# Building Network Dashboards in Grafana
with gNMIc and Nokia SR OS: A
Step-by-Step Guide

This guide builds a complete Grafana dashboard for a Nokia SR OS node from gNMIc telemetry: interface throughput, packet rates, CPU and memory, BGP peers and ISIS adjacencies.

### Assumptions

*   gNMIc is already streaming from SR OS and exposing metrics on port **9804** with a specific prefix
    
*   Prometheus is scraping gNMIc
    
*   Grafana runs at http://:3000
    
*   Basic knowledge of PromQL
    

# Part 1: Confirm the data exists

Dashboards fail because the metric names are wrong, not because of styling. This initial step is very important.

## 1.1 Check gNMIc is exposing the metrics

```python
curl -s localhost:9804/metrics | grep '^sros_' | head -20

# Output sample 

sros_state_port_statistics_in_octets{port_port_id="1/1/c1",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c1/1",source="172.20.20.20",subscription_name="port_stats_nokia"} 8.224912605e+09 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c1/2",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c1/3",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c1/4",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c2",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504
sros_state_port_statistics_in_octets{port_port_id="1/1/c2/1",source="172.20.20.20",subscription_name="port_stats_nokia"} 0 1791161161504

```

If this is empty, **stop**. The problem is in **gNMIc** or on the router, not Grafana.

## 1.2 List the unique metric names

```python
curl -s localhost:9804/metrics | grep '^sros_' | cut -d'{' -f1 | sort -u
```

Typical names:

```python
sros_state_port_statistics_in_octets
sros_state_port_statistics_in_packets
sros_state_port_statistics_out_octets
sros_state_port_statistics_out_packets
sros_state_router_bgp_neighbor_statistics_session_state
sros_state_router_isis_interface_adjacency_oper_state
```

## 1.3 Look at the labels in one series

```python
curl -s localhost:9804/metrics | grep 'in_octets' | head -3
```

**Example :**

```python
sros_state_port_statistics_in_octets{port_port_id="1/1/c1",source="172.20.20.20",subscription_name="port_stats_nokia"}
```

*   gNMIc builds a label name from the path element plus the key name, so `port[port-id=*]` becomes `port_port_id`
    
*   **source** is the target address exactly as written in the gNMIc config
    

<div data-node-type="callout">
<div data-node-type="callout-emoji">💡</div>
<div data-node-type="callout-text">Note: Always read the real label names from your own output</div>
</div>

The label names you see (`port_port_id`, `source`) are what you filter and group by.

## 1.4 Confirm Prometheus is scraping

Open `http://:9090/targets`. The gNMIc job must show **UP.** Then run `sros_state_port_statistics_in_octets` in the Prometheus query box. If it returns rows, Grafana will see it.

# Part 2: Connect Grafana to Prometheus

1.  Log in to Grafana (default `admin / admin` on first run).
    
2.  Left menu: **Connections**, **Data sources**, Add new data source, Prometheus.
    
3.  Set the fields: Name - **Prometheus**, URL **\-** [**http://prometheus:9090**](http://prometheus:9090) (host IP address) , Scrape Interval - **15s** matching `prometheus.yml`
    
4.  Click **Save** & **test**. You want a green "**Successfully queried the Prometheus API**" message
    
5.  **Test a query**. Open **Explore**, choose the Prometheus data source, switch the editor to **Code**, and run:
    

```python
sros_state_port_statistics_in_octets
```

You should see one line per port. If not, return to Part 1. **Nothing later works until this does.**

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/05179279-d928-4f9c-bbce-0b5a0f55db89.jpg align="center")

# Part 3: Create the dashboard and variables

1.  **Dashboards**, **New,** **New dashboard**. Do not add a panel yet.
    
2.  Click the gear icon (**Settings**), open the **Variables** tab, then **Add variable**.
    

### Variable 1: node

| Field | Value |
| --- | --- |
| Variable Type | Classic query |
| Name | node |
| Label | Node |
| Data source | Prometheus |
| Query | label\_values(sros\_state\_port\_statistics\_in\_octets, source) |
| Multi-value | On |
| Include All option | On |

Click **Preview.** You should see a router IP; in my example, it is `172.20.20.20`. Click **Back to list**.

