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
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→ 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()
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→ 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 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1
2 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
1 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+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
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Lookup(testuser1=User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-11-11 09:34:07 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f4f4410a630>>)

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

user = users.testuser1
user
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User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-11-11 09:34:07 UTC)

You can also get a dictionary:

users_dict = ln.User.lookup().dict()
users_dict
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{'testuser1': User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-11-11 09:34:07 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")
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User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-11-11 09:34:07 UTC)

Query sets of records

Filter for all artifacts created by a user:

ln.Artifact.filter(created_by=user).df()
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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 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+00:00 1
2 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+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()
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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 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+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)
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uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
3 etC3CnCw5ad10000 None True Igg4 intestine IgE IgG4. None None notebook None None None None None 2024-11-11 09:34:20.820054+00:00 1
15 RXjNOWiMZTNr0000 None True Cluster IgD rank IgE intestine White fat cell ... None None notebook None None None None None 2024-11-11 09:34:20.821209+00:00 1
23 BUqqNuUTowC60000 None True Theca Interna Cell somatostatin-secreting D ce... None None notebook None None None None None 2024-11-11 09:34:20.821970+00:00 1
26 6WGBxLBliWsf0000 None True Intestinal IgA intestine IgG4 Duodenum classif... None None notebook None None None None None 2024-11-11 09:34:20.822261+00:00 1
28 GHklsVMzK1Wl0000 None True Igg2 IgD Tendons intestine IgE. None None notebook None None None None None 2024-11-11 09:34:20.822452+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()
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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 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+00:00 1
2 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+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()
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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 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+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()
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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 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1

in

ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
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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 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1

order by

ln.Artifact.filter().order_by("-updated_at").df()
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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 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1
2 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
1 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+00:00 1

contains

ln.Transform.filter(name__contains="search").df().head(5)
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uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
14 xwCl9aahdcp70000 None True Classify research somatostatin-secreting D cel... None None notebook None None None None None 2024-11-11 09:34:20.821114+00:00 1
16 WbPatfyM6zhq0000 None True Visualize Lacrimal gland cell IgG2 research IgG4. None None notebook None None None None None 2024-11-11 09:34:20.821304+00:00 1
37 KDgPEDAnVmCs0000 None True Spinal Cord Tendons research intestinal IgG4 IgM. None None notebook None None None None None 2024-11-11 09:34:20.823323+00:00 1
40 OljJlXS27GQB0000 None True Ige IgM Duodenum Duodenum Parietal cell resear... None None notebook None None None None None 2024-11-11 09:34:20.823607+00:00 1
54 pjzn4G4oIKlh0000 None True Igg4 research Capillaries White fat cell IgA. None None notebook None None None None None 2024-11-11 09:34:20.824934+00:00 1

And case-insensitive:

ln.Transform.filter(name__icontains="Search").df().head(5)
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uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
14 xwCl9aahdcp70000 None True Classify research somatostatin-secreting D cel... None None notebook None None None None None 2024-11-11 09:34:20.821114+00:00 1
16 WbPatfyM6zhq0000 None True Visualize Lacrimal gland cell IgG2 research IgG4. None None notebook None None None None None 2024-11-11 09:34:20.821304+00:00 1
37 KDgPEDAnVmCs0000 None True Spinal Cord Tendons research intestinal IgG4 IgM. None None notebook None None None None None 2024-11-11 09:34:20.823323+00:00 1
40 OljJlXS27GQB0000 None True Ige IgM Duodenum Duodenum Parietal cell resear... None None notebook None None None None None 2024-11-11 09:34:20.823607+00:00 1
54 pjzn4G4oIKlh0000 None True Igg4 research Capillaries White fat cell IgA. None None notebook None None None None None 2024-11-11 09:34:20.824934+00:00 1

startswith

ln.Transform.filter(name__startswith="Research").df()
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uid version is_latest name key description type source_code hash reference reference_type _source_code_artifact_id created_at created_by_id
id
124 wL6VLCqzRB3k0000 None True Research IgG3 IgG4 result research IgG4. None None notebook None None None None None 2024-11-11 09:34:20.836478+00:00 1
176 LeblrZkwLfRw0000 None True Research IgG4 White fat cell IgG4 IgG4. None None notebook None None None None None 2024-11-11 09:34:20.845218+00:00 1
223 h1J6SpRzPQtu0000 None True Research IgG4 IgE study spinal cord. None None notebook None None None None None 2024-11-11 09:34:20.853166+00:00 1
267 P39YsmJhcQ1h0000 None True Research IgD IgG4 IgG2 IgA. None None notebook None None None None None 2024-11-11 09:34:20.860916+00:00 1
354 y1QdQcP8wl5K0000 None True Research Duodenum IgG4 IgG2 intestine spinal c... None None notebook None None None None None 2024-11-11 09:34:20.872793+00:00 1
368 Y3vqDGH5sSGq0000 None True Research Parietal cell IgG4 IgG4 IgD IgE Outer... None None notebook None None None None None 2024-11-11 09:34:20.874082+00:00 1

or

ln.Artifact.filter(ln.Q(suffix=".jpg") | ln.Q(suffix=".fastq.gz")).df()
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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 JYS3bZZZyUdXtRQT0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 09:34:11.311697+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1

negate/ unequal

ln.Artifact.filter(~ln.Q(suffix=".jpg")).df()
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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 OrhCye8uUKlXCFpP0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 09:34:11.502276+00:00 1
3 vsGuJ9CkguoDmeoJ0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 09:34:11.513574+00:00 1

Clean up the test instance.

!rm -r ./test-registries
!lamin delete --force test-registries
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• deleting instance testuser1/test-registries