Query & search registries¶
Find & access data using registries.
Setup¶
!lamin init --storage ./mydata
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💡 connected lamindb: testuser1/mydata
import lamindb as ln
ln.settings.verbosity = "info"
💡 connected lamindb: testuser1/mydata
We’ll need some 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()
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❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact 'DqW6TIJzFUaazoD5Tso6' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/DqW6TIJzFUaazoD5Tso6.jpg'
❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact 'i02EktimsGzlc3pJRaFx' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/i02EktimsGzlc3pJRaFx.parquet'
❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact '1kP4KiO3pqgvkkED9sqj' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/1kP4KiO3pqgvkkED9sqj.fastq.gz'
Artifact(uid='1kP4KiO3pqgvkkED9sqj', description='My fastq', suffix='.fastq.gz', size=20, hash='hi7ZmAzz8sfMd3vIQr-57Q', hash_type='md5', visibility=1, key_is_virtual=True, created_by_id=1, storage_id=1, updated_at='2024-05-25 15:25:45 UTC')
Look up metadata¶
For entities where we don’t store more than 100k records, a look up object can be a convenient way of selecting a record.
Consider the User registry:
users = ln.User.lookup(field="handle")
With auto-complete, we find a user:
user = users.testuser1
user
User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at='2024-05-25 15:25:43 UTC')
Note
You can also auto-complete in a dictionary:
users_dict = ln.User.lookup().dict()
Filter by metadata¶
Filter for all artifacts created by a user:
ln.Artifact.filter(created_by=user).df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 1 | DqW6TIJzFUaazoD5Tso6 | None | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.054507+00:00 |
| 2 | i02EktimsGzlc3pJRaFx | None | The iris collection | None | .parquet | DataFrame | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.200058+00:00 |
| 3 | 1kP4KiO3pqgvkkED9sqj | None | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.208046+00:00 |
To access the results encoded in a filter statement, execute its return value with one of:
.df(): A pandasDataFramewith each record stored as a row..all(): An indexable djangoQuerySet..list(): A list of records..one(): Exactly one record. Will raise an error if there is none..one_or_none(): Either one record orNoneif there is no query result.
Note
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 metadata¶
ln.Artifact.search("iris").df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 2 | i02EktimsGzlc3pJRaFx | None | The iris collection | None | .parquet | DataFrame | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.200058+00:00 |
Let us create 500 notebook objects with fake titles and save them:
ln.save(
[
ln.Transform(name=title, type="notebook")
for title in ln.core.datasets.fake_bio_notebook_titles(n=500)
]
)
We can now search for any combination of terms:
ln.Transform.search("intestine").df().head()
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 6 | FGiqj2FiMc6V | None | Ascending Colon IgG1 Martinotti cells research... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992348+00:00 |
| 13 | U0PochPCb41w | None | Ascending Colon Capillaries Skin rank Cortical... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993465+00:00 |
| 14 | ARPbiygNLxSW | None | Red Skeletal Muscle Cell efficiency intestine ... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993623+00:00 |
| 17 | LwnW4dgzOedg | None | Crystallin-Containing Lens Fiber Cell IgG Asce... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.994100+00:00 |
| 23 | pdvOUsaZYwvL | None | Igy IgD IgY intestine. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.995063+00:00 |
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:
ln.Artifact.filter(run__created_by__handle__startswith="testuse").df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id |
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.)
Beyond __startswith, Django supports about two dozen field comparators field__comparator=value.
Here are some of them.
and¶
ln.Artifact.filter(suffix=".jpg", created_by=user).df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 1 | DqW6TIJzFUaazoD5Tso6 | None | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.054507+00:00 |
less than/ greater than¶
Or subset to artifacts greater 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()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 2 | i02EktimsGzlc3pJRaFx | None | The iris collection | None | .parquet | DataFrame | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.200058+00:00 |
| 3 | 1kP4KiO3pqgvkkED9sqj | None | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.208046+00:00 |
or¶
from django.db.models import Q
ln.Artifact.filter().filter(Q(suffix=".jpg") | Q(suffix=".fastq.gz")).df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 1 | DqW6TIJzFUaazoD5Tso6 | None | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.054507+00:00 |
| 3 | 1kP4KiO3pqgvkkED9sqj | None | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.208046+00:00 |
in¶
ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 1 | DqW6TIJzFUaazoD5Tso6 | None | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.054507+00:00 |
| 3 | 1kP4KiO3pqgvkkED9sqj | None | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.208046+00:00 |
order by¶
ln.Artifact.filter().order_by("-updated_at").df()
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 3 | 1kP4KiO3pqgvkkED9sqj | None | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.208046+00:00 |
| 2 | i02EktimsGzlc3pJRaFx | None | The iris collection | None | .parquet | DataFrame | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.200058+00:00 |
| 1 | DqW6TIJzFUaazoD5Tso6 | None | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | 1 | True | 1 | None | None | 1 | 2024-05-25 15:25:45.054507+00:00 |
