Query & search registries

This guide walks through all the ways of finding metadata records in LaminDB registries.

# !pip install lamindb
!lamin init --storage ./test-registries
Hide code cell output 
→ connected lamindb: testuser1/test-registries

We’ll need some toy data.

import lamindb as ln

# create toy data
ln.Artifact(ln.core.datasets.file_jpg_paradisi05(), description="My image").save()
ln.Artifact.from_df(ln.core.datasets.df_iris(), description="The iris collection").save()
ln.Artifact(ln.core.datasets.file_fastq(), description="My fastq").save()

# see the content of the artifact registry
ln.Artifact.df()
Hide code cell output 
→ connected lamindb: testuser1/test-registries

! no run & transform got linked, call `ln.track()` & re-run

! no run & transform got linked, call `ln.track()` & re-run

! no run & transform got linked, call `ln.track()` & re-run
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1

Look up metadata

For registries with less than 100k records, auto-completing a Lookup object is the most convenient way of finding a record.

For example, take the User registry:

# query the database for all users, optionally pass the field that creates the key
users = ln.User.lookup(field="handle")

# the lookup object is a NamedTuple
users
Hide code cell output 
Lookup(testuser1=User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-18 15:41:47 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f31cbaf9990>>)

With auto-complete, we find a specific user record:

user = users.testuser1
user
Hide code cell output 
User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-18 15:41:47 UTC)

You can also get a dictionary:

users_dict = ln.User.lookup().dict()
users_dict
Hide code cell output 
{'testuser1': User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-18 15:41:47 UTC)}

Query exactly one record

get errors if more than one matching records are found.

# by the universal base62 uid
ln.User.get("DzTjkKse")

# by any expression involving fields
ln.User.get(handle="testuser1")
Hide code cell output 
User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-18 15:41:47 UTC)

Query sets of records

Filter for all artifacts created by a user:

ln.Artifact.filter(created_by=user).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

To access the results encoded in a filter statement, execute its return value with one of:

  • .df(): A pandas DataFrame with each record in a row.

  • .all(): A QuerySet.

  • .one(): Exactly one record. Will raise an error if there is none. Is equivalent to the .get() method shown above.

  • .one_or_none(): Either one record or None if there is no query result.

Note

filter() returns a QuerySet.

The ORMs in LaminDB are Django Models and any Django query works. LaminDB extends Django’s API for data scientists.

Under the hood, any .filter() call translates into a SQL select statement.

.one() and .one_or_none() are two parts of LaminDB’s API that are borrowed from SQLAlchemy.

Search for records

Search the toy data:

ln.Artifact.search("iris").df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1

Let us create 500 notebook objects with fake titles, save, and search them:

transforms = [ln.Transform(name=title, type="notebook") for title in ln.core.datasets.fake_bio_notebook_titles(n=500)]
ln.save(transforms)

# search
ln.Transform.search("intestine").df().head(5)
Hide code cell output
uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
11 009r97JEbjaE0000 None True Taste Buds Cerebellum IgE intestine Arteries P... None None notebook None None None None None 2024-10-18 15:41:51.282629+00:00 1
49 WSbfgIVWvxAM0000 None True Nuclear Chain Cell Enterochromaffin cell Phary... None None notebook None None None None None 2024-10-18 15:41:51.285007+00:00 1
69 bCuOGURnIzGa0000 None True Nuclear Chain Cell intestine IgY Hensen's cells. None None notebook None None None None None 2024-10-18 15:41:51.289202+00:00 1
71 n7ZzOPNXy0ME0000 None True Vestibular Apparatus Supporting Cells Pharynx ... None None notebook None None None None None 2024-10-18 15:41:51.289322+00:00 1
90 5MMjWbFwBAl80000 None True Efficiency intestinal Cerebellum Platelets IgY... None None notebook None None None None None 2024-10-18 15:41:51.290434+00:00 1

Note

Currently, the LaminHub UI search is more powerful than the search of the lamindb open-source package.

Leverage relations

Django has a double-under-score syntax to filter based on related tables.

This syntax enables you to traverse several layers of relations and leverage different comparators.

ln.Artifact.filter(created_by__handle__startswith="testuse").df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

The filter selects all artifacts based on the users who ran the generating notebook.

Under the hood, in the SQL database, it’s joining the artifact table with the run and the user table.

Comparators

You can qualify the type of comparison in a query by using a comparator.

