---
title: "Streamed Spans"
description: "Learn how to use stream mode to send spans to Sentry as they finish, removing the 1,000-span limit and making trace data visible sooner."
url: https://docs.sentry.io/platforms/python/tracing/streamed-spans/
---

# Streamed Spans | Sentry for Python

By default, the Sentry Python SDK collects all spans in memory and sends them to Sentry as a single transaction once the root span ends. This is called transaction mode. Stream mode changes this by sending spans to Sentry in batches as they finish. Service spans, which represent a service's entry point, replace transactions as the main grouping for each service.

Why use stream mode?

* **No 1,000-span limit.** In transaction mode, transactions are capped at 1,000 spans. Stream mode has no upper limit since spans are sent in batches. This is especially beneficial for complex generative AI pipelines that easily exceed standard span limits.
* **Lower memory usage.** Spans are flushed periodically and don't need to be held in memory until the root span ends. This is especially useful for long-running processes like queue consumers or cron jobs.
* **Faster visibility.** Span data arrives in Sentry as your application runs, instead of only after the entire operation completes.
* **Fewer spans lost to crashes.** If your process terminates unexpectedly, spans that were already flushed are emitted normally. In transaction mode, a crash before the transaction ends means all span data is lost. Stream mode emits spans incrementally as they finish, so more of them survive a crash.

You can find the following span types mentioned throughout this page:

* **Root span**: The topmost span in a trace. It has no parent span and is always a service span.
* **Service span**: A parent-level span at the entry of a service. In transaction mode, this is called a transaction.
* **Child span**: Any span nested under a parent span within the same trace.

This graph shows how these span types relate to each other within a trace:

```bash
Trace
│
└── Root span [service A]
     ├── Child span
     │    └── Child span
     └── Service span [service B]
          ├── Child span
          └── Child span
```

##### Migrating from transaction mode?

Stream mode requires the streamed Span API, so migrating to stream mode and migrating to the streamed Span API are the same step. If you have existing custom instrumentation, see the [Migration Guide](https://docs.sentry.io/platforms/python/tracing/streamed-spans/migration-guide.md) for a full list of changes.

## [Prerequisites](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#prerequisites)

You need:

* [Tracing configured](https://docs.sentry.io/platforms/python/tracing.md#configure) in your app
* Sentry SDK `>=2.62.0`

## [Enable Stream Mode](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#enable-stream-mode)

Opt in by adding the `trace_lifecycle` option when initializing the SDK:

```python
import sentry_sdk

sentry_sdk.init(
    dsn="https://<key>@o<orgId>.ingest.sentry.io/<projectId>",
    traces_sample_rate=1.0,
    # enables stream mode
    trace_lifecycle="stream",
)
```

To revert to transaction mode, remove the `trace_lifecycle` option or set it to `"static"` (the default).

How does span flushing work?

When stream mode is enabled, the SDK maintains an internal buffer that groups spans by trace ID.

Spans are flushed:

* On a regular interval (every 5 seconds by default).
* When a trace's buffer reaches 1,000 spans.
* When the SDK shuts down.

Each flush sends only the spans accumulated since the last flush, grouped into envelopes by trace ID.

## [Manual Instrumentation (Optional)](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#manual-instrumentation-optional)

### [Start a Span](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#start-a-span)

Use `sentry_sdk.traces.start_span()` to create a span that ends automatically when the `with` block exits:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(name="my-operation") as span:
    # Your code here
    do_work()
```

Child spans created inside an active span are automatically associated with the parent:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(name="parent-operation"):
    with sentry_sdk.traces.start_span(name="child-step-1"):
        step_one()

    with sentry_sdk.traces.start_span(name="child-step-2"):
        step_two()
```

A span is automatically promoted to a service span (the equivalent of a transaction) if no other parent is currently active. If you want to force a new service span, regardless of whether it has a parent span, set `parent_span=None`:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(name="task-name", parent_span=None) as span:
    do_work()
```

You can also use the `@trace` decorator to instrument a function. It accepts optional `name`, `attributes`, and `active` arguments:

```python
from sentry_sdk.traces import trace

@trace(name="checkout", attributes={"flow.pipeline": "legacy"})
def checkout():
    ...
```

For more details on span creation, see [Custom Instrumentation](https://docs.sentry.io/platforms/python/tracing/instrumentation/custom-instrumentation.md).

### [Add Span Attributes](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#add-span-attributes)

Attach structured metadata to spans using `attributes`, which can be `str`, `int`, `float`, or `bool`, as well as arrays of these types.

Sentry automatically sets several standard attributes on spans. To avoid accidentally overwriting these, refer to our [Sentry Attribute Conventions](https://getsentry.github.io/sentry-conventions/attributes/).

You can set attributes when starting a span:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(
    name="process-order",
    attributes={
        "sentry.op": "queue.process",
        "order.id": "abc-123",
        "order.item_count": 5,
        "order.priority": True,
    },
):
    process_order()
```

