> For the complete documentation index, see [llms.txt](https://docs.feast.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feast.dev/v0.35-branch/reference/data-sources/spark.md).

# Spark (contrib)

## Description

Spark data sources are tables or files that can be loaded from some Spark store (e.g. Hive or in-memory). They can also be specified by a SQL query.

## Disclaimer

The Spark data source does not achieve full test coverage. Please do not assume complete stability.

## Examples

Using a table reference from SparkSession (for example, either in-memory or a Hive Metastore):

```python
from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
    SparkSource,
)

my_spark_source = SparkSource(
    table="FEATURE_TABLE",
)
```

Using a query:

```python
from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
    SparkSource,
)

my_spark_source = SparkSource(
    query="SELECT timestamp as ts, created, f1, f2 "
          "FROM spark_table",
)
```

Using a file reference:

```python
from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
    SparkSource,
)

my_spark_source = SparkSource(
    path=f"{CURRENT_DIR}/data/driver_hourly_stats",
    file_format="parquet",
    timestamp_field="event_timestamp",
    created_timestamp_column="created",
)
```

The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource).

## Supported Types

Spark data sources support all eight primitive types and their corresponding array types. For a comparison against other batch data sources, please see [here](/v0.35-branch/reference/data-sources/overview.md#functionality-matrix).


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

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

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.feast.dev/v0.35-branch/reference/data-sources/spark.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.