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Here are/will be the key advantages of the mrpowers-benchmarks compared to the h2o/DuckDB benchmarks.
use Parquet files instead of CSV
compute total query runtime, not just the query runtime when data is loaded into memory
present the results in a more representative way (if you show the cumulative runtime for 10 queries, then a really slow query can greatly bias the overall result)
add new queries including compound queries like filter, group by, and then join
add synthetic datasets with complex column types like StructType, MapType, and JSON strings
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mrpowers-benchmarks are starting to get referenced and used, see here for an example.
We could start publishing some better results for the data community here: https://www.mrpowers.io/benchmarks
Here are/will be the key advantages of the mrpowers-benchmarks compared to the h2o/DuckDB benchmarks.
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