Or add them to an already running span:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(name="handle-request") as span:
    # Set a single attribute
    span.set_attribute("http.response.status_code", 200)

    # Set multiple attributes at once
    span.set_attributes({
        "http.route": "/api/users",
        "user.id": "user-42",
    })
```

Find more examples in our [Sending Span Metrics](https://docs.sentry.io/platforms/python/tracing/span-metrics.md) documentation.

## [Distributed Tracing (Optional)](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#distributed-tracing-optional)

### [Continue a Trace](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#continue-a-trace)

When your service receives a request from an upstream service that includes Sentry trace headers, use `sentry_sdk.traces.continue_trace()` to connect your spans to the existing distributed trace. Unlike the legacy `sentry_sdk.continue_trace()`, the new version is not a context manager. Instead, it sets the propagation context and the next span picks it up automatically.

```python
import sentry_sdk

headers = {
    "sentry-trace": "4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0be902b7-1",
    "baggage": "sentry-trace_id=...",
}

sentry_sdk.traces.continue_trace(headers)
with sentry_sdk.traces.start_span(name="handle request"):
    ...
```

### [Start a New Trace](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#start-a-new-trace)

If you need to start a completely new trace unconnected to the current one, use `sentry_sdk.traces.new_trace()`. This is useful for background jobs or scheduled tasks where you want a clean trace boundary:

```python
import sentry_sdk

with sentry_sdk.traces.start_span(name="span in trace 1"):
    ...

sentry_sdk.traces.new_trace()

with sentry_sdk.traces.start_span(name="span in trace 2"):
    # This span is the root of a new, separate trace
    ...
```

See [Custom Trace Propagation](https://docs.sentry.io/platforms/python/tracing/distributed-tracing/custom-trace-propagation.md) for more information on distributed tracing.

## [Extended Configuration (Optional)](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#extended-configuration-optional)

### [Filter Spans](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#filter-spans)

To modify or redact span data before it's sent, use the `before_send_span` option:

```python
import sentry_sdk

def postprocess_span(span, hint):
    attributes_to_sanitize = [
        "http.request.header.custom-auth",
        "http.request.header.custom-user-id",
    ]
    for attribute in attributes_to_sanitize:
        if span["attributes"].get(attribute):
            span["attributes"][attribute] = "[Sanitized]"
    return span

sentry_sdk.init(
    dsn="https://<key>@o<orgId>.ingest.sentry.io/<projectId>",
    traces_sample_rate=1.0,
    trace_lifecycle="stream",
    before_send_span=postprocess_span,
)
```

`before_send_span` can only modify span data — you cannot use it to drop spans (use [`ignore_spans`](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#drop-spans) instead).

### [Drop Spans](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#drop-spans)

To prevent specific spans from being created, use the `ignore_spans` option. Rules are evaluated at span start, so only the span name and attributes set at creation time are taken into account. Rules can be strings, compiled regexes, or dictionaries with name and/or attributes conditions:

```python
import re
import sentry_sdk

sentry_sdk.init(
    dsn="https://<key>@o<orgId>.ingest.sentry.io/<projectId>",
    traces_sample_rate=1.0,
    trace_lifecycle="stream",
    ignore_spans=[
        # String match against span name
        "/health",
        # Regex match against span name
        re.compile(r"/flow/.*"),
        # Match by attributes (all must match)
        {
            "attributes": {
                "service.id": "15def9a",
                "flow.pipeline": "legacy",
            }
        },
        # Match by name and attributes
        {
            "name": re.compile(r"/flow/.*"),
            "attributes": {
                "service.id": re.compile(r".*\.facade"),
            },
        },
    ],
)
```

If a matching span is a service span, all of its child spans are dropped as well. If a child span matches, only that span is dropped and its children are reparented to the nearest ancestor.

## [Sampling (Optional)](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#sampling-optional)

If you use `traces_sample_rate`, no changes are needed — it works the same way in stream mode.

If you use a custom `traces_sampler`, the shape of the sampling context is different in stream mode. See the [Streamed Span API migration guide](https://docs.sentry.io/platforms/python/tracing/streamed-spans/migration-guide.md) for details.

## [Verify Your Setup](https://docs.sentry.io/platforms/python/tracing/streamed-spans.md#verify-your-setup)

To make sure you've enabled stream mode successfully:

* **Check the Sentry dashboard**: Spans should appear in the Traces view shortly after they complete. Traces look similar to transaction mode, but contain only spans and no transactions.
* **Check your logs**: If the SDK logs warnings about unsupported span operations, you may still be using the legacy Span API somewhere in your code.

## Pages in this section

- [Migrate to Stream Mode](https://docs.sentry.io/platforms/python/tracing/streamed-spans/migration-guide.md)