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 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+00:00 1
2 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
1 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+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=2024-11-25 06:34:39 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f57d483a6f0>>)

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=2024-11-25 06:34:39 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=2024-11-25 06:34:39 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=2024-11-25 06:34:39 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 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+00:00 1
2 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+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
6 mqUJZsyhys0E0000 None True Osteoclast intestine Chief cell IgY. None None notebook None None None None None 2024-11-25 06:34:53.745713+00:00 1
11 Uh8QUqeex1wk0000 None True Intestine Osteoclast Astrocytes IgA Osteoclast... None None notebook None None None None None 2024-11-25 06:34:53.746211+00:00 1
15 JaiYrC8PhJEi0000 None True Igg3 visualize Chief cell Osteoclast classify ... None None notebook None None None None None 2024-11-25 06:34:53.746630+00:00 1
17 QJH9IYzeXXau0000 None True Intestine IgG1 IgA. None None notebook None None None None None 2024-11-25 06:34:53.746825+00:00 1
34 MRsTYP0gZ42a0000 None True Igd IgG1 Oligodendrocytes IgG intestine Astroc... None None notebook None None None None None 2024-11-25 06:34:53.748493+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 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+00:00 1
2 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+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 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+00:00 1
2 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
1 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+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
10 pmw053hnqKia0000 None True Intestinal IgY blue-sensitive cone cells Lungs... None None notebook None None None None None 2024-11-25 06:34:53.746113+00:00 1
19 fhN4yhPoabDm0000 None True Intestinal intestinal research IgY. None None notebook None None None None None 2024-11-25 06:34:53.747025+00:00 1
33 QgiZ4fAfSDTJ0000 None True Igd research IgD IgG Huxley's layer. None None notebook None None None None None 2024-11-25 06:34:53.748396+00:00 1
46 yBDFHzO1MCwi0000 None True Igg3 Osteoclast cluster IgD IgG3 research Astr... None None notebook None None None None None 2024-11-25 06:34:53.749683+00:00 1
55 1cQpVAgH2IPa0000 None True Igy result IgA research. None None notebook None None None None None 2024-11-25 06:34:53.750581+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
10 pmw053hnqKia0000 None True Intestinal IgY blue-sensitive cone cells Lungs... None None notebook None None None None None 2024-11-25 06:34:53.746113+00:00 1
19 fhN4yhPoabDm0000 None True Intestinal intestinal research IgY. None None notebook None None None None None 2024-11-25 06:34:53.747025+00:00 1
33 QgiZ4fAfSDTJ0000 None True Igd research IgD IgG Huxley's layer. None None notebook None None None None None 2024-11-25 06:34:53.748396+00:00 1
46 yBDFHzO1MCwi0000 None True Igg3 Osteoclast cluster IgD IgG3 research Astr... None None notebook None None None None None 2024-11-25 06:34:53.749683+00:00 1
55 1cQpVAgH2IPa0000 None True Igy result IgA research. None None notebook None None None None None 2024-11-25 06:34:53.750581+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
56 0fOhJlHccduI0000 None True Research Chief cell Oligodendrocytes IgG1 IgG3... None None notebook None None None None None 2024-11-25 06:34:53.750679+00:00 1
62 JUlTJs74tlUK0000 None True Research IgG visualize research. None None notebook None None None None None 2024-11-25 06:34:53.751265+00:00 1
120 YkrRnuew9rfO0000 None True Research IgD classify Lungs Huxley's layer Chi... None None notebook None None None None None 2024-11-25 06:34:53.760873+00:00 1
145 6WLY609LhqJO0000 None True Research Oligodendrocytes IgG3 IgG3 IgA result. None None notebook None None None None None 2024-11-25 06:34:53.766832+00:00 1
158 h9iJLeGNb5ps0000 None True Research result Astrocytes IgY IgG3 IgY IgY IgY. None None notebook None None None None None 2024-11-25 06:34:53.768041+00:00 1
350 pzXqH6ZduuRa0000 None True Research intestinal study IgD IgY IgY IgY. None None notebook None None None None None 2024-11-25 06:34:53.796712+00:00 1
352 FOFia2r4VcHi0000 None True Research Centroacinar cell study Oligodendrocy... None None notebook None None None None None 2024-11-25 06:34:53.796904+00:00 1
354 GTJ7Yk4oLyv20000 None True Research efficiency efficiency Astrocytes. None None notebook None None None None None 2024-11-25 06:34:53.797089+00:00 1
396 7kitLhLwJJHE0000 None True Research IgG1 IgG1 IgA visualize. None None notebook None None None None None 2024-11-25 06:34:53.801018+00:00 1
407 e85Zp89OFDa90000 None True Research IgG1 intestine IgG3 Astrocytes IgA. None None notebook None None None None None 2024-11-25 06:34:53.805696+00:00 1
434 PbNMj5RHUpkH0000 None True Research IgY IgY candidate IgG1 IgD Trachea. None None notebook None None None None None 2024-11-25 06:34:53.808241+00:00 1
448 W0l4Q7DhqaOJ0000 None True Research efficiency Centroacinar cell candidat... None None notebook None None None None None 2024-11-25 06:34:53.809553+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 CzM80em0fbaUmhXA0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-25 06:34:43.819861+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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 PNIsssnxQkJAgst50000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-25 06:34:43.950067+00:00 1
3 vX3dCD6dpo4BIItz0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-25 06:34:43.963448+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