During late August 2021, we made Amazon Managed Grafana generally available, and around re:Invent we launched some new features, specifically for new plugins. This post provides you with the high-level overview and shows you some of them in action.

Amazon Managed Grafana is a fully managed service that handles the provisioning, setup, scaling, and maintenance of Grafana servers. It is generally available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Europe (London), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), and Asia Pacific (Seoul).  At re:Invent 2021, we upgraded the Amazon Managed Grafana data plane to use Grafana 8.2 (from 8.0), and now it also supports CloudFlare, Zabbix, and Splunk Infrastructure Monitoring data sources. We worked with Grafana Labs to make a series of plugins available as open source (more later on in this post) for self-managed Grafana. Moreover, we’re offering them for you to use in Amazon Managed Grafana. Let’s look closer at the new data sources and visualization.

Geomap visualization

You can now use the Geomap panel visualization to visualize geospatial data in a map view. You can configure multiple overlay styles to visually represent important location-based characteristics of your data, such as the heatmap overlay to cluster data points for visualizing hotspot locations with high data densities.

The following example shows the OpenStreetMap on AWS open data set (OSM) in action, using the Geomap panel that’s readily available on the left-hand side drop down within the Amazon Managed Grafana workspace. To re-create this, upload the OSM data into an S3 bucket, configure Athena as a data source, and then visualize the query results of available restaurants in Las Vegas by using the Geomap visualization.

Grafana Geomap visualization showing a map of Las Vegas with restaurant locations plotted

Figure 1: Grafana Geomap visualization showing a map of Las Vegas with restaurant locations plotted

See also:

Amazon Athena/S3 data source

Amazon Athena is a distributed query engine (think: PrestoDB as a service) that lets you query a range of structured data formats (including JSON, CSV, ORC, and Parquet) using SQL, with the data stored in Amazon Simple Storage Service (S3) buckets. This means that for a number of non-relational datasets, from security (VPC flow logs) to budgeting (Cost and Usage Report (CUR)) to the previously mentioned OpenStreetMap geo data, you can now use Grafana to query and visualize your data.

The default dashboard that we bundled with the Athena data source is for the CUR data, and it looks like this:

AWS Cost and Usage Report dashboard in Grafana

Figure 2: AWS Cost and Usage Report dashboard in Grafana

Another use case for the Athena data source is querying VPC flow logs, as is shown in the following:

Grafana visualization showing VPC Flow logs query using the Athena data-source plugin

Figure 3: Grafana visualization showing VPC Flow logs query using the Athena data-source plugin

See also:

Amazon Redshift data source

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud, which now is also serverless. This data source now lets you perform analytical SQL queries against all of your Redshift clusters. By default, we included the performance monitoring dashboard:

Grafana visualization showing the performance monitoring dashboard of a Redshift cluster

Figure 4: Grafana visualization showing the performance monitoring dashboard of a Redshift cluster

Another example dashboard that you can use to visualize contents of the Redshift sample database looks like the following:

Grafana visualization showing sample data from a Redshift cluster using the Redshift datasource plugin

Figure 5: Grafana visualization showing sample data from a Redshift cluster using the Redshift datasource plugin

See also:

CloudWatch Metrics Insights

Amazon CloudWatch announced the preview availability CloudWatch Metrics Insights, a powerful high-performance SQL query engine that you can use to query your metrics at scale. Using the updated CloudWatch datasource plugin, you can query CloudWatch Metrics using SQL. As shown in the following image, you now have a free text area to simply type in the Metrics Insights query that you want to use to query the data.

For example, if you use the following CloudWatch Metrics Insights query that lists the top Lambda function invocations ordered by invocation count,

SELECT SUM(Invocations)
FROM SCHEMA("AWS/Lambda", FunctionName)
GROUP BY FunctionName
ORDER BY SUM() DESC

then the resulting data graphed on a time series visualization looks like the following:

Grafana visualization showing Lambda invocation data using CloudWatch Metrics Insights SQL query option

Figure 6: Grafana visualization showing Lambda invocation data using CloudWatch Metrics Insights SQL query option

Furthermore, you can use the Builder mode to construct the query easily. The following screenshot shows how the exact same query as above can be built using the Builder option with a donut visualization:

Grafana visualization showing Lambda invocation data using CloudWatch Metrics Insights query builder option

Figure 7: Grafana visualization showing Lambda invocation data using CloudWatch Metrics Insights query builder option

Learn more about CloudWatch Metrics Insights in the Amazon Managed Grafana docs, and take a look at the blog post that goes through this feature in greater detail.

IoT TwinMaker

Currently in preview, AWS IoT TwinMaker lets you build operational digital twins of physical and digital systems. IoT TwinMaker creates digital visualizations using measurements and analysis from a variety of real-world sensors, cameras, and enterprise applications to help you track your physical factory, building, or industrial plant. Also, see our Amazon Managed Grafana docs for how to use it.

What’s next

As you know, a large number of the features we deliver are based on what customers like yourself tell us. So please tell us through your AWS account contacts on what you like to see next, from features to data source and what you think we should focus on in 2022!

About the authors

Vikram Venkataraman

Michael Hausenblas

Michael is a Solution Engineering Lead in the AWS open source observability service team.

Vikram Venkataraman

Imaya Kumar Jagannathan

Imaya is a Principal Solution Architect focused on AWS observability services including Amazon CloudWatch, AWS X-Ray, Amazon Managed Service for Prometheus, Amazon Managed Grafana and AWS Distro for Open Telemetry. He is passionate about monitoring and observability and has a strong application development and architecture background. He likes working on distributed systems and is excited to talk about microservice architecture design. He loves programming on C#, working with containers and serverless technologies. Find him on Twitter & LinkedIn – @imaya.