monitor Logs	Collect log and performance data from your Azure account using the Kusto Query Language (KQL).
Azure Resource Graph	Query your Azure resources across subscriptions using KQL. Useful for inventory, compliance, and resource management.
Application Insights Traces	Collect distributed trace data and correlate requests across your application components.

Get started

The following documents will help you get started with the Azure Monitor data source:

Configure the Azure Monitor data source - Set up authentication and connect to Azure
Azure Monitor query editor - Create and edit queries for Metrics, Logs, Traces, and Resource Graph
Template variables - Create dynamic dashboards with Azure Monitor variables
Alerting - Create alert rules using Azure Monitor data
Troubleshooting - Solve common configuration and query errors

Additional features

After you have configured the Azure Monitor data source, you can:

Add Annotations to overlay Azure log events on your graphs.
Configure and use Template variables for dynamic dashboards.
Add Transformations to manipulate query results.
Set up Alerting and recording rules using Metrics, Logs, Traces, and Resource Graph queries.
Use Explore to investigate your Azure data without building a dashboard.

Pre-built dashboards

The Azure Monitor plugin includes the following pre-built dashboards:

Azure Monitor Overview - Displays key metrics across your Azure subscriptions and resources.
Azure Storage Account - Shows storage account metrics including availability, latency, and transactions.

To import a pre-built dashboard:

Go to Connections > Data sources.
Select your Azure Monitor data source.
Click the Dashboards tab.
Click Import next to the dashboard you want to use.

Elasticsearch data source

Fri, 03 Apr 2026 19:43:06 +0000

Elasticsearch data source

Elasticsearch is a search and analytics engine used for a variety of use cases. You can create many types of queries to visualize logs or metrics stored in Elasticsearch, and annotate graphs with log events stored in Elasticsearch.

The following resources will help you get started with Elasticsearch and Grafana:

Key capabilities

The Elasticsearch data source supports:

Metrics queries: Aggregate and visualize numeric data using bucket and metric aggregations.
Log queries: Search, filter, and explore log data with Lucene query syntax.
Annotations: Overlay Elasticsearch events on your dashboard graphs.
Alerting: Create alerts based on Elasticsearch query results.

Before you begin

Before you configure the Elasticsearch data source, you need:

An Elasticsearch instance (v7.17+, v8.x, or v9.x)
Network access from Grafana to your Elasticsearch server
Appropriate user credentials or API keys with read access

Note
If you use Amazon OpenSearch Service (the successor to Amazon Elasticsearch Service), use the OpenSearch data source instead.

Supported Elasticsearch versions

This data source supports these versions of Elasticsearch:

≥ v7.17
v8.x
v9.x
Elastic Cloud Serverless

The Grafana maintenance policy for the Elasticsearch data source aligns with Elastic Product End of Life Dates. Grafana ensures proper functionality for supported versions only. If you use an EOL version of Elasticsearch, you can still run queries, but the query builder displays a warning. Grafana doesn’t guarantee functionality or provide fixes for EOL versions.

Additional resources

Once you have configured the Elasticsearch data source, you can:

Use Explore to run ad hoc queries against your Elasticsearch data.
Configure and use template variables for dynamic dashboards.
Add Transformations to process query results.
Build dashboards to visualize your Elasticsearch data.

OpenSearch - For Amazon OpenSearch Service.
Loki - Grafana’s log aggregation system.

Google Cloud Monitoring data source

Fri, 03 Apr 2026 19:43:06 +0000

Google Cloud Monitoring data source

Google Cloud Monitoring (formerly Stackdriver) is Google Cloud Platform’s native monitoring and observability service that collects metrics, events, and metadata from GCP resources, hosted uptime probes, and application instrumentation. The Google Cloud Monitoring data source in Grafana allows you to query and visualize this data alongside metrics from other systems, creating unified dashboards for comprehensive infrastructure and application monitoring.

Grafana includes built-in support for Google Cloud Monitoring, so you don’t need to install a plugin.

Get started

The following documents will help you get started with the Google Cloud Monitoring data source:

Configure the data source - Set up authentication and connect to Google Cloud
Query editor - Create and edit Metric and SLO queries
Template variables - Create dynamic dashboards with Google Cloud Monitoring variables
Annotations - Overlay Google Cloud Monitoring events on graphs
Alerting - Create alert rules based on GCP metrics and SLOs
Google authentication - Configure authentication methods for GCP access
Troubleshooting - Solve common configuration and query errors

Supported query types

The Google Cloud Monitoring data source supports the following query types:

Query type	Description
Metrics	Query time series data from GCP resources using the Monitoring Query Language (MQL) or the visual builder.
Service Level Objectives (SLOs)	Query SLO data defined in Google Cloud Monitoring to track service reliability and error budgets.

Additional features

After you configure the Google Cloud Monitoring data source, you can:

Create a wide variety of visualizations using GCP metrics.
Configure and use template variables for dynamic dashboards.
Add transformations to manipulate query results.
Add annotations to overlay events on your graphs.
Set up alerting based on GCP metrics.
Use Explore to investigate your Google Cloud data without building a dashboard.

Pre-configured dashboards

The Google Cloud Monitoring data source includes pre-configured dashboards for popular GCP services. These curated dashboards are based on similar dashboards in the GCP dashboard samples repository.

To import a pre-configured dashboard:

Go to Connections > Data sources.
Select your Google Cloud Monitoring data source.
Click the Dashboards tab.
Click Import next to the dashboard you want to use.

The dashboards include a template variable populated with the projects accessible by the configured service account each time you load the dashboard. After Grafana loads the dashboard, you can select a project from the dropdown list.

To customize an imported dashboard, save it under a different name. Otherwise, Grafana upgrades can overwrite your customizations with the new version.

Graphite data source

Fri, 03 Apr 2026 19:43:06 +0000

Graphite data source

Grafana includes built-in support for Graphite. This topic explains options, variables, querying, and other features specific to the Graphite data source, which include its feature-rich query editor.

For instructions on how to add a data source to Grafana, refer to the administration documentation.

Once you’ve added the Graphite data source, you can configure it so that your Grafana instance’s users can create queries in its query editor when they build dashboards and use Explore.

Give it a try using Grafana Play

With Grafana Play, you can explore and see how it works, learning from practical examples to accelerate your development. This feature can be seen on Graphite: Sample Website Dashboard.

Try it

Grafana exposes metrics for Graphite on the /metrics endpoint. For detailed instructions, refer to Internal Grafana metrics.

