---
title: "Sampling"
description: "Learn how to configure the volume of error and transaction events sent to Sentry."
url: https://docs.sentry.io/platforms/python/sampling/
---

# Sampling | Sentry for Python

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](https://docs.sentry.io/platforms/python/sampling.md#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:

```python
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](https://docs.sentry.io/pricing/quotas/manage-event-stream-guide.md#rate-limiting) for your project (which only drops events when volume is high) may better suit your needs.

### [Dynamically Sampling Error Events](https://docs.sentry.io/platforms/python/sampling.md#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](https://github.com/getsentry/sentry-python/blob/master/sentry_sdk/_types.py) that will be sent to Sentry, `hint` includes Python's [sys.exc\_info()](https://docs.python.org/3/library/sys.html#sys.exc_info) information in `hint["exc_info"]`.

Your `error_sampler` function **must return a valid value**. A valid value is either:

* A **floating-point number** between `0.0` and `1.0` (inclusive) indicating the probability an error gets sampled, **or**
* A **boolean** indicating whether or not to sample the error.

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:

```python
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,
)
```

You can define at most one of the `error_sampler` and the `sample_rate`. If both are set, the `error_sampler` will control sampling, and the `sample_rate` will be ignored.

## [Sampling Transaction Events](https://docs.sentry.io/platforms/python/sampling.md#sampling-transaction-events)

We recommend sampling your transactions 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](https://docs.sentry.io/platforms/python/sampling.md#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](https://docs.sentry.io/platforms/python/sampling.md#inheritance) and [precedence](https://docs.sentry.io/platforms/python/sampling.md#precedence) behavior

2. Sampling function (`traces_sampler`) which:

   * Samples different transactions at different rates
   * [Filters](https://docs.sentry.io/platforms/python/configuration/filtering.md) out some transactions entirely
   * Modifies default [precedence](https://docs.sentry.io/platforms/python/sampling.md#precedence) and [inheritance](https://docs.sentry.io/platforms/python/sampling.md#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](https://docs.sentry.io/platforms/python/sampling.md#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:

```python
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](https://docs.sentry.io/platforms/python/sampling.md#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:

```python
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["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](https://docs.sentry.io/platforms/python/sampling.md#sampling-context-data)

### [Default Sampling Context Data](https://docs.sentry.io/platforms/python/sampling.md#default-sampling-context-data)

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

For the Python SDK, it includes at least the following:

```python
{
    "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
    ...  # other attributes as passed to `start_span`
}
```

### [Custom Sampling Context Data](https://docs.sentry.io/platforms/python/sampling.md#custom-sampling-context-data)

When using custom instrumentation to create a transaction, you can add data to the `sampling_context` by passing it as an optional second argument to `start_transaction`. This is useful if there's data to which you want the sampler to have access but which you don't want to attach to the transaction as `tags` or `data`, such as information that's sensitive or that’s too large to send with the transaction. For example:

```python
sentry_sdk.start_transaction(
    # kwargs passed to Transaction constructor - will be recorded on transaction
    name="GET /search",
    op="search",
    data={
        "query_params": {
            "animal": "dog",
            "type": "very good"
        }
    },
    # `custom_sampling_context` - won't be recorded
    custom_sampling_context={
        # PII
        "user_id": "12312012",
        # too big to send
        "search_results": { ... }
    }
)
```

## [Inheritance](https://docs.sentry.io/platforms/python/sampling.md#inheritance)

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

(See [Distributed Tracing](https://docs.sentry.io/platforms/python/tracing/trace-propagation.md) for more about how that propagation is done.)

If the transaction currently being created is one of those subsequent transactions (in other words, if it has a parent transaction), 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.

```python
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.

## [Forcing a Sampling Decision](https://docs.sentry.io/platforms/python/sampling.md#forcing-a-sampling-decision)

If you know at transaction creation time whether or not you want the transaction sent to Sentry, you also have the option of passing a sampling decision directly to the transaction constructor (note, not in the `custom_sampling_context` object). If you do that, the transaction 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.

```python
sentry_sdk.start_transaction(
    name="GET /search",
    sampled=True
)
```

## [Precedence](https://docs.sentry.io/platforms/python/sampling.md#precedence)

There are multiple ways for a 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_transaction`

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.