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 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+00:00 | 1 |
| 2 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 1 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+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=2024-10-18 15:57:35 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7fee8c246710>>)
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=2024-10-18 15:57:35 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=2024-10-18 15:57:35 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=2024-10-18 15:57:35 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 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+00:00 | 1 |
| 2 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+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 | ||||||||||||||
| 1 | vAoSeClOK82a0000 | None | True | Intestine IgD IgE IgD IgG4 IgM intestine. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039398+00:00 | 1 |
| 4 | UfHraK45LfVl0000 | None | True | Intestine intestinal Platelets visualize. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039653+00:00 | 1 |
| 5 | CdPQUHNQ85IS0000 | None | True | Cajal–Retzius Cells intestine Type II Pneumocy... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039717+00:00 | 1 |
| 11 | 3YjHRSwwWkWp0000 | None | True | Head Direction Cells Dendritic cell IgE intest... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040098+00:00 | 1 |
| 20 | ZGjDZsp7wzkt0000 | None | True | Head Direction Cells intestine cluster IgE Hea... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040669+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 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+00:00 | 1 |
| 2 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+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 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+00:00 | 1 |
| 2 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 1 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+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 | ||||||||||||||
| 8 | DEiUfoUYxySK0000 | None | True | Research IgE Type II Pneumocyte IgE. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039907+00:00 | 1 |
| 13 | EiHp0fQi55v30000 | None | True | Igg4 IgM candidate IgG1 Spleen IgG research. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040225+00:00 | 1 |
| 14 | zlMDjkwcX6l30000 | None | True | Research study cluster Cochlea. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040289+00:00 | 1 |
| 15 | 552MZAUyI8J60000 | None | True | Brown Fat Cell IgG4 research Apocrine sweat gl... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040353+00:00 | 1 |
| 25 | QhHofciJgP7a0000 | None | True | Type Ii Pneumocyte research IgA Head direction... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040987+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 | ||||||||||||||
| 8 | DEiUfoUYxySK0000 | None | True | Research IgE Type II Pneumocyte IgE. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039907+00:00 | 1 |
| 13 | EiHp0fQi55v30000 | None | True | Igg4 IgM candidate IgG1 Spleen IgG research. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040225+00:00 | 1 |
| 14 | zlMDjkwcX6l30000 | None | True | Research study cluster Cochlea. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040289+00:00 | 1 |
| 15 | 552MZAUyI8J60000 | None | True | Brown Fat Cell IgG4 research Apocrine sweat gl... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040353+00:00 | 1 |
| 25 | QhHofciJgP7a0000 | None | True | Type Ii Pneumocyte research IgA Head direction... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040987+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 | ||||||||||||||
| 8 | DEiUfoUYxySK0000 | None | True | Research IgE Type II Pneumocyte IgE. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.039907+00:00 | 1 |
| 14 | zlMDjkwcX6l30000 | None | True | Research study cluster Cochlea. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.040289+00:00 | 1 |
| 107 | AZht4wWr0qHz0000 | None | True | Research IgG1 result IgE. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.049004+00:00 | 1 |
| 267 | AfqKAwWADvHS0000 | None | True | Research classify Brown fat cell candidate vis... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.066103+00:00 | 1 |
| 313 | tDZn9YnUHeEL0000 | None | True | Research IgA IgA Apocrine sweat gland IgG3 Bro... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.068848+00:00 | 1 |
| 423 | mED34dlGZV8l0000 | None | True | Research intestinal investigate Transverse col... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.080407+00:00 | 1 |
| 453 | f0rTYL6gycX20000 | None | True | Research IgG2 IgY Transverse colon. | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.082173+00:00 | 1 |
| 455 | sFXTvQ1FQkgP0000 | None | True | Research IgE Head direction cells IgM Dendriti... | None | None | notebook | None | None | None | None | None | 2024-10-18 15:57:39.082290+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 | CFQFonx3v0qIMSzM0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.209451+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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 | zuUvZW2buP4wfECG0000 | None | True | The iris collection | None | .parquet | dataset | 5629 | 5axHfO_2Pmlun2sJZHVuNg | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-18 15:57:37.437003+00:00 | 1 |
| 3 | qCa3vZFEJ5DS6vyw0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-18 15:57:37.444112+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