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This commit is based on local benchmarks of ClickHouse's re2 caching. Question 1: ----------------------------------------------------------- Is pattern caching useful for queries with const LIKE/REGEX patterns? E.g. SELECT LIKE(col_haystack, '%HelloWorld') FROM T; The short answer is: no. Runtime is (unsurprisingly) dominated by pattern evaluation + other stuff going on in queries, but definitely not pattern compilation. For space reasons, I omit details of the local experiments. (Side note: the current caching scheme is unbounded in size which poses a DoS risk (think of multi-tenancy). This risk is more pronounced when unbounded caching is used with non-const patterns ..., see next question) Question 2: ----------------------------------------------------------- Is pattern caching useful for queries with non-const LIKE/REGEX patterns? E.g. SELECT LIKE(col_haystack, col_needle) FROM T; I benchmarked five caching strategies: 1. no caching as a baseline (= recompile for each row) 2. unbounded cache (= threadsafe global hash-map) 3. LRU cache (= threadsafe global hash-map + LRU queue) 4. lightweight local cache 1 (= not threadsafe local hashmap with collision list which grows to a certain size (here: 10 elements) and afterwards never changes) 5. lightweight local cache 2 (not threadsafe local hashmap without collision list in which a collision replaces the stored element, idea by Alexey) ... using a haystack of 2 mio strings and A). 2 mio distinct simple patterns B). 10 simple patterns C) 2 mio distinct complex patterns D) 10 complex patterns Fo A) and C), caching does not help but these queries still allow to judge the static overhead of caching on query runtimes. B) and D) are extreme but common cases in practice. They include queries like "SELECT ... WHERE LIKE (col_haystack, flag ? '%pattern1%' : '%pattern2%'). Caching should help significantly. Because LIKE patterns are internally translated to re2 expressions, I show only measurements for MATCH queries. Results in sec, averaged over on multiple measurements; 1.A): 2.12 B): 1.68 C): 9.75 D): 9.45 2.A): 2.17 B): 1.73 C): 9.78 D): 9.47 3.A): 9.8 B): 0.63 C): 31.8 D): 0.98 4.A): 2.14 B): 0.29 C): 9.82 D): 0.41 5.A) 2.12 / 2.15 / 2.26 B) 1.51 / 0.43 / 0.30 C) 9.97 / 9.88 / 10.13 D) 5.70 / 0.42 / 0.43 (10/100/1000 buckets, resp. 10/1/0.1% collision rate) Evaluation: 1. This is the baseline. It was surprised that complex patterns (C, D) slow down the queries so badly compared to simple patterns (A, B). The runtime includes evaluation costs, but as caching only helps with compilation, and looking at 4.D and 5.D, compilation makes up over 90% of the runtime! 2. No speedup compared to 1, probably due to locking overhead. The cache is unbounded, and in experiments with data sets > 2 mio rows, 2. is the only scheme to throw OOM exceptions which is not acceptable. 3. Unique patterns (A and C) lead to thrashing of the LRU cache and very bad runtimes due to LRU queue maintenance and locking. Works pretty well however with few distinct patterns (B and D). 4. This scheme is tailored to queries B and D where it performs pretty good. More importantly, the caching is lightweight enough to not deteriorate performance on datasets A and C. 5. After some tuning of the hash map size, 100 buckets seem optimal to be in the same ballpark with 10 distinct patterns as 4. Performance also does not deteriorate on A and C compared to the baseline. Unlike 4., this scheme behaves LRU-like and can adjust to changing pattern distributions. As a conclusion, this commit implementes two things: 1. Based on Q1, pattern search with const needle no longer uses caching. This applies to LIKE and MATCH + a few (exotic) other SQL functions. The code for the unbounded caching was removed. 2. Based on Q2, pattern search with non-const needles now use method 5.
