{"id":115053,"date":"2026-10-11T01:33:48","date_gmt":"2026-10-10T17:33:48","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/115053.html"},"modified":"2026-10-11T01:33:48","modified_gmt":"2026-10-10T17:33:48","slug":"duckdb-%e5%90%91%e9%87%8f%e5%8c%96%e6%89%a7%e8%a1%8c%e5%8a%a0%e9%80%9f%e8%bf%87%e6%bb%a4%ef%bc%9asimd-%e6%8c%87%e4%bb%a4%e9%9b%86%e5%9c%a8%e5%88%97%e5%bc%8f%e5%86%85%e5%ad%98%e6%89%b9%e5%a4%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/115053.html","title":{"rendered":"DuckDB \u5411\u91cf\u5316\u6267\u884c\u52a0\u901f\u8fc7\u6ee4\uff1aSIMD \u6307\u4ee4\u96c6\u5728\u5217\u5f0f\u5185\u5b58\u6279\u5904\u7406\u4e2d\u7684\u4f53\u73b0"},"content":{"rendered":"

\"\u5c01\u9762\u4fe1\u606f\u56fe\"<\/p>\n

\u524d\u4e24\u5929\u5728\u672c\u5730\u7b14\u8bb0\u672c\u4e0a\u8c03\u8bd5\u4e00\u6bb5\u9488\u5bf9\u4e09\u5343\u4e07\u884c\u70b9\u51fb\u6d41\u65e5\u5fd7\u7684\u8fc7\u6ee4\u4e0e\u7279\u5f81\u63d0\u53d6\u903b\u8f91\u3002\u539f\u672c\u540c\u4e8b\u7528 Pandas \u5199\u7684\u5904\u7406\u811a\u672c\u8dd1\u4e86\u5c06\u8fd1 45 \u79d2，\u98ce\u6247\u72c2\u8f6c，\u732b\u54aa Null \u88ab\u5413\u5f97\u76f4\u63a5\u7a9c\u4e0a\u4e86\u4e66\u67b6\u3002\u6211\u968f\u624b\u628a\u6570\u636e\u5bfc\u5165 DuckDB，\u8f93\u5165\u76f8\u540c\u7684\u6761\u4ef6\u8fc7\u6ee4\u5e76\u7edf\u8ba1\u5206\u7ec4，\u7ec8\u7aef\u556a\u7684\u4e00\u58f0，0.38 \u79d2\u51fa\u7ed3\u679c\u3002<\/p>\n

\u540c\u4e8b\u7aef\u7740\u5496\u5561\u6123\u5728\u539f\u5730：\u201c\u4f60\u8fd9\u5f00\u6302\u4e86\u5427？45 \u79d2\u7f29\u77ed\u5230 0.38 \u79d2，\u8fd9\u53ef\u662f\u4e00\u767e\u500d\u7684\u5dee\u8ddd！\u96be\u9053 DuckDB \u5728\u540e\u53f0\u628a\u5168\u91cf\u6570\u636e\u90fd\u7f13\u5b58\u6210\u4e86\u9884\u7f16\u8bd1\u673a\u5668\u7801？\u201d<\/p>\n

\u7b54\u6848\u5f53\u7136\u4e0d\u662f\u9b54\u6cd5，\u800c\u662f\u73b0\u4ee3 OLAP \u5f15\u64ce\u7684\u57fa\u77f3\u54f2\u5b66\u2014\u2014\u5411\u91cf\u5316\u6267\u884c\u6a21\u578b（Vectorized Execution Engine） \u4e0e\u73b0\u4ee3 CPU \u7684 SIMD（Single Instruction, Multiple Data，\u5355\u6307\u4ee4\u591a\u6570\u636e\u6d41） \u6307\u4ee4\u96c6\u6df1\u5ea6\u7ed3\u5408\u7684\u5fc5\u7136\u4ea7\u7269\u3002<\/p>\n

