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 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+00:00 1
2 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
1 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+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=2024-11-11 14:18:03 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f5c04eee540>>)

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=2024-11-11 14:18:03 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=2024-11-11 14:18:03 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=2024-11-11 14:18:03 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 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+00:00 1
2 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+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
7 3W2tiATSgItC0000 None True Result Tonsils Zona reticularis Juxtaglomerula... None None notebook None None None None None 2024-11-11 14:18:17.260707+00:00 1
8 VFrFHArWrgcU0000 None True Eccrine Sweat Gland intestine Bushy cells. None None notebook None None None None None 2024-11-11 14:18:17.260805+00:00 1
10 jVjlMwucj6Fs0000 None True Chromaffin Cells Somatotropes cluster IgM Soma... None None notebook None None None None None 2024-11-11 14:18:17.260997+00:00 1
24 0R8Zw2Ti7x7X0000 None True Ige IgD intestine IgG result Bushy cells. None None notebook None None None None None 2024-11-11 14:18:17.262368+00:00 1
29 ePkgzlUC8IDQ0000 None True Vas Deferens intestine result intestinal IgM. None None notebook None None None None None 2024-11-11 14:18:17.262849+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 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+00:00 1
2 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+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 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+00:00 1
2 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
1 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+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
6 kldkmKWm3WkI0000 None True Research Chromaffin cells IgG2 investigate. None None notebook None None None None None 2024-11-11 14:18:17.260611+00:00 1
9 2ul7KvREbpRb0000 None True Visualize IgA research Tonsils candidate Chrom... None None notebook None None None None None 2024-11-11 14:18:17.260901+00:00 1
37 nV3Rtj9DET4U0000 None True Somatotropes Eccrine sweat gland IgA IgA resea... None None notebook None None None None None 2024-11-11 14:18:17.263616+00:00 1
48 91LKFFGPeKar0000 None True Igm IgA IgE IgA research Tonsils IgE research. None None notebook None None None None None 2024-11-11 14:18:17.264669+00:00 1
63 FQfXszvxpKPX0000 None True Igy IgG2 Eccrine sweat gland research. None None notebook None None None None None 2024-11-11 14:18:17.266122+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
6 kldkmKWm3WkI0000 None True Research Chromaffin cells IgG2 investigate. None None notebook None None None None None 2024-11-11 14:18:17.260611+00:00 1
9 2ul7KvREbpRb0000 None True Visualize IgA research Tonsils candidate Chrom... None None notebook None None None None None 2024-11-11 14:18:17.260901+00:00 1
37 nV3Rtj9DET4U0000 None True Somatotropes Eccrine sweat gland IgA IgA resea... None None notebook None None None None None 2024-11-11 14:18:17.263616+00:00 1
48 91LKFFGPeKar0000 None True Igm IgA IgE IgA research Tonsils IgE research. None None notebook None None None None None 2024-11-11 14:18:17.264669+00:00 1
63 FQfXszvxpKPX0000 None True Igy IgG2 Eccrine sweat gland research. None None notebook None None None None None 2024-11-11 14:18:17.266122+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
6 kldkmKWm3WkI0000 None True Research Chromaffin cells IgG2 investigate. None None notebook None None None None None 2024-11-11 14:18:17.260611+00:00 1
111 TR1s2TLuh1JP0000 None True Research IgM investigate efficiency. None None notebook None None None None None 2024-11-11 14:18:17.275660+00:00 1
132 wUJOheyUXI5V0000 None True Research IgG result candidate Bushy cells clas... None None notebook None None None None None 2024-11-11 14:18:17.277596+00:00 1
134 zYQJ6BmGYSEJ0000 None True Research Eccrine sweat gland intestinal candid... None None notebook None None None None None 2024-11-11 14:18:17.281421+00:00 1
143 DXmQx6jedXGp0000 None True Research Tonsils IgA Monocyte IgY candidate rank. None None notebook None None None None None 2024-11-11 14:18:17.282282+00:00 1
186 LmeVzJxqSEGy0000 None True Research IgG IgM Pineal gland Chromaffin cells... None None notebook None None None None None 2024-11-11 14:18:17.286260+00:00 1
191 9BKWqJrZSFgf0000 None True Research IgE IgG2 result IgA. None None notebook None None None None None 2024-11-11 14:18:17.286721+00:00 1
243 v0AZ9NC6Dnq70000 None True Research study Eccrine sweat gland Zona reticu... None None notebook None None None None None 2024-11-11 14:18:17.295111+00:00 1
266 af2jCFf8oqeG0000 None True Research IgG classify Type II Pneumocyte visua... None None notebook None None None None None 2024-11-11 14:18:17.300895+00:00 1
301 FFt92aTIzJZn0000 None True Research Vas deferens IgG Monocyte. None None notebook None None None None None 2024-11-11 14:18:17.304169+00:00 1
389 8f9zmIi4QlJm0000 None True Research Teeth Chromaffin cells IgM. None None notebook None None None None None 2024-11-11 14:18:17.316173+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 qEDNSMI1STpubA6o0000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-11 14:18:07.930203+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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 SCmWMCliblx3IqMx0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-11 14:18:08.060184+00:00 1
3 p79tMPP5kRv0Qt3O0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-11 14:18:08.071991+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