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/31b9d8fc-5ae0-45d7-86b7-45d56b08bb14.jpg align="center")

### Variable 2: port

| Field | Value |
| --- | --- |
| Variable Type | Classic Query |
| Name | port |
| Label | Port |
| Query | label\_values(sros\_state\_port\_statistics\_in\_octets{source=~"$node"}, port\_port\_id) |
| Multi-value | On |
| Include All option | On |

The `{source=~"$node"}` filter makes the port list depend on the selected node.

Click **Back to dashboard**, save, and give it a **name**. Two dropdowns now appear at the top. Every panel below uses them.

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/9768a87c-8e68-4568-a251-288a4409f6ed.jpg align="center")

# Part 4: Panel 1, Interface throughput

1.  **Add**, **Visualization**, select the Prometheus data source, and choose **Time series**.
    
2.  Switch the query editor from **Builder** to **Code**.
    

**Query A** (inbound) , legend `{{ port_port_id }} in` :

```python
rate(sros_state_port_statistics_in_octets{source=~"$node",
port_port_id=~"$port"}[$__rate_interval]) * 8
```

**Query B** (outbound , click + **Query**) , legend `{{ port_port_id }} out` :

```python
rate(sros_state_port_statistics_out_octets{source=~"$node",
port_port_id=~"$port"}[$__rate_interval]) * 8
```

Why each part exists:

*   `rate(...[$__rate_interval])` turns a constantly growing counter into a per-second rate.
    
*   `* 8` converts bytes to bits, which is how link speeds are quoted.
    
*   `$node` and `$port` come from the dropdowns.
    

## Panel Options

*   **Title**: Interface Throughput.
    
*   **Standard options**, **Unit**: Data rate, bits/sec (SI). Type unit in the options search box to jump to it.
    
*   **Legend**, **Mode**: Table; **Values**: Mean and Max
    

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/5e5c3cc6-c956-4d8b-abf9-d6ec37a4de07.jpg align="center")

# Part 5: Panel 2, Packet rate

Duplicate Panel 1: panel menu, **More**, **Duplicate**. Edit the copy.

**Query A** (inbound) , legend {{ port\_port\_id }} in :

```python
rate(sros_state_port_statistics_in_packets{source=~"$node", port_port_id=~"$port"}[$__rate_interval])
```

**Query B** (outbound , click + Query) , legend {{ port\_port\_id }} out :

```python
rate(sros_state_port_statistics_out_packets{source=~"$node", port_port_id=~"$port"}[$__rate_interval]) 
```

There is no `* 8` here, since packets are not **bytes**. **Title**: Packet Rate. **Unit**: Throughput, packets/sec.

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/8f95338e-a730-4e7e-a08d-d5169f69e67d.jpg align="center")

# Part 6: Panel 4, CPU and memory

Find the exact leaf names from your `system_resources` subscription:

```python
# Execute 

curl -s localhost:9804/metrics | grep -Ei 'cpu|memory' | cut -d'{' -f1 | sort -u

# Output 
sros_state_system_cpu_summary_busiest_core_utilization_cpu_time
sros_state_system_cpu_summary_busiest_core_utilization_cpu_usage
sros_state_system_cpu_summary_busiest_core_utilization_time_used
sros_state_system_cpu_summary_idle_cpu_time
sros_state_system_cpu_summary_idle_cpu_usage
sros_state_system_cpu_summary_idle_time_used
sros_state_system_cpu_summary_total_cpu_time
sros_state_system_cpu_summary_total_cpu_usage
sros_state_system_cpu_summary_total_time_used
sros_state_system_cpu_summary_usage_cpu_time
sros_state_system_cpu_summary_usage_cpu_usage
sros_state_system_cpu_summary_usage_time_used
sros_state_system_memory_pools_summary_available_memory
sros_state_system_memory_pools_summary_current_total_size
sros_state_system_memory_pools_summary_total_in_use
```

**CPU gauge**

1.  **Add**, **Visualization**, **Gauge**.
    