\u8bb8\u591a\u4e60\u60ef\u4e86\u4f20\u7edf\u884c\u5f0f\u6570\u636e\u5e93（\u5982 MySQL）\u6216\u89e3\u91ca\u578b\u6570\u636e\u5904\u7406\u6846\u67b6（\u5982 Python \u539f\u751f\u5faa\u73af\u3001\u65e0 JIT \u4f18\u5316\u7684\u8fed\u4ee3\u5668）\u7684\u5de5\u7a0b\u5e08，\u8111\u6d77\u4e2d\u7684\u6570\u636e\u5904\u7406\u6a21\u578b\u4f9d\u7136\u505c\u7559\u5728\u7ecf\u5178\u7684\u201c\u706b\u5c71\u6a21\u578b（Volcano Iterator Model）\u201d：\u4e00\u884c\u4e00\u884c\u5730\u8c03\u7528 next()，\u4e00\u884c\u4e00\u884c\u5730\u5224\u5b9a WHERE \u6761\u4ef6\u3002\u800c\u5728\u73b0\u4ee3\u5217\u5f0f\u5185\u5b58\u5206\u6790\u5f15\u64ce DuckDB \u5185\u90e8，\u6570\u636e\u7684\u6d41\u52a8\u65b9\u5f0f\u65e9\u5df2\u53d1\u751f\u4e86\u964d\u7ef4\u6253\u51fb\u5f0f\u7684\u7a81\u53d8\u3002<\/p>\n

\n

\u4e00\u3001 \u4f20\u7edf\u706b\u5c71\u6a21\u578b vs \u5411\u91cf\u5316\u6279\u5904\u7406\u7684\u672c\u8d28\u9e3f\u6c9f<\/h4>\n

\u5728\u7ecf\u5178\u7684\u6570\u636e\u5e93\u6267\u884c\u5668\u4e2d，\u6bcf\u5904\u7406\u4e00\u6761\u8bb0\u5f55，\u90fd\u4f1a\u7ecf\u5386\u4e00\u6b21\u865a\u51fd\u6570\u8c03\u7528（Virtual Function Call）\u548c\u6df1\u5c42\u7684\u6761\u4ef6\u5206\u652f\u8df3\u8f6c\u3002<\/p>\n

\u3010\u4f20\u7edf\u706b\u5c71\u6a21\u578b (Tuple-at-a-Time)\u3011
\nRecord 1 -> Filter.next() -> Scan.next() -> [\u5206\u652f\u9884\u6d4b \/ \u865a\u51fd\u6570\u5f00\u9500] -> \u5199\u5165\u7ed3\u679c
\nRecord 2 -> Filter.next() -> Scan.next() -> [\u5206\u652f\u9884\u6d4b \/ \u865a\u51fd\u6570\u5f00\u9500] -> \u5199\u5165\u7ed3\u679c
\n… \u6781\u5176\u96f6\u788e，CPU \u6307\u4ee4\u7f13\u5b58 (L1 i-cache) \u9891\u7e41\u98a0\u7c38，\u65e0\u6cd5\u5229\u7528 SIMD \u5bc4\u5b58\u5668<\/p>\n

\u3010DuckDB \u5411\u91cf\u5316\u6a21\u578b (Vector-at-a-Time)\u3011
\nDataChunk (\u9ed8\u8ba4 2048 \u884c\u5217\u5f0f\u6241\u5e73\u6570\u7ec4)
\n |
\n v
\nSIMD \u5411\u91cf\u5bc4\u5b58\u5668 (AVX2 \/ AVX-512, \u4e00\u6b21\u88c5\u5165 8 \u6216 16 \u4e2a\u6570\u503c)
\n |
\n v
\n\u5355\u6761 CPU \u6c47\u7f16\u6307\u4ee4 (\u5982 _mm256_cmp_epi32_mask) \u4e00\u77ac\u95f4\u751f\u6210 8~16 \u4e2a\u5339\u914d\u63a9\u7801<\/p>\n

1. \u865a\u51fd\u6570\u8c03\u7528\u4e0e\u6307\u4ee4\u7f13\u5b58\u7f3a\u5931（i-cache misses）<\/h5>\n

\u5728\u884c\u5f0f\u8fed\u4ee3\u5668\u4e2d，\u6bcf\u4e00\u884c\u6570\u636e\u7684\u6d41\u8f6c\u90fd\u8981\u7ecf\u5386\u5c42\u5c42\u62bd\u8c61\u63a5\u53e3\u3002\u4e09\u5343\u4e07\u884c\u6570\u636e\u610f\u5473\u7740\u6570\u5343\u4e07\u6b21\u51fd\u6570\u8c03\u7528\u5f00\u9500\u3002\u800c CPU \u7684\u6307\u4ee4\u7f13\u5b58\u7a7a\u95f4\u6781\u5176\u6709\u9650，\u9891\u7e41\u7684\u4ee3\u7801\u8df3\u8f6c\u4f1a\u5bfc\u81f4 L1 \u6307\u4ee4\u7f13\u5b58\u547d\u4e2d\u7387\u96ea\u5d29\u3002<\/p>\n

