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 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+00:00 1
2 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
1 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+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 11:07:05 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f33634b11f0>>)

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 11:07:05 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 11:07:05 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 11:07:05 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 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+00:00 1
2 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+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
19 BGiOSxq1gQtO0000 None True Plasma Cell Thymus IgM intestine IgD IgG4 inve... None None notebook None None None None None 2024-11-07 11:07:19.193856+00:00 1
50 6g8CurB6ksQW0000 None True Igg IgG2 IgG2 Spermatocyte intestine intestina... None None notebook None None None None None 2024-11-07 11:07:19.196830+00:00 1
55 SKmZxfMlVhvc0000 None True Outer Pillar Cells Of Organ Of Corti glucagon-... None None notebook None None None None None 2024-11-07 11:07:19.197306+00:00 1
57 Yg4QxVJWTGR20000 None True Intestine Mammary glands research Epididymal p... None None notebook None None None None None 2024-11-07 11:07:19.197495+00:00 1
61 1frFK1K8WjXQ0000 None True Outer Pillar Cells Of Organ Of Corti intestine... None None notebook None None None None None 2024-11-07 11:07:19.197875+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 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+00:00 1
2 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+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 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+00:00 1
2 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
1 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+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
12 lsPkTmWEm6v00000 None True Research Osteoclast IgY Cortical IgG2. None None notebook None None None None None 2024-11-07 11:07:19.193183+00:00 1
15 UDapfybYAROa0000 None True Mammary Glands IgM candidate Epididymal princi... None None notebook None None None None None 2024-11-07 11:07:19.193475+00:00 1
34 wnRGnmnFbih00000 None True Candidate IgG4 research Thymus IgG4. None None notebook None None None None None 2024-11-07 11:07:19.195306+00:00 1
41 FyhrYXj5heUL0000 None True Epididymal Principal Cell IgG4 IgG4 IgM Thymus... None None notebook None None None None None 2024-11-07 11:07:19.195972+00:00 1
52 PsnXAG4pMDBJ0000 None True Ige Epididymal principal cell research IgG3 Ig... None None notebook None None None None None 2024-11-07 11:07:19.197020+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
12 lsPkTmWEm6v00000 None True Research Osteoclast IgY Cortical IgG2. None None notebook None None None None None 2024-11-07 11:07:19.193183+00:00 1
15 UDapfybYAROa0000 None True Mammary Glands IgM candidate Epididymal princi... None None notebook None None None None None 2024-11-07 11:07:19.193475+00:00 1
34 wnRGnmnFbih00000 None True Candidate IgG4 research Thymus IgG4. None None notebook None None None None None 2024-11-07 11:07:19.195306+00:00 1
41 FyhrYXj5heUL0000 None True Epididymal Principal Cell IgG4 IgG4 IgM Thymus... None None notebook None None None None None 2024-11-07 11:07:19.195972+00:00 1
52 PsnXAG4pMDBJ0000 None True Ige Epididymal principal cell research IgG3 Ig... None None notebook None None None None None 2024-11-07 11:07:19.197020+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 lsPkTmWEm6v00000 None True Research Osteoclast IgY Cortical IgG2. None None notebook None None None None None 2024-11-07 11:07:19.193183+00:00 1
137 zv20lFIAztgJ0000 None True Research IgG2 IgG4 efficiency. None None notebook None None None None None 2024-11-07 11:07:19.213332+00:00 1
188 Gc3tQmKfZjzp0000 None True Research Seminal vesicles IgG4. None None notebook None None None None None 2024-11-07 11:07:19.218088+00:00 1
198 xrFyKfcglNHZ0000 None True Research intestine IgG4 Seminal vesicles Mamma... None None notebook None None None None None 2024-11-07 11:07:19.219026+00:00 1
354 Jcip3GM0ap0J0000 None True Research IgG2 visualize IgG3 IgE Cortical. None None notebook None None None None None 2024-11-07 11:07:19.244584+00:00 1
358 9ctKLGXZeXcw0000 None True Research IgD investigate efficiency IgE IgG. None None notebook None None None None None 2024-11-07 11:07:19.244956+00:00 1
414 x1xxLSwwPOZV0000 None True Research IgM efficiency Spermatocyte efficiency. None None notebook None None None None None 2024-11-07 11:07:19.253884+00:00 1
423 hlicBKkTLhxI0000 None True Research IgG2 IgG4 IgM intestine. None None notebook None None None None None 2024-11-07 11:07:19.254731+00:00 1
441 hZTsbT4Jf29M0000 None True Research visualize Osteoclast Epididymal princ... None None notebook None None None None None 2024-11-07 11:07:19.256439+00:00 1
465 Y4QJqbhRARSY0000 None True Research IgE Teeth IgD study Thymus Mammary gl... None None notebook None None None None None 2024-11-07 11:07:19.262332+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 AhWNAilFmmsBNrB20000 None True My image None .jpg None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 None 1 True 1 None None 2024-11-07 11:07:09.831729+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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 U7ovEPRTs9Ga16dw0000 None True The iris collection None .parquet dataset 5097 K1jn6pPlqIC6ebZQfW84NQ None None md5 DataFrame 1 True 1 None None 2024-11-07 11:07:09.964317+00:00 1
3 2euQPKOBri7CjfHG0000 None True My fastq None .fastq.gz None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 None 1 True 1 None None 2024-11-07 11:07:09.975460+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