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# Example Usage

In this example some simple processing is performed on the [`example.csv`](https://github.com/apache/datafusion/blob/main/datafusion/core/tests/data/example.csv) file.

Even [`more code examples`](https://github.com/apache/datafusion/tree/main/datafusion-examples) attached to the project.

## Add published DataFusion dependency

Find latest available Datafusion version on [DataFusion's
crates.io] page. Add the dependency to your `Cargo.toml` file:

```toml
datafusion = "latest_version"
tokio = { version = "1.0", features = ["rt-multi-thread"] }
```

## Run a SQL query against data stored in a CSV

```rust
use datafusion::prelude::*;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
  // register the table
  let ctx = SessionContext::new();
  ctx.register_csv("example", "tests/data/example.csv", CsvReadOptions::new()).await?;

  // create a plan to run a SQL query
  let df = ctx.sql("SELECT a, MIN(b) FROM example WHERE a <= b GROUP BY a LIMIT 100").await?;

  // execute and print results
  df.show().await?;
  Ok(())
}
```

See [the SQL API](../library-user-guide/using-the-sql-api.md) section of the
library guide for more information on the SQL API.

## Use the DataFrame API to process data stored in a CSV

```rust
use datafusion::prelude::*;
use datafusion::functions_aggregate::expr_fn::min;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
  // create the dataframe
  let ctx = SessionContext::new();
  let df = ctx.read_csv("tests/data/example.csv", CsvReadOptions::new()).await?;

  let df = df.filter(col("a").lt_eq(col("b")))?
           .aggregate(vec![col("a")], vec![min(col("b"))])?
           .limit(0, Some(100))?;

  // execute and print results
  df.show().await?;
  Ok(())
}
```

## Output from both examples

```text
+---+--------+
| a | MIN(b) |
+---+--------+
| 1 | 2      |
+---+--------+
```

## Arrow Versions

Many of DataFusion's public APIs use types from the
[`arrow`] and [`parquet`] crates, so if you use
`arrow` in your project, the `arrow` version must match that used by
DataFusion. You can check the required version on [DataFusion's
crates.io] page.

The easiest way to ensure the versions match is to use the `arrow`
exported by DataFusion, for example:

```rust
use datafusion::arrow::datatypes::Schema;
```

For example, [DataFusion `25.0.0` dependencies] require `arrow`
`39.0.0`. If instead you used `arrow` `40.0.0` in your project you may
see errors such as:

```text
mismatched types [E0308] expected `Schema`, found `arrow_schema::Schema` Note: `arrow_schema::Schema` and `Schema` have similar names, but are actually distinct types Note: `arrow_schema::Schema` is defined in crate `arrow_schema` Note: `Schema` is defined in crate `arrow_schema` Note: perhaps two different versions of crate `arrow_schema` are being used? Note: associated function defined here
```

Or calling `downcast_ref` on an `ArrayRef` may return `None`
unexpectedly.

[`arrow`]: https://docs.rs/arrow/latest/arrow/
[`parquet`]: https://docs.rs/parquet/latest/parquet/
[datafusion's crates.io]: https://crates.io/crates/datafusion
[datafusion `26.0.0` dependencies]: https://crates.io/crates/datafusion/26.0.0/dependencies

## Identifiers and Capitalization

Please be aware that all identifiers are effectively made lower-case in SQL, so if your csv file has capital letters (ex: `Name`) you must put your column name in double quotes or the examples won't work.

To illustrate this behavior, consider the [`capitalized_example.csv`](../../../datafusion/core/tests/data/capitalized_example.csv) file:

## Run a SQL query against data stored in a CSV:

```rust
use datafusion::prelude::*;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
  // register the table
  let ctx = SessionContext::new();
  ctx.register_csv("example", "tests/data/capitalized_example.csv", CsvReadOptions::new()).await?;

  // create a plan to run a SQL query
  let df = ctx.sql("SELECT \"A\", MIN(b) FROM example WHERE \"A\" <= c GROUP BY \"A\" LIMIT 100").await?;

  // execute and print results
  df.show().await?;
  Ok(())
}
```

## Use the DataFrame API to process data stored in a CSV:

```rust
use datafusion::prelude::*;
use datafusion::functions_aggregate::expr_fn::min;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
  // create the dataframe
  let ctx = SessionContext::new();
  let df = ctx.read_csv("tests/data/capitalized_example.csv", CsvReadOptions::new()).await?;

  let df = df
      // col will parse the input string, hence requiring double quotes to maintain the capitalization
      .filter(col("\"A\"").lt_eq(col("c")))?
      // alternatively use ident to pass in an unqualified column name directly without parsing
      .aggregate(vec![ident("A")], vec![min(col("b"))])?
      .limit(0, Some(100))?;

  // execute and print results
  df.show().await?;
  Ok(())
}
```

## Output from both examples

```text
+---+--------+
| A | MIN(b) |
+---+--------+
| 2 | 1      |
| 1 | 2      |
+---+--------+
```