2. \u5206\u652f\u9884\u6d4b\u5931\u8d25（Branch Misprediction）\u7684\u6bc1\u706d\u6027\u60e9\u7f5a<\/h5>\n

if (val > 100) \u8fd9\u79cd\u8fc7\u6ee4\u6761\u4ef6\u5982\u679c\u9762\u5bf9\u9ad8\u5ea6\u968f\u673a\u5206\u5e03\u7684\u4e1a\u52a1\u6570\u636e，CPU \u7684\u5206\u652f\u9884\u6d4b\u5668\u4f1a\u9891\u7e41\u731c\u9519\u3002\u5728\u73b0\u4ee3\u6df1\u5ea6\u6d41\u6c34\u7ebf\u67b6\u6784\u4e2d，\u4e00\u6b21\u5206\u652f\u9884\u6d4b\u5931\u8d25\u7684\u60e9\u7f5a\u662f 15~20 \u4e2a\u65f6\u949f\u5468\u671f\u7684\u6d41\u6c34\u7ebf\u6e05\u7a7a\u4e0e\u91cd\u586b\u3002<\/p>\n

3. \u5411\u91cf\u5316\u6267\u884c（DataChunk）\u5982\u4f55\u7834\u89e3<\/h5>\n

DuckDB \u4e0d\u4ee5\u201c\u884c\u201d\u4e3a\u5355\u4f4d\u6d41\u52a8，\u4e5f\u4e0d\u4ee5\u201c\u5168\u8868\u201d\u4e3a\u5355\u4f4d\u52a0\u8f7d，\u800c\u662f\u5c06\u6570\u636e\u5207\u5206\u6210\u56fa\u5b9a\u5927\u5c0f\u7684 DataChunk（\u5185\u90e8\u901a\u5e38\u4e3a 2048 \u884c）\u3002\u5bf9\u4e8e\u8fd9 2048 \u884c\u6570\u636e，\u7531\u4e8e\u5728\u5185\u5b58\u4e2d\u662f\u540c\u4e00\u79cd\u6570\u636e\u7c7b\u578b\u4e14\u7269\u7406\u7d27\u5bc6\u8fde\u7eed\u5b58\u50a8，CPU \u53ef\u4ee5\u4e00\u53e3\u6c14\u5c06\u8fde\u7eed\u5185\u5b58\u5730\u5740\u76f4\u63a5 Prefetch \u5230 L1\/L2 \u6570\u636e\u7f13\u5b58，\u5e76\u901a\u8fc7 SIMD \u5bc4\u5b58\u5668\u4e00\u6b21\u6027\u5e76\u884c\u5bf9\u6bd4\u3002<\/p>\n

\n

\u4e8c\u3001 SIMD \u8fc7\u6ee4\u7684\u5e95\u5c42\u9ed1\u79d1\u6280：\u4ece\u6807\u91cf\u5bf9\u6bd4\u5230\u4f4d\u63a9\u7801\u5e76\u884c<\/h4>\n

\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u770b\u61c2\u786c\u4ef6\u5c42\u9762\u7a76\u7adf\u53d1\u751f\u4e86\u4ec0\u4e48，\u6211\u4eec\u6765\u770b\u4e00\u6bb5\u7eaf C\/C++ \u98ce\u683c\u7684\u5bf9\u6bd4\u903b\u8f91\u3002<\/p>\n

\u5047\u8bbe\u6211\u4eec\u8981\u5bf9\u4e00\u5217\u6574\u578b\u6570\u7ec4\u6267\u884c amount > 50 \u7684\u8fc7\u6ee4\u3002<\/p>\n

\u4f20\u7edf\u6807\u91cf\u4ee3\u7801（Scalar）：<\/h5>\n

\/\/ \u4e32\u884c\u6267\u884c，\u4e00\u6b21\u53ea\u5bf9\u6bd4\u4e00\u4e2a 32 \u4f4d\u6574\u6570
\nfor (int i = 0; i < N; i++) {
\n if (data[i] > 50) {
\n result[count++] = data[i]; \/\/ \u4ea7\u751f\u5206\u652f\u8df3\u8f6c
\n }
\n}<\/p>\n

DuckDB \u98ce\u683c\u7684 SIMD \u5411\u91cf\u5316\u5b9e\u73b0（\u4ee5 x86 AVX2 \u4e3a\u4f8b）：<\/h5>\n

