Struct spark_connect_rs::dataframe::dataframe::DataFrameWriter

pub struct DataFrameWriter { /* private fields */ }

DataFrameWriter provides the ability to output a DataFrame to a specific file format supported by Spark

Implementations

impl DataFrameWriter

pub fn new(dataframe: DataFrame) -> Self

Create a new DataFrameWriter from a provided DataFrame

Defaults

• format: None,
• mode: SaveMode::Overwrite,
• bucket_by: None,
• partition_by: vec![],
• sort_by: vec![],
• write_options: HashMap::new()

pub fn format(self, format: &str) -> Self

Target format to output the DataFrame

Examples found in repository

examples/writer.rs (line 20)

async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let spark: SparkSession = SparkSessionBuilder::default().build().await?;

    let df = spark
        .clone()
        .range(None, 1000, 1, Some(16))
        .selectExpr(vec!["id AS range_id"]);

    let path = "/opt/spark/examples/src/main/rust/employees/";

    df.write()
        .format("csv")
        .option("header", "true")
        .save(path)
        .await?;

    let mut df = spark
        .clone()
        .read()
        .format("csv")
        .option("header", "true")
        .load(vec![path.to_string()]);

    df.show(Some(10), None, None).await?;

    // print results may slighty vary but should be close to the below
    // +--------------------------+
    // | show_string              |
    // +--------------------------+
    // | +--------+               |
    // | |range_id|               |
    // | +--------+               |
    // | |312     |               |
    // | |313     |               |
    // | |314     |               |
    // | |315     |               |
    // | |316     |               |
    // | |317     |               |
    // | |318     |               |
    // | |319     |               |
    // | |320     |               |
    // | |321     |               |
    // | +--------+               |
    // | only showing top 10 rows |
    // |                          |
    // +--------------------------+

    Ok(())
}

pub fn mode(self, mode: &str) -> Self

Specifies the behavior when data or table already exists

Arguments:

• mode: (&str) translates to a specific SaveMode from the protobuf

pub fn bucketBy(self, num_buckets: i32, buckets: Vec<String>) -> Self

Buckets the output by the given columns. If specified, the output is laid out on the file system similar to Hive’s bucketing scheme.

pub fn sortBy(self, cols: Vec<String>) -> Self

Sorts the output in each bucket by the given columns on the file system

pub fn partitionBy(self, cols: Vec<String>) -> Self

Partitions the output by the given columns on the file system

pub fn option(self, key: &str, value: &str) -> Self

Add an input option for the underlying data source

Examples found in repository

examples/writer.rs (line 21)

async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let spark: SparkSession = SparkSessionBuilder::default().build().await?;

    let df = spark
        .clone()
        .range(None, 1000, 1, Some(16))
        .selectExpr(vec!["id AS range_id"]);

    let path = "/opt/spark/examples/src/main/rust/employees/";

    df.write()
        .format("csv")
        .option("header", "true")
        .save(path)
        .await?;

    let mut df = spark
        .clone()
        .read()
        .format("csv")
        .option("header", "true")
        .load(vec![path.to_string()]);

    df.show(Some(10), None, None).await?;

    // print results may slighty vary but should be close to the below
    // +--------------------------+
    // | show_string              |
    // +--------------------------+
    // | +--------+               |
    // | |range_id|               |
    // | +--------+               |
    // | |312     |               |
    // | |313     |               |
    // | |314     |               |
    // | |315     |               |
    // | |316     |               |
    // | |317     |               |
    // | |318     |               |
    // | |319     |               |
    // | |320     |               |
    // | |321     |               |
    // | +--------+               |
    // | only showing top 10 rows |
    // |                          |
    // +--------------------------+

    Ok(())
}

pub fn options(self, options: HashMap<String, String>) -> Self

Set many input options based on a HashMap for the underlying data source

pub async fn save(&mut self, path: &str) -> Result<(), ArrowError>

Save the contents of the DataFrame to a data source.

The data source is specified by the format and a set of options.

Examples found in repository

examples/writer.rs (line 22)

async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let spark: SparkSession = SparkSessionBuilder::default().build().await?;

    let df = spark
        .clone()
        .range(None, 1000, 1, Some(16))
        .selectExpr(vec!["id AS range_id"]);

    let path = "/opt/spark/examples/src/main/rust/employees/";

    df.write()
        .format("csv")
        .option("header", "true")
        .save(path)
        .await?;

    let mut df = spark
        .clone()
        .read()
        .format("csv")
        .option("header", "true")
        .load(vec![path.to_string()]);

    df.show(Some(10), None, None).await?;

    // print results may slighty vary but should be close to the below
    // +--------------------------+
    // | show_string              |
    // +--------------------------+
    // | +--------+               |
    // | |range_id|               |
    // | +--------+               |
    // | |312     |               |
    // | |313     |               |
    // | |314     |               |
    // | |315     |               |
    // | |316     |               |
    // | |317     |               |
    // | |318     |               |
    // | |319     |               |
    // | |320     |               |
    // | |321     |               |
    // | +--------+               |
    // | only showing top 10 rows |
    // |                          |
    // +--------------------------+

    Ok(())
}

pub async fn saveAsTable(&mut self, table_name: &str) -> Result<(), ArrowError>

Saves the context of the DataFrame as the specified table.

pub async fn insertInto(&mut self, table_name: &str) -> Result<(), ArrowError>

Inserts the content of the DataFrame to the specified table.

It requires that the schema of the DataFrame is the same as the schema of the target table.

Unlike saveAsTable(), this method ignores the column names and just uses position-based resolution