Tensor Query Language

This page describes the Tensor Query Language (TQL), an SQL-like language used for Querying in Activeloop Platform as well as in ds.query in our Python API. To use queries, install deeplake with pip install deeplake[enterprise].



TQL supports only SELECT statement. Every TQL expression starts with SELECT *. TQL supports only * which means to select all tensors. The common syntax for select statement is the following:

SELECT * [FROM string] [WHERE expression] [LIMIT number [OFFSET number]] [ORDER BY expression [ASC/DESC]]

Each part of the SELECT statement can be omitted.

FROM expression is allowed, but it does not have any effect on the query, because for now TQL queries are run on a specific dataset, so the FROM is known from the context


WHERE expression is used to filter the samples in the dataset by conditions. The conditions should be convertible to boolean. Any expression which outputs a number will be converted to boolean with non-zero values taken as True. If the expression is not convertible to boolean, such as strings, json objects and arrays, the query will print the corresponding error.


ORDER BY expression orders the output of the query by the given criteria. The criteria can be any expression output of which can be ordered. The ordered outputs are either scalar numbers or strings. In addition it can also be json, which contains number or string.

ORDER BY statement optionally accepts ASC/DESC keywords specifying whether the ordering should be ascending or descending. It is ascending by default.


LIMIT and OFFSET expressions are used to limit the output of the query by index, as in SQL.


TQL supports any comparison operator (==, !=, <, <=, >=) where the left side is a tensor and the right side is a known value.

The value can be numeric scalar or array as well as string value.

String literal should be provided within single quotes (') and can be used on class_label, json and text tensors.

For class labels it will get corresponding numeric value from the class_names list and do numeric comparison.

For json and text it will do string comparison. The left side of the expression can be indexed (subscripted) if the tensor is multidimensional array or json. Jsons support indexing by string, e.g. index_meta['id'] == 'some_id'. Jsons can also be indexed by number if the underlying data is array.

Numeric multidimensional tensors can be indexed by numbers, e.g. categories[0] == 1 as well as Python style slicing and multidimensional indexing, such as boxes[:2]. This last expression returns array containing the third elements of the initial two dimensional array boxes.

TQL supports logical operators - AND, OR and NOT. These operators can be used to combine boolean expressions. For example,

labels == 0 OR labels == 1

From SQL we also support the following two keywords:


labels BETWEEN 0 and 5
  • IN

labels in ARRAY[0, 2, 4, 6, 8]


There are predefined functions which can be used in WHERE expression as well as in ORDER BY expressions:

  • CONTAINS - checks if the given tensor contains given value - CONTAINS(categories, 'person')

  • RANDOM - returns random number. May be used in ORDER BY to shuffle the output - ORDER BY RANDOM()

  • SHAPE - returns the shape array of the given tensor - SHAPE(boxes)

  • ALL - takes an array of booleans and returns single boolean, True if all elements of the input array are True

  • ALL_STRICT - same as ALL with one difference. ALL returns True on empty array, while ALL_STRICT return False

  • ANY - takes an array of booleans and returns single boolean, True if any of the elements int the input array is True

  • LOGICAL_AND - takes two boolean arrays, does element wise logical and, returns the result array. This will return False if the input arrays have different sizes.

  • LOGICAL_OR - takes two boolean arrays, does element wise logical or, returns the result array. This will return False if the input arrays have different sizes.


Query can contain multiple SELECT statements, combined by one of the set operations - UNION, INTERSECT and EXCEPT.


Querying for images containing 0 in MNIST Train Dataset with ds.query.

>>> import deeplake
>>> ds = deeplake.load("hub://activeloop/mnist-train")
>>> result = ds.query("select * where labels == 0")
>>> len(result)

Querying for samples with car or motorcycle in categories of COCO Train Dataset.

>>> import deeplake
>>> ds = deeplake.load("hub://activeloop/coco-train")
>>> result = ds.query("(select * where contains(categories, 'car')) union (select * where contains(categories, 'motorcycle'))")
>>> len(result)