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 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+00:00 1
2 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
1 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+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=2024-10-17 13:14:06 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7fd10442d5d0>>)

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=2024-10-17 13:14:06 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=2024-10-17 13:14:06 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=2024-10-17 13:14:06 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 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+00:00 1
2 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+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 OKITVEztddjW0000 None True Intestinal IgM IgD IgG green-sensitive cone ce... None None notebook None None None None None 2024-10-17 13:14:10.656179+00:00 1
8 9okvLdQEjTep0000 None True Foveolar Cell intestine IgG4 result. None None notebook None None None None None 2024-10-17 13:14:10.656434+00:00 1
11 4OeN0Rgk3zxU0000 None True Rectum visualize IgG intestine IgG1. None None notebook None None None None None 2024-10-17 13:14:10.656631+00:00 1
20 hMK3RIXIz20z0000 None True Investigate IgG3 IgD IgG1 efficiency intestine. None None notebook None None None None None 2024-10-17 13:14:10.657215+00:00 1
36 rfONsXyfLSjn0000 None True Intestine IgA result. None None notebook None None None None None 2024-10-17 13:14:10.658236+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 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+00:00 1
2 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+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 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+00:00 1
2 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
1 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+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 JiSdUl6sBRuR0000 None True Larynx study IgA IgG2 research Red skeletal mu... None None notebook None None None None None 2024-10-17 13:14:10.656043+00:00 1
6 EB6ZM7qn3yAr0000 None True Choroid Plexus research efficiency Bulbourethr... None None notebook None None None None None 2024-10-17 13:14:10.656307+00:00 1
12 p5EZSZcwhE8Y0000 None True Igd research IgG4 Grid cells. None None notebook None None None None None 2024-10-17 13:14:10.656699+00:00 1
21 24YpSOfsECHn0000 None True Cluster IgG Larynx research Rectum cluster. None None notebook None None None None None 2024-10-17 13:14:10.657279+00:00 1
47 psyd8quBCgua0000 None True Candidate IgG2 classify IgG1 research Larynx g... None None notebook None None None None None 2024-10-17 13:14:10.658963+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 JiSdUl6sBRuR0000 None True Larynx study IgA IgG2 research Red skeletal mu... None None notebook None None None None None 2024-10-17 13:14:10.656043+00:00 1
6 EB6ZM7qn3yAr0000 None True Choroid Plexus research efficiency Bulbourethr... None None notebook None None None None None 2024-10-17 13:14:10.656307+00:00 1
12 p5EZSZcwhE8Y0000 None True Igd research IgG4 Grid cells. None None notebook None None None None None 2024-10-17 13:14:10.656699+00:00 1
21 24YpSOfsECHn0000 None True Cluster IgG Larynx research Rectum cluster. None None notebook None None None None None 2024-10-17 13:14:10.657279+00:00 1
47 psyd8quBCgua0000 None True Candidate IgG2 classify IgG1 research Larynx g... None None notebook None None None None None 2024-10-17 13:14:10.658963+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
117 g2s74qq1W0JT0000 None True Research IgG3 result intestine. None None notebook None None None None None 2024-10-17 13:14:10.666397+00:00 1
304 7haeuRMwfFCi0000 None True Research IgG4 IgG3 Grid cells study cluster. None None notebook None None None None None 2024-10-17 13:14:10.685563+00:00 1
312 cZVt5AczNoQy0000 None True Research study intestine. None None notebook None None None None None 2024-10-17 13:14:10.686032+00:00 1
326 zuPppXhzj7Yo0000 None True Research IgG4 intestinal study IgG4 IgG4. None None notebook None None None None None 2024-10-17 13:14:10.686889+00:00 1
429 OIyQq7PtHyZz0000 None True Research IgM IgG1. None None notebook None None None None None 2024-10-17 13:14:10.698453+00:00 1
445 Zg7rvY77CtnM0000 None True Research Larynx investigate result intestine I... None None notebook None None None None None 2024-10-17 13:14:10.699427+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 I59Xh21qsPXzvuNv0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-10-17 13:14:08.835407+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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 HymFl4mTrP1a2W8S0000 None True The iris collection None .parquet dataset 5629 5axHfO_2Pmlun2sJZHVuNg None None md5 DataFrame 1 True 1 None None 2024-10-17 13:14:08.978532+00:00 1
3 jOqJ86kdo474ZOYc0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-10-17 13:14:08.986341+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