Rich t kid/dictionary encoding hash optmize#21589
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Rich-T-kid wants to merge 9 commits intoapache:mainfrom
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Rich t kid/dictionary encoding hash optmize#21589Rich-T-kid wants to merge 9 commits intoapache:mainfrom
Rich-T-kid wants to merge 9 commits intoapache:mainfrom
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Currenly the CI is breaking because my current emit implementations returns the values dirrectly instead of returning a dictionary encoded column. & im not handling nulls. |
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run benchmarks |
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Hi @Rich-T-kid, thanks for the request (#21589 (comment)). Only whitelisted users can trigger benchmarks. Allowed users: Dandandan, Fokko, Jefffrey, Omega359, adriangb, alamb, asubiotto, brunal, buraksenn, cetra3, codephage2020, comphead, erenavsarogullari, etseidl, friendlymatthew, gabotechs, geoffreyclaude, grtlr, haohuaijin, jonathanc-n, kevinjqliu, klion26, kosiew, kumarUjjawal, kunalsinghdadhwal, liamzwbao, mbutrovich, mzabaluev, neilconway, rluvaton, sdf-jkl, timsaucer, xudong963, zhuqi-lucas. File an issue against this benchmark runner |
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run benchmarks |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing rich-t-kid/Dictionary-encoding-Hash-optmize (aa69892) to 37cd3de (merge-base) diff using: clickbench_partitioned File an issue against this benchmark runner |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing rich-t-kid/Dictionary-encoding-Hash-optmize (aa69892) to 37cd3de (merge-base) diff using: tpch File an issue against this benchmark runner |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing rich-t-kid/Dictionary-encoding-Hash-optmize (aa69892) to 37cd3de (merge-base) diff using: tpcds File an issue against this benchmark runner |
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🤖 Benchmark completed (GKE) | trigger Instance: CPU Details (lscpu)Details
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🤖 Benchmark completed (GKE) | trigger Instance: CPU Details (lscpu)Details
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🤖 Benchmark completed (GKE) | trigger Instance: CPU Details (lscpu)Details
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Wrote some criterion benchmarks and they reflect what the query benchmarks show. I think storing Scalar values directly in the hash map is causing performance issues. Need to profile to see heap allocations. |
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ScalarValue::try_from_array (38%) — allocating a new ScalarValue heap object for every single row lots of heap allocations are happening on the hot path (_xzm_free) - a solution to this may be to pre-allocate space for the unique values array and hashmap. |
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Optimization 1 — Hash cachingInstead of hashing each row's value individually, pre-compute hashes for all d distinct values in the values array once per batch and cache them in a Vec indexed by dictionary key. For a batch of n rows with d distinct values this reduces hash computations from O(n) to O(d). Optimization 2 — Eliminate ScalarValueReplace HashMap<ScalarValue, usize> with a structure that stores raw hashes and raw string slices pointing directly into the Arrow buffer. This eliminates per-row heap allocation (ScalarValue::try_from_array) and deallocation (drop_in_place) which the profiler shows accounts for ~60% of intern time combined. Optimization 3 — Pre-allocate using occupancyUse dict_array.occupancy().count_set_bits() to determine the number of truly distinct non-null values in the batch upfront and pre-allocate internal storage accordingly. This avoids incremental Vec growth during intern. |
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Which issue does this PR close?
This PR make an effort towards #7000 & closing materializing dictionary columns
A separate follow up PR aims to close the multi-column + dictionary column case
Rationale for this change
This PR implements a specialized GroupValues implementation for single-column dictionary-encoded GROUP BY columns, motivated by the dictionary encoding efficiency work tracked in #7647.
GroupValuesRowswas inefficient for dictionary-encoded columns because it runs the full RowConverter pipeline on every row, decoding the dictionary back to its underlying string values and serializing them into a packed row format for comparison, completely discarding the integer key structure that dictionary encoding provides and doing O(n) expensive string operations when the same d distinct values could be processed just once.Initial approach
the first implementation used HashMap<ScalarValue, usize> to map group values to group indices. While correct, profiling revealed this was significantly slower than GroupValuesRows due to per-row heap allocation from ScalarValue::try_from_array, with ~60% of intern time spent on allocation and deallocation.
Final approach
after profiling and iteration the implementation now uses a two-pass strategy that directly exploits dictionary encoding's structure. Pass 1 iterates the small values array (d distinct values) once, building a key_to_group lookup via raw byte comparison and pre-computed hashes. Pass 2 iterates the keys array (n rows) using only cheap array index lookups — no hashing, no byte comparison, no hashmap lookup in the hot path. This reduces the expensive work from O(n) to O(d) per batch.
Benchmarks show consistent 1.9x–2.7x improvement over GroupValuesRows across all cardinality and batch size configurations with no regressions.
What changes are included in this PR?
Update the match statement in
new_group_valuesto include a custom dictionary encoding branch that works for single fields that are of typeDictionaryarrayAre these changes tested?
Yes a large portion of the PR are test, these include
Are there any user-facing changes?
No everything is internal.