Query & search registries¶
Find & access data using registries.
Setup¶
!lamin init --storage ./mydata
Show code cell output
💡 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()
Show code cell output
❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact 'sXgR1PiJERA5fEwtMhol' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/sXgR1PiJERA5fEwtMhol.jpg'
❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact 'AvyYmHPTQjXzn0MaXwuK' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/AvyYmHPTQjXzn0MaXwuK.parquet'
❗ no run & transform get linked, consider calling ln.track()
✅ storing artifact 'jnCSBSGTm8aQwWNGAgSy' at '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/jnCSBSGTm8aQwWNGAgSy.fastq.gz'
Artifact(uid='jnCSBSGTm8aQwWNGAgSy', suffix='.fastq.gz', description='My fastq', size=20, hash='hi7ZmAzz8sfMd3vIQr-57Q', hash_type='md5', visibility=1, key_is_virtual=True, updated_at=2024-05-14 15:58:21 UTC, storage_id=1, created_by_id=1)
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=2024-05-14 15:58:19 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()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 1 | None | sXgR1PiJERA5fEwtMhol | 1 | None | .jpg | None | My image | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.202380+00:00 | 2024-05-14 15:58:21.202423+00:00 | 1 |
| 2 | None | AvyYmHPTQjXzn0MaXwuK | 1 | None | .parquet | DataFrame | The iris collection | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.485892+00:00 | 2024-05-14 15:58:21.485923+00:00 | 1 |
| 3 | None | jnCSBSGTm8aQwWNGAgSy | 1 | None | .fastq.gz | None | My fastq | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.493461+00:00 | 2024-05-14 15:58:21.493485+00:00 | 1 |
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()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 2 | None | AvyYmHPTQjXzn0MaXwuK | 1 | None | .parquet | DataFrame | The iris collection | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.485892+00:00 | 2024-05-14 15:58:21.485923+00:00 | 1 |
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()
| version | uid | name | key | description | type | latest_report_id | source_code_id | reference | reference_type | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 16 | None | hSpzYi0u7TdR | Candidate intestine candidate. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.549331+00:00 | 2024-05-14 15:58:22.549344+00:00 | 1 |
| 51 | None | r47Q0yxgqtuH | Igm Bowman's gland intestine Thyrotropes Magno... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.554715+00:00 | 2024-05-14 15:58:22.554729+00:00 | 1 |
| 64 | None | vMinMGdDTaZb | Intestine research Thyroid gland Satellite gli... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556724+00:00 | 2024-05-14 15:58:22.556738+00:00 | 1 |
| 71 | None | 3J14gvPIFglj | Iga intestine IgG1 Magnocellular neurosecretor... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.557797+00:00 | 2024-05-14 15:58:22.557811+00:00 | 1 |
| 74 | None | TOYmkzmLbgYA | Intestine study Place cells Place cells. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.558342+00:00 | 2024-05-14 15:58:22.558356+00:00 | 1 |
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()
| version | uid | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | created_at | updated_at | storage_id | transform_id | run_id | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 1 | None | sXgR1PiJERA5fEwtMhol | 1 | None | .jpg | None | My image | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.202380+00:00 | 2024-05-14 15:58:21.202423+00:00 | 1 |
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()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 2 | None | AvyYmHPTQjXzn0MaXwuK | 1 | None | .parquet | DataFrame | The iris collection | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.485892+00:00 | 2024-05-14 15:58:21.485923+00:00 | 1 |
| 3 | None | jnCSBSGTm8aQwWNGAgSy | 1 | None | .fastq.gz | None | My fastq | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.493461+00:00 | 2024-05-14 15:58:21.493485+00:00 | 1 |
