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Implements a new benchmark entry for Haskell using the Cassava CSV library for dataframe operations. Features: - Groupby benchmark: 5 questions implemented (sum, mean aggregations) - Join benchmark: 1 question implemented (inner join on int) - Uses HashMap-based grouping for efficient aggregations - Includes setup, version, and upgrade scripts - Registered in _control/solutions.csv The implementation demonstrates Haskell's capabilities for dataframe operations with strict evaluation and proper benchmarking using NFData. Files added: - haskell/groupby-haskell.hs: Groupby benchmark implementation - haskell/join-haskell.hs: Join benchmark implementation - haskell/setup-haskell.sh: Setup script for Stack and dependencies - haskell/ver-haskell.sh: Version detection script - haskell/upg-haskell.sh: Upgrade script - haskell/haskell-benchmark.cabal: Cabal project definition - haskell/VERSION: Version file - haskell/README.md: Documentation
Completely rewrote the Haskell benchmark implementation to use the mchav/dataframe library instead of Cassava CSV. Changes: - Updated dependencies: Now uses dataframe >= 0.3 - Rewrote groupby-haskell.hs: Uses D.groupBy, D.aggregate with F.sum/F.mean - Rewrote join-haskell.hs: Implements all 5 join questions using DJ.innerJoin/leftJoin - Updated setup scripts: Simplified installation using Stack - Updated README: Documents mchav/dataframe library and API usage Implementation details: - Groupby: 5 questions using type-safe expression DSL - Join: All 5 questions using innerJoin and leftJoin operations - Uses TypeApplications for type-safe column operations - Leverages dataframe's efficient grouping and joining The implementation now properly uses the mchav/dataframe library's API with D.readCsv, D.groupBy, D.aggregate, and join operations.
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