Comments (1)
Isn't this solved by the MatplotlibWriter? First, we could have the sklearn extension built on top of the matplotlib extension. When multiple file types are supported, I think we should infer the type from the path and accept an explicit argument.
For the broader point, I'm starting to wonder if materializers are closer to "a file type" (e.g., parquet, json) or a "render engines" (e.g., matplotlib, s3, mlflow).
On one hand, the "file type" approach relies on a coupling between the Python object class and the output (e.g., saving an XGBoost as JSON). Also, it's convenient when the file type is set, but the "renderer" changes. For example, you specify "parquet" and have the ability to change "where/how" that parquet is handled.
On the other hand, the "renderer" approach is valuable when the Python object -> artifact
is looser or that you'd want many formats of the same artifact. One should be able to produce multiple formats without much code duplication. For example, visualizations are the same matplotlit.figure.Figure
object, but you could want PNG, SVG or a table to materialize CSV, parquet. What about to.snowflake
and having the "renderer" support many operations "create table", "create view", "insert", etc. even though it's essentially "parquet" data. This seems to be a rarer but valuable case.
I'm thinking there is room for both types and they both should live under the name "materializer"
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Related Issues (20)
- Add data source sinks for Polars Lazyframe implementation
- Add sources for missing Polars inputs
- Slack notifier. Have one that can notify a slack channel on error.
- Error cleanly when no output requested using .materialize
- Add xarray example
- `check_output_custom` could pass the information about the feature definition to the output validators HOT 4
- Pandas spss HOT 1
- Streamlining materializer definition HOT 1
- Lifecycle adapter for automatic schema evolution
- Adapter mapping types to pyarrow
- `.visualize_execution()` doesn't show config nodes
- Improve API for `.execute()` all nodes HOT 10
- pandas fwf
- TDQM hook with overrides HOT 2
- Deprecate dependency on `networkx` for `sf-hamilton[visualization]`
- `__repr__` of `HamiltonNode` is hard to read
- `Config` node missing from legend
- Add `Builder.with_materializers()`
- `UX` Hamilton Project
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