[FEAT] (WIP) connect: createDataFrame

src/daft-connect/src/translation/logical_plan.rs

@@ -3,11 +3,15 @@ use eyre::{bail, Context};
 use spark_connect::{relation::RelType, Relation};
 use tracing::warn;
 
-use crate::translation::logical_plan::{aggregate::aggregate, project::project, range::range};
+use crate::translation::logical_plan::{
+    aggregate::aggregate, project::project, range::range, to_df::to_df,
+};
 
 mod aggregate;
 mod project;
 mod range;
+mod to_df;
+mod
 
 pub fn to_logical_plan(relation: Relation) -> eyre::Result<LogicalPlanBuilder> {
     if let Some(common) = relation.common {
@@ -24,6 +28,7 @@ pub fn to_logical_plan(relation: Relation) -> eyre::Result<LogicalPlanBuilder> {
         RelType::Aggregate(a) => {
             aggregate(*a).wrap_err("Failed to apply aggregate to logical plan")
         }
+        RelType::ToDf(t) => to_df(*t).wrap_err("Failed to apply to_df to logical plan"),
         plan => bail!("Unsupported relation type: {plan:?}"),
     }
 }

src/daft-connect/src/translation/logical_plan/to_df.rs

@@ -0,0 +1,25 @@
+use eyre::{bail, WrapErr};
+
+use crate::translation::to_logical_plan;
+
+pub fn to_df(to_df: spark_connect::ToDf) -> eyre::Result<daft_logical_plan::LogicalPlanBuilder> {
+    let spark_connect::ToDf {
+        input,
+        column_names,
+    } = to_df;
+
+    let Some(input) = input else {
+        bail!("Input is required");
+    };
+
+    let plan = to_logical_plan(*input)
+        .wrap_err_with(|| format!("Failed to translate relation to logical plan: {input:?}"))?;
+
+    let column_names: Vec<_> = column_names.iter().map(daft_dsl::col).collect();
+
+    let plan = plan
+        .with_columns(column_names)
+        .wrap_err_with(|| format!("Failed to add columns to logical plan: {column_names:?}"))?;
+
+    Ok(plan)
+}

tests/connect/test_create_df.py

@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+
+def test_create_df(spark_session):
+    # Create simple DataFrame
+    data = [(1,), (2,), (3,)]
+    df = spark_session.createDataFrame(data, ["id"])
+
+    # Convert to pandas
+    df_pandas = df.toPandas()
+    assert len(df_pandas) == 3, "DataFrame should have 3 rows"
+    assert list(df_pandas["id"]) == [1, 2, 3], "DataFrame should contain expected values"

tests/connect/test_distinct.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+from pyspark.sql.functions import col
+
+
+def test_distinct(spark_session):
+    # Create DataFrame with duplicates
+    data = [(1,), (1,), (2,), (2,), (3,)]
+    df = spark_session.createDataFrame(data, ["id"])
+
+    # Get distinct rows
+    df_distinct = df.distinct()
+
+    # Verify distinct operation removed duplicates
+    df_distinct_pandas = df_distinct.toPandas()
+    assert len(df_distinct_pandas) == 3, "Distinct should remove duplicates"
+    assert set(df_distinct_pandas["id"]) == {1, 2, 3}, "Distinct values should be preserved"