Sampling

Adding Sentry to your app gives you a great deal of very valuable information about errors and performance you wouldn't otherwise get. And lots of information is good -- as long as it's the right information, at a reasonable volume.

Sampling Error Events

To send a representative sample of your errors to Sentry, set the sample_rate option in your SDK configuration to a number between 0 (0% of errors sent) and 1 (100% of errors sent). This is a static rate, which will apply equally to all errors. For example, to sample 25% of your errors:

import sentry_sdk

sentry_sdk.init(
    # ...
    sample_rate=0.25,
)

The error sample rate defaults to 1.0, meaning all errors are sent to Sentry.

Changing the error sample rate requires re-deployment. In addition, setting an SDK sample rate limits visibility into the source of events. Setting a rate limit for your project (which only drops events when volume is high) may better suit your needs.

Dynamically Sampling Error Events

To sample error events dynamically, set the error_sampler to a function that returns the desired sample rate for the event. The error_sampler takes two arguments, event and hint. event is the Event that will be sent to Sentry, hint includes Python's sys.exc_info() information in hint["exc_info"].

One potential use case for the error_sampler is to apply different sample rates for different exception types. For instance, if you would like to sample some exception called MyException at 50%, discard all events of another exception called MyIgnoredException, and sample all other exception types at 100%, you could use the following code when initializing the SDK:

import sentry_sdk
from sentry_sdk.types import Event, Hint


def error_sampler(event: Event, hint: Hint) -> float:
    error_class = hint["exc_info"][0]

    if error_class == MyException:
        return 0.5
    elif error_class == MyIgnoredException:
        return 0

    # All the other errors
    return 1.0


sentry_sdk.init(
    # ...
    error_sampler=error_sampler,
)

Sampling Transaction Events

We recommend sampling your transactions (root spans) for two reasons:

1. Capturing a single trace involves minimal overhead, but capturing traces for every page load or every API request may add an undesirable load to your system.
2. Enabling sampling allows you to better manage the number of events sent to Sentry, so you can tailor your volume to your organization's needs.

Choose a sampling rate with the goal of finding a balance between performance and volume concerns with data accuracy. You don't want to collect too much data, but you want to collect sufficient data from which to draw meaningful conclusions. If you’re not sure what rate to choose, start with a low value and gradually increase it as you learn more about your traffic patterns and volume.

Configuring the Transaction Sample Rate

The Sentry SDKs have two configuration options to control the volume of transactions sent to Sentry, allowing you to take a representative sample:

1. Uniform sample rate (traces_sample_rate):
   • Provides an even cross-section of transactions, no matter where in your app or under what circumstances they occur.
   • Uses default inheritance and precedence behavior
2. Sampling function (traces_sampler) which:
   • Samples different transactions at different rates
   • Filters out some transactions entirely
   • Modifies default precedence and inheritance behavior

By default, none of these options are set, meaning no transactions will be sent to Sentry. You must set either one of the options to start sending transactions.

Setting a Uniform Sample Rate

To do this, set the traces_sample_rate option in your sentry_sdk.init() to a number between 0 and 1. With this option set, every transaction created will have that percentage chance of being sent to Sentry. (So, for example, if you set traces_sample_rate to 0.2, approximately 20% of your transactions will get recorded and sent.) That looks like this:

sentry_sdk.init(
    # ...

    # Set traces_sample_rate to 1.0 to capture 100%
    # of transactions for tracing.
    # We recommend adjusting this value in production,
    traces_sample_rate=1.0,
)

Setting a Sampling Function

To use the sampling function, set the traces_sampler option in your sentry-sdk.init() to a function that will accept a sampling_context dictionary and return a sample rate between 0 and 1. For example:

import sentry_sdk
from sentry_sdk.types import SamplingContext

def traces_sampler(sampling_context: SamplingContext) -> float:
    # Use the parent sampling decision if we have an incoming trace.
    # Note: we strongly recommend respecting the parent sampling decision,
    # as this ensures your traces will be complete!
    parent_sampling_decision = sampling_context.get("parent_sampled")
    if parent_sampling_decision is not None:
        return float(parent_sampling_decision)

