# Push

**Warning**: This is an *experimental* feature. It's intended for early testing and feedback, and could change without warnings in future releases.

## Description

Push sources allow feature values to be pushed to the online store in real time. This allows fresh feature values to be made available to applications. Push sources supercede the [FeatureStore.write\_to\_online\_store](https://rtd.feast.dev/en/latest/index.html#feast.feature_store.FeatureStore.write_to_online_store).

Push sources can be used by multiple feature views. When data is pushed to a push source, Feast propagates the feature values to all the consuming feature views.

Push sources must have a batch source specified, since that's the source used when retrieving historical features. When using a PushSource as a stream source in the definition of a feature view, a batch source doesn't need to be specified in the definition explicitly.

## Stream sources

Streaming data sources are important sources of feature values. A typical setup with streaming data looks like:

1. Raw events come in (stream 1)
2. Streaming transformations applied (e.g. generating features like `last_N_purchased_categories`) (stream 2)
3. Write stream 2 values to an offline store as a historical log for training
4. Write stream 2 values to an online store for low latency feature serving
5. Periodically materialize feature values from the offline store into the online store for improved correctness

Feast now allows users to push features previously registered in a feature view to the online store for fresher features.

## Example

### Defining a push source

Note that the push schema needs to also include the entity

```python
from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature, Field
from feast.types import Int64

push_source = PushSource(
    name="push_source",
    batch_source=BigQuerySource(table="test.test"),
)

fv = FeatureView(
    name="feature view",
    entities=["user_id"],
    schema=[Field(name="life_time_value", dtype=Int64)],
    source=push_source,
)
```

### Pushing data

```python
from feast import FeatureStore
import pandas as pd

fs = FeatureStore(...)
feature_data_frame = pd.DataFrame()
fs.push("push_source_name", feature_data_frame)
```

See also [Python feature server](https://docs.feast.dev/v0.21-branch/reference/feature-servers/python-feature-server) for instructions on how to push data to a deployed feature server.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.feast.dev/v0.21-branch/reference/data-sources/push.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.