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32 changes: 32 additions & 0 deletions
32
src/daft-connect/src/translation/logical_plan/with_columns.rs
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use eyre::bail; | ||
use spark_connect::{expression::ExprType, Expression}; | ||
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use crate::translation::{to_daft_expr, to_logical_plan}; | ||
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pub fn with_columns( | ||
with_columns: spark_connect::WithColumns, | ||
) -> eyre::Result<daft_logical_plan::LogicalPlanBuilder> { | ||
let spark_connect::WithColumns { input, aliases } = with_columns; | ||
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let Some(input) = input else { | ||
bail!("input is required"); | ||
}; | ||
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let plan = to_logical_plan(*input)?; | ||
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let daft_exprs: Vec<_> = aliases | ||
.into_iter() | ||
.map(|alias| { | ||
let expression = Expression { | ||
common: None, | ||
expr_type: Some(ExprType::Alias(Box::new(alias))), | ||
}; | ||
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to_daft_expr(expression) | ||
}) | ||
.try_collect()?; | ||
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let plan = plan.with_columns(daft_exprs)?; | ||
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Ok(plan) | ||
} |
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from __future__ import annotations | ||
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from pyspark.sql.functions import col, count | ||
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def test_group_by(spark_session): | ||
# Create DataFrame from range(10) | ||
df = spark_session.range(10) | ||
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# Add a column that will have repeated values for grouping | ||
df = df.withColumn("group", col("id") % 3) | ||
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# Group by the new column and sum the ids in each group | ||
df_grouped = df.groupBy("group").sum("id") | ||
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# Convert to pandas to verify the sums | ||
df_grouped_pandas = df_grouped.toPandas() | ||
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# Verify we have 3 groups | ||
assert len(df_grouped_pandas) == 3, "Should have 3 groups (0, 1, 2)" | ||
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# Verify the sums are correct | ||
expected_sums = { | ||
0: 9, # 0 + 3 + 6 + 9 = 18 | ||
1: 10, # 1 + 4 + 7 = 12 | ||
2: 11 # 2 + 5 + 8 = 15 | ||
} | ||
for _, row in df_grouped_pandas.iterrows(): | ||
assert row["sum(id)"] == expected_sums[row["group"]], f"Sum for group {row['group']} is incorrect" |