contains¶
ln.Transform.filter(name__contains="search").df().head(10)
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | 9f62oh0gWlAG | None | Research IgG1 IgD IgG1 IgG3 IgM IgG1 IgY. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992005+00:00 |
| 6 | FGiqj2FiMc6V | None | Ascending Colon IgG1 Martinotti cells research... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992348+00:00 |
| 12 | gQ8Yd8sInbRW | None | Igd research IgE IgY Martinotti cells classify... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993306+00:00 |
| 16 | ZCMK5FlIpmjb | None | Research Skin IgY IgG1 result Chandelier cells. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993940+00:00 |
| 19 | TQ3Hr5adK5cF | None | Igy IgE IgE Cuticular Martinotti cells Cortica... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.994420+00:00 |
| 35 | lqRJT6vU5IUW | None | Study research intestinal. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.997102+00:00 |
| 48 | 2qM7jjtfOWpi | None | Igd research Red skeletal muscle cell IgG IgG3... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.999176+00:00 |
| 67 | VJTOSvYLGsHa | None | Research Martinotti cells result cluster IgG I... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.002225+00:00 |
| 69 | kOFzxfz8vvg9 | None | Igd IgG IgA research cluster Ascending colon I... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.002546+00:00 |
| 93 | BV4Zr9eboy3W | None | Research neurotensin IgD. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.009290+00:00 |
And case-insensitive:
ln.Transform.filter(name__icontains="Search").df().head(10)
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | 9f62oh0gWlAG | None | Research IgG1 IgD IgG1 IgG3 IgM IgG1 IgY. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992005+00:00 |
| 6 | FGiqj2FiMc6V | None | Ascending Colon IgG1 Martinotti cells research... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992348+00:00 |
| 12 | gQ8Yd8sInbRW | None | Igd research IgE IgY Martinotti cells classify... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993306+00:00 |
| 16 | ZCMK5FlIpmjb | None | Research Skin IgY IgG1 result Chandelier cells. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993940+00:00 |
| 19 | TQ3Hr5adK5cF | None | Igy IgE IgE Cuticular Martinotti cells Cortica... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.994420+00:00 |
| 35 | lqRJT6vU5IUW | None | Study research intestinal. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.997102+00:00 |
| 48 | 2qM7jjtfOWpi | None | Igd research Red skeletal muscle cell IgG IgG3... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.999176+00:00 |
| 67 | VJTOSvYLGsHa | None | Research Martinotti cells result cluster IgG I... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.002225+00:00 |
| 69 | kOFzxfz8vvg9 | None | Igd IgG IgA research cluster Ascending colon I... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.002546+00:00 |
| 93 | BV4Zr9eboy3W | None | Research neurotensin IgD. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.009290+00:00 |
startswith¶
ln.Transform.filter(name__startswith="Research").df()
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | 9f62oh0gWlAG | None | Research IgG1 IgD IgG1 IgG3 IgM IgG1 IgY. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.992005+00:00 |
| 16 | ZCMK5FlIpmjb | None | Research Skin IgY IgG1 result Chandelier cells. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:49.993940+00:00 |
| 67 | VJTOSvYLGsHa | None | Research Martinotti cells result cluster IgG I... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.002225+00:00 |
| 93 | BV4Zr9eboy3W | None | Research neurotensin IgD. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.009290+00:00 |
| 107 | SEjT5K0eLr0N | None | Research IgG1 rank IgY intestinal cluster clas... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.011436+00:00 |
| 189 | nZF4skfiEGkA | None | Research Smooth muscle cell IgG IgE. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.026554+00:00 |
| 297 | wJKDKLAeQkLp | None | Research candidate Cortical study. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.045752+00:00 |
| 349 | QBdsx4ytUfrx | None | Research rank neurotensin IgG1. | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.056321+00:00 |
| 353 | QRK9JpEGa6XA | None | Research Smooth muscle cell IgG3 IgG4 IgM IgG ... | None | None | notebook | None | None | None | None | 1 | 2024-05-25 15:25:50.056940+00:00 |
Show code cell content
# clean up test instance
!lamin delete --force mydata
!rm -r mydata
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.11.9/x64/bin/lamin", line 8, in <module>
sys.exit(main())
^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/rich_click/rich_command.py", line 367, in __call__
return super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/rich_click/rich_command.py", line 152, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/click/core.py", line 1688, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/lamin_cli/__main__.py", line 103, in delete
return delete(instance, force=force)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/lamindb_setup/_delete.py", line 98, in delete
n_objects = check_storage_is_empty(
^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/lamindb_setup/core/upath.py", line 798, in check_storage_is_empty
raise InstanceNotEmpty(message)
lamindb_setup.core.upath.InstanceNotEmpty: Storage /home/runner/work/lamindb/lamindb/docs/mydata/.lamindb contains 3 objects ('_is_initialized' ignored) - delete them prior to deleting the instance
['/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/1kP4KiO3pqgvkkED9sqj.fastq.gz', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/DqW6TIJzFUaazoD5Tso6.jpg', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/_is_initialized', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/i02EktimsGzlc3pJRaFx.parquet']