Metrics (Trace Connected) For Python

[AbhiPrasad]

Sentry is introducing metrics that let you send counters, gauges, and distributions from your code to track things like email.sent, checkout.failed, or queue.depth — and pivot directly into the related traces, logs, and errors when something looks off.

TraceConnectedMetrics.mp4

Note

To get early access to the Sentry metrics product and to see how it works, see the announcement post.

To use the new Metric APIs to send metrics directly to Sentry, you'll have to upgrade to 2.43.0 of the Python SDK or higher.

Metrics are gated by an experimental option, _experiments.enable_metrics (will not be required in future versions of the SDK).

import sentry_sdk

sentry_sdk.init(
    dsn="___PUBLIC_DSN___",
    _experiments={
        "enable_metrics": True,
    },
)

Then you can import and use methods from the metric namespace to send logs to Sentry.
Once the feature is enabled on the SDK and the SDK is initialized, you can send metrics using the sentry_sdk.metrics APIs.

The metrics namespace exposes three methods that you can use to capture different types of metric information: count, gauge, and distribution.

from sentry_sdk import metrics

metrics.count("checkout.failed", 1)
metrics.gauge("queue.depth", 42)
metrics.distribution("cart.amount_usd", 187.5)

You can also pass additional attributes directly to count, gauge, and distribution via the attributes kwarg.

from sentry_sdk import metrics

metrics.count(
    "checkout.failed",
    1,
    "attributes": {
        "route": "/checkout",
        "tenant": "acme",
        "provider": "stripe",
    },
)