ClickHouse/src/Functions/MatchImpl.h

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#pragma once
#include <type_traits>
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#include <base/types.h>
#include <Common/Volnitsky.h>
#include <Columns/ColumnString.h>
#include <Core/ColumnNumbers.h>
#include "Regexps.h"
#include "config.h"
#include <re2_st/re2.h>
namespace DB
{
namespace ErrorCodes
{
extern const int ILLEGAL_COLUMN;
}
namespace impl
{
/// Is the [I]LIKE expression reduced to finding a substring in a string?
inline bool likePatternIsSubstring(std::string_view pattern, String & res)
{
if (pattern.size() < 2 || !pattern.starts_with('%') || !pattern.ends_with('%'))
return false;
res.clear();
res.reserve(pattern.size() - 2);
const char * pos = pattern.data() + 1;
const char * const end = pattern.data() + pattern.size() - 1;
while (pos < end)
{
switch (*pos)
{
case '%':
case '_':
return false;
case '\\':
++pos;
if (pos == end)
return false;
else
res += *pos;
break;
default:
res += *pos;
break;
}
++pos;
}
return true;
}
}
// For more readable instantiations of MatchImpl<>
struct MatchTraits
{
enum class Syntax
{
Like,
Re2
};
enum class Case
{
Sensitive,
Insensitive
};
enum class Result
{
DontNegate,
Negate
};
};
/**
* NOTE: We want to run regexp search for whole columns by one call (as implemented in function 'position')
* but for that, regexp engine must support \0 bytes and their interpretation as string boundaries.
*/
template <typename Name, MatchTraits::Syntax syntax_, MatchTraits::Case case_, MatchTraits::Result result_>
struct MatchImpl
{
static constexpr bool use_default_implementation_for_constants = true;
static constexpr bool supports_start_pos = false;
static constexpr auto name = Name::name;
static ColumnNumbers getArgumentsThatAreAlwaysConstant() { return {2};}
using ResultType = UInt8;
static constexpr bool is_like = (syntax_ == MatchTraits::Syntax::Like);
static constexpr bool case_insensitive = (case_ == MatchTraits::Case::Insensitive);
static constexpr bool negate = (result_ == MatchTraits::Result::Negate);
using Searcher = std::conditional_t<case_insensitive, VolnitskyCaseInsensitiveUTF8, VolnitskyUTF8>;
static void vectorConstant(
const ColumnString::Chars & haystack_data,
const ColumnString::Offsets & haystack_offsets,
const String & needle,
[[maybe_unused]] const ColumnPtr & start_pos_,
PaddedPODArray<UInt8> & res)
{
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const size_t haystack_size = haystack_offsets.size();
assert(haystack_size == res.size());
assert(start_pos_ == nullptr);
if (haystack_offsets.empty())
return;
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/// Fast path for [I]LIKE, because the result is always true or false
/// col [i]like '%%'
/// col not [i]like '%%'
/// col like '%'
/// col not [i]like '%'
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/// match(like, '^$')
if ((is_like && (needle == "%%" or needle == "%")) || (!is_like && needle == ".*"))
{
for (auto & re : res)
re = !negate;
return;
}
/// Special case that the [I]LIKE expression reduces to finding a substring in a string
String strstr_pattern;
if (is_like && impl::likePatternIsSubstring(needle, strstr_pattern))
{
const UInt8 * const begin = haystack_data.data();
const UInt8 * const end = haystack_data.data() + haystack_data.size();
const UInt8 * pos = begin;
/// The current index in the array of strings.
size_t i = 0;
/// TODO You need to make that `searcher` is common to all the calls of the function.
Searcher searcher(strstr_pattern.data(), strstr_pattern.size(), end - pos);
/// We will search for the next occurrence in all rows at once.
while (pos < end && end != (pos = searcher.search(pos, end - pos)))
{
/// Let's determine which index it refers to.
while (begin + haystack_offsets[i] <= pos)
{
res[i] = negate;
++i;
}
/// We check that the entry does not pass through the boundaries of strings.
if (pos + strstr_pattern.size() < begin + haystack_offsets[i])
res[i] = !negate;
else
res[i] = negate;
pos = begin + haystack_offsets[i];
++i;
}
/// Tail, in which there can be no substring.
if (i < res.size())
memset(&res[i], negate, (res.size() - i) * sizeof(res[0]));
return;
}
const auto & regexp = Regexps::Regexp(Regexps::createRegexp<is_like, /*no_capture*/ true, case_insensitive>(needle));
String required_substring;
bool is_trivial;
bool required_substring_is_prefix; /// for `anchored` execution of the regexp.
regexp.getAnalyzeResult(required_substring, is_trivial, required_substring_is_prefix);
if (required_substring.empty())
{
if (!regexp.getRE2()) /// An empty regexp. Always matches.
memset(res.data(), !negate, haystack_size * sizeof(res[0]));
else
{
size_t prev_offset = 0;
for (size_t i = 0; i < haystack_size; ++i)
{
const bool match = regexp.getRE2()->Match(
{reinterpret_cast<const char *>(&haystack_data[prev_offset]), haystack_offsets[i] - prev_offset - 1},
0,
haystack_offsets[i] - prev_offset - 1,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
prev_offset = haystack_offsets[i];
}
}
}
else
{
/// NOTE This almost matches with the case of impl::likePatternIsSubstring.