Get Grafana metrics into Graphite

Grafana exposes metrics for Graphite on the /metrics endpoint. Refer to Internal Grafana metrics for more information.

Graphite and Loki integration

When you change the data source selection in Explore, Graphite queries are converted to Loki queries. Grafana extracts Loki label names and values from the Graphite queries according to mappings provided in the Graphite data source configuration. Grafana automatically transforms queries using tags with seriesByTags() without requiring additional setup.

Get the most out of the data source

After installing and configuring the Graphite data source you can:

Create a wide variety of visualizations
Configure and use templates and variables
Add transformations
Add annotations
Set up alerting
Troubleshoot common issues with the Graphite data source

InfluxDB data source

Fri, 03 Apr 2026 19:43:06 +0000

InfluxDB data source

InfluxDB is an open-source time series database (TSDB) developed by InfluxData. It is optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, IoT sensor data, and real-time analytics.

Grafana includes a built-in InfluxDB data source plugin, enabling you to query and visualize data from InfluxDB without installing additional plugins. Grafana offers multiple configuration options for this data source, including a choice of three query languages (InfluxQL, SQL, and Flux) and a query editor with both code and visual builder modes.

Supported versions

This data source supports the following InfluxDB products:

InfluxDB OSS 1.x, 2.x, and 3.x
InfluxDB Enterprise 1.x and 3.x
InfluxDB Cloud Serverless
InfluxDB Cloud Dedicated
InfluxDB Cloud (TSM)

Key capabilities

The InfluxDB data source supports:

Multiple query languages: Choose from InfluxQL, SQL (v3.x+), or Flux depending on your InfluxDB version.
Time series queries: Visualize metrics over time with built-in macros for time filtering and grouping.
Table queries: Display query results in table format.
Template variables: Create dynamic dashboards with variable-driven queries.
Annotations: Overlay events from InfluxDB on your dashboard panels.
Alerting: Create alerts based on InfluxDB query results.
Log querying: Query and display log data in Explore and the Logs panel.

Get started with the InfluxDB data source

The following documents will help you get started with the InfluxDB data source in Grafana:

Additional resources

Once you have configured the InfluxDB data source, you can:

Use Explore to run ad hoc queries against your InfluxDB data.
Configure and use template variables for dynamic dashboards.
Add transformations to process query results.
Build dashboards to visualize your InfluxDB data.
Set up alerting based on your InfluxDB query results.

Configure the Loki data source

Fri, 03 Apr 2026 19:43:06 +0000

Loki data source

Grafana Loki is a set of components that can be combined into a fully featured logging stack. Unlike other logging systems, Loki is built around the idea of only indexing metadata about your logs: labels (just like Prometheus labels). Log data itself is then compressed and stored in chunks in object stores such as S3 or GCS, or even locally on a filesystem.

The following guides will help you get started with Loki:

Supported Loki versions

This data source supports these versions of Loki:

v2.9+

Adding a data source

For instructions on how to add a data source to Grafana, refer to the administration documentation Only users with the organization administrator role can add data sources. Administrators can also configure the data source via YAML with Grafana’s provisioning system.

Once you’ve added the Loki data source, you can configure it so that your Grafana instance’s users can create queries in its query editor when they build dashboards, use Explore, and annotate visualizations.

Note
To troubleshoot configuration and other issues, check the log file located at /var/log/grafana/grafana.log on Unix systems, or in <grafana_install_dir>/data/log on other platforms and manual installations.

Provision the data source

You can define and configure the data source in YAML files as part of Grafana’s provisioning system. For more information about provisioning, and for available configuration options, refer to Provisioning Grafana.

Provisioning examples

apiVersion: 1

datasources:
  - name: Loki
    type: loki
    access: proxy
    url: http://localhost:3100
    jsonData:
      timeout: 60
      maxLines: 1000

Using basic authorization and a derived field:

You must escape the dollar ($) character in YAML values because it can be used to interpolate environment variables:

apiVersion: 1

datasources:
  - name: Loki
    type: loki
    access: proxy
    url: http://localhost:3100
    basicAuth: true
    basicAuthUser: my_user
    jsonData:
      maxLines: 1000
      derivedFields:
        # Field with internal link pointing to data source in Grafana.
        # datasourceUid value can be anything, but it should be unique across all defined data source uids.
        - datasourceUid: my_jaeger_uid
          matcherRegex: "traceID=(\\w+)"
          name: TraceID
          # url will be interpreted as query for the datasource
          url: '$${__value.raw}'
          # optional for URL Label to set a custom display label for the link.
          urlDisplayLabel: 'View Trace'

        # Field with external link.
        - matcherRegex: "traceID=(\\w+)"
          name: TraceID
          url: 'http://localhost:16686/trace/$${__value.raw}'
    secureJsonData:
      basicAuthPassword: test_password

Using a Jaeger data source:

In this example, the Jaeger data source’s uid value should match the Loki data source’s datasourceUid value.

datasources:
    - name: Jaeger
      type: jaeger
      url: http://jaeger-tracing-query:16686/
      access: proxy
      # UID should match the datasourceUid in derivedFields.
      uid: my_jaeger_uid

Query the data source

The Loki data source’s query editor helps you create log and metric queries that use Loki’s query language, LogQL.

For details, refer to the query editor documentation.

Use template variables

Instead of hard-coding details such as server, application, and sensor names in metric queries, you can use variables. Grafana lists these variables in dropdown select boxes at the top of the dashboard to help you change the data displayed in your dashboard. Grafana refers to such variables as template variables.

For details, see the template variables documentation.

Jaeger data source

Fri, 03 Apr 2026 19:43:06 +0000

Jaeger data source

Grafana ships with built-in support for Jaeger, which provides open source, end-to-end distributed tracing. This topic explains configuration and queries specific to the Jaeger data source.

For instructions on how to add a data source to Grafana, refer to the administration documentation. Only users with the organization administrator role can add data sources. Administrators can also configure the data source via YAML with Grafana’s provisioning system.

Once you’ve added the data source, you can configure it so your Grafana instance’s users can create queries in the query editor when the users build dashboards and use Explore.

You can also upload a JSON trace file, link to a trace ID from logs, and link to a trace ID from metrics.

Configure the data source

To configure basic settings for the data source, complete the following steps:

Click Connections in the left-side menu.
Under Your connections, click Data sources.
Enter Jaeger in the search bar.
Select Jaeger.

The Settings tab of the data source is displayed.

