fix: call df_func with literal

discord9 commented 3 months ago

I hereby agree to the terms of the GreptimeDB CLA.

Refer to a related PR or issue link (optional)

What's changed and what's your intention?

fix a bug where calling datafusion function from flow with literal args will cause datafusion to throw error with unmatched type

Please explain IN DETAIL what the changes are in this PR and why they are needed:

add a check to not rewrite literal in df func's calling arguments
add sqlness test for that case

Checklist

[ ] I have written the necessary rustdoc comments.
[ ] I have added the necessary unit tests and integration tests.
[ ] This PR requires documentation updates.

Summary by CodeRabbit

New Features
- Enhanced logging for scalar function evaluation, providing better insights during the debugging process.
Improvements
- Improved handling of literals in scalar functions with the introduction of a new method.

coderabbitai[bot] commented 3 months ago

Walkthrough

The latest modifications enhance the logging capabilities within the DfScalarFunction implementation and related processes. By adding detailed info and debug statements, these changes aim to give developers improved insights into function evaluations, making debugging more straightforward and improving overall visibility into the datafusion scalar function evaluations.

Changes

Files	Change Summaries
`src/flow/src/expr/scalar.rs`	Introduced `info` and `debug` logging, capturing important stages of function evaluation and record batch processing.
`src/flow/src/transform/expr.rs`	Added `info` and `debug` logging, a new function `is_proto_literal`, and updated `rewrite_scalar_function` for handling literals.

Sequence Diagram(s)

sequenceDiagram
    participant Client
    participant DfScalarFunction
    participant Logger
    Client->>DfScalarFunction: Evaluate function
    DfScalarFunction->>Logger: info!("Evaluating scalar function: {:?}", self)
    DfScalarFunction->>DfScalarFunction: Process function
    DfScalarFunction->>Logger: info!("Values: {:?}, Exprs: {:?}", values, exprs)
    DfScalarFunction->>Logger: info!("Evaluated values: {:?}", values)
    DfScalarFunction->>Logger: info!("Processed with record batch: {:?}", rb)
    DfScalarFunction-->>Client: Return result

Poem

In the code’s forest, logs now gleam and glow,
With info and debug, what insights they bestow.
Scalar functions speak, their secrets to the light,
Guiding devs in darkness, making wrongs go right.
🌟✨🐰

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codecov[bot] commented 3 months ago

Codecov Report

Attention: Patch coverage is 90.00000% with 1 line in your changes missing coverage. Please review.

Project coverage is 84.66%. Comparing base (c4db9e8) to head (16c32c5). Report is 8 commits behind head on main.

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #4265 +/- ## ========================================== - Coverage 84.96% 84.66% -0.31% ========================================== Files 1054 1057 +3 Lines 187160 187523 +363 ========================================== - Hits 159026 158769 -257 - Misses 28134 28754 +620 ```

GreptimeTeam / greptimedb