ADX

Marple integrates seamlessly with ADX, a powerful time-series database optimized for large-scale telemetry data.

The connection credentials are listed below with examples with randomised values.

{
    "appId": "8aa131d-dac5-429d-ada2-95adbc83b",
    "appKey": "anv8Q~TJ6ubna15TCi.wAg9015EcDL",
    "tenantId": "11344e0f-83d3-4b52-9a38-a6bbb1a360a4",
    "dbName": "my-adx-database",
    "dbHost": "https://myadxdatabase.westeurope.kusto.windows.net"
}

Queries are executed using Kusto Query Language (KQL).

Marple offers predefined queries for fetching datasets, signals, and time-series data.

ADX example queries

• To retreive datasets:

{{TABLE}}
{%- for key, values in metadata.items() %}
| where {{key}} in ('{{values|join("', '")}}')
{% endfor %}
| summarize
    timestamp_start = min({{TIME_COLUMN}}),
    timestamp_stop  = max({{TIME_COLUMN}}),
    by session, car, outing
| extend 
    display_name = strcat(session, ' | ', car, ' | ', outing), 
    realtime = (['acquisition type'] == 'Telemetry' and unixtime_seconds_todatetime(timestamp_stop) > ago(1m))

• To retrieve a signal list:

{{TABLE}}
| where outing == '{{dataset.outing}}'
| summarize ['count'] = count()
    by name=signal

• To retrieve the time series data

| where outing == '{{dataset.outing}}'
| where {{timestamp_start}} <= timestamp
| where {{timestamp_stop}} >= timestamp
| where signal == '{{signal.name}}'
| project name = signal, timestamp = timestamp, value = toreal(value)