#include <immintrin.h><\/p>\n

void filter_simd_avx2(const int32_t* __restrict src, uint8_t* __restrict selection_vector, int n) {
\n \/\/ \u5e7f\u64ad\u6807\u91cf\u9608\u503c 50 \u5230 256 \u4f4d SIMD \u5bc4\u5b58\u5668\u7684 8 \u4e2a\u901a\u9053\u4e2d
\n __m256i threshold = _mm256_set1_epi32(50);<\/p>\n

for (int i = 0; i < n; i += 8) {
\n \/\/ 1. \u4e00\u6761\u6307\u4ee4\u76f4\u63a5\u5c06\u8fde\u7eed 8 \u4e2a 32-bit \u6574\u6570\u52a0\u8f7d\u5230 YMM \u5bc4\u5b58\u5668
\n __m256i values = _mm256_loadu_si256((const __m256i*)(src + i));<\/p>\n

\/\/ 2. \u4e00\u6761 SIMD \u5927\u4e8e\u6bd4\u8f83\u6307\u4ee4，\u786c\u4ef6\u5e76\u884c\u8f93\u51fa 8 \u4e2a\u5e03\u5c14\u6bd4\u8f83\u7ed3\u679c
\n __m256i cmp_result = _mm256_cmpgt_epi32(values, threshold);<\/p>\n

\/\/ 3. \u5c06 8 \u4e2a 32 \u4f4d\u7684\u63a9\u7801\u63d0\u53d6\u4e3a\u4e00\u4e2a 8-bit \u7684\u538b\u7f29\u4f4d\u63a9\u7801
\n int mask = _mm256_movemask_ps(_mm256_castsi256_ps(cmp_result));<\/p>\n

\/\/ 4. \u65e0\u5206\u652f\u6307\u4ee4\u5199\u5165\u9009\u62e9\u5411\u91cf（Selection Vector）
\n selection_vector[i \/ 8] = (uint8_t)mask;
\n }
\n}<\/p>\n

\u6ce8\u610f\u8fd9\u5176\u4e2d\u7684\u795e\u7ea7\u4f18\u5316：\u5b8c\u5168\u6ca1\u6709 if-else \u5206\u652f\u8df3\u8f6c\u8bed\u53e5！CPU \u6d41\u6c34\u7ebf\u5728\u6267\u884c\u8fd9\u6bb5\u6c47\u7f16\u65f6，\u5b8c\u5168\u4e0d\u5b58\u5728\u5206\u652f\u9884\u6d4b\u5931\u8d25\u7684\u98ce\u9669\u3002\u4e00\u6761 _mm256_cmpgt_epi32 \u6307\u4ee4\u8017\u8d39 1 \u4e2a CPU \u65f6\u949f\u5468\u671f，\u76f4\u63a5\u641e\u5b9a 8 \u4e2a\u6570\u5b57\u7684\u6bd4\u8f83\u3002\u5728\u652f\u6301 AVX-512 \u7684\u670d\u52a1\u5668\u7ea7 CPU \u4e0a，\u4e00\u6b21\u66f4\u80fd\u76f4\u63a5\u541e\u4e0b 16 \u4e2a 32 \u4f4d\u6574\u6570\u3002<\/p>\n

\n

\u4e09\u3001 DuckDB \u7684 Selection Vector（\u9009\u62e9\u5411\u91cf）\u8bbe\u8ba1\u7cbe\u9ad3<\/h4>\n

\u5982\u679c\u8fc7\u6ee4\u51fa\u6765\u7684\u6570\u636e\u9700\u8981\u62f7\u8d1d\u5230\u65b0\u7684\u5185\u5b58\u5757\u4e2d，\u90a3\u4e48\u6570\u636e\u642c\u8fc1（Memory Copy）\u4f9d\u7136\u4f1a\u5403\u6389\u5927\u91cf\u5185\u5b58\u5e26\u5bbd\u3002DuckDB \u5728\u8fd9\u91cc\u8fd0\u7528\u4e86\u4e00\u4e2a\u7cbe\u5de7\u7684\u96f6\u62f7\u8d1d\u7ed3\u6784\u2014\u2014\u9009\u62e9\u5411\u91cf（Selection Vector）\u3002<\/p>\n

