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 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+00:00 | 1 |
| 2 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 1 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+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=2024-11-25 17:11:31 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f3f3f74be90>>)
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=2024-11-25 17:11:31 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=2024-11-25 17:11:31 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=2024-11-25 17:11:31 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 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+00:00 | 1 |
| 2 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+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 | ||||||||||||||
| 9 | ZHMfNJBtYpIz0000 | None | True | Oogonium IgG3 classify intestine classify. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.909788+00:00 | 1 |
| 26 | W5H8YHFHE8eU0000 | None | True | Eosinophil Granulocyte Vas deferens rank IgG3 ... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911421+00:00 | 1 |
| 27 | LBhKeZJPkOLS0000 | None | True | Study Tanycytes IgG1 intestine IgG2 IgG3 IgG3 ... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911517+00:00 | 1 |
| 43 | hvdqLBTXrMuK0000 | None | True | Double-Bouquet Cells IgM investigate IgM intes... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.913064+00:00 | 1 |
| 50 | JPaYbJfKEVDa0000 | None | True | Igg2 efficiency intestine cluster. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.913733+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 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+00:00 | 1 |
| 2 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+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 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+00:00 | 1 |
| 2 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 1 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+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 | ||||||||||||||
| 5 | tgqUcNpcZz7t0000 | None | True | Visualize efficiency IgD research Alpha cell. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.909403+00:00 | 1 |
| 10 | AmQPAhL0SO1n0000 | None | True | Igy research IgG2 Vas deferens IgG3 study clas... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.909883+00:00 | 1 |
| 16 | AIyS26lebf7M0000 | None | True | Igg2 study research. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.910461+00:00 | 1 |
| 28 | wrmk3kh8FlKj0000 | None | True | Igg3 rank Descending colon research investigat... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911612+00:00 | 1 |
| 31 | grbxlnXd8P1O0000 | None | True | Research IgG2 IgY. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911899+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 | ||||||||||||||
| 5 | tgqUcNpcZz7t0000 | None | True | Visualize efficiency IgD research Alpha cell. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.909403+00:00 | 1 |
| 10 | AmQPAhL0SO1n0000 | None | True | Igy research IgG2 Vas deferens IgG3 study clas... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.909883+00:00 | 1 |
| 16 | AIyS26lebf7M0000 | None | True | Igg2 study research. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.910461+00:00 | 1 |
| 28 | wrmk3kh8FlKj0000 | None | True | Igg3 rank Descending colon research investigat... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911612+00:00 | 1 |
| 31 | grbxlnXd8P1O0000 | None | True | Research IgG2 IgY. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911899+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 | ||||||||||||||
| 31 | grbxlnXd8P1O0000 | None | True | Research IgG2 IgY. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.911899+00:00 | 1 |
| 142 | UBWaetizyz270000 | None | True | Research Melanocyte intestine IgG2 Natural kil... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.930271+00:00 | 1 |
| 151 | d14HQohvRQr60000 | None | True | Research efficiency IgG3. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.931103+00:00 | 1 |
| 276 | 47PsZtGiPtav0000 | None | True | Research IgA IgG3 candidate rank IgE Outer roo... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.949869+00:00 | 1 |
| 351 | Cdiz4WlQ1Hqy0000 | None | True | Research Descending colon Pharynx result Outer... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.960375+00:00 | 1 |
| 367 | 5T8nS8nuZItc0000 | None | True | Research Spermatogonium cell IgM Descending co... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.961874+00:00 | 1 |
| 372 | KjyMlg9XbaCg0000 | None | True | Research IgM IgY IgD. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.962336+00:00 | 1 |
| 378 | G7jWMrPE0p3i0000 | None | True | Research IgG3 result IgG2. | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.962889+00:00 | 1 |
| 398 | 7jMFYUBRb5IP0000 | None | True | Research Double-bouquet cells research investi... | None | None | notebook | None | None | None | None | None | 2024-11-25 17:11:45.968376+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 | KYwM91IDsJGYF0AE0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:35.916471+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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 | pj5PUSKlWUdFwLKO0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-11-25 17:11:36.068579+00:00 | 1 |
| 3 | bNLnbYJUyDPoLgiy0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-11-25 17:11:36.082420+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