2.  **Query**:
    
    ```promql
    sros_state_system_cpu_summary_usage_cpu_usage{source=~"$node"}
    ```
    
3.  **Uni**t: Percent (0-100). **Min**: 0. Max: 100.
    
4.  **Thresholds**: base green, 70 amber, 90 red.
    
5.  **Title**: CPU.
    

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/df91839a-0b57-4b3e-b1b0-bcf0dff7692b.jpg align="center")

**Memory**: plot it the same way. If you only have allocated and available bytes, compute it:

**Query**:

```python
sros_state_system_memory_pools_summary_total_in_use{source=~"$node"}
/
(
  sros_state_system_memory_pools_summary_current_total_size{source=~"$node"}
  + sros_state_system_memory_pools_summary_available_memory{source=~"$node"}
)
* 100
```

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/51770aa6-5471-4d2d-9557-6a6967bb2cbe.jpg align="center")

# Part 7: Panel 5, BGP peers

**Stat: BGP peers established**

1.  **Add,** **Visualization**, **Stat**.
    
2.  **Query**:
    
    ```promql
    sum(sros_state_router_bgp_neighbor_statistics_session_state{source=~"$node"})
    ```
    
3.  **Title**: BGP Peers Established
    
4.  **Thresholds**: base red, 1 green (or your expected peer count).
    
5.  **Color mode**: Background.
    

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/11dd3d45-3e05-4707-b145-7df7d9c64f12.jpg align="center")

**Table: per-peer state**

1.  **Add,** **Visualization**, **Table**
    
2.  **Query**:
    
    ```promql
    sros_state_router_bgp_neighbor_statistics_session_state{source=~"$node"}
    ```
    
3.  **Transform**, **Organize fields by name:** hide **Time**, **name**, **app**, **job**, **instance**, **subscription\_name**; rename **Value** to **State**.
    
4.  **Value mappings**: 1 to **Established** (green), 0 to **Down** (red).
    
5.  **Cell options**, **Cell type**: Colored background.
    

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/de51cd27-01d3-41ac-8a17-06a0e75f5b58.jpg align="center")

# Part 8: Panel 6, ISIS adjacencies

**Stat**

1.  **Add**, **Visualization**, **State timeline**.
    
2.  **Query**:
    
    ```promql
    sum(sros_state_router_isis_interface_adjacency_oper_state{source=~"$node"})
    ```
    
3.  **Thresholds**: red base, green at your expected adjacency count.
    
4.  **Title**: ISIS Adjacencies
    

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/27f3b740-7ca4-4ec2-a092-5523b9bd7f5d.jpg align="center")

# Part 9: Arrange the layout

| Row | Panels |
| --- | --- |
| Top | BGP Peers Established, ISIS Adjacencies Up, CPU gauge, memory gauge |
| Middle | Interface Throughput (wide), Packet Rate |

![](https://cdn.hashnode.com/uploads/covers/68933f4690103a1d4a8d7df7/4ccacf12-f80f-4675-a53e-36e276671abb.jpg align="center")

# Part 10: Finishing touches

1.  Top right: **Time range** Last 1 hour; **Refresh** 10s.
    
2.  Save with **Ctrl+S** and add a version note.
    
3.  **Export** the dashboard: **Export**, **Export as JSON**, tick **Export for sharing externally**, **Save** to file. The data source becomes a variable, so the JSON imports cleanly into any other Grafana or into Git.
    

# Part 11: Troubleshooting a blank panel

1.  **Run the query in Explore**. If Explore is empty, the problem is the query or the data, not the panel.
    
2.  **Remove label filters**. Delete `{source=~"$node", port_port_id=~"$port"}` and retry. If data appears, a label name or variable value is wrong.
    
3.  Check spelling against /metrics. `port_port_id` versus `port-id` is the classic mistake.
    
4.  **Widen the time range**. `on_change` series can look empty over a very short window.
    
5.  **Rates** show "No data". `rate()` needs at least two samples inside the window, so `$__rate_interva`l should be at least four times the scrape interval.