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docs: typo in working-with-exprs.md #18033

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docs: Update working-with-exprs.md for clarity and structure
Weijun-H Oct 12, 2025
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Weijun-H Oct 12, 2025
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Weijun-H Oct 12, 2025
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6 changes: 3 additions & 3 deletions docs/source/library-user-guide/building-logical-plans.md
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Expand Up @@ -153,9 +153,9 @@ Filter: person.id > Int32(500) [id:Int32;N, name:Utf8;N]
Logical plans can not be directly executed. They must be "compiled" into an
[`ExecutionPlan`], which is often referred to as a "physical plan".

Compared to `LogicalPlan`s `ExecutionPlans` have many more details such as
specific algorithms and detailed optimizations compared to. Given a
`LogicalPlan` the easiest way to create an `ExecutionPlan` is using
Compared to `LogicalPlan`s, `ExecutionPlan`s have many more details such as
specific algorithms and detailed optimizations. Given a
`LogicalPlan`, the easiest way to create an `ExecutionPlan` is using
[`SessionState::create_physical_plan`] as shown below

```rust
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8 changes: 4 additions & 4 deletions docs/source/library-user-guide/working-with-exprs.md
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Expand Up @@ -52,13 +52,13 @@ As the writer of a library, you can use `Expr`s to represent computations that y

## Arrow Schema and DataFusion DFSchema

Apache Arrow `Schema` provides a lightweight structure for defining data, and Apache Datafusion`DFSchema` extends it with extra information such as column qualifiers and functional dependencies. Column qualifiers are multi part path to the table e.g table, schema, catalog. Functional Dependency is the relationship between attributes(characteristics) of a table related to each other.
Apache Arrow `Schema` provides a lightweight structure for defining data, and Apache Datafusion `DFSchema` extends it with extra information such as column qualifiers and functional dependencies. Column qualifiers are multi part path to the table e.g table, schema, catalog. Functional Dependency is the relationship between attributes(characteristics) of a table related to each other.

### Difference between Schema and DFSchema

- Schema: A fundamental component of Apache Arrow, `Schema` defines a dataset's structure, specifying column names and their data types.

> Please see [Struct Schema](https://docs.rs/arrow-schema/54.2.1/arrow_schema/struct.Schema.html) for a detailed document of Arrow Schema.
> Please see [Struct Schema](https://docs.rs/arrow-schema/latest/arrow_schema/struct.Schema.html) for a detailed document of Arrow Schema.

- DFSchema: Extending `Schema`, `DFSchema` incorporates qualifiers such as table names, enabling it to carry additional context when required. This is particularly valuable for managing queries across multiple tables.
> Please see [Struct DFSchema](https://docs.rs/datafusion/latest/datafusion/common/struct.DFSchema.html) for a detailed document of DFSchema.
Expand Down Expand Up @@ -121,7 +121,7 @@ If you'd like to learn more about `Expr`s, before we get into the details of cre

## Rewriting `Expr`s

There are several examples of rewriting and working with `Exprs`:
There are several examples of rewriting and working with `Expr`s:

- [expr_api.rs](https://github.com/apache/datafusion/blob/main/datafusion-examples/examples/expr_api.rs)
- [analyzer_rule.rs](https://github.com/apache/datafusion/blob/main/datafusion-examples/examples/analyzer_rule.rs)
Expand Down Expand Up @@ -162,7 +162,7 @@ fn rewrite_add_one(expr: Expr) -> Result<Transformed<Expr>> {

### Creating an `OptimizerRule`

In DataFusion, an `OptimizerRule` is a trait that supports rewriting`Expr`s that appear in various parts of the `LogicalPlan`. It follows DataFusion's general mantra of trait implementations to drive behavior.
In DataFusion, an `OptimizerRule` is a trait that supports rewriting `Expr`s that appear in various parts of the `LogicalPlan`. It follows DataFusion's general mantra of trait implementations to drive behavior.

We'll call our rule `AddOneInliner` and implement the `OptimizerRule` trait. The `OptimizerRule` trait has two methods:

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