Telemetry

dlt collects and reports anonymous usage information. This information is essential to figuring out how we should improve the library. Telemetry does not send any personal data. We create a random tracking cookie that is stored in your ~/.dlt directory. You can disable telemetry at any moment or send it to your own servers instead.

How to opt out

You can disable telemetry by adding --disable-telemetry to any dlt command.

This command will disable telemetry both in the current project and globally for the whole machine:

dlt --disable-telemetry

While this command will also permanently disable telemetry and then initialize the chess pipeline:

dlt --disable-telemetry init chess duckdb

You can check the current telemetry status with this command:

dlt telemetry

Another way to disable telemetry is to set the runtime.dlthub_telemetry option in the config.toml file in the .dlt folder.

[runtime]

dlthub_telemetry=false

What we send when

Anonymous telemetry is sent when:

• Any dlt command is executed from the command line. The data contains the command name. In the case of the dlt init command, we also send the requested destination and data source names.
• When pipeline.run is called, we send information when the extract, normalize, and load steps are completed. The data contains the destination name (e.g., duckdb), hashes of the dataset name, pipeline name, default schema name, destination fingerprint (which is a hash of selected destination configuration fields), elapsed time, and whether the step succeeded or not.
• When pipeline.dataset is called, we send information about dataset access attempts. The data contains the destination name and type (e.g., duckdb), hashes of the dataset name, default and requested schema names, as well as whether the access attempt succeeded.
• When dbt and airflow helpers are used

Here is an example dlt init telemetry message:

{
  "anonymousId": "933dd165453d196a58adaf49444e9b4c",
  "context": {
    "ci_run": false,
    "cpu": 8,
    "exec_info": [],
    "library": {
      "name": "dlt",
      "version": "0.2.0a25"
    },
    "os": {
      "name": "Linux",
      "version": "4.19.128-microsoft-standard"
    },
    "python": "3.8.11"
  },
  "event": "command_init",
  "properties": {
    "destination_name": "bigquery",
    "elapsed": 3.1720383167266846,
    "event_category": "command",
    "event_name": "init",
    "pipeline_name": "pipedrive",
    "success": true
  }
}

Example for load pipeline run step:

{
  "anonymousId": "570816b273a41d16caacc26a797204d9",
  "context": {
    "ci_run": false,
    "cpu": 3,
    "exec_info": [],
    "library": {
      "name": "dlt",
      "version": "0.2.0a26"
    },
    "os": {
      "name": "Darwin",
      "version": "21.6.0"
    },
    "python": "3.10.10"
  },
  "event": "pipeline_load",
  "properties": {
    "destination_name": "duckdb",
    "destination_fingerprint": "",
    "pipeline_name_hash": "OpVShb3cX7qQAmOZSbV8",
    "dataset_name_hash": "Hqk0a3Ov5AD55KjSg2rC",
    "default_schema_name_hash": "Hqk0a3Ov5AD55KjSg2rC",
    "elapsed": 2.234885,
    "event_category": "pipeline",
    "event_name": "load",
    "success": true,
    "transaction_id": "39c3b69c858836c36b9b7c6e046eb391"
  }
}

Example for data access telemetry:

{
  "anonymousId": "570816b273a41d16caacc26a797204d9",
  "context": {
    "ci_run": false,
    "cpu": 3,
    "exec_info": [],
    "library": {
      "name": "dlt",
      "version": "1.15.0"
    },
    "os": {
      "name": "Darwin",
      "version": "21.6.0"
    },
    "python": "3.10.10"
  },
  "event": "data_access_connect",
  "properties": {
    "destination_name": "duckdb",
    "destination_type": "duckdb",
    "dataset_name_hash": "Hqk0a3Ov5AD55KjSg2rC",
    "default_schema_name_hash": "Hqk0a3Ov5AD55KjSg2rC",
    "requested_schema_name_hash": "49ShjWp8x9h+rmyqjP6G",
    "event_category": "data_access",
    "event_name": "connect",
    "success": true
  }
}

The message context

The message context contains the following information:

• anonymousId: a random tracking cookie stored in ~/.dlt/.anonymous_id.
• ci_run: a flag indicating if the message was sent from a CI environment (e.g., GitHub Actions, Travis CI).
• cpu: contains the number of cores.
• exec_info: contains a list of strings that identify the execution environment: (e.g., kubernetes, docker, airflow).
• The library, os, and python give us some understanding of the runtime environment of the dlt.

Send telemetry data to your own tracker

You can set up your own tracker to receive telemetry events. You can create a scalable, globally distributed edge service using dlt and Cloudflare.

Once your tracker is running, point dlt to it. You can use the global config.toml to redirect all pipelines on a given machine.

[runtime]
dlthub_telemetry_endpoint="telemetry-tracker.services4745.workers.dev"

Track events with Segment

You can send anonymous telemetry to your own Segment account. You need to create an HTTP Server source and generate a WRITE KEY, which you then pass to the config.toml like this:

[runtime]
dlthub_telemetry_endpoint="https://api.segment.io/v1/track"
dlthub_telemetry_segment_write_key="<write_key>"