Read Primitives — Array of Struct

Read Primitives benchmark · all Read Primitives queries

Create array of structs from grouped data

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  o_orderkey,
  ARRAY_AGG(
    STRUCT(l_linenumber, l_partkey, l_quantity, l_extendedprice) ORDER BY l_linenumber
  ) AS line_items
FROM orders AS o
JOIN lineitem AS l
  ON o.o_orderkey = l.l_orderkey
WHERE
  o_orderdate = CAST('1995-03-15' AS DATE)
GROUP BY
  o_orderkey
ORDER BY
  o_orderkey
LIMIT 50

Representative DataFrame

Expression family (Polars, PySpark, DataFusion), default parameters.

def array_of_struct_expression_impl(ctx: DataFrameContext) -> Any:
    orders, lineitem = _tables(ctx, "orders", "lineitem")
    col = ctx.col
    lit = ctx.lit
    struct = ctx.struct

    filtered_orders = orders.filter(col("o_orderdate") == lit(date(1995, 3, 15)))
    joined = lineitem.join(filtered_orders, col("l_orderkey") == col("o_orderkey")).sort("l_linenumber")

    return (
        joined.group_by("o_orderkey")
        .agg(
            struct(col("l_linenumber"), col("l_partkey"), col("l_quantity"), col("l_extendedprice"))
            .list()
            .alias("line_items")
        )
        .sort("o_orderkey")
        .limit(50)
    )

Get the exact query for your run

The blocks above are representative. To get the precise statement a specific platform runs at a specific scale, substitute your own values:

from benchbox import ReadPrimitives

bench = ReadPrimitives(scale_factor=0.01)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['array_of_struct'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('read_primitives', 'array_of_struct', family="expression").source)  # or family="pandas"

Source

Query text: benchbox/core/read_primitives/queries.py