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Making Spark Connect Rust more thread-safe friendly #13

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6f16899
Wrapping into Arc, plus other
edmondop Apr 14, 2024
fe53515
Reverting arrow change
edmondop Apr 15, 2024
1483bcf
Revert dummy print
edmondop Apr 15, 2024
d1e656b
Updated examples
edmondop Apr 15, 2024
cff3e9b
Fixing format
edmondop Apr 15, 2024
ed5c679
Add setCatalog and setDatabase to SparkSession
phillipleblanc Apr 18, 2024
24b8a43
Merge pull request #2 from phillipleblanc/phillip/240417-setCatalog
edmondop Apr 18, 2024
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28 changes: 28 additions & 0 deletions src/session.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,34 @@ impl SparkSession {
DataFrame::new(self, LogicalPlanBuilder::from(range_relation))
}

pub fn setCatalog(self: Arc<Self>, catalog: &str) -> DataFrame {
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So for both setCatalog and setDatabase, these should be implemented on the spark.catalog object as setCurrentCatalog and setCurrentDatabasesince we want to mirror the existing Spark API.

For these actions to take effect on the existing session, the plan has to be submitted to the server via client.execute_and_fetch and receive a successful response. Both of these execution plans return nothing from the server. The code might look like this below

pub async fn setCurrentCatalog(self, catalog: &str) -> Result<(), SparkError> {
       let cat_type = Some(spark::catalog::CatType::SetCurrentCatalog(
            spark::SetCurrentCatalog { catalog_name: catalog.to_string() },
        ));

        let rel_type = spark::relation::RelType::Catalog(spark::Catalog { cat_type });

        let plan = LogicalPlanBuilder::plan_root(LogicalPlanBuilder::from(rel_type));

        self.spark_session.client().execute_and_fetch(plan).await
}

let catalog_relation = spark::relation::RelType::Catalog(spark::Catalog {
cat_type: Some(spark::catalog::CatType::SetCurrentCatalog(
spark::SetCurrentCatalog {
catalog_name: catalog.to_string(),
},
)),
});

let logical_plan = LogicalPlanBuilder::from(catalog_relation);

DataFrame::new(self, logical_plan)
}

pub fn setDatabase(self: Arc<Self>, database: &str) -> DataFrame {
let catalog_relation = spark::relation::RelType::Catalog(spark::Catalog {
cat_type: Some(spark::catalog::CatType::SetCurrentDatabase(
spark::SetCurrentDatabase {
db_name: database.to_string(),
},
)),
});

let logical_plan = LogicalPlanBuilder::from(catalog_relation);

DataFrame::new(self, logical_plan)
}

/// Returns a [DataFrameReader] that can be used to read datra in as a [DataFrame]
pub fn read(self: Arc<Self>) -> DataFrameReader {
DataFrameReader::new(self)
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