Set the data source’s basic configuration options:

Name	Description
Name	Sets the name you use to refer to the data source in panels and queries.
Default	Defines whether this data source is pre-selected for new panels.
URL	Sets the URL of the Jaeger instance, such as `http://localhost:16686`.
Basic Auth	Enables basic authentication for the Jaeger data source.
User	Defines the user name for basic authentication.
Password	Defines the password for basic authentication.

You can also configure settings specific to the Jaeger data source. These options are described in the sections below.

Trace to logs

Note
If you use Grafana Cloud, open a support ticket in the Cloud Portal to access this feature.

The Trace to logs setting configures the trace to logs feature that is available when you integrate Grafana with Jaeger.

There are two ways to configure the trace to logs feature:

Use a simplified configuration with default query, or
Configure a custom query where you can use a template language to interpolate variables from the trace or span.

Use a simple configuration

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Set start and end time shift. Since the logs timestamps may not exactly match the timestamps of the spans in trace, it may be necessary to search in larger or shifted time range to find the desired logs.
Select which tags to use in the logs query. The tags you configure must be present in the spans attributes or resources for a trace to logs span link to appear. You can optionally configure a new name for the tag. This is useful if the tag has dots in the name and the target data source does not allow dots in labels. In that case, you can, for example, remap http.status to http_status.
Optionally, switch on the Filter by trace ID and/or Filter by span ID setting to further filter the logs if your logs consistently contain trace or span IDs.

Configure a custom query

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Set start and end time shift. Since the logs timestamps may not exactly match the timestamps of the spans in the trace, you may need to widen or shift the time range to find the desired logs.
Optionally, select tags to map. These tags can be used in the custom query with ${__tags} variable. This variable will interpolate the mapped tags as a list in an appropriate syntax for the data source and will only include the tags that were present in the span omitting those that weren’t present. You can optionally configure a new name for the tag. This is useful in cases where the tag has dots in the name and the target data source does not allow dots in labels. For example, you can remap http.status to http_status. If you don’t map any tags here, you can still use any tag in the query like this method="${__span.tags.method}".
Skip Filter by trace ID and Filter by span ID settings as these cannot be used with a custom query.
Switch on Use custom query.
Specify a custom query to be used to query the logs. You can use various variables to make that query relevant for current span. The link is present only if all the variables are interpolated with non-empty values to prevent creating an invalid query.

Variables that can be used in a custom query

To use a variable you need to wrap it in ${}. For example: ${__span.name}.

Variable name	Description
__tags	This variable uses the tag mapping from the UI to create a label matcher string in the specific data source syntax. The variable only uses tags that are present in the span. The link is still created even if only one of those tags is present in the span. You can use this if all tags are not required for the query to be useful.
__span.spanId	The ID of the span.
__span.traceId	The ID of the trace.
__span.duration	The duration of the span.
__span.name	Name of the span.
__span.tags	Namespace for the tags in the span. To access a specific tag named `version`, you would use `${__span.tags.version}`. In case the tag contains dot, you have to access it as `${__span.tags["http.status"]}`.
__trace.traceId	The ID of the trace.
__trace.duration	The duration of the trace.
__trace.name	The name of the trace.

The following table describes the ways in which you can configure your trace to logs settings:

Setting name	Description
Data source	Defines the target data source. You can select only Loki or Splunk logs data sources.
Span start time shift	Shifts the start time for the logs query, based on the span’s start time. You can use time units, such as `5s`, `1m`, `3h`. To extend the time to the past, use a negative value. Default: `0`.
Span end time shift	Shifts the end time for the logs query, based on the span’s end time. You can use time units. Default: `0`.
Tags	Defines the tags to use in the logs query. Default: `cluster`, `hostname`, `namespace`, `pod`, `service.name`, `service.namespace`. You can change the tag name for example to remove dots from the name if they are not allowed in the target data source. For example, map `http.status` to `http_status`.
Filter by trace ID	Toggles whether to append the trace ID to the logs query.
Filter by span ID	Toggles whether to append the span ID to the logs query.
Use custom query	Toggles use of custom query with interpolation.
Query	Input to write custom query. Use variable interpolation to customize it with variables from span.

Trace to metrics

The Trace to metrics setting configures the trace to metrics feature available when integrating Grafana with Jaeger.

To configure trace to metrics:

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Create any desired linked queries.

Setting name	Description
Data source	Defines the target data source.
Tags	Defines the tags used in linked queries. The key sets the span attribute name, and the optional value sets the corresponding metric label name. For example, you can map `k8s.pod` to `pod`. To interpolate these tags into queries, use the `$__tags` keyword.

Each linked query consists of:

Link Label: (Optional) Descriptive label for the linked query.
Query: The query ran when navigating from a trace to the metrics data source. Interpolate tags using the $__tags keyword. For example, when you configure the query requests_total{$__tags}with the tags k8s.pod=pod and cluster, the result looks like requests_total{pod="nginx-554b9", cluster="us-east-1"}.

Node Graph

The Node Graph setting enables the Node Graph visualization, which is disabled by default.

Once enabled, Grafana displays the Node Graph above the trace view.

Span bar

The Span bar setting helps you display additional information in the span bar row.

You can choose one of three options:

Name	Description
None	Adds nothing to the span bar row.
Duration	(Default) Displays the span duration on the span bar row.
Tag	Displays the span tag on the span bar row. You must also specify which tag key to use to get the tag value, such as `component`.

Provision the data source

You can define and configure the data source in YAML files as part of Grafana’s provisioning system. For more information about provisioning and available configuration options, refer to Provisioning Grafana.

Provisioning example

apiVersion: 1

datasources:
  - name: Jaeger
    type: jaeger
    uid: EbPG8fYoz
    url: http://localhost:16686
    access: proxy
    basicAuth: true
    basicAuthUser: my_user
    readOnly: false
    isDefault: false
    jsonData:
      tracesToLogsV2:
        # Field with an internal link pointing to a logs data source in Grafana.
        # datasourceUid value must match the uid value of the logs data source.
        datasourceUid: 'loki'
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
        tags: ['job', 'instance', 'pod', 'namespace']
        filterByTraceID: false
        filterBySpanID: false
        customQuery: true
        query: 'method="$${__span.tags.method}"'
      tracesToMetrics:
        datasourceUid: 'prom'
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
        tags: [{ key: 'service.name', value: 'service' }, { key: 'job' }]
        queries:
          - name: 'Sample query'
            query: 'sum(rate(traces_spanmetrics_latency_bucket{$$__tags}[5m]))'
      nodeGraph:
        enabled: true
      traceQuery:
        timeShiftEnabled: true
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
      spanBar:
        type: 'None'
    secureJsonData:
      basicAuthPassword: my_password

Query the data source

You can query and display traces from Jaeger via Explore.

