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 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+00:00 1
2 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
1 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+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=2024-10-29 21:24:30 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7ff948d76db0>>)

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=2024-10-29 21:24:30 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=2024-10-29 21:24:30 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=2024-10-29 21:24:30 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 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+00:00 1
2 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+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
4 azho7TjIsUo00000 None True Intestine IgA Granule cells efficiency Capilla... None None notebook None None None None None 2024-10-29 21:24:43.777368+00:00 1
17 nY0ecJD7d0dF0000 None True Ige IgE IgG3 IgE visualize intestine. None None notebook None None None None None 2024-10-29 21:24:43.778624+00:00 1
23 QYorx1I8pkuu0000 None True Ige Bulbourethral gland intestine classify IgY... None None notebook None None None None None 2024-10-29 21:24:43.779193+00:00 1
24 W8aVmfchynHb0000 None True Investigate intestine IgD IgA IgA IgG3 Crystal... None None notebook None None None None None 2024-10-29 21:24:43.779288+00:00 1
29 CJFkFwnepqXH0000 None True Igy IgE SA node cell intestine IgG3 IgY Bulbou... None None notebook None None None None None 2024-10-29 21:24:43.779763+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 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+00:00 1
2 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+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 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+00:00 1
2 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
1 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+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
8 KkhPTfpPxzto0000 None True Research candidate IgD efficiency. None None notebook None None None None None 2024-10-29 21:24:43.777750+00:00 1
20 K47M8UgOUWoC0000 None True Intestinal research candidate IgD Gland of Lit... None None notebook None None None None None 2024-10-29 21:24:43.778908+00:00 1
25 SitVEonp4cuy0000 None True Gland Of Littre Ganglia research Elastic carti... None None notebook None None None None None 2024-10-29 21:24:43.779383+00:00 1
34 063sCQtukSdJ0000 None True Research visualize IgD Ganglia intestinal. None None notebook None None None None None 2024-10-29 21:24:43.780238+00:00 1
63 dOF8jHVh5AOO0000 None True Ige Thymus research research. None None notebook None None None None None 2024-10-29 21:24:43.782992+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
8 KkhPTfpPxzto0000 None True Research candidate IgD efficiency. None None notebook None None None None None 2024-10-29 21:24:43.777750+00:00 1
20 K47M8UgOUWoC0000 None True Intestinal research candidate IgD Gland of Lit... None None notebook None None None None None 2024-10-29 21:24:43.778908+00:00 1
25 SitVEonp4cuy0000 None True Gland Of Littre Ganglia research Elastic carti... None None notebook None None None None None 2024-10-29 21:24:43.779383+00:00 1
34 063sCQtukSdJ0000 None True Research visualize IgD Ganglia intestinal. None None notebook None None None None None 2024-10-29 21:24:43.780238+00:00 1
63 dOF8jHVh5AOO0000 None True Ige Thymus research research. None None notebook None None None None None 2024-10-29 21:24:43.782992+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
8 KkhPTfpPxzto0000 None True Research candidate IgD efficiency. None None notebook None None None None None 2024-10-29 21:24:43.777750+00:00 1
34 063sCQtukSdJ0000 None True Research visualize IgD Ganglia intestinal. None None notebook None None None None None 2024-10-29 21:24:43.780238+00:00 1
68 GOWHGWZGCGO50000 None True Research investigate rank IgG3 Bulbourethral g... None None notebook None None None None None 2024-10-29 21:24:43.788378+00:00 1
139 gue27f8QbUvz0000 None True Research Fork neurons efficiency IgE investiga... None None notebook None None None None None 2024-10-29 21:24:43.798585+00:00 1
200 Dsz7xbDF3D6S0000 None True Research efficiency IgG3 IgG3 Heart IgG2 IgG1. None None notebook None None None None None 2024-10-29 21:24:43.807706+00:00 1
219 dXpXOQ6Fnv1Q0000 None True Research IgA Stellate cells Elastic cartilage. None None notebook None None None None None 2024-10-29 21:24:43.809444+00:00 1
235 VovxwuYxFqde0000 None True Research Fork neurons IgG IgG3 rank rank. None None notebook None None None None None 2024-10-29 21:24:43.810939+00:00 1
297 lcJ5BxNiuVb50000 None True Research efficiency IgA. None None notebook None None None None None 2024-10-29 21:24:43.820264+00:00 1
324 dELCtgT0tUxf0000 None True Research Cold-sensitive sensory neurons Granul... None None notebook None None None None None 2024-10-29 21:24:43.822763+00:00 1
411 b1TIXdVf9hSE0000 None True Research classify Granule cells investigate Ig... None None notebook None None None None None 2024-10-29 21:24:43.838261+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 KMYyFlqVG1Pk7dyv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-29 21:24:34.309287+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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 oCP4JM7PYSDoCAAi0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-10-29 21:24:34.579565+00:00 1
3 QoSRVvGH4vOZByjT0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-29 21:24:34.588758+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