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 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+00:00 | 1 |
| 2 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 1 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+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=2024-10-19 15:06:04 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7ff90e7d12d0>>)
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=2024-10-19 15:06:04 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=2024-10-19 15:06:04 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=2024-10-19 15:06:04 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 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+00:00 | 1 |
| 2 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+00:00 | 1 |
To access the results encoded in a filter statement, execute its return value with one of:
.df(): A pandasDataFramewith each record in a row..all(): AQuerySet..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 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 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 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+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 | ||||||||||||||
| 5 | rud9l2XgEcat0000 | None | True | Intestine result intestine investigate. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.628268+00:00 | 1 |
| 17 | MBZvIPFvsigI0000 | None | True | Efficiency IgG IgY Organ of Corti Liver lipocy... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.629016+00:00 | 1 |
| 39 | zwHEkvWmrLnk0000 | None | True | Semicircular Canals IgG3 Granulosa lutein cell... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630366+00:00 | 1 |
| 40 | 2iAnCszEcrci0000 | None | True | Igd intestine IgD research Granule cells IgG4. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630427+00:00 | 1 |
| 44 | uuFD6TJsOSNU0000 | None | True | Visualize Zona fasciculata Tendons Schwann cel... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630673+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 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+00:00 | 1 |
| 2 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+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 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+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 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+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 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+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 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+00:00 | 1 |
| 2 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 1 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+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 | ||||||||||||||
| 20 | 68cx3BQB1VOj0000 | None | True | Granule Cells research efficiency study IgG Ig... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.629199+00:00 | 1 |
| 24 | Bur2J0odSrUJ0000 | None | True | Bone Marrow Bone marrow research IgE study Sem... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.629445+00:00 | 1 |
| 40 | 2iAnCszEcrci0000 | None | True | Igd intestine IgD research Granule cells IgG4. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630427+00:00 | 1 |
| 45 | 0opYI2wcnRjj0000 | None | True | Organ Of Corti research IgG classify IgG3 rank... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630734+00:00 | 1 |
| 46 | YHysn5jxXAqz0000 | None | True | Ige research IgG Oxyphil cell result. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630795+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 | ||||||||||||||
| 20 | 68cx3BQB1VOj0000 | None | True | Granule Cells research efficiency study IgG Ig... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.629199+00:00 | 1 |
| 24 | Bur2J0odSrUJ0000 | None | True | Bone Marrow Bone marrow research IgE study Sem... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.629445+00:00 | 1 |
| 40 | 2iAnCszEcrci0000 | None | True | Igd intestine IgD research Granule cells IgG4. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630427+00:00 | 1 |
| 45 | 0opYI2wcnRjj0000 | None | True | Organ Of Corti research IgG classify IgG3 rank... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630734+00:00 | 1 |
| 46 | YHysn5jxXAqz0000 | None | True | Ige research IgG Oxyphil cell result. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.630795+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 | ||||||||||||||
| 52 | VaRLae6a72ut0000 | None | True | Research Organ of Corti IgG. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.631161+00:00 | 1 |
| 91 | xqG0JCG7T9Pk0000 | None | True | Research Organ of Corti IgE classify Granule c... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.636635+00:00 | 1 |
| 142 | TS4oUmo41bih0000 | None | True | Research IgG3 Schwann cells intestinal Granule... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.642236+00:00 | 1 |
| 184 | TfFDpPuap0yQ0000 | None | True | Research IgD IgG1 intestinal IgG3. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.644768+00:00 | 1 |
| 297 | lTSOJB90C2N50000 | None | True | Research Liver lipocyte IgE IgG4. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.656761+00:00 | 1 |
| 321 | n4pcKr43oU8K0000 | None | True | Research Bone marrow IgG Uterus. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.658169+00:00 | 1 |
| 467 | ZJTad2tsfIXl0000 | None | True | Research IgG IgD candidate Granulosa lutein ce... | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.674491+00:00 | 1 |
| 489 | EVKYyD7dADOB0000 | None | True | Research Uterus IgD Starburst amacrine cells. | None | None | notebook | None | None | None | None | None | 2024-10-19 15:06:08.675826+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 | K3UdSEBnVA64JkL20000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:06.869330+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+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 | QjXEF3KFIfEvQW5V0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-19 15:06:06.997010+00:00 | 1 |
| 3 | NR6Js0g9IM03Lsfx0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-19 15:06:07.004153+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