or¶
from django.db.models import Q
ln.Artifact.filter().filter(Q(suffix=".jpg") | Q(suffix=".fastq.gz")).df()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 1 | None | sXgR1PiJERA5fEwtMhol | 1 | None | .jpg | None | My image | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.202380+00:00 | 2024-05-14 15:58:21.202423+00:00 | 1 |
| 3 | None | jnCSBSGTm8aQwWNGAgSy | 1 | None | .fastq.gz | None | My fastq | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.493461+00:00 | 2024-05-14 15:58:21.493485+00:00 | 1 |
in¶
ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 1 | None | sXgR1PiJERA5fEwtMhol | 1 | None | .jpg | None | My image | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.202380+00:00 | 2024-05-14 15:58:21.202423+00:00 | 1 |
| 3 | None | jnCSBSGTm8aQwWNGAgSy | 1 | None | .fastq.gz | None | My fastq | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.493461+00:00 | 2024-05-14 15:58:21.493485+00:00 | 1 |
order by¶
ln.Artifact.filter().order_by("-updated_at").df()
| version | uid | storage_id | key | suffix | accessor | description | size | hash | hash_type | n_objects | n_observations | transform_id | run_id | visibility | key_is_virtual | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 3 | None | jnCSBSGTm8aQwWNGAgSy | 1 | None | .fastq.gz | None | My fastq | 20 | hi7ZmAzz8sfMd3vIQr-57Q | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.493461+00:00 | 2024-05-14 15:58:21.493485+00:00 | 1 |
| 2 | None | AvyYmHPTQjXzn0MaXwuK | 1 | None | .parquet | DataFrame | The iris collection | 5629 | ah24lV9Ncc8nPL0MumEsdw | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.485892+00:00 | 2024-05-14 15:58:21.485923+00:00 | 1 |
| 1 | None | sXgR1PiJERA5fEwtMhol | 1 | None | .jpg | None | My image | 29358 | r4tnqmKI_SjrkdLzpuWp4g | md5 | None | None | None | None | 1 | True | 2024-05-14 15:58:21.202380+00:00 | 2024-05-14 15:58:21.202423+00:00 | 1 |
contains¶
ln.Transform.filter(name__contains="search").df().head(10)
| version | uid | name | key | description | type | latest_report_id | source_code_id | reference | reference_type | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 15 | None | hv0sP6hmZpW3 | Visualize IgE IgE Gonadotropes research Cecum ... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.549176+00:00 | 2024-05-14 15:58:22.549190+00:00 | 1 |
| 21 | None | TM5HXVrjWSux | Research Cajal–Retzius cells neurotensin-secre... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.550101+00:00 | 2024-05-14 15:58:22.550114+00:00 | 1 |
| 29 | None | jo75RtfsbhoB | Cluster efficiency Satellite glial cells IgG2 ... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.551328+00:00 | 2024-05-14 15:58:22.551342+00:00 | 1 |
| 60 | None | Uhhnv7pkCuHw | Igd Place cells IgG4 research Lugaro cells. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556112+00:00 | 2024-05-14 15:58:22.556125+00:00 | 1 |
| 62 | None | XxlF3SX1pNSr | Cecum IgE Magnocellular neurosecretory cells r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556419+00:00 | 2024-05-14 15:58:22.556432+00:00 | 1 |
| 64 | None | vMinMGdDTaZb | Intestine research Thyroid gland Satellite gli... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556724+00:00 | 2024-05-14 15:58:22.556738+00:00 | 1 |
| 68 | None | ZAtFIC9Us6Xq | Research candidate IgE rank Thyrotropes IgG3 r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.557338+00:00 | 2024-05-14 15:58:22.557352+00:00 | 1 |
| 127 | None | HLfhGnAsW8Wv | Igm Cecum IgD Cecum research IgG1 IgE. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.569350+00:00 | 2024-05-14 15:58:22.569363+00:00 | 1 |
| 140 | None | y22fNojjA2gj | Visualize IgG1 IgG3 intestinal Place cells res... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.571245+00:00 | 2024-05-14 15:58:22.571258+00:00 | 1 |
| 143 | None | E6w3xYRP8I83 | Igm IgG2 IgG1 research Lugaro cells Place cell... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.571698+00:00 | 2024-05-14 15:58:22.571712+00:00 | 1 |