This topic explains configuration and queries specific to the Jaeger data source. For general documentation on querying data sources in Grafana, see Query and transform data.

Query by search

To search for traces:

Select Search from the Query type selector.
Fill out the search form:

Name	Description
Service	Returns a list of services.
Operation	Populated when you select a service with related operations. Select `All` to query all operations.
Tags	Sets tags with values in the logfmt format, such as `error=true db.statement="select * from User"`.
Min Duration	Filters all traces with a duration higher than the set value. Possible values are `1.2s, 100ms, 500us`.
Max Duration	Filters all traces with a duration lower than the set value. Possible values are `1.2s, 100ms, 500us`.
Limit	Limits the number of traces returned.

Query by trace ID

To query a particular trace:

Select the TraceID query type.
Enter the trace’s ID into the Trace ID field.

Upload a JSON trace file

You can upload a JSON file that contains a single trace and visualize it. If the file has multiple traces, Grafana visualizes its first trace.

Trace JSON example

{
  "data": [
    {
      "traceID": "2ee9739529395e31",
      "spans": [
        {
          "traceID": "2ee9739529395e31",
          "spanID": "2ee9739529395e31",
          "flags": 1,
          "operationName": "CAS",
          "references": [],
          "startTime": 1616095319593196,
          "duration": 1004,
          "tags": [
            {
              "key": "sampler.type",
              "type": "string",
              "value": "const"
            }
          ],
          "logs": [],
          "processID": "p1",
          "warnings": null
        }
      ],
      "processes": {
        "p1": {
          "serviceName": "loki-all",
          "tags": [
            {
              "key": "jaeger.version",
              "type": "string",
              "value": "Go-2.25.0"
            }
          ]
        }
      },
      "warnings": null
    }
  ],
  "total": 0,
  "limit": 0,
  "offset": 0,
  "errors": null
}

Span Filters

Using span filters, you can filter your spans in the trace timeline viewer. The more filters you add, the more specific are the filtered spans.

You can add one or more of the following filters:

Service name
Span name
Duration
Tags (which include tags, process tags, and log fields)

To only show the spans you have matched, you can press the Show matches only toggle.

Link to a trace ID from logs

You can link to Jaeger traces from logs in Loki, Elasticsearch, Splunk, and other logs data sources by configuring an internal link.

To configure this feature, see the Derived fields section of the Loki data source docs or the Data links section of the Elasticsearch or Splunk data source docs.

Link to a trace ID from metrics

You can link to Jaeger traces from metrics in Prometheus data sources by configuring an exemplar.

To configure this feature, see the introduction to exemplars documentation.

Visualizing the dependency graph

If service dependency information is available in Jaeger, it can be visualized in Grafana. Use the Jaeger data source with the “Dependency Graph” query type on a Node Graph panel for this.

Querying Data via gRPC Endpoint (Public Preview)

Jaeger now offers an alternative method for querying data via HTTP, which utilizes their gRPC service. For detailed information about the API and any necessary Jaeger setup requirements, please consult their documentation.

The following queries are currently enabled through the gRPC endpoint:

Service search
Operation search
Trace ID search

To enable gRPC querying for Jaeger within Grafana, you must enable the jaegerEnableGrpcEndpoint feature flag. Grafana Cloud customers should contact support to request access and provide feedback on this feature.

Microsoft SQL Server data source

Fri, 03 Apr 2026 19:43:06 +0000

Microsoft SQL Server (MSSQL) data source

Grafana ships with built-in support for Microsoft SQL Server (MSSQL). You can query and visualize data from any Microsoft SQL Server 2005 or newer, including Microsoft Azure SQL Database.

Use this data source to create dashboards, explore SQL data, and monitor MSSQL-based workloads in real time.

The following documentation helps you get started working with the Microsoft SQL Server (MSSQL) data source:

Supported versions

This data source supports the following Microsoft SQL Server versions:

Microsoft SQL Server 2005 and newer
Microsoft Azure SQL Database
Azure SQL Managed Instance

Grafana recommends using the latest available service pack for your SQL Server version for optimal compatibility.

Key capabilities

The Microsoft SQL Server data source supports:

Time series queries: Visualize metrics over time using the built-in time grouping macros.
Table queries: Display query results in table format for any valid SQL query.
Template variables: Create dynamic dashboards with variable-driven queries.
Annotations: Overlay events from SQL Server on your dashboard graphs.
Alerting: Create alerts based on SQL Server query results.
Stored procedures: Execute stored procedures and visualize results.
Macros: Simplify queries with built-in macros for time filtering and grouping.

Additional resources

After configuring the Microsoft SQL Server data source, you can:

Create a wide variety of visualizations
Configure and use templates and variables
Add transformations
Add annotations
Set up alerting
Optimize performance with query caching

PostgreSQL - For PostgreSQL databases.
MySQL - For MySQL and MariaDB databases.

MySQL data source

Fri, 03 Apr 2026 19:43:06 +0000

MySQL data source

Grafana ships with built-in support for MySQL. You can query and visualize data from MySQL-compatible databases like MariaDB or Percona Server.

Use this data source to create dashboards, explore SQL data, and monitor MySQL-based workloads in real time.

Watch this video to learn more about using the Grafana MySQL data source plugin:

Give it a try using Grafana Play

With Grafana Play, you can explore and see how it works, learning from practical examples to accelerate your development. This feature can be seen on MySQL Overview.

Try it

Supported databases

This data source supports the following MySQL-compatible databases:

MySQL 5.7 and newer
MariaDB 10.2 and newer
Percona Server 5.7 and newer
Amazon Aurora MySQL
Azure Database for MySQL
Google Cloud SQL for MySQL

Grafana recommends using the latest available version for your database for optimal compatibility.

Key capabilities

The MySQL data source supports:

Time series queries: Visualize metrics over time using built-in time grouping macros.
Table queries: Display query results in table format for any valid SQL query.
Template variables: Create dynamic dashboards with variable-driven queries.
Annotations: Overlay events from MySQL on your dashboard graphs.
Alerting: Create alerts based on MySQL query results.
Macros: Simplify queries with built-in macros for time filtering and grouping.

Get started

The following documentation helps you get started with the MySQL data source:

Additional resources

After configuring the MySQL data source, you can also:

Create a wide variety of visualizations.
Configure and use templates and variables.
Add transformations.
Optimize performance with query caching.

