# Tensor Query Language (TQL)

## How to query datasets using the Deep Lake Tensor Query Language (TQL)

Querying datasets is a critical aspect of data science workflows that enables users to filter datasets and focus their work on the most relevant data. Deep Lake offers a highly-performant  query engine built in C++ and optimized for the Deep Lake data format. 

{% hint style="danger" %}
The Deep Lake query engine is only accessible to registered and authenticated users, and it applies usage restrictions based on your Deep Lake Plan.
{% endhint %}

### Dataset Query Summary

#### Querying in the UI

{% embed url="<https://www.loom.com/share/40f8f10af5064f9a8baf3dfd37029700>" %}

#### Querying in the Vector Store Python API

```
view = vector_store.search(query = <query_string>, exec_option = "compute_engine")
```

#### Querying in the low-level Python API

Queries can also be performed in the Python API using:

```python
view = ds.query(<query_string>)
```

#### Saving and utilizing dataset query results in the low-level Python API

The query results (`Dataset Views`) can be saved in the UI as shown above, or if the view is generated in Python, it can be saved using the Python API below. Full details are [available here](/v3.8.19/example-code/getting-started/deep-learning/dataset-filtering.md).

```python
ds_view.save_view(message = 'Samples with monarchs')
```

{% hint style="warning" %}
In order to maintain data lineage, `Dataset Views` are immutable and are connected to specific commits. Therefore, views can only be saved if the dataset has a commit and there are no uncommitted changes in the `HEAD`. You can check for this using `ds.has_head_changes`
{% endhint %}

`Dataset Views` can be loaded in the python API and they can passed to ML frameworks just like regular datasets:

```python
ds_view = ds.load_view(view_id, optimize = True, num_workers = 2)

for data in ds_view.pytorch():
    # Training loop here
```

{% hint style="warning" %}
The `optimize` parameter in `ds.load_view(...,`` `**`optimize = True`**`)` materializes the `Dataset View` into a new sub-dataset that is optimized for streaming. If the original dataset uses [linked tensors](broken://pages/PAxJHAoZTMrUQpRYjzXz), the data will be copied to Deep Lake format.

Optimizing the `Dataset View` is critical for achieving rapid streaming.
{% endhint %}

If the saved `Dataset View` is no longer needed, it can be deleted using:

```python
ds.delete_view(view_id)
```

### Query Syntax

{% content-ref url="/pages/M2H2yvqoN4QJuIaO3yEp" %}
[TQL Syntax](/v3.8.19/performance-features/querying-datasets/query-syntax.md)
{% endcontent-ref %}


---

# 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-v3.activeloop.ai/v3.8.19/performance-features/querying-datasets.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.