127 lines
5.0 KiB
C++
127 lines
5.0 KiB
C++
#pragma once
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#include <Functions/IFunction.h>
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#include <Functions/FunctionFactory.h>
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#include <Functions/FunctionHelpers.h>
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#include <Columns/ColumnString.h>
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#include <Columns/ColumnsNumber.h>
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#include <DataTypes/DataTypesNumber.h>
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#include <DataTypes/DataTypeString.h>
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#include <Functions/Regexps.h>
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int ILLEGAL_TYPE_OF_ARGUMENT;
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extern const int ILLEGAL_COLUMN;
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extern const int LOGICAL_ERROR;
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}
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using Pos = const char *;
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template <class CountMatchesBase>
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class FunctionCountMatches : public IFunction
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{
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public:
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static constexpr auto name = CountMatchesBase::name;
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static FunctionPtr create(ContextPtr) { return std::make_shared<FunctionCountMatches<CountMatchesBase>>(); }
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String getName() const override { return name; }
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size_t getNumberOfArguments() const override { return 2; }
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bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const override { return true; }
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DataTypePtr getReturnTypeImpl(const ColumnsWithTypeAndName & arguments) const override
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{
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if (!isStringOrFixedString(arguments[1].type))
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"Illegal type {} of second argument (pattern) of function {}. Must be String/FixedString.",
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arguments[1].type->getName(), getName());
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if (!isStringOrFixedString(arguments[0].type))
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"Illegal type {} of first argument (haystack) of function {}. Must be String/FixedString.",
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arguments[0].type->getName(), getName());
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const auto * column = arguments[1].column.get();
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if (!column || !checkAndGetColumnConstStringOrFixedString(column))
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throw Exception(ErrorCodes::ILLEGAL_COLUMN,
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"The second argument of function {} should be a constant string with the pattern",
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getName());
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return std::make_shared<DataTypeUInt64>();
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}
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ColumnPtr executeImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count) const override
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{
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const ColumnConst * column_pattern = checkAndGetColumnConstStringOrFixedString(arguments[1].column.get());
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const Regexps::Regexp re = Regexps::createRegexp</*is_like*/ false, /*no_capture*/ true, CountMatchesBase::case_insensitive>(column_pattern->getValue<String>());
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OptimizedRegularExpression::MatchVec matches;
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const IColumn * column_haystack = arguments[0].column.get();
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if (const ColumnString * col_str = checkAndGetColumn<ColumnString>(column_haystack))
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{
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auto result_column = ColumnUInt64::create();
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const ColumnString::Chars & src_chars = col_str->getChars();
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const ColumnString::Offsets & src_offsets = col_str->getOffsets();
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ColumnUInt64::Container & vec_res = result_column->getData();
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vec_res.resize(input_rows_count);
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size_t size = src_offsets.size();
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ColumnString::Offset current_src_offset = 0;
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for (size_t i = 0; i < size; ++i)
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{
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Pos pos = reinterpret_cast<Pos>(&src_chars[current_src_offset]);
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current_src_offset = src_offsets[i];
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Pos end = reinterpret_cast<Pos>(&src_chars[current_src_offset]) - 1;
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StringRef str(pos, end - pos);
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vec_res[i] = countMatches(str, re, matches);
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}
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return result_column;
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}
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else if (const ColumnConst * col_const_str = checkAndGetColumnConstStringOrFixedString(column_haystack))
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{
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StringRef str = col_const_str->getDataColumn().getDataAt(0);
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uint64_t matches_count = countMatches(str, re, matches);
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return result_type->createColumnConst(input_rows_count, matches_count);
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}
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else
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throw Exception(ErrorCodes::LOGICAL_ERROR, "Error in FunctionCountMatches::getReturnTypeImpl()");
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}
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static uint64_t countMatches(StringRef src, const Regexps::Regexp & re, OptimizedRegularExpression::MatchVec & matches)
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{
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/// Only one match is required, no need to copy more.
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static const unsigned matches_limit = 1;
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Pos pos = reinterpret_cast<Pos>(src.data);
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Pos end = reinterpret_cast<Pos>(src.data + src.size);
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uint64_t match_count = 0;
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while (true)
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{
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if (pos >= end)
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break;
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if (!re.match(pos, end - pos, matches, matches_limit))
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break;
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/// Progress should be made, but with empty match the progress will not be done.
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/// Also note that simply check is pattern empty is not enough,
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/// since for example "'[f]{0}'" will match zero bytes:
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if (!matches[0].length)
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break;
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pos += matches[0].offset + matches[0].length;
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match_count++;
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}
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return match_count;
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}
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};
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}
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