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[POC] Add TPC-H rewrite into Hive partitions - #408

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GregoryKimball:tpch-decimal-hive-rewrite
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[POC] Add TPC-H rewrite into Hive partitions#408
GregoryKimball wants to merge 10 commits into
rapidsai:mainfrom
GregoryKimball:tpch-decimal-hive-rewrite

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@GregoryKimball

@GregoryKimball GregoryKimball commented Aug 28, 2026

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Target file size is 512 MiB per parquet, snappy compressed.

orders: Hive-partitioned monthly by o_orderdate as o_ordermonth=YYYY-MM; sorted within each partition by (o_orderdate, o_orderkey). o_ordermonth is exposed by Hive as a virtual partition column.
lineitem: Hive-partitioned monthly by l_shipdate as l_shipmonth=YYYY-MM; sorted within each partition by (l_shipdate, l_orderkey, l_linenumber).
customer: Unpartitioned; split into files sorted by c_custkey.
part: Unpartitioned; split into files sorted by p_partkey.
partsupp: Unpartitioned; split into files sorted by (ps_partkey, ps_suppkey). File cuts align to ps_partkey boundaries to preserve global ordering.
supplier: Unpartitioned; split into files sorted by s_suppkey.
nation, region: Small tables copied unchanged.

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Generate resumable, key-sorted local or S3 datasets with measured file-size calibration so large scale factors remain bounded and benchmark-ready.
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Use the executable rewrite module directly to keep a single maintained CLI entry point.
Model partitioned, flat-split, and copied tables explicitly so identity and completion logic reflect each processing path.
Keep the benchmark-critical partitioning and flat-table sizing while removing production lifecycle machinery, using bounded date ranges, one-write calibration, and final-writer codec overrides.
Treat pylibcudf as a required runtime dependency and keep direct operations visible at their call sites.
Prevent S3 bucket-wide deletion and enforce source/staging separation for every destination type.
Allow CPU-only test collection while keeping the GPU dependency scoped to rewrite execution without adding wrapper helpers.
Add bounded work assignments and measured row-level file sizing so large partitioned datasets can be generated concurrently without oversized outputs.
Cover worker assignment validation and measured within-day splitting to prevent regressions in parallel SF-scale rewrites.
Feed sorted date ranges to measured output sizing in file-scale chunks so large dates do not require a second date-sized GPU allocation.
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