Pre-configured dashboards

If you want to monitor your MySQL server’s performance metrics (connections, queries, replication, and more), Grafana provides pre-configured dashboards through the MySQL integration:

MySQL Overview - Key performance metrics for your MySQL server.
MySQL Logs - Log analysis for troubleshooting.

The MySQL integration uses the Prometheus MySQL Exporter to collect server metrics and includes 15 pre-configured alert rules.

To use these dashboards:

In Grafana Cloud, navigate to Connections > Add new connection.
Search for MySQL and select the MySQL integration.
Follow the setup instructions to install the MySQL Exporter.
Import the pre-configured dashboards from the integration page.

For more MySQL dashboards, browse the Grafana dashboard catalog.

Note
The MySQL integration monitors your MySQL server using Prometheus metrics. The MySQL data source documented here queries data stored in MySQL tables. These are complementary features for different use cases.

PostgreSQL - For PostgreSQL databases.
Microsoft SQL Server - For Microsoft SQL Server and Azure SQL databases.

OpenTSDB data source

Fri, 03 Apr 2026 19:43:06 +0000

OpenTSDB data source

Grafana ships with support for OpenTSDB, an open source time series database built on top of HBase. Use the OpenTSDB data source to visualize metrics, create alerts, and build dashboards from your time series data.

Supported features

The OpenTSDB data source supports the following features:

Feature	Supported	Notes
Metrics queries	Yes	Query time series data with aggregation, downsampling, and filtering
Alerting	Yes	Create alert rules based on OpenTSDB queries
Annotations	Yes	Overlay events on graphs using metric-specific or global annotations
Template variables	Yes	Use dynamic variables in queries
Explore	Yes	Ad-hoc data exploration without dashboards

Supported OpenTSDB versions

The data source supports OpenTSDB versions 2.1 through 2.4. Some features are version-specific:

Feature	Minimum version
Filters	2.2
Fill policies	2.2
Explicit tags	2.3

Get started

The following documents help you get started with the OpenTSDB data source:

Configure the OpenTSDB data source - Set up authentication and connect to OpenTSDB.
OpenTSDB query editor - Create and edit queries with aggregation, downsampling, and filtering.
Template variables - Create dynamic dashboards with OpenTSDB variables.
Troubleshooting - Solve common configuration and query errors.

Additional features

After you have configured the OpenTSDB data source, you can:

Add Annotations to overlay OpenTSDB events on your graphs.
Configure and use Template variables for dynamic dashboards.
Set up Alerting rules based on your time series queries.
Use Explore to investigate your OpenTSDB data without building a dashboard.

Parca

Fri, 03 Apr 2026 19:43:06 +0000

Parca data source

Grafana ships with built-in support for Parca, a continuous profiling OSS database for analysis of CPU and memory usage, down to the line number and throughout time. Add it as a data source, and you are ready to query your profiles in Explore.

Supported Parca versions

This data source supports these versions of Parca:

v0.19+

Configure the Parca data source

To configure basic settings for the data source, complete the following steps:

Click Connections in the left-side menu.
Under Your connections, click Data sources.
Enter Parca in the search bar.
Click Parca.

The Settings tab of the data source is displayed.

Set the data source’s basic configuration options:

Name	Description
`Name`	A name to specify the data source in panels, queries, and Explore.
`Default`	The default data source will be pre-selected for new panels.
`URL`	The URL of the Parca instance, e.g., `http://localhost:4100`
`Basic Auth`	Enable basic authentication to the Parca data source.
`User`	User name for basic authentication.
`Password`	Password for basic authentication.

Querying

Query Editor

Query editor gives you access to a profile type selector, a label selector, and collapsible options.

Select a profile type from the drop-down menu. While the label selector can be left empty to query all profiles without filtering by labels, the profile type must be selected for the query to be valid. Grafana does not show any data if the profile type isn’t selected when a query is run.

Use the labels selector input to filter by labels. Parca uses similar syntax to Prometheus to filter labels. Refer to Parca documentation for available operators and syntax.

Select a query type to return the profile data which can be shown in the Flame Graph, metric data visualized in a graph, or both. You can only select both options in a dashboard, because panels allow only one visualization.

Profiles query results

Profiles can be visualized in a flame graph. See the Flame Graph documentation to learn about the visualization and its features.

Parca returns profiles aggregated over a selected time range, and the absolute values in the flame graph grow as the time range gets bigger while keeping the relative values meaningful. You can zoom in on the time range to get a higher granularity profile up to the point of a single Parca scrape interval.

Metrics query results

Metrics results represent the aggregated value, over time, of the selected profile type. Parca returns ungrouped data with a series for each label combination.

This allows you to quickly see any spikes in the value of the scraped profiles and zoom in to a particular time range.

Provision the Parca data source

You can modify the Grafana configuration files to provision the Parca data source. To learn more, and to view the available provisioning settings, see provisioning documentation.

Here is an example config:

apiVersion: 1

datasources:
  - name: Parca
    type: parca
    url: http://localhost:3100

PostgreSQL data source

Fri, 03 Apr 2026 19:43:06 +0000

PostgreSQL data source

Grafana includes a built-in PostgreSQL data source plugin, enabling you to query and visualize data from any PostgreSQL-compatible database. You don’t need to install a plugin to add the PostgreSQL data source to your Grafana instance.

Grafana offers several configuration options for this data source as well as a visual and code-based query editor.

Supported databases

This data source supports the following PostgreSQL-compatible databases:

PostgreSQL 9.0 and newer
Amazon RDS for PostgreSQL
Amazon Aurora PostgreSQL
Azure Database for PostgreSQL
Google Cloud SQL for PostgreSQL

Grafana recommends using the latest available version for your database for optimal compatibility.

Key capabilities

The PostgreSQL data source supports:

Time series queries: Visualize metrics over time using built-in time grouping macros.
Table queries: Display query results in table format for any valid SQL query.
Template variables: Create dynamic dashboards with variable-driven queries.
Annotations: Overlay events from PostgreSQL on your dashboard panels.
Alerting: Create alerts based on PostgreSQL query results (time series format only).
Macros: Simplify queries with built-in macros for time filtering and grouping.

Get started with the PostgreSQL data source

The following documents will help you get started with the PostgreSQL data source in Grafana:

After you configure the data source, you can:

Create a variety of visualizations
Add annotations to overlay events on your panels
Use template variables for dynamic dashboards
Set up alerting with time series queries (time series format only)
Add transformations

View a PostgreSQL overview on Grafana Play:

Give it a try using Grafana Play

With Grafana Play, you can explore and see how it works, learning from practical examples to accelerate your development. This feature can be seen on PostgreSQL Overview.

