Read Primitives — JSON Extract Nested

Read Primitives benchmark · all Read Primitives queries

Extract from JSON with complex path expressions

Representative SQL

Rendered in the benchmark’s native SQL with default parameters (no dialect translation available).

SELECT
  c_custkey,
  JSON_EXTRACT(c_comment, '$.profile.segment') AS customer_segment,
  JSON_EXTRACT(c_comment, '$.profile.preferences[0]') AS primary_preference,
  JSON_EXTRACT(c_comment, '$.history.last_order.date') AS last_order_date
FROM customer
WHERE
  JSON_VALID(c_comment)
LIMIT 500

Representative DataFrame

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

def json_extract_nested_expression_impl(ctx: DataFrameContext) -> Any:
    col, lit = ctx.col, ctx.lit
    return (
        ctx.get_table("customer")
        .filter(col("c_comment").str.starts_with("{"))
        .select(
            "c_custkey",
            lit(None).alias("customer_segment"),
            lit(None).alias("primary_preference"),
            lit(None).alias("last_order_date"),
        )
        .limit(500)
    )

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['json_extract_nested'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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