ClickBench — Watch Client Analysis (Filtered)

ClickBench benchmark · all ClickBench queries

Multi-agg by WatchID + ClientIP, filtered - high cardinality

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  WatchID,
  ClientIP,
  COUNT(*) AS c,
  SUM(IsRefresh),
  AVG(ResolutionWidth)
FROM hits
WHERE
  SearchPhrase <> ''
GROUP BY
  WatchID,
  ClientIP
ORDER BY
  c DESC
LIMIT 10

Representative DataFrame

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

def impl(ctx: DataFrameContext) -> Any:
        result = _expr_filter(ctx, _hits(ctx), filter_code)
        result = _expr_derive(ctx, result, derive_code)
        result = result.group_by(*groups).agg(*[_expr_agg(ctx, token) for token in agg_spec.split(";")])
        if sort_spec:
            sort_cols, descending = _sort_parts(sort_spec)
            result = result.sort(sort_cols, descending=descending)
        if offset:
            return result.slice(offset, limit)
        return result.limit(limit) if limit else result

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 ClickBench

bench = ClickBench(scale_factor=1.0)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['Q32'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('clickbench', 'Q32', family="expression").source)  # or family="pandas"

Source

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