Querying Datasets

Deep Lake offers a highly-performant SQL-style query engine for filtering your data.

How to query dataset 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 dataset query engine built in C++ and optimized for Deep Lake datasets.

Querying features in the python API are installed using pip install "deeplake[enterprise]". Details on all installation options are available here.

Dataset Query Summary

Querying in the UI

Querying in the Python API

Queries can also be performed in the Python API using:

view = ds.query('Query string')

Saving and utilizing dataset query results

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.

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

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

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

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

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

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, the data will be copied to Deep Lake format.

Optimizing the Dataset View is critical for achieving rapid streaming.

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

ds.delete_view(view_id)

Query Syntax

Query Syntax