Struct PySparkJob 

#[non_exhaustive]
pub struct PySparkJob {
    pub main_python_file_uri: String,
    pub args: Vec<String>,
    pub python_file_uris: Vec<String>,
    pub jar_file_uris: Vec<String>,
    pub file_uris: Vec<String>,
    pub archive_uris: Vec<String>,
    pub properties: HashMap<String, String>,
    pub logging_config: Option<LoggingConfig>,
    /* private fields */
}

A Dataproc job for running Apache PySpark applications on YARN.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

main_python_file_uri: String

Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.

args: Vec<String>

Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.

python_file_uris: Vec<String>

Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.

jar_file_uris: Vec<String>

Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.

file_uris: Vec<String>

Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.

archive_uris: Vec<String>

Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.

properties: HashMap<String, String>

Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.

logging_config: Option<LoggingConfig>

Optional. The runtime log config for job execution.

Implementations

impl PySparkJob

pub fn new() -> Self

pub fn set_main_python_file_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of main_python_file_uri.

pub fn set_args<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of args.

pub fn set_python_file_uris<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of python_file_uris.

pub fn set_jar_file_uris<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of jar_file_uris.

pub fn set_file_uris<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of file_uris.

pub fn set_archive_uris<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of archive_uris.

pub fn set_properties<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of properties.

pub fn set_logging_config<T>(self, v: T) -> Self

where T: Into<LoggingConfig>,

Sets the value of logging_config.

pub fn set_or_clear_logging_config<T>(self, v: Option<T>) -> Self

where T: Into<LoggingConfig>,

Sets or clears the value of logging_config.

Trait Implementations

impl Clone for PySparkJob

fn clone(&self) -> PySparkJob

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PySparkJob

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for PySparkJob

fn default() -> PySparkJob

Returns the “default value” for a type.

impl Message for PySparkJob

fn typename() -> &'static str

The typename of this message.

impl PartialEq for PySparkJob

fn eq(&self, other: &PySparkJob) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for PySparkJob