Read Primitives — Array Slice

Read Primitives benchmark · all Read Primitives queries

Get slice/subset of array

Representative SQL

Rendered for DataFusion with default parameters.

WITH order_prices AS (
  SELECT
    o_custkey,
    ARRAY_AGG(o_totalprice ORDER BY o_totalprice DESC) AS prices
  FROM orders
  GROUP BY
    o_custkey
  HAVING
    COUNT(*) >= 5
)
SELECT
  o_custkey,
  ARRAY_SLICE(prices, 1, 3) AS top_3_orders
FROM order_prices
ORDER BY
  o_custkey
LIMIT 100

Representative DataFrame

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

def array_slice_expression_impl(ctx: DataFrameContext) -> Any:
    orders = ctx.get_table("orders")
    col = ctx.col

    order_prices = orders.group_by("o_custkey").agg(
        col("o_totalprice").sort(descending=True).list().alias("prices"), col("o_totalprice").count().alias("cnt")
    )

    return (
        order_prices.filter(col("cnt") >= 5)
        .with_columns(col("prices").list.slice(0, 3).alias("top_3_orders"))
        .select("o_custkey", "top_3_orders")
        .sort("o_custkey")
        .limit(100)
    )

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_slice'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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