    # Examine provided sampling context along with anything in the
    # global namespace to compute the sample rate for this transaction
    if "...":
        # These are important - take a big sample
        return 0.5
    elif "...":
        # These are less important - only take 1%
        return 0.01
    elif "...":
        # These aren't worth tracking - drop these transactions
        return 0

    # Default sample rate
    return 0.1

sentry_sdk.init(
    # ...
    traces_sampler=traces_sampler,
)

For convenience, the function can also return a boolean. Returning True is equivalent to returning 1, and will guarantee the transaction will be sent to Sentry. Returning False is equivalent to returning 0 and will guarantee the transaction will not be sent to Sentry.

Sampling Context Data

Default Sampling Context Data

The information contained in the sampling_context object passed to the traces_sampler when a root span is created varies by integration.

For Python-based SDKs, it includes at least the following:

{
  "transaction_context": {
    "name": <string>  # transaction title at creation time
    "op": <string>  # short description of transaction type, like "http.request"
  },
  "parent_sampled": <bool>  # if this transaction has a parent, its sampling decision
  ...  # custom context as passed to `start_transaction`
}

Custom Sampling Context Data

When using custom instrumentation to create a root span, you can add data to the sampling_context by providing additional attributes to start_span. All span attributes provided at span start are accessible via the sampling_context and will also ultimately be sent to Sentry. If you want to exclude an attribute, you can filter it out in a before_send.

sentry_sdk.start_span(
    name="GET /search",
    op="search",
    data={
        "query_params": {
            "animal": "dog",
            "type": "very good"
        }
    },
    # Attributes defined at root span start will be accessible in traces_sampler
    # and they will be sent to Sentry unless filtered out manually.
    # Note that these can only be primitive types or a list of a single primitive
    # type.
    attributes={
        "user_id": "12312012",
        "foo": "bar",
    }
)

Inheritance

Whatever a root span's sampling decision, that decision will be passed to its child spans and from there to any root spans they subsequently cause in other services.

(See Distributed Tracing for more about how that propagation is done.)

If the root span currently being created is one of those subsequent root spans (in other words, if it has a parent root span), the upstream (parent) sampling decision will be included in the sampling context data. Your traces_sampler can use this information to choose whether to inherit that decision. In most cases, inheritance is the right choice, to avoid breaking distributed traces. A broken trace will not include all your services.

def traces_sampler(sampling_context):
    # always inherit
    if sampling_context["parent_sampled"] is not None:
        return sampling_context["parent_sampled"]

    ...
    # rest of sampling logic here

If you're using a traces_sample_rate rather than a traces_sampler, the decision will always be inherited. The provided traces_sample_rate will only be used to generate a sample rate if there is sampling decision coming in from upstream.

Forcing a Sampling Decision

If you know at span creation time whether or not you want the root span (transaction) sent to Sentry, you also have the option of passing a sampling decision directly in the start_span API. If you do that, the root span won't be subject to the traces_sample_rate, nor will traces_sampler be run, so you can count on the decision that's passed not to be overwritten.

sentry_sdk.start_span(
    name="GET /search",
    # Sample this span. Note that the `sampled` parameter is only taken into
    # account for root-level spans (transactions).
    sampled=True,
)

Precedence

There are multiple ways for a root span (transaction) to end up with a sampling decision.

• Random sampling according to a static sample rate set in traces_sample_rate
• Random sampling according to a sample function rate returned by traces_sampler
• Absolute decision (100% chance or 0% chance) returned by traces_sampler
• If the transaction has a parent, inheriting its parent's sampling decision
• Absolute decision passed to start_span

When there's the potential for more than one of these to come into play, the following precedence rules apply:

1. If a sampling decision is passed to start_transaction, that decision will be used, overriding everything else.
2. If traces_sampler is defined, its decision will be used. It can choose to keep or ignore any parent sampling decision, use the sampling context data to make its own decision, or choose a sample rate for the transaction. We advise against overriding the parent sampling decision because it will break distributed traces.
3. If traces_sampler is not defined, but there's a parent sampling decision, the parent sampling decision will be used.
4. If traces_sampler is not defined and there's no parent sampling decision, traces_sample_rate will be used.