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

! record with similar description exists! did you mean to load it?
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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+00:00 1
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 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+00:00 1
2 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
1 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+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=2024-11-07 20:51:54 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f8e8469ea80>>)

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=2024-11-07 20:51:54 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=2024-11-07 20:51:54 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=2024-11-07 20:51:54 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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+00:00 1
2 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+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
38 cok5lCtGNO5G0000 None True Ige intestine IgD. None None notebook None None None None None 2024-11-07 20:52:23.820360+00:00 1
150 nzCnG1iM0O3D0000 None True Urethra intestine IgG1. None None notebook None None None None None 2024-11-07 20:52:23.838691+00:00 1
492 EGRO94Avjf000000 None True Iga intestine IgA result. None None notebook None None None None None 2024-11-07 20:52:23.889025+00:00 1
379 YqjhwLsObbfQ0000 None True Intestine IgG4 Astrocytes. None None notebook None None None None None 2024-11-07 20:52:23.871052+00:00 1
472 v9vapb9nkmbG0000 None True Urethra IgD intestine IgM. None None notebook None None None None None 2024-11-07 20:52:23.887165+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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+00:00 1
2 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+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 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+00:00 1
2 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
1 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+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
2 GAtTAWzEimHm0000 None True Duodenum study research IgY Astrocytes. None None notebook None None None None None 2024-11-07 20:52:23.816809+00:00 1
7 RwXzrgSvV9ua0000 None True Ige Odontoblast research candidate. None None notebook None None None None None 2024-11-07 20:52:23.817305+00:00 1
12 1eQFXFHnjwKC0000 None True Research Intercalated duct blue-sensitive cone... None None notebook None None None None None 2024-11-07 20:52:23.817791+00:00 1
14 HbGzFlHNMCqX0000 None True Urethra research Cochlea IgG. None None notebook None None None None None 2024-11-07 20:52:23.817985+00:00 1
18 4JdgVQli1MQh0000 None True Intercalated Duct Iris Cochlea Iris research IgA. None None notebook None None None None None 2024-11-07 20:52:23.818372+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
2 GAtTAWzEimHm0000 None True Duodenum study research IgY Astrocytes. None None notebook None None None None None 2024-11-07 20:52:23.816809+00:00 1
7 RwXzrgSvV9ua0000 None True Ige Odontoblast research candidate. None None notebook None None None None None 2024-11-07 20:52:23.817305+00:00 1
12 1eQFXFHnjwKC0000 None True Research Intercalated duct blue-sensitive cone... None None notebook None None None None None 2024-11-07 20:52:23.817791+00:00 1
14 HbGzFlHNMCqX0000 None True Urethra research Cochlea IgG. None None notebook None None None None None 2024-11-07 20:52:23.817985+00:00 1
18 4JdgVQli1MQh0000 None True Intercalated Duct Iris Cochlea Iris research IgA. None None notebook None None None None None 2024-11-07 20:52:23.818372+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
12 1eQFXFHnjwKC0000 None True Research Intercalated duct blue-sensitive cone... None None notebook None None None None None 2024-11-07 20:52:23.817791+00:00 1
26 EI3AiFts7pnk0000 None True Research Descending colon IgM research IgY IgG3. None None notebook None None None None None 2024-11-07 20:52:23.819146+00:00 1
79 g7a8ROXnswNZ0000 None True Research IgM Parietal epithelial cell IgG Odon... None None notebook None None None None None 2024-11-07 20:52:23.828422+00:00 1
213 ylKILV4LfN030000 None True Research Medullary blue-sensitive cone cells b... None None notebook None None None None None 2024-11-07 20:52:23.848309+00:00 1
221 Ceh8vjvS65dr0000 None True Research IgG2 Iris blue-sensitive cone cells M... None None notebook None None None None None 2024-11-07 20:52:23.849050+00:00 1
305 NJZqtFhmiC2F0000 None True Research IgG candidate. None None notebook None None None None None 2024-11-07 20:52:23.860511+00:00 1
313 1d7vu8rE3P9n0000 None True Research IgG2 Medullary Epithelial reticular c... None None notebook None None None None None 2024-11-07 20:52:23.861257+00:00 1
366 GsJFQ1zSqGbV0000 None True Research research Epithelial reticular cell IgD. None None notebook None None None None None 2024-11-07 20:52:23.869837+00:00 1
367 n6DAfNBComQN0000 None True Research rank efficiency. None None notebook None None None None None 2024-11-07 20:52:23.869931+00:00 1
435 gNBnCYctqnI80000 None True Research Descending colon IgD Descending colon. None None notebook None None None None None 2024-11-07 20:52:23.880010+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 QrmoUefrMl7Wz9Vj0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 20:51:58.870019+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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 Ui1h2mkCta9Vn2cB0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 20:51:59.042466+00:00 1
3 dG3T4qBFVVLvwV8f0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 20:51:59.148166+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