And case-insensitive:
ln.Transform.filter(name__icontains="Search").df().head(10)
| version | uid | name | key | description | type | latest_report_id | source_code_id | reference | reference_type | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 15 | None | hv0sP6hmZpW3 | Visualize IgE IgE Gonadotropes research Cecum ... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.549176+00:00 | 2024-05-14 15:58:22.549190+00:00 | 1 |
| 21 | None | TM5HXVrjWSux | Research Cajal–Retzius cells neurotensin-secre... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.550101+00:00 | 2024-05-14 15:58:22.550114+00:00 | 1 |
| 29 | None | jo75RtfsbhoB | Cluster efficiency Satellite glial cells IgG2 ... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.551328+00:00 | 2024-05-14 15:58:22.551342+00:00 | 1 |
| 60 | None | Uhhnv7pkCuHw | Igd Place cells IgG4 research Lugaro cells. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556112+00:00 | 2024-05-14 15:58:22.556125+00:00 | 1 |
| 62 | None | XxlF3SX1pNSr | Cecum IgE Magnocellular neurosecretory cells r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556419+00:00 | 2024-05-14 15:58:22.556432+00:00 | 1 |
| 64 | None | vMinMGdDTaZb | Intestine research Thyroid gland Satellite gli... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.556724+00:00 | 2024-05-14 15:58:22.556738+00:00 | 1 |
| 68 | None | ZAtFIC9Us6Xq | Research candidate IgE rank Thyrotropes IgG3 r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.557338+00:00 | 2024-05-14 15:58:22.557352+00:00 | 1 |
| 127 | None | HLfhGnAsW8Wv | Igm Cecum IgD Cecum research IgG1 IgE. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.569350+00:00 | 2024-05-14 15:58:22.569363+00:00 | 1 |
| 140 | None | y22fNojjA2gj | Visualize IgG1 IgG3 intestinal Place cells res... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.571245+00:00 | 2024-05-14 15:58:22.571258+00:00 | 1 |
| 143 | None | E6w3xYRP8I83 | Igm IgG2 IgG1 research Lugaro cells Place cell... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.571698+00:00 | 2024-05-14 15:58:22.571712+00:00 | 1 |
startswith¶
ln.Transform.filter(name__startswith="Research").df()
| version | uid | name | key | description | type | latest_report_id | source_code_id | reference | reference_type | created_at | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 21 | None | TM5HXVrjWSux | Research Cajal–Retzius cells neurotensin-secre... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.550101+00:00 | 2024-05-14 15:58:22.550114+00:00 | 1 |
| 68 | None | ZAtFIC9Us6Xq | Research candidate IgE rank Thyrotropes IgG3 r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.557338+00:00 | 2024-05-14 15:58:22.557352+00:00 | 1 |
| 324 | None | Rl92KyRjHFTe | Research IgG4 IgG Magnocellular neurosecretory... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.605932+00:00 | 2024-05-14 15:58:22.605944+00:00 | 1 |
| 359 | None | 4jtL6Oz0DGPG | Research IgG2 Thyrotropes Satellite glial cells. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.610967+00:00 | 2024-05-14 15:58:22.610979+00:00 | 1 |
| 371 | None | 5iRYDFBwSrX8 | Research result IgG2. | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.612702+00:00 | 2024-05-14 15:58:22.612715+00:00 | 1 |
| 380 | None | 5GQMJOPIaplh | Research IgG3 visualize IgM Seminal vesicles r... | None | None | notebook | None | None | None | None | 2024-05-14 15:58:22.613992+00:00 | 2024-05-14 15:58:22.614005+00:00 | 1 |
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 97, 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 824, 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/AvyYmHPTQjXzn0MaXwuK.parquet', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/_is_initialized', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/jnCSBSGTm8aQwWNGAgSy.fastq.gz', '/home/runner/work/lamindb/lamindb/docs/mydata/.lamindb/sXgR1PiJERA5fEwtMhol.jpg']