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Sampler - Diagnostics Data Forwarder

Sampler forwards Inspect diagnostics data to the Cobalt telemetry system.

If your component is instrumented with Inspect, you can use Cobalt telemetry (fleet monitoring) with just configuration file changes! If you want a low-barrier entry into fleet monitoring (no new FIDL integrations, no new data models, no flow control complexity in your component), Sampler is for you!

What do I get?

Sampler offers a reduced complexity approach to getting fleetwide monitoring into the debugging toolkit of every component.

Centralized data model

Many developers find it unwieldy to have to maintain two separate data models, one for things like on-device or crash report debugging, and one for things like fleet monitoring.

With Sampler, a single diagnostics data model (Inspect) can serve as the source for all branches of diagnostics data.

Avoid complexity of direct integration

For some components (like drivers), their workload is so system-critical that finding the "right time" to perform non-essential work like diagnostics reporting can be a complex undertaking. By centralizing sampling to Sampler we are able to offload work to a program that can be run during non-peak-user load. Even for components not providing system-critical services, integrating with Sampler makes Fuchsia healthier by allowing for better informed sampling scheduling.

Direct integration with Cobalt requires component authors to consider their flow control as a client sending requests to the Cobalt on-device forwarder. Flow control is implicit in Sampler, so you can focus more on uploading common metric types without adding new service dependencies.

Getting Started

  1. If you are a V2 component, make sure that you expose your diagnostics directory to the Diagnostics platform.
  2. Register the Cobalt metric you want to transform your Inspect metric into.
  3. Create a Sampler configuration defining the transformation from Inspect to Cobalt. See existing config files.

Sampler Configuration

We will be using the FVM Integration as an example configuration.

{
  "project_id": 3676913920,
  "poll_rate_sec": 3600,
  "metrics": [
    {
      "selector": "bootstrap/driver_manager:root/fvm/partitions/blobfs:total_slices_reserved",
      "metric_id": 3000,
      "metric_type": "Integer",
      "event_codes": [0]
    },
    {
      "selector": "bootstrap/driver_manager:root/fvm/partitions/minfs:total_slices_reserved",
      "metric_id": 3000,
      "metric_type": "Integer",
      "event_codes": [1]
    }
  ]
}

The Sampler configuration specifies a top-level project id, which specifies the Cobalt client that Sampler is sampling and forwarding on the behalf of. It also specifies a top-level poll_rate_seconds, although in Sampler v1.1 this poll rate will be migrated on client's behalfs to a per-metric configuration.

Next, the configuration requires a list of metric transformations. In these configurations, the client provides the selector identifying the relevant inspect metric, along with the Cobalt metadata needed to forward the inspect on the client's behalf.

  1. metric_type is the type of metric transformation requested.
    • If your Cobalt metric is of type OCCURRENCE being used to track numerical aggregations, use the metric_type Occurrence.
    • If your Cobalt metric is of type INTEGER being used to track raw integers (e.g. cpu_load, thermal_temp), use the metric_type Integer.
    • If your Cobalt metric is of type INTEGER_HISTOGRAM, use the metric_type IntHistogram.
    • If your Cobalt metric is of type STRING, use the metric_type String.
  2. metric_id is the same id used when you register your metric in the Cobalt yaml files.
  3. event_codes is the list of dimension values specified in your registration of the Cobalt metric. In FVM's case, each metric has a single dimension value corresponding to its partition type.

NOTE: The order of the dimension values in the Sampler configuration must align with the order in which the dimensions were declared in the yaml definition.