Grafana Dashboard for Pi-hole Stats

One of the most important parts of my home network and homelab is my Pi-hole, because who likes internet ads? I also enjoy pretty graphs, so I recently started looking for a way to add Pi-hole stats to my Grafana dashboard. Unfortunately all the other monitoring tools that I came across had a fatal flaw in how they recorded the number of queries throughout the day: they simply plotted the “queries today” value at each poll interval. However, Pi-hole tracks queries in 10-minute rolling windows. Each value represents the number of queries for the past 23 hours and 50 minutes, plus the number of queries in the current 10-minute window. Charting this over time looks like a sawtooth, and isn’t what we want:

My goal was to replicate the same chart that the Pi-hole dashboard shows.

Using the REST API, I simply poll the overTimeData10mins endpoint and record those datapoints in InfluxDB. Sure, there will be a lot of duplicate data, but those datapoints will simply be overwritten in the database. Once I got this working, I made quick work of populating all the other data that the API provides.

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# Ads over time
for timestamp,count in ads_over_time.items():
    points.append(Point.from_dict(
        {
            "measurement": "over_time_data",
            "tags": tags,
            "fields": {
                "ads_over_time": count
            },
            "time": int(timestamp)
        },
        WritePrecision.S
    ))

The last major challenge was displaying the tables of Top Queries, Top Ads, and Top Sources. For most other datapoints I was able to simply take the JSON data from the API and drop it in as the field data for the database record. However, attempting to group multiple records by time, and sort them turned into a massive headache, and failed in several edge cases. To make querying the data easier, I converted the JSON data from the REST API into a comma-separated string. This way there would always be a single, latest record in the database with the top items.

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points.append(Point.from_dict(
    {
        "measurement": "top_queries",
        "tags": tags,
        "fields": {
            "top_10": self._json_to_csv(stats.pop('top_queries'))
        },
        "time": now_seconds
    },
    WritePrecision.S
))

In the end I tossed together a little Grafana dashboard to display all the data I collected:

The script is available as a Docker image on DockerHub, and can be run easily:

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docker run -d \
    -e PIHOLE_ALIAS="pihole" \
    -e PIHOLE_ADDRESS="http://pi.hole" \
    -e PIHOLE_TOKEN="pihole_api_token" \
    -e INFLUXDB_ADDRESS="http://influxdb:8086" \
    -e INFLUXDB_ORG="my-org" \
    -e INFLUXDB_TOKEN="super_secret_token" \
    -e INFLUXDB_BUCKET="pihole" \
    avojak/pihole-influxdb:latest

Alternatively you can run the Python application directly from the command-line:

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python3 pihole-influxdb.py \
    --pihole-alias "pihole" \
    --pihole-address "http://pi.hole" \
    --pihole-token "pihole_api_token" \
    --influxdb-address "http://influxdb:8096" \
    --influxdb-org "my-org" \
    --influxdb-token "super_secret_token" \
    --influxdb-bucket "pihole"

Check out the full source over on my GitHub:

avojak/pihole-influxdb-monitor