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 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+00:00 1
2 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
1 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+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=2024-10-22 13:14:40 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f95331aaad0>>)

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=2024-10-22 13:14:40 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=2024-10-22 13:14:40 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=2024-10-22 13:14:40 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 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+00:00 1
2 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+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 AQW6P6oeWALS0000 None True Intestine Cerebral hemispheres efficiency IgG1... None None notebook None None None None None 2024-10-22 13:14:43.925036+00:00 1
4 C8VSYwYlTSqp0000 None True Igd classify intestinal intestine Intermediate... None None notebook None None None None None 2024-10-22 13:14:43.925100+00:00 1
8 C0tTBbLeFMH70000 None True Zona Reticularis intestinal IgD Pons IgG3 IgD ... None None notebook None None None None None 2024-10-22 13:14:43.925352+00:00 1
11 flAgXoozfuxl0000 None True Iga IgG1 intestine Place cells cluster classify. None None notebook None None None None None 2024-10-22 13:14:43.925539+00:00 1
18 8mxpkoyAyjf80000 None True Renshaw Cells IgM efficiency intestine IgG2 cl... None None notebook None None None None None 2024-10-22 13:14:43.925977+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 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+00:00 1
2 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+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 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+00:00 1
2 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
1 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+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
3 AQW6P6oeWALS0000 None True Intestine Cerebral hemispheres efficiency IgG1... None None notebook None None None None None 2024-10-22 13:14:43.925036+00:00 1
19 nBc1yZzKIYa70000 None True Cerebellum efficiency Cerebral hemispheres res... None None notebook None None None None None 2024-10-22 13:14:43.926039+00:00 1
20 IFKmMvm5wSwt0000 None True Inner Phalangeal Cells Of Organ Of Corti IgD R... None None notebook None None None None None 2024-10-22 13:14:43.926102+00:00 1
36 IqMDwWhtP9jI0000 None True Research Unipolar brush cells IgA cluster Inte... None None notebook None None None None None 2024-10-22 13:14:43.927097+00:00 1
41 eN5baCUXhS6L0000 None True Inner Phalangeal Cells Of Organ Of Corti resea... None None notebook None None None None None 2024-10-22 13:14:43.927408+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
3 AQW6P6oeWALS0000 None True Intestine Cerebral hemispheres efficiency IgG1... None None notebook None None None None None 2024-10-22 13:14:43.925036+00:00 1
19 nBc1yZzKIYa70000 None True Cerebellum efficiency Cerebral hemispheres res... None None notebook None None None None None 2024-10-22 13:14:43.926039+00:00 1
20 IFKmMvm5wSwt0000 None True Inner Phalangeal Cells Of Organ Of Corti IgD R... None None notebook None None None None None 2024-10-22 13:14:43.926102+00:00 1
36 IqMDwWhtP9jI0000 None True Research Unipolar brush cells IgA cluster Inte... None None notebook None None None None None 2024-10-22 13:14:43.927097+00:00 1
41 eN5baCUXhS6L0000 None True Inner Phalangeal Cells Of Organ Of Corti resea... None None notebook None None None None None 2024-10-22 13:14:43.927408+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
36 IqMDwWhtP9jI0000 None True Research Unipolar brush cells IgA cluster Inte... None None notebook None None None None None 2024-10-22 13:14:43.927097+00:00 1
84 63KHIYzpEWNm0000 None True Research research IgG IgD Unipolar brush cells. None None notebook None None None None None 2024-10-22 13:14:43.933281+00:00 1
107 VWGy0Nu6TNat0000 None True Research Pons IgD Zona reticularis IgD IgG1 Re... None None notebook None None None None None 2024-10-22 13:14:43.934649+00:00 1
166 twOkzCPUtrVq0000 None True Research Ganglia IgG IgG intestinal result Zon... None None notebook None None None None None 2024-10-22 13:14:43.940729+00:00 1
210 g8ksGrOe9r5d0000 None True Research IgM Ganglia Place cells result IgG1 IgD. None None notebook None None None None None 2024-10-22 13:14:43.946210+00:00 1
292 X9EuppGuO5zT0000 None True Research IgG IgM IgG Unipolar brush cells IgG ... None None notebook None None None None None 2024-10-22 13:14:44.024977+00:00 1
342 121ohgElPE9o0000 None True Research IgG4 Unipolar brush cells IgG2 Granul... None None notebook None None None None None 2024-10-22 13:14:44.030636+00:00 1
343 PEdNJ1bkdQr30000 None True Research IgM rank rank. None None notebook None None None None None 2024-10-22 13:14:44.030696+00:00 1
356 Xg39qz8zHgWy0000 None True Research IgG2 IgG4 IgM IgD IgG classify. None None notebook None None None None None 2024-10-22 13:14:44.031481+00:00 1
483 duhmFY9RuhO60000 None True Research IgD Unipolar brush cells Renshaw cell... None None notebook None None None None None 2024-10-22 13:14:44.044417+00:00 1
491 2D3Q80exPiW50000 None True Research Ganglia IgM visualize Cerebellum Cere... None None notebook None None None None None 2024-10-22 13:14:44.044917+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 EJ8YoLJQuiystFPK0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-22 13:14:42.215309+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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 8rIKkRUihXv3gTzc0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-22 13:14:42.292407+00:00 1
3 BGDYP8td2BHxyHGj0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-22 13:14:42.301886+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