Example Usage¶

In this example some simple processing is performed on the example.csv file.

Even more code 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:

datafusion = "latest_version"
tokio = "1.0"

Add latest non published DataFusion dependency¶

DataFusion changes are published to crates.io according to release schedule In case if it is required to test out DataFusion changes which are merged but yet to be published, Cargo supports adding dependency directly to Github branch

datafusion = { git = "https://github.com/apache/arrow-datafusion", branch = "main"}

Also it works on the package level

datafusion-common = { git = "https://github.com/apache/arrow-datafusion", branch = "main", package = "datafusion-common"}

And with features

datafusion = { git = "https://github.com/apache/arrow-datafusion", branch = "main", default-features = false, features = ["unicode_expressions"] }

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

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(())
}

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

use datafusion::prelude::*;

#[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¶

+---+--------+
| 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:

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:

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.

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 file:

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

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:¶

use datafusion::prelude::*;

#[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¶

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

Extensibility¶

DataFusion is designed to be extensible at all points. To that end, you can provide your own custom:

User Defined Functions (UDFs)
User Defined Aggregate Functions (UDAFs)
User Defined Table Source (TableProvider) for tables
User Defined Optimizer passes (plan rewrites)
User Defined LogicalPlan nodes
User Defined ExecutionPlan nodes

Optimized Configuration¶

For an optimized build several steps are required. First, use the below in your Cargo.toml. It is worth noting that using the settings in the [profile.release] section will significantly increase the build time.

[dependencies]
datafusion = { version = "22.0" }
tokio = { version = "^1.0", features = ["rt-multi-thread"] }
snmalloc-rs = "0.3"

[profile.release]
lto = true
codegen-units = 1

Then, in main.rs. update the memory allocator with the below after your imports:

use datafusion::prelude::*;

#[global_allocator]
static ALLOC: snmalloc_rs::SnMalloc = snmalloc_rs::SnMalloc;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
  Ok(())
}

Based on the instruction set architecture you are building on you will want to configure the target-cpu as well, ideally with native or at least avx2.

RUSTFLAGS='-C target-cpu=native' cargo run --release

Enable backtraces¶

By default Datafusion returns errors as a plain message. There is option to enable more verbose details about the error, like error backtrace. To enable a backtrace you need to add Datafusion backtrace feature to your Cargo.toml file:

datafusion = { version = "31.0.0", features = ["backtrace"]}

Set environment variables

RUST_BACKTRACE=1 ./target/debug/datafusion-cli
DataFusion CLI v31.0.0
> select row_numer() over (partition by a order by a) from (select 1 a);
Error during planning: Invalid function 'row_numer'.
Did you mean 'ROW_NUMBER'?

backtrace:    0: std::backtrace_rs::backtrace::libunwind::trace
             at /rustc/5680fa18feaa87f3ff04063800aec256c3d4b4be/library/std/src/../../backtrace/src/backtrace/libunwind.rs:93:5
   1: std::backtrace_rs::backtrace::trace_unsynchronized
             at /rustc/5680fa18feaa87f3ff04063800aec256c3d4b4be/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
   2: std::backtrace::Backtrace::create
             at /rustc/5680fa18feaa87f3ff04063800aec256c3d4b4be/library/std/src/backtrace.rs:332:13
   3: std::backtrace::Backtrace::capture
             at /rustc/5680fa18feaa87f3ff04063800aec256c3d4b4be/library/std/src/backtrace.rs:298:9
   4: datafusion_common::error::DataFusionError::get_back_trace
             at /arrow-datafusion/datafusion/common/src/error.rs:436:30
   5: datafusion_sql::expr::function::<impl datafusion_sql::planner::SqlToRel<S>>::sql_function_to_expr
   ............

Note: The backtrace wrapped into systems calls, so some steps on top of the backtrace can be ignored

Introduction

DataFusion CLI