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 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+00:00 1
2 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
1 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+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=2024-10-11 09:32:36 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f9498fb9150>>)

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=2024-10-11 09:32:36 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=2024-10-11 09:32:36 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=2024-10-11 09:32:36 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 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+00:00 1
2 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+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
9 j8oVy5oFnrLm0000 None True Alpha Cell IgG4 Vas deferens intestine IgG int... None None notebook None None None None None 2024-10-11 09:32:39.885733+00:00 1
35 e0iIK94nRZcD0000 None True Gastric Inhibitory Peptide-Secreting K Cell Ig... None None notebook None None None None None 2024-10-11 09:32:39.887358+00:00 1
61 xWDzei3o5zvQ0000 None True Classify IgA intestine Alpha cell. None None notebook None None None None None 2024-10-11 09:32:39.889010+00:00 1
79 mAtJLwRrLXqy0000 None True Spinal Cord Connective-tissue macrophage inves... None None notebook None None None None None 2024-10-11 09:32:39.893156+00:00 1
84 3v8BQROsK2qt0000 None True Interstitial Kidney Cells Brunner's gland visu... None None notebook None None None None None 2024-10-11 09:32:39.893454+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 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+00:00 1
2 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+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 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+00:00 1
2 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
1 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+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
4 8BLgf5S9sN4C0000 None True Alpha Cell research intestinal efficiency cand... None None notebook None None None None None 2024-10-11 09:32:39.885417+00:00 1
15 wnmD1dzvl9290000 None True Research IgG IgG3 IgA IgD. None None notebook None None None None None 2024-10-11 09:32:39.886112+00:00 1
16 peYalmyEdNub0000 None True Fallopian Tubes visualize efficiency research ... None None notebook None None None None None 2024-10-11 09:32:39.886175+00:00 1
17 cJ1ADIMX9oMy0000 None True Igg3 IgG3 efficiency research Brunner's gland ... None None notebook None None None None None 2024-10-11 09:32:39.886237+00:00 1
25 5CAMqEqchSwQ0000 None True Research IgG3 IgE Vas deferens. None None notebook None None None None None 2024-10-11 09:32:39.886735+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
4 8BLgf5S9sN4C0000 None True Alpha Cell research intestinal efficiency cand... None None notebook None None None None None 2024-10-11 09:32:39.885417+00:00 1
15 wnmD1dzvl9290000 None True Research IgG IgG3 IgA IgD. None None notebook None None None None None 2024-10-11 09:32:39.886112+00:00 1
16 peYalmyEdNub0000 None True Fallopian Tubes visualize efficiency research ... None None notebook None None None None None 2024-10-11 09:32:39.886175+00:00 1
17 cJ1ADIMX9oMy0000 None True Igg3 IgG3 efficiency research Brunner's gland ... None None notebook None None None None None 2024-10-11 09:32:39.886237+00:00 1
25 5CAMqEqchSwQ0000 None True Research IgG3 IgE Vas deferens. None None notebook None None None None None 2024-10-11 09:32:39.886735+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
15 wnmD1dzvl9290000 None True Research IgG IgG3 IgA IgD. None None notebook None None None None None 2024-10-11 09:32:39.886112+00:00 1
25 5CAMqEqchSwQ0000 None True Research IgG3 IgE Vas deferens. None None notebook None None None None None 2024-10-11 09:32:39.886735+00:00 1
57 m6PSrgJXYtVl0000 None True Research visualize IgY cluster Vas deferens re... None None notebook None None None None None 2024-10-11 09:32:39.888760+00:00 1
83 5lDZcqVhWgwe0000 None True Research spinal cord Connective-tissue macroph... None None notebook None None None None None 2024-10-11 09:32:39.893394+00:00 1
110 uSzp9BCANUXY0000 None True Research IgA investigate IgG3. None None notebook None None None None None 2024-10-11 09:32:39.895001+00:00 1
146 uymdPiSEyJHR0000 None True Research IgE IgE IgG. None None notebook None None None None None 2024-10-11 09:32:39.899730+00:00 1
172 FntCrfbPHwxt0000 None True Research Hyaline cartilage IgG2 Ascending colo... None None notebook None None None None None 2024-10-11 09:32:39.901265+00:00 1
178 rqA4VGrA6i1w0000 None True Research investigate Sigmoid colon IgG4 IgG3. None None notebook None None None None None 2024-10-11 09:32:39.901616+00:00 1
254 5Oz8LZT2Lsv00000 None True Research IgG3 Hyaline cartilage IgG3 IgY IgG2 ... None None notebook None None None None None 2024-10-11 09:32:39.908832+00:00 1
269 KIKxAIuUlpET0000 None True Research study IgG3. None None notebook None None None None None 2024-10-11 09:32:39.912203+00:00 1
270 SJgWV4Ct9yaG0000 None True Research candidate study Beta cell Ascending c... None None notebook None None None None None 2024-10-11 09:32:39.912263+00:00 1
292 XOZ6W7hwGBrK0000 None True Research IgG3 candidate Fallopian tubes. None None notebook None None None None None 2024-10-11 09:32:39.913555+00:00 1
310 X2qKKeUxcqav0000 None True Research IgM spinal cord Hyaline cartilage clu... None None notebook None None None None None 2024-10-11 09:32:39.914606+00:00 1
432 QnMdRylKTSIu0000 None True Research intestinal IgG3 classify candidate IgG2. None None notebook None None None None None 2024-10-11 09:32:39.926884+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 ESj9Js3Ex2FguQtm0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-11 09:32:38.098281+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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 q1mMI0YFUHY6LpsO0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-11 09:32:38.232889+00:00 1
3 aoD9P21yBTfr86gu0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-11 09:32:38.239461+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