Try it

Prometheus data source

Fri, 03 Apr 2026 19:43:06 +0000

Prometheus data source

Prometheus is an open source database that uses a telemetry collector agent to scrape and store metrics used for monitoring and alerting.

Grafana provides native support for Prometheus, so you don’t need to install a plugin.

The following documentation will help you get started working with Prometheus and Grafana:

Exemplars

In Prometheus, an exemplar is a specific trace that represents a measurement taken within a given time interval. While metrics provide an aggregated view of your system, and traces offer a detailed view of individual requests, exemplars serve as a bridge between the two, linking high-level metrics to specific traces for deeper insights.

Exemplars associate higher-cardinality metadata from a specific event with traditional time series data. Refer to Introduction to exemplars in the Prometheus documentation for detailed information on how they work.

Grafana can show exemplar data alongside a metric both in Explore and in Dashboards.

You add exemplars when you configure the Prometheus data source.

Prometheus API

The Prometheus data source also works with other projects that implement the Prometheus querying API.

For more information on how to query other Prometheus-compatible projects from Grafana, refer to the specific product’s documentation:

View Grafana metrics with Prometheus

Grafana exposes metrics for Prometheus on the /metrics endpoint and includes a pre-built dashboard to help you start visualizing your metrics immediately.

Complete the following steps to import the pre-built dashboard:

Navigate to the Prometheus configuration page.
Click the Dashboards tab.
Locate the Grafana metrics dashboard in the list and click Import.

For details about these metrics, refer to Internal Grafana metrics.

Amazon Managed Service for Prometheus

Grafana has deprecated the Prometheus data source for Amazon Managed Service for Prometheus. Use the Amazon Managed Service for Prometheus data source instead. The linked documentation outlines the migration steps.

Get the most out of the Prometheus data source

After you install and configure Prometheus you can:

Create a wide variety of visualizations
Configure and use templates and variables
Add transformations
Add annotations
Set up alerting
Create recorded queries

Pyroscope

Fri, 03 Apr 2026 19:43:06 +0000

Grafana Pyroscope data source

Grafana Pyroscope is a horizontally scalable, highly available, multi-tenant, OSS, continuous profiling aggregation system. Add a Pyroscope data source to query your profiles in Explore.

Refer to Introduction to Pyroscope to understand profiling and Pyroscope.

To use profiling data, you should:

Continuous profiling

While code profiling has been a long-standing practice, continuous profiling represents a modern and more advanced approach to performance monitoring.

This technique adds two critical dimensions to traditional profiles:

Time: Profiling data is collected continuously, providing a time-centric view that allows querying performance data from any point in the past.
Metadata: Profiles are enriched with metadata, adding contextual depth to the performance data.

These dimensions, coupled with the detailed nature of performance profiles, make continuous profiling a uniquely valuable tool.

Flame graphs

Flame graphs help you visualize resource allocation and performance bottlenecks, and you even get suggested recommendations and performance fixes via AI-driven flame graph analysis, as well as line-level insights from our GitHub integration.

On views with a flame graph, you can use Explain flame graph to provide an AI flame graph analysis that explains the performance bottleneck, root cause, and recommended fix. For more information, refer to Flame graph AI.

Integrate profiles into dashboards

Using the Pyroscope data source, you can integrate profiles into your dashboards. For example, you can embed flame graphs using the flame graph panel.

In this case, the screenshot shows memory profiles alongside panels for logs and metrics to be able to debug out of memory (OOM) errors alongside the associated logs and metrics.

Visualize traces and profiles data using Traces to profiles

You can link profile and tracing data using your Pyroscope data source with the Tempo data source. To learn more about how profiles and tracing can work together, refer to Profiling and tracing synergies.

Combined traces and profiles let you see granular line-level detail when available for a trace span. This allows you pinpoint the exact function that’s causing a bottleneck in your application as well as a specific request.

For more information, refer to the Traces to profile section and Link tracing and profiling with span profiles.

Provision the Pyroscope data source

You can modify the Grafana configuration files to provision the Pyroscope data source. To learn more, and to view the available provisioning settings, refer to provisioning documentation.

Here is an example configuration:

apiVersion: 1

datasources:
  - name: Grafana Pyroscope
    type: grafana-pyroscope-datasource
    url: http://localhost:4040
    jsonData:
      minStep: '15s'

Tempo data source

Fri, 03 Apr 2026 19:43:06 +0000

Tempo data source

Grafana ships with built-in support for Tempo, a high-volume, minimal-dependency trace storage, open source tracing solution from Grafana Labs.

To learn more about traces, refer to Introduction to tracing.

To use traces, you need you have an application or service that is instrumented to emit traces. Refer to the Instrument for tracing for more information.

Add a data source

This video explains how to add data sources, including Loki, Tempo, and Mimir, to Grafana and Grafana Cloud. Tempo data source set up starts at 4:58 in the video.

Learn more

After you’ve added the data source, you can configure it so that your Grafana instance’s users can create queries in its query editor when they build dashboards and use Explore.

Configure the Tempo data source
Guide for configuring Tempo in Grafana
Best practices for tracing
Use best practices to plan how you implement tracing.
Use traces in Grafana
Learn about how you can use tracing data in Grafana Cloud to query data, generate metrics, and link your tracing data with logs, metrics, and profiles.
Query tracing data
Guide for using the Tempo data source's query editor
Service Graph and Service Graph view
Use the Service Graph and Service Graph view
Span filters
Use span filters to filter spans in the timeline viewer

TestData data source

Fri, 03 Apr 2026 19:43:06 +0000

TestData data source

Grafana ships with a TestData data source, which creates simulated time series data for any panel. You can use it to build your own fake and random time series data and render it in any panel, which helps you verify dashboard functionality since you can safely and easily share the data.

For instructions on how to add a data source to Grafana, refer to the administration documentation. Only users with the organization administrator role can add data sources.

Configure the data source

To configure basic settings for the data source, complete the following steps:

Click Connections in the left-side menu.
Under Your connections, click Data sources.
Enter TestData in the search bar.

Select TestData.

The Settings tab of the data source is displayed. The data source doesn’t provide any settings beyond the most basic options common to all data sources:

Name	Description
Name	Sets the name you use to refer to the data source in panels and queries.
Default	Defines whether this data source is pre-selected for new panels.

Create mock data

Once you’ve added the TestData data source, your Grafana instance’s users can use it as a data source in any metric panel.

Choose a scenario

Instead of providing a query editor, the TestData data source helps you select a Scenario that generates simulated data for panels.

You can assign an Alias to each scenario, and many have their own options that appear when selected.