Below follows a list of the most import, but Django supports about two dozen field comparators field__comparator=value.

and

ln.Artifact.filter(suffix=".jpg", created_by=user).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1

less than/ greater than

Or subset to artifacts smaller than 10kB. Here, we can’t use keyword arguments, but need an explicit where statement.

ln.Artifact.filter(created_by=user, size__lt=1e4).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

in

ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

order by

ln.Artifact.filter().order_by("-updated_at").df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1

contains

ln.Transform.filter(name__contains="search").df().head(5)
Hide code cell output
uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
17 lmj7fUne1SGi0000 None True Research Pharynx IgE IgG4 Pharynx IgM. None None notebook None None None None None 2024-10-18 15:41:51.283001+00:00 1
18 1BCypOJyb2s20000 None True Igm research Nuclear chain cell IgM Stomach Ig... None None notebook None None None None None 2024-10-18 15:41:51.283063+00:00 1
19 kZdaIhklzp730000 None True Cerebellum IgM candidate intestinal research I... None None notebook None None None None None 2024-10-18 15:41:51.283125+00:00 1
28 4QyIJFTmFgED0000 None True Candidate research Proximal tubule brush borde... None None notebook None None None None None 2024-10-18 15:41:51.283685+00:00 1
29 BnS9AdMGLa0y0000 None True Igm research Proximal tubule brush border cell... None None notebook None None None None None 2024-10-18 15:41:51.283747+00:00 1

And case-insensitive:

ln.Transform.filter(name__icontains="Search").df().head(5)
Hide code cell output
uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
17 lmj7fUne1SGi0000 None True Research Pharynx IgE IgG4 Pharynx IgM. None None notebook None None None None None 2024-10-18 15:41:51.283001+00:00 1
18 1BCypOJyb2s20000 None True Igm research Nuclear chain cell IgM Stomach Ig... None None notebook None None None None None 2024-10-18 15:41:51.283063+00:00 1
19 kZdaIhklzp730000 None True Cerebellum IgM candidate intestinal research I... None None notebook None None None None None 2024-10-18 15:41:51.283125+00:00 1
28 4QyIJFTmFgED0000 None True Candidate research Proximal tubule brush borde... None None notebook None None None None None 2024-10-18 15:41:51.283685+00:00 1
29 BnS9AdMGLa0y0000 None True Igm research Proximal tubule brush border cell... None None notebook None None None None None 2024-10-18 15:41:51.283747+00:00 1

startswith

ln.Transform.filter(name__startswith="Research").df()
Hide code cell output
uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
17 lmj7fUne1SGi0000 None True Research Pharynx IgE IgG4 Pharynx IgM. None None notebook None None None None None 2024-10-18 15:41:51.283001+00:00 1
35 qwUHYe7mPq5W0000 None True Research classify Stomach IgM. None None notebook None None None None None 2024-10-18 15:41:51.284117+00:00 1
95 vRx5MNS5PtPK0000 None True Research Hensen's cells Proximal tubule brush ... None None notebook None None None None None 2024-10-18 15:41:51.290723+00:00 1
162 7E3cwIc7dPSq0000 None True Research IgE research Stomach investigate IgA. None None notebook None None None None None 2024-10-18 15:41:51.297128+00:00 1
206 UJQpaq8rB2oO0000 None True Research intestine result IgA Pharynx Proximal... None None notebook None None None None None 2024-10-18 15:41:51.302311+00:00 1
226 CSf24aDSGSJP0000 None True Research IgG4 Cerebellum cluster. None None notebook None None None None None 2024-10-18 15:41:51.303475+00:00 1
314 sO3Kg0TG3rBb0000 None True Research Hensen's cells cluster Pharynx rank P... None None notebook None None None None None 2024-10-18 15:41:51.311078+00:00 1
418 PCYOKM0J7w7K0000 None True Research IgG4 IgG Platelets IgG4 Mammary gland... None None notebook None None None None None 2024-10-18 15:41:51.322232+00:00 1
454 rxTYgz6xnSyo0000 None True Research IgG1 IgG4 IgM Stomach IgG1 IgA. None None notebook None None None None None 2024-10-18 15:41:51.324358+00:00 1

or

ln.Artifact.filter(ln.Q(suffix=".jpg") | ln.Q(suffix=".fastq.gz")).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
1 vqf6lpuWjJuTgL7E0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-18 15:41:49.543430+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

negate/ unequal

ln.Artifact.filter(~ln.Q(suffix=".jpg")).df()
Hide code cell output
uid version is_latest description key suffix type size hash n_objects n_observations _hash_type _accessor visibility _key_is_virtual storage_id transform_id run_id created_at created_by_id
id
2 xD0XJhOuz8cA20kL0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-18 15:41:49.639767+00:00 1
3 Zo3xbntnYYlEDHtn0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-18 15:41:49.646594+00:00 1

Clean up the test instance.

!rm -r ./test-registries
!lamin delete --force test-registries
Hide code cell output 
• deleting instance testuser1/test-registries