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 EventsTo 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:
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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 EventsTo 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"].
Your error_sampler function must return a valid value. A valid value is either:
0.0 and 1.0 (inclusive) indicating the probability an error gets sampled, orOne 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:
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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
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.
We recommend sampling your transactions for two reasons:
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 RateThe Sentry SDKs have two configuration options to control the volume of transactions sent to Sentry, allowing you to take a representative sample:
traces_sample_rate):
traces_sampler) which:
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 RateTo 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:
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sentry_sdk.init(
traces_sample_rate=1.0,
)
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:
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import sentry_sdk
from sentry_sdk.types import SamplingContext
def traces_sampler(sampling_context: SamplingContext) -> float:
parent_sampling_decision = sampling_context["parent_sampled"]
if parent_sampling_decision is not None:
return float(parent_sampling_decision)
if "...":
return 0.5
elif "...":
return 0.01
elif "...":
return 0
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.
The information contained in the sampling_context object passed to the traces_sampler when a transaction is created varies by platform and integration.
For the Python SDK, it includes at least the following:
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{
"transaction_context": {
"name": <string>
"op": <string>
},
"parent_sampled": <bool>
...
}
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:
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sentry_sdk.start_transaction(
name="GET /search",
op="search",
data={
"query_params": {
"animal": "dog",
"type": "very good"
}
},
custom_sampling_context={
"user_id": "12312012",
"search_results": { ... }
}
)
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 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.
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def traces_sampler(sampling_context):
if sampling_context["parent_sampled"] is not None:
return sampling_context["parent_sampled"]
...
If you're using a traces_sample_rate rather than a traces_sampler, the decision will always be inherited.
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.
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sentry_sdk.start_transaction(
name="GET /search",
sampled=True
)
There are multiple ways for a transaction to end up with a sampling decision.
traces_sample_ratetraces_samplertraces_samplerstart_transactionWhen there's the potential for more than one of these to come into play, the following precedence rules apply:
start_transaction, that decision will be used, overriding everything else.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.traces_sampler is not defined, but there's a parent sampling decision, the parent sampling decision will be used.traces_sampler is not defined and there's no parent sampling decision, traces_sample_rate will be used.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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