Using the SQL API

DataFusion has a full SQL API that allows you to interact with DataFusion using SQL query strings. The simplest way to use the SQL API is to use the SessionContext struct which provides the highest-level API for executing SQL queries.

To use SQL, you first register your data as a table and then run queries using the SessionContext::sql method. For lower level control such as preventing DDL, you can use SessionContext::sql_with_options or the SessionState APIs

Registering Data Sources using SessionContext::register*

The SessionContext::register* methods tell DataFusion the name of the source and how to read data. Once registered, you can execute SQL queries using the SessionContext::sql method referring to your data source as a table.

The SessionContext::sql method returns a DataFrame for ease of use. See the “Using the DataFrame API” section for more information on how to work with DataFrames.

Read a CSV File

use datafusion::error::Result;
use datafusion::prelude::*;
use arrow::record_batch::RecordBatch;

#[tokio::main]
async fn main() -> Result<()> {
    let ctx = SessionContext::new();
    // register the "example" table
    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 the plan and collect the results as Vec<RecordBatch>
    let results: Vec<RecordBatch> = df.collect().await?;
    // Use the assert_batches_eq macro to compare the results with expected output
    datafusion::assert_batches_eq!(vec![
        "+---+----------------+",
        "| a | MIN(example.b) |",
        "+---+----------------+",
        "| 1 | 2              |",
        "+---+----------------+",
        ],
        &results
    );
  Ok(())
}

Read an Apache Parquet file

Similarly to CSV, you can register a Parquet file as a table using the register_parquet method.

use datafusion::error::Result;
use datafusion::prelude::*;
#[tokio::main]
async fn main() -> Result<()> {
    // create local session context
    let ctx = SessionContext::new();
    let testdata = datafusion::test_util::parquet_test_data();

    // register parquet file with the execution context
    ctx.register_parquet(
        "alltypes_plain",
        &format!("{testdata}/alltypes_plain.parquet"),
        ParquetReadOptions::default(),
    )
    .await?;

    // execute the query
    let df = ctx.sql(
        "SELECT int_col, double_col, CAST(date_string_col as VARCHAR) \
         FROM alltypes_plain \
         WHERE id > 1 AND tinyint_col < double_col",
    ).await?;

    // execute the plan, and compare to the expected results
    let results = df.collect().await?;
    datafusion::assert_batches_eq!(vec![
        "+---------+------------+--------------------------------+",
        "| int_col | double_col | alltypes_plain.date_string_col |",
        "+---------+------------+--------------------------------+",
        "| 1       | 10.1       | 03/01/09                       |",
        "| 1       | 10.1       | 04/01/09                       |",
        "| 1       | 10.1       | 02/01/09                       |",
        "+---------+------------+--------------------------------+",
        ],
        &results
    );
    Ok(())
}

Read an Apache Avro file

DataFusion can also read Avro files using the register_avro method.

use datafusion::arrow::util::pretty;
use datafusion::error::Result;
use datafusion::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    let ctx = SessionContext::new();
    // find the path to the avro test files
    let testdata = datafusion::test_util::arrow_test_data();
    // register avro file with the execution context
    let avro_file = &format!("{testdata}/avro/alltypes_plain.avro");
    ctx.register_avro("alltypes_plain", avro_file, AvroReadOptions::default()).await?;

    // execute the query
    let df = ctx.sql(
        "SELECT int_col, double_col, CAST(date_string_col as VARCHAR) \
         FROM alltypes_plain \
         WHERE id > 1 AND tinyint_col < double_col"
      ).await?;

    // execute the plan, and compare to the expected results
    let results = df.collect().await?;
    datafusion::assert_batches_eq!(vec![
        "+---------+------------+--------------------------------+",
        "| int_col | double_col | alltypes_plain.date_string_col |",
        "+---------+------------+--------------------------------+",
        "| 1       | 10.1       | 03/01/09                       |",
        "| 1       | 10.1       | 04/01/09                       |",
        "| 1       | 10.1       | 02/01/09                       |",
        "+---------+------------+--------------------------------+",
        ],
        &results
    );
    Ok(())
}

Reading Multiple Files as a table

It is also possible to read multiple files as a single table. This is done with the ListingTableProvider which takes a list of file paths and reads them as a single table, matching schemas as appropriate

Coming Soon

Using CREATE EXTERNAL TABLE to register data sources via SQL

You can also register files using SQL using the CREATE EXTERNAL TABLE statement.

use datafusion::error::Result;
use datafusion::prelude::*;
#[tokio::main]
async fn main() -> Result<()> {
    // create local session context
    let ctx = SessionContext::new();
    let testdata = datafusion::test_util::parquet_test_data();

    // register parquet file using SQL
    let ddl = format!(
        "CREATE EXTERNAL TABLE alltypes_plain \
        STORED AS PARQUET LOCATION '{testdata}/alltypes_plain.parquet'"
    );
    ctx.sql(&ddl).await?;

    // execute the query referring to the alltypes_plain table we just registered
    let df = ctx.sql(
        "SELECT int_col, double_col, CAST(date_string_col as VARCHAR) \
         FROM alltypes_plain \
         WHERE id > 1 AND tinyint_col < double_col",
    ).await?;

    // execute the plan, and compare to the expected results
    let results = df.collect().await?;
    datafusion::assert_batches_eq!(vec![
        "+---------+------------+--------------------------------+",
        "| int_col | double_col | alltypes_plain.date_string_col |",
        "+---------+------------+--------------------------------+",
        "| 1       | 10.1       | 03/01/09                       |",
        "| 1       | 10.1       | 04/01/09                       |",
        "| 1       | 10.1       | 02/01/09                       |",
        "+---------+------------+--------------------------------+",
        ],
        &results
    );
    Ok(())
}

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