# Try Vega

You can try the HEAVY.AI Vega engine and work with various examples. The database used for the examples includes over 100,000,000 rows. The data is based on the [political donations dataset](https://www.omnisci.com/demos/political-donations). Some columns have been stripped so that the data can fit on one NVIDIA Tesla P100 GPU.

## [**Try It: Vega Editor**](https://vega-demo.mapd.com/)

The following examples are available.

### [Simple Pointmap](https://vega-demo.mapd.com/#/examples/geographical/simple-pointmap)

![](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-095e9098c8d9290db9df5b6fab16ca6617f44206%2F6_vega-tryit-1.png?alt=media)

### [Pointmap With Scales](https://vega-demo.mapd.com/#/examples/geographical/pointmap-with-scales)

![](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-78918e6e4a0b309df301c8c5ea97ace9c4dda3aa%2F6_vega-tryit-2.png?alt=media)

### [Grouped Pointmap](https://vega-demo.mapd.com/#/examples/geographical/grouped-pointmap)

![](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-543690a3dc9989682cb33bd9126447b4912c28bf%2F6_vega-tryit-3.png?alt=media)

### [Square Heatmap](https://vega-demo.mapd.com/#/examples/geographical/square-heat-map)

![](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-a10371058d68adbfa3365f217fee0b911db1bd1e%2F6_vega-tryit-4.png?alt=media)

### [Hexagon Heatmap](https://vega-demo.mapd.com/#/examples/geographical/hexagon-heat-map)

![](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-fb825eace6a5d05197e08c3079f2167770295a67%2F6_vega-tryit-5.png?alt=media)


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# 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.heavy.ai/v8.3.0/apis-and-interfaces/vega/try-vega.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.