JoinOrder Q16d — Canonical JoinOrder 16d

JoinOrder benchmark · all JoinOrder queries

Canonical JoinOrder SQL query 16d; generated DataFrame translation

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  MIN(an.name) AS cool_actor_pseudonym,
  MIN(t.title) AS series_named_after_char
FROM aka_name AS an, cast_info AS ci, company_name AS cn, keyword AS k, movie_companies AS mc, movie_keyword AS mk, name AS n, title AS t
WHERE
  cn.country_code = '[us]'
  AND k.keyword = 'character-name-in-title'
  AND t.episode_nr >= 5
  AND t.episode_nr < 100
  AND an.person_id = n.id
  AND n.id = ci.person_id
  AND ci.movie_id = t.id
  AND t.id = mk.movie_id
  AND mk.keyword_id = k.id
  AND t.id = mc.movie_id
  AND mc.company_id = cn.id
  AND an.person_id = ci.person_id
  AND ci.movie_id = mc.movie_id
  AND ci.movie_id = mk.movie_id
  AND mc.movie_id = mk.movie_id

Representative DataFrame

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

def _impl(ctx: DataFrameContext) -> Any:
        return _execute_joinorder_expression_query(ctx, query_id)

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 JoinOrder

bench = JoinOrder(scale_factor=1.0)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['16d'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('joinorder', '16d', family="expression").source)  # or family="pandas"

Source

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