Easy Guide for Connecting Redis to a Grafana Data Source

Introduction 

Redis is a widely used in-memory data store, commonly deployed as a cache, session store, message broker, or fast key-value database. Because Redis often sits on the critical path of an application, having visibility into its behavior (memory usage, client connections, command throughput, cache efficiency) is essential for troubleshooting and performance tuning.

In this guide, we’ll walk through connecting an existing Redis instance directly to Grafana as a datasource. We’ll focus on visualizing key operational metrics exposed by Redis using the INFO command and Grafana’s Redis datasource. This approach is well-suited for learning, exploration, and real-time visibility into Redis behavior. For long-term retention and alerting, we recommend collecting Redis metrics using Telegraf.

Getting Started

Prerequisites

This guide assumes:

• You already have a running Redis instance
• You have access to a Grafana account
• Redis is reachable from the Grafana environment (network and firewall rules allow access)

No Redis exporters or agents are required for this setup, but we're also including an optional guide for configuring the Telegraf agent to collect Redis metrics for long-term storage.

Allow External Access

Redis listens on port 6379 by default. If your server uses a firewall, allow access to this port so Grafana can connect (ubuntu):

sudo ufw allow 6379/tcp
sudo ufw reload

For testing purposes, this rule may be temporarily open. In production environments, access should be restricted using firewall rules, private networking, or authentication.

Connect Redis to Hosted Grafana

Navigate to your Grafana instance => Connect Data => search/select/install Redis:

• Type: Standalone
• Address: <server_ip>:6379
• Click "Save & Test"

curl -s "https://www.hostedgraphite.com/scripts/hg-cli/installer/" | sudo sh

[[inputs.redis]]
  servers = ["tcp://localhost:6379"]

telegraf --config /etc/telegraf/telegraf.conf

Visualizing Redis Metrics in Grafana

Redis does not expose metrics as individual endpoints. Instead, operational metrics are returned as part of the Redis INFO command. Grafana parses this output and allows you to select individual fields for visualization.

Add a Data Source Query (memory)

In your Grafana:

• Navigate to => Dashboards => New Dashboard => "Add new panel"
• Data source: Redis
• Type: redis
• Command: INFO
• Section: Memory
• Optionally use Transforms to include or exclude specific fields

Add Telegraf Metric Query (cpu)

If you also configured Telegraf to collect Redis performance metrics, they are now being forwarded to your configured backend. If metrics are being forwarded to Hosted Graphite's storage backend, they are searchable in the HG Metrics Search UI and can be queried via the default HostedGraphite data source.

For more details on how to collect/visualize Redis metrics and logs, see our related article HERE.

Security Considerations

The Redis datasource connects directly to a running instance but in production environments, Redis should not be exposed publicly without authentication or network restrictions. Common production setups include:

• Restricting access via firewall rules or private networking
• Enabling Redis authentication or ACLs
• Collecting Redis metrics with Telegraf and storing them in Hosted Graphite for long-term retention and alerting

This guide intentionally focuses on a simplified configuration intended for testing and exploration.

Conclusion

Redis provides valuable operational signals that can be easily visualized using Grafana’s Redis datasource. By querying Redis INFO metrics directly, you can quickly gain insight into memory usage, client activity, command throughput, and cache efficiency without deploying additional agents or exporters.

This approach is ideal for real-time visibility and troubleshooting. For production monitoring, alerting, and historical analysis, we recommend collecting Redis metrics with Telegraf and storing them in Hosted Graphite. Together, these options give you flexibility in how you monitor Redis while maintaining a clean and scalable observability setup.