Available scenarios:

Annotations
Conditional Error
CSV Content
CSV File
CSV Metric Values
Datapoints Outside Range
Exponential heatmap bucket data
Flame Graph
Grafana API
Grafana Live
Linear heatmap bucket data
Load Apache Arrow Data
Logs
No Data Points
Node Graph
Predictable CSV Wave
Predictable Pulse
Random Walk
Random Walk (with error)
Random Walk Table
Raw Frames
Simulation
Slow Query
Streaming Client
Table Static
Trace
USA generated data

Import a pre-configured dashboard

TestData also provides an example dashboard.

To import the example dashboard:

Navigate to the data source’s configuration page.
Select the Dashboards tab.
Select Import for the Simple Streaming Example dashboard.

To customize an imported dashboard:

To customize the imported dashboard, we recommend that you save it under a different name. If you don’t, upgrading Grafana can overwrite the customized dashboard with the new version.

Use test data to report issues

If you report an issue on GitHub involving the use or rendering of time series data, we strongly recommend that you use this data source to replicate the issue. That makes it much easier for the developers to replicate and solve your issue.

Use a custom version of TestData

Note
This feature is experimental and requires Grafana version 10.3.0 or later.

If you want to use a version of TestData different from the one shipped with Grafana, follow these steps:

Set the configuration field as_external for the plugin to true. An example configuration would be:
ini
```
[plugin.grafana-testdata-datasource]
as_external = true
```
Restart Grafana.

These settings, if enabled, allow you to to install TestData as an external plugin and manage its lifecycle independently of Grafana.

With the feature toggle disabled (default) TestData can still be installed as an external plugin, but it has no effect as the bundled, Core version of TestData is already installed and takes precedence.

Zipkin data source

Fri, 03 Apr 2026 19:43:06 +0000

Zipkin data source

Grafana ships with built-in support for Zipkin, an open source, distributed tracing system. This topic explains configuration and queries specific to the Zipkin data source.

Once you’ve added the Zipkin data source, you can configure it so that your Grafana instance’s users can create queries in its query editor when they build dashboards and use Explore.

You can also upload a JSON trace file, link to a trace ID from logs, and link to a trace ID from metrics.

Configure the data source

To configure basic settings for the data source, complete the following steps:

Click Connections in the left-side menu.
Under Your connections, click Data sources.
Enter Zipkin in the search bar.
Select Zipkin.

The Settings tab of the data source is displayed.

Set the data source’s basic configuration options:

Name	Description
Name	Sets the name you use to refer to the data source in panels and queries.
Default	Defines whether this data source is pre-selected for new panels.
URL	Sets the URL of the Zipkin instance, such as `http://localhost:9411`.
Basic Auth	Enables basic authentication for the Zipkin data source.
User	Defines the user name for basic authentication.
Password	Defines the password for basic authentication.

Trace to logs

Note
If you use Grafana Cloud, open a support ticket in the Cloud Portal to access this feature.

The Trace to logs setting configures the trace to logs feature that is available when you integrate Grafana with Zipkin.

There are two ways to configure the trace to logs feature:

Use a simplified configuration with default query, or
Configure a custom query where you can use a template language to interpolate variables from the trace or span.

Use a simple configuration

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Set start and end time shift. As the logs timestamps may not exactly match the timestamps of the spans in trace it may be necessary to search in larger or shifted time range to find the desired logs.
Select which tags to use in the logs query. The tags you configure must be present in the spans attributes or resources for a trace to logs span link to appear. You can optionally configure a new name for the tag. This is useful if the tag has dots in the name and the target data source does not allow using dots in labels. In that case, you can for example remap http.status to http_status.
Optionally, switch on the Filter by trace ID and/or Filter by span ID setting to further filter the logs if your logs consistently contain trace or span IDs.

Configure a custom query

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Set start and end time shift. Since the logs timestamps may not exactly match the timestamps of the spans in the trace, you may need to widen or shift the time range to find the desired logs.
Optionally, select tags to map. These tags can be used in the custom query with ${__tags} variable. This variable will interpolate the mapped tags as list in an appropriate syntax for the data source and will only include the tags that were present in the span omitting those that weren’t present. You can optionally configure a new name for the tag. This is useful when the tag has dots in the name and the target data source does not allow using dots in labels. For example, you can remap http.status to http_status. If you don’t map any tags here, you can still use any tag in the query like this method="${__span.tags.method}".
Skip Filter by trace ID and Filter by span ID settings as these cannot be used with a custom query.
Switch on Use custom query.
Specify a custom query to be used to query the logs. You can use various variables to make that query relevant for current span. The link will only be shown only if all the variables are interpolated with non-empty values to prevent creating an invalid query.

Variables that can be used in a custom query

To use a variable you need to wrap it in ${}. For example ${__span.name}.

Variable name	Description
__tags	This variable uses the tag mapping from the UI to create a label matcher string in the specific data source syntax. The variable only uses tags that are present in the span. The link is still created even if only one of those tags is present in the span. You can use this if all tags are not required for the query to be useful.
__span.spanId	The ID of the span.
__span.traceId	The ID of the trace.
__span.duration	The duration of the span.
__span.name	Name of the span.
__span.tags	Namespace for the tags in the span. To access a specific tag named `version`, you would use `${__span.tags.version}`. In case the tag contains dot, you have to access it as `${__span.tags["http.status"]}`.
__trace.traceId	The ID of the trace.
__trace.duration	The duration of the trace.
__trace.name	The name of the trace.

The following table describes the ways in which you can configure your trace to logs settings:

Setting name	Description
Data source	Defines the target data source. You can select only Loki or Splunk logs data sources.
Span start time shift	Shifts the start time for the logs query, based on the span’s start time. You can use time units, such as `5s`, `1m`, `3h`. To extend the time to the past, use a negative value. Default: `0`.
Span end time shift	Shifts the end time for the logs query, based on the span’s end time. You can use time units. Default: `0`.
Tags	Defines the tags to use in the logs query. Default: `cluster`, `hostname`, `namespace`, `pod`, `service.name`, `service.namespace`. You can change the tag name for example to remove dots from the name if they are not allowed in the target data source. For example, map `http.status` to `http_status`.
Filter by trace ID	Toggles whether to append the trace ID to the logs query.
Filter by span ID	Toggles whether to append the span ID to the logs query.
Use custom query	Toggles use of custom query with interpolation.
Query	Input to write custom query. Use variable interpolation to customize it with variables from span.

Trace to metrics

The Trace to metrics setting configures the trace to metrics feature available when integrating Grafana with Zipkin.

