⚠️ This is a work in progress. Do not rely on being stable yet. ⚠️
Detect Schema
When you add a dataframe to Oxen, it automatically detects and versions the schema of any tabular data. This is done by using Polars under the hood to infer the column names and datatypes.
To list all the schemas that have been detected and committed, you can use the oxen schemas subcommand.
+-----------------------+------+----------------------------------+------------------------+
| path | name | hash | fields |
+==========================================================================================+
| annotations/train.csv | ? | 53732ea1c2a9ba5807bd59978ebb69f5 | [file, ..., is_fluffy] |
|-----------------------+------+----------------------------------+------------------------|
| annotations/test.csv | ? | 36d0edc8779f42e30b0d630aa83bc83c | [file, ..., height] |
+-----------------------+------+----------------------------------+------------------------+
The schema detection is done on a per file basis. This means that if you have a directory of csv or parquet files, each file will have its own schema.
View Schema
To view a specific schema, you can pass in a schema hash, name, or path to the oxen schemas command.
oxen schemas annotations/train.csv
+--------+-------+----------+
| name | dtype | metadata |
+===========================+
| file | str | |
|--------+-------+----------|
| label | str | |
|--------+-------+----------|
| min_x | f64 | |
|--------+-------+----------|
| min_y | f64 | |
|--------+-------+----------|
| width | i64 | |
|--------+-------+----------|
| height | i64 | |
+--------+-------+----------+
Add Schema
Schemas are automatically detected when you add csv, tsv, jsonl, parquet, and arrow files to Oxen. Before a schema is committed, you can see the detected schemas in the oxen status command.
oxen add annotations/train.csv
oxen status
On branch main -> 503591398980c485
Directories to be committed
added: annotations with 1 file
Files to be committed:
(use "oxen restore --staged <file> ..." to unstage)
modified: annotations/train.csv
Schemas to be committed
(use "oxen schemas show --staged <HASH>" to view staged schema)
detected schema: annotations/train.csv 23d86a4c1481b817b57ee8ccd7d9016b
To view more detailed information about the detected schema, use the --staged flag on the oxen schemas command.
oxen schemas --staged annotations/train.csv
annotations/train.csv 23d86a4c1481b817b57ee8ccd7d9016b
+-----------+-------+----------+
| name | dtype | metadata |
+==============================+
| file | str | |
|-----------+-------+----------|
| label | str | |
|-----------+-------+----------|
| min_x | f64 | |
|-----------+-------+----------|
| min_y | f64 | |
|-----------+-------+----------|
| width | f64 | |
|-----------+-------+----------|
| height | f64 | |
|-----------+-------+----------|
| is_fluffy | str | |
|-----------+-------+----------|
| breed | str | |
+-----------+-------+----------+
To view how Polars interprets the schema before adding the file, you can use the oxen df command with the --schema flag.
oxen df annotations/train.csv --schema
+-----------+-------+
| column | dtype |
+===================+
| file | str |
|-----------+-------|
| label | str |
|-----------+-------|
| min_x | f64 |
|-----------+-------|
| min_y | f64 |
|-----------+-------|
| width | f64 |
|-----------+-------|
| height | f64 |
|-----------+-------|
| is_fluffy | str |
|-----------+-------|
| breed | str |
+-----------+-------+
You can also add additional information to the schema. This is useful if you want to provide context about the data for a UI, data fetching, or any other reason.
Notice the empty column metadata in the schema above. You can add arbitrary JSON blobs to the schema itself, as well as each column.
Metadata may provide useful information for your end application:
- Transforms you want to perform.
- How you want to render the data.
- Information about the data itself, such as a description of the schema or colun.
At the root of each schema is an Optional<json::Value> metadata value. This is useful for adding information about the schema itself. For example, you can add a description of the schema or a json blob that gives context to a data renderer.
oxen schemas add annotations/train.csv -m '{"task": "bounding_box", "description": "Extracting bounding boxes from images"}'
You will see the additional metadata listed above the schema if it is added.
"annotations/train.csv"
{"task": "bounding_box", "description": "Extracting bounding boxes from images"}
+-----------+-------+----------+
| name | dtype | metadata |
+==============================+
| file | str | |
|-----------+-------+----------|
| label | str | |
|-----------+-------+----------|
| min_x | f64 | |
|-----------+-------+----------|
| min_y | f64 | |
|-----------+-------+----------|
| width | f64 | |
|-----------+-------+----------|
| height | f64 | |
|-----------+-------+----------|
| is_fluffy | str | |
|-----------+-------+----------|
| breed | str | |
+-----------+-------+----------+
You can also add metadata to specific columns. Say you wanted to add information to the file column about the root directory of the images, you could do the following:
oxen schemas add annotations/train.csv -c 'file' -m '{"root": "images/"}'
"annotations/train.csv"
+-----------+-------+---------------------+
| name | dtype | metadata |
+=========================================+
| file | str | {"root": "images/"} |
|-----------+-------+---------------------|
| label | str | |
|-----------+-------+---------------------|
| min_x | f64 | |
|-----------+-------+---------------------|
| min_y | f64 | |
|-----------+-------+---------------------|
| width | f64 | |
|-----------+-------+---------------------|
| height | f64 | |
|-----------+-------+---------------------|
| is_fluffy | str | |
|-----------+-------+---------------------|
| breed | str | |
+-----------+-------+---------------------+
The -c flag stands for column and the -m flag stands for metadata. The metadata is a JSON blob that can be used to store any information you want.
The OxenHub UI uses schema metadata to render more complex datatypes in the UI. For example viewing inline images directly in a dataframe.
TODO: Add image of OxenHub UI.
Commit The Schema
TODO: Pushing logic is not working, unless you have changed the dataframe.
Schemas changes will not be saved until you commit them.
To view the schemas staged for commit, you can use the --staged flag.
+-----------------------+------+----------------------------------+--------------------+
| path | name | hash | fields |
+======================================================================================+
| annotations/train.csv | ? | 9d1fac486f95120403d7f18232fa5520 | [file, ..., breed] |
+-----------------------+------+----------------------------------+--------------------+
You can then commit the schema to the dataframe with the commit subcommand.
oxen commit -m "Overriding schema for annotations/train.csv"
These changes are persistent across commits and will be carried forward.
Name Schema
It is nice to have human readable names to refer to schemas by. Use the oxen schemas name command to name a schema.
oxen schemas name annotations/train.csv bounding_box
Remove Schema
If you have accidentally staged a schema, you can remove it with the oxen schemas rm command.
oxen schemas rm annotations/train.csv --staged
Render Images
Oxen.ai can render images through the webhub if you add the proper schema metadata.
oxen schemas add data.csv -c 'file' --render image
Under the hood this applied a metadata blob to the column, telling Oxen to render an image. More verbosely it would look like:
oxen schemas add data.csv -c 'file' -m '{
"_oxen": {
"render": {
"func": "image"
}
}
}'
Render Links
Oxen.ai can render links to other files through the webhub if you add the proper schema metadata.
oxen schemas add data.csv -c 'file' --render link
Under the hood this applied a metadata blob to the column, telling Oxen to render an link. More verbosely it would look like:
oxen schemas add data.csv -c 'file' -m '{
"_oxen": {
"render": {
"func": "link"
}
}
}'
