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
Show code cell output
→ 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()
Show code cell output
→ 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 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
| 2 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 1 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+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
Show code cell output
Lookup(testuser1=User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-30 10:01:45 UTC), dict=<bound method Lookup.dict of <lamin_utils._lookup.Lookup object at 0x7f78e47635f0>>)
With auto-complete, we find a specific user record:
user = users.testuser1
user
Show code cell output
User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-30 10:01:45 UTC)
You can also get a dictionary:
users_dict = ln.User.lookup().dict()
users_dict
Show code cell output
{'testuser1': User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-30 10:01:45 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")
Show code cell output
User(uid='DzTjkKse', handle='testuser1', name='Test User1', created_at=2024-10-30 10:01:45 UTC)
Query sets of records¶
Filter for all artifacts created by a user:
ln.Artifact.filter(created_by=user).df()
Show code cell output
| 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 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+00:00 | 1 |
| 2 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+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()
Show code cell output
| 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 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+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)
Show code cell output
| uid | version | is_latest | name | key | description | type | source_code | hash | reference | reference_type | _source_code_artifact_id | created_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 16 | jes1VKMFVeAD0000 | None | True | Lugaro Cells Cold-sensitive sensory neurons in... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.259100+00:00 | 1 |
| 21 | QGYsgM1ad0dk0000 | None | True | Intestine IgG IgD Bulbourethral glands. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.259572+00:00 | 1 |
| 34 | 83UqqOBKMCfO0000 | None | True | Igg2 intestine investigate visualize IgM IgD IgE. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.260797+00:00 | 1 |
| 49 | 5Rg0kBVXs5Jq0000 | None | True | Igg3 study IgY study classify intestine invest... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.262229+00:00 | 1 |
| 56 | H9AVNqTsy3nE0000 | None | True | Cold-Sensitive Sensory Neurons Spinal nerves i... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.262889+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()
Show code cell output
| 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 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+00:00 | 1 |
| 2 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+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()
Show code cell output
| 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 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+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()
Show code cell output
| 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 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
in¶
ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
Show code cell output
| 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 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
order by¶
ln.Artifact.filter().order_by("-updated_at").df()
Show code cell output
| 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 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
| 2 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 1 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+00:00 | 1 |
contains¶
ln.Transform.filter(name__contains="search").df().head(5)
Show code cell output
| uid | version | is_latest | name | key | description | type | source_code | hash | reference | reference_type | _source_code_artifact_id | created_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 4 | UwC0QVurx8VG0000 | None | True | Igg3 Oogonium IgG3 research. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.257964+00:00 | 1 |
| 5 | I2s9yjbSruef0000 | None | True | Igg2 Teeth Hensen's cells IgG3 research Granul... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.258059+00:00 | 1 |
| 14 | jPwddx0itEIL0000 | None | True | Cold-Sensitive Sensory Neurons Zona reticulari... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.258911+00:00 | 1 |
| 20 | YO0QIlfNjaYl0000 | None | True | Igd rank Medullary IgG3 research. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.259478+00:00 | 1 |
| 33 | qRUnsBpuvyLV0000 | None | True | Medullary IgG2 Teeth research IgE Spermatid IgG. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.260703+00:00 | 1 |
And case-insensitive:
ln.Transform.filter(name__icontains="Search").df().head(5)
Show code cell output
| uid | version | is_latest | name | key | description | type | source_code | hash | reference | reference_type | _source_code_artifact_id | created_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 4 | UwC0QVurx8VG0000 | None | True | Igg3 Oogonium IgG3 research. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.257964+00:00 | 1 |
| 5 | I2s9yjbSruef0000 | None | True | Igg2 Teeth Hensen's cells IgG3 research Granul... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.258059+00:00 | 1 |
| 14 | jPwddx0itEIL0000 | None | True | Cold-Sensitive Sensory Neurons Zona reticulari... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.258911+00:00 | 1 |
| 20 | YO0QIlfNjaYl0000 | None | True | Igd rank Medullary IgG3 research. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.259478+00:00 | 1 |
| 33 | qRUnsBpuvyLV0000 | None | True | Medullary IgG2 Teeth research IgE Spermatid IgG. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.260703+00:00 | 1 |
startswith¶
ln.Transform.filter(name__startswith="Research").df()
Show code cell output
| uid | version | is_latest | name | key | description | type | source_code | hash | reference | reference_type | _source_code_artifact_id | created_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 93 | toduFRFqZ79O0000 | None | True | Research IgG3 study IgY Enterochromaffin-like ... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.270930+00:00 | 1 |
| 129 | 06bhaduaEMwo0000 | None | True | Research Bulbourethral glands research. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.274195+00:00 | 1 |
| 155 | whhQMKghFhOy0000 | None | True | Research cluster cluster IgG2 IgG2 classify IgE. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.280098+00:00 | 1 |
| 274 | Lw3vPgBhLpU20000 | None | True | Research IgG3 candidate Granulosa lutein cells... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.298047+00:00 | 1 |
| 327 | Pyz9h1WCuUTT0000 | None | True | Research rank IgY Oogonium study IgD rank. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.302637+00:00 | 1 |
| 361 | MKLeCIaWJLTj0000 | None | True | Research IgG3 Spinal nerves Ganglia Hensen's c... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.309259+00:00 | 1 |
| 412 | a8gq4mFfFmbz0000 | None | True | Research Enterochromaffin-like cell Ganglia Te... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.317795+00:00 | 1 |
| 464 | QgfEn19KIwDM0000 | None | True | Research Spermatid Spermatid intestine IgE clu... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.327129+00:00 | 1 |
| 485 | z3Ydu961YaGo0000 | None | True | Research Medullary Teeth IgG IgE IgG3 investig... | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.329030+00:00 | 1 |
| 487 | ZxnZmbp6iXCT0000 | None | True | Research IgE IgD result rank IgM. | None | None | notebook | None | None | None | None | None | 2024-10-30 10:01:58.329210+00:00 | 1 |
or¶
ln.Artifact.filter(ln.Q(suffix=".jpg") | ln.Q(suffix=".fastq.gz")).df()
Show code cell output
| 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 | MrOBNyibzN18PNNO0000 | None | True | My image | None | .jpg | None | 29358 | r4tnqmKI_SjrkdLzpuWp4g | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.032066+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
negate/ unequal¶
ln.Artifact.filter(~ln.Q(suffix=".jpg")).df()
Show code cell output
| 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 | 7hYW6kDOSKKrvI0d0000 | None | True | The iris collection | None | .parquet | dataset | 5097 | K1jn6pPlqIC6ebZQfW84NQ | None | None | md5 | DataFrame | 1 | True | 1 | None | None | 2024-10-30 10:01:49.110691+00:00 | 1 |
| 3 | uni0rFQp6M81p5uv0000 | None | True | My fastq | None | .fastq.gz | None | 20 | hi7ZmAzz8sfMd3vIQr-57Q | None | None | md5 | None | 1 | True | 1 | None | None | 2024-10-30 10:01:49.119087+00:00 | 1 |
Clean up the test instance.
!rm -r ./test-registries
!lamin delete --force test-registries
Show code cell output
• deleting instance testuser1/test-registries