To configure trace to metrics:

Select the target data source from the drop-down list.

You can also click Open advanced data source picker to see more options, including adding a data source.
Create any desired linked queries.

Setting name	Description
Data source	Defines the target data source.
Tags	Defines the tags used in linked queries. The key sets the span attribute name, and the optional value sets the corresponding metric label name. For example, you can map `k8s.pod` to `pod`. To interpolate these tags into queries, use the `$__tags` keyword.

Each linked query consists of:

Link Label: (Optional) Descriptive label for the linked query.
Query: The query ran when navigating from a trace to the metrics data source. Interpolate tags using the $__tags keyword. For example, when you configure the query requests_total{$__tags}with the tags k8s.pod=pod and cluster, the result looks like requests_total{pod="nginx-554b9", cluster="us-east-1"}.

Node Graph

The Node Graph setting enables the Node Graph visualization, which is disabled by default.

Once enabled, Grafana displays the Node Graph after loading the trace view.

Span bar

The Span bar setting helps you display additional information in the span bar row.

You can choose one of three options:

Name	Description
None	Adds nothing to the span bar row.
Duration	(Default) Displays the span duration on the span bar row.
Tag	Displays the span tag on the span bar row. You must also specify which tag key to use to get the tag value, such as `component`.

Provision the data source

Provisioning example

apiVersion: 1

datasources:
  - name: Zipkin
    type: zipkin
    uid: EbPG8fYoz
    url: http://localhost:16686
    access: proxy
    basicAuth: true
    basicAuthUser: my_user
    readOnly: true
    isDefault: false
    jsonData:
      tracesToLogsV2:
        # Field with an internal link pointing to a logs data source in Grafana.
        # datasourceUid value must match the uid value of the logs data source.
        datasourceUid: 'loki'
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
        tags: ['job', 'instance', 'pod', 'namespace']
        filterByTraceID: false
        filterBySpanID: false
        customQuery: true
        query: 'method="$${__span.tags.method}"'
      tracesToMetrics:
        datasourceUid: 'prom'
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
        tags: [{ key: 'service.name', value: 'service' }, { key: 'job' }]
        queries:
          - name: 'Sample query'
            query: 'sum(rate(traces_spanmetrics_latency_bucket{$$__tags}[5m]))'
      nodeGraph:
        enabled: true
      traceQuery:
        timeShiftEnabled: true
        spanStartTimeShift: '1h'
        spanEndTimeShift: '-1h'
      spanBar:
        type: 'None'
    secureJsonData:
      basicAuthPassword: my_password

Query the data source

You can query and display traces from Zipkin via Explore

This topic explains configuration and queries specific to the Zipkin data source. For general documentation on querying data sources in Grafana, see Query and transform data.

Query by trace ID

To query a particular trace:

Select the TraceID query type.
Enter the trace’s ID into the Trace ID field.

Query by trace selector

To select a particular trace from all traces logged in the time range you have selected in Explore, you can also query by trace selector. The trace selector has three levels of nesting:

The service you’re interested in.
Particular operation, part of the selected service
Specific trace in which the selected operation occurred, represented by the root operation name and trace duration

View data mapping in the trace UI

You can view Zipkin annotations in the trace view as logs with annotation value displayed under the annotation key.

Upload a JSON trace file

You can upload a JSON file that contains a single trace and visualize it. If the file has multiple traces, Grafana visualizes its first trace.

Trace JSON example

[
  {
    "traceId": "efe9cb8857f68c8f",
    "parentId": "efe9cb8857f68c8f",
    "id": "8608dc6ce5cafe8e",
    "kind": "SERVER",
    "name": "get /api",
    "timestamp": 1627975249601797,
    "duration": 23457,
    "localEndpoint": { "serviceName": "backend", "ipv4": "127.0.0.1", "port": 9000 },
    "tags": {
      "http.method": "GET",
      "http.path": "/api",
      "jaxrs.resource.class": "Resource",
      "jaxrs.resource.method": "printDate"
    },
    "shared": true
  }
]

Span Filters

Using span filters, you can filter your spans in the trace timeline viewer. The more filters you add, the more specific are the filtered spans.

You can add one or more of the following filters:

Service name
Span name
Duration
Tags (which include tags, process tags, and log fields)

To only show the spans you have matched, you can press the Show matches only toggle.

Link to a trace ID from logs

You can link to Zipkin traces from logs in Loki, Elasticsearch, Splunk, and other logs data sources by configuring an internal link.

To configure this feature, see the Derived fields section of the Loki data source docs or the Data links section of the Elasticsearch or Splunk data source docs.

Link to a trace ID from metrics

You can link to Zipkin traces from metrics in Prometheus data sources by configuring an exemplar.

To configure this feature, see the introduction to exemplars documentation.

Data sources on Grafana Labs

Alertmanager data source

Alertmanager data source

Alertmanager implementations

Configure the data source

Provision the Alertmanager data source

Amazon CloudWatch data source

Amazon CloudWatch data source

Import pre-configured dashboards

Get the most out of the data source

Control pricing

Manage service quotas

Cross-account observability

Azure Monitor data source

Azure Monitor data source

Supported Azure clouds

Supported Azure services

Get started

Additional features

Pre-built dashboards

Related resources

Elasticsearch data source

Elasticsearch data source

Key capabilities

Before you begin

Supported Elasticsearch versions

Additional resources

Related data sources

Google Cloud Monitoring data source

Google Cloud Monitoring data source

Get started

Supported query types

Additional features

Pre-configured dashboards

Related resources

Graphite data source

Graphite data source

Get Grafana metrics into Graphite

Graphite and Loki integration

Get the most out of the data source

InfluxDB data source

InfluxDB data source

Supported versions

Key capabilities

Get started with the InfluxDB data source

Additional resources

Configure the Loki data source

Loki data source

Supported Loki versions

Adding a data source

Provision the data source

Provisioning examples

Query the data source

Use template variables

Jaeger data source

Jaeger data source

Configure the data source

Trace to logs

Use a simple configuration

Configure a custom query

Variables that can be used in a custom query

Trace to metrics

Node Graph

Span bar

Provision the data source

Provisioning example

Query the data source

Query by search

Query by trace ID

Upload a JSON trace file

Trace JSON example

Span Filters

Link to a trace ID from logs

Link to a trace ID from metrics

Visualizing the dependency graph

Querying Data via gRPC Endpoint (Public Preview)

Microsoft SQL Server data source

Microsoft SQL Server (MSSQL) data source

Supported versions

Key capabilities