\u5728 DuckDB \u7684\u6267\u884c\u5f15\u64ce\u4e2d，\u8fc7\u6ee4\u7b97\u5b50\u6839\u672c\u4e0d\u771f\u6b63\u4fee\u6539\u539f\u6570\u636e\u5217，\u4e5f\u4e0d\u4f1a\u5f00\u8f9f\u65b0\u7684\u5185\u5b58\u5757\u53bb\u642c\u8fd0\u901a\u8fc7\u8fc7\u6ee4\u7684\u884c\u3002\u5b83\u4ec5\u4ec5\u7ef4\u62a4\u4e00\u4e2a\u5fae\u5c0f\u7684\u6574\u6570\u6570\u7ec4（\u8bb0\u5f55\u7b26\u5408\u6761\u4ef6\u7684\u7269\u7406\u4e0b\u6807）：<\/p>\n

# \u6982\u5ff5\u903b\u8f91\u6a21\u62df：DuckDB Vector \u4e0e SelectionVector \u7ed3\u6784
\nfrom dataclasses import dataclass
\nfrom typing import List, Optional<\/p>\n

@dataclass
\nclass DuckDBVector:
\n data_type: str
\n raw_array: List[int] # \u7269\u7406\u5185\u5b58\u6570\u636e\u5757 (\u5982 2048 \u4e2a\u8fde\u7eed\u5143\u7d20)
\n selection_vector: Optional[List[int]] = None # \u4e0b\u6807\u5f15\u7528\u6570\u7ec4
\n count: int = 0 # \u5f53\u524d\u6709\u6548\u884c\u6570<\/p>\n

def vectorized_filter_greater_than(vec: DuckDBVector, threshold: int) -> DuckDBVector:
\n sel_vec: List[int] = []<\/p>\n

# \u6a21\u62df SIMD \u6279\u91cf\u4ea7\u51fa\u4e0b\u6807\u6620\u5c04
\n for idx in range(len(vec.raw_array)):
\n if vec.raw_array[idx] > threshold:
\n sel_vec.append(idx)<\/p>\n

# \u8fd4\u56de\u7684\u65b0\u5411\u91cf，\u539f\u7269\u7406\u6570\u7ec4\u5b8c\u5168\u4e0d\u52a8，\u96f6\u62f7\u8d1d\u5171\u4eab\u7269\u7406\u5185\u5b58！
\n return DuckDBVector(
\n data_type=vec.data_type,
\n raw_array=vec.raw_array, # \u5185\u5b58\u5f15\u7528\u4f20\u9012
\n selection_vector=sel_vec,
\n count=len(sel_vec)
\n )<\/p>\n

\u4e0b\u6e38\u7684\u805a\u5408\u7b97\u5b50\u6216\u6295\u5f71\u7b97\u5b50\u5728\u6d88\u8d39\u6570\u636e\u65f6，\u76f4\u63a5\u6309\u7167 selection_vector \u7684\u7d22\u5f15\u53bb\u8bfb\u53d6\u539f\u5185\u5b58\u5757\u3002\u8fd9\u79cd\u8bbe\u8ba1\u8ba9 DuckDB \u7684\u7b97\u5b50\u95f4\u901a\u4fe1\u5f00\u9500\u65e0\u9650\u8d8b\u8fd1\u4e8e\u96f6\u3002<\/p>\n

\n

\u56db\u3001 \u751f\u4ea7\u8c03\u4f18\u4e0e\u57fa\u51c6\u5b9e\u6d4b<\/h4>\n

\u4e3a\u4e86\u5728\u5b9e\u9645\u4e1a\u52a1\u5f00\u53d1\u4e2d\u628a DuckDB \u7684\u5411\u91cf\u5316\u6f5c\u80fd\u53d1\u6325\u5230\u6781\u81f4，\u6211\u4eec\u9700\u8981\u5728 SQL \u7f16\u5199\u548c\u67b6\u6784\u8bbe\u8ba1\u4e0a\u9075\u5faa\u5217\u5f0f\u53cb\u597d\u7684\u89c4\u5219：<\/p>\n

— \u5c3d\u91cf\u4fdd\u8bc1\u8fc7\u6ee4\u6761\u4ef6\u5b57\u6bb5\u7269\u7406\u8fde\u7eed，\u907f\u514d\u590d\u6742\u975e\u786e\u5b9a\u6027\u6807\u91cf\u51fd\u6570\u7834\u574f SIMD \u8def\u5f84
\nSELECT
\n category_id,
\n count(1) AS click_count,
\n sum(price) AS total_potential_value
\nFROM read_parquet('s3:\/\/analytics-logs\/events_*.parquet')
\nWHERE status = 1
\n AND price > 100.0
\n AND client_type IN ('iOS', 'Android')
\nGROUP BY category_id;<\/p>\n

\u5173\u952e\u8c03\u4f18\u51c6\u5219：<\/h5>\n
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