const UInt8 * const begin = haystack_data.data();
const UInt8 * const end = haystack_data.begin() + haystack_data.size();
const UInt8 * pos = begin;
/// The current index in the array of strings.
size_t i = 0;
Searcher searcher(required_substring.data(), required_substring.size(), end - pos);
/// We will search for the next occurrence in all rows at once.
while (pos < end && end != (pos = searcher.search(pos, end - pos)))
{
/// Determine which index it refers to.
while (begin + haystack_offsets[i] <= pos)
{
res[i] = negate;
++i;
}
/// We check that the entry does not pass through the boundaries of strings.
if (pos + required_substring.size() < begin + haystack_offsets[i])
{
/// And if it does not, if necessary, we check the regexp.
if (is_trivial)
res[i] = !negate;
else
{
const char * str_data = reinterpret_cast<const char *>(&haystack_data[haystack_offsets[i - 1]]);
size_t str_size = haystack_offsets[i] - haystack_offsets[i - 1] - 1;
/** Even in the case of `required_substring_is_prefix` use UNANCHORED check for regexp,
* so that it can match when `required_substring` occurs into the string several times,
* and at the first occurrence, the regexp is not a match.
*/
const size_t start_pos = (required_substring_is_prefix) ? (reinterpret_cast<const char *>(pos) - str_data) : 0;
const size_t end_pos = str_size;
const bool match = regexp.getRE2()->Match(
{str_data, str_size},
start_pos,
end_pos,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
}
}
else
res[i] = negate;
pos = begin + haystack_offsets[i];
++i;
}
/// Tail, in which there can be no substring.
if (i < res.size())
memset(&res[i], negate, (res.size() - i) * sizeof(res[0]));
}
}
/// Very carefully crafted copy-paste.
static void vectorFixedConstant(
const ColumnString::Chars & haystack,
size_t N,
const String & needle,
PaddedPODArray<UInt8> & res)
{
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const size_t haystack_size = haystack.size() / N;
assert(haystack_size == res.size());
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if (haystack.empty())
return;
/// Special case that the [I]LIKE expression reduces to finding a substring in a string
String strstr_pattern;
if (is_like && impl::likePatternIsSubstring(needle, strstr_pattern))
{
const UInt8 * const begin = haystack.data();
const UInt8 * const end = haystack.data() + haystack.size();
const UInt8 * pos = begin;
size_t i = 0;
const UInt8 * next_pos = begin;
/// If needle is larger than string size - it cannot be found.
if (strstr_pattern.size() <= N)
{
Searcher searcher(strstr_pattern.data(), strstr_pattern.size(), end - pos);
/// We will search for the next occurrence in all rows at once.
while (pos < end && end != (pos = searcher.search(pos, end - pos)))
{
/// Let's determine which index it refers to.
while (next_pos + N <= pos)
{
res[i] = negate;
next_pos += N;
++i;
}
next_pos += N;
/// We check that the entry does not pass through the boundaries of strings.
if (pos + strstr_pattern.size() <= next_pos)
res[i] = !negate;
else
res[i] = negate;
pos = next_pos;
++i;
}
}
/// Tail, in which there can be no substring.
if (i < res.size())
memset(&res[i], negate, (res.size() - i) * sizeof(res[0]));
return;
}
const auto & regexp = Regexps::Regexp(Regexps::createRegexp<is_like, /*no_capture*/ true, case_insensitive>(needle));
String required_substring;
bool is_trivial;
bool required_substring_is_prefix; /// for `anchored` execution of the regexp.
regexp.getAnalyzeResult(required_substring, is_trivial, required_substring_is_prefix);
if (required_substring.empty())
{
if (!regexp.getRE2()) /// An empty regexp. Always matches.
memset(res.data(), !negate, haystack_size * sizeof(res[0]));
else
{
size_t offset = 0;
for (size_t i = 0; i < haystack_size; ++i)
{
const bool match = regexp.getRE2()->Match(
{reinterpret_cast<const char *>(&haystack[offset]), N},
0,
N,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
offset += N;
}
}
}
else
{
/// NOTE This almost matches with the case of likePatternIsSubstring.
const UInt8 * const begin = haystack.data();
const UInt8 * const end = haystack.data() + haystack.size();
const UInt8 * pos = begin;
size_t i = 0;
const UInt8 * next_pos = begin;
/// If required substring is larger than string size - it cannot be found.
if (required_substring.size() <= N)
{
Searcher searcher(required_substring.data(), required_substring.size(), end - pos);
/// We will search for the next occurrence in all rows at once.
while (pos < end && end != (pos = searcher.search(pos, end - pos)))
{
/// Let's determine which index it refers to.
while (next_pos + N <= pos)
{
res[i] = negate;
next_pos += N;
++i;
}
next_pos += N;
if (pos + required_substring.size() <= next_pos)
{
/// And if it does not, if necessary, we check the regexp.
if (is_trivial)
res[i] = !negate;
else
{
const char * str_data = reinterpret_cast<const char *>(next_pos - N);
/** Even in the case of `required_substring_is_prefix` use UNANCHORED check for regexp,
* so that it can match when `required_substring` occurs into the string several times,
* and at the first occurrence, the regexp is not a match.
*/
const size_t start_pos = (required_substring_is_prefix) ? (reinterpret_cast<const char *>(pos) - str_data) : 0;
const size_t end_pos = N;
const bool match = regexp.getRE2()->Match(
{str_data, N},
start_pos,
end_pos,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
}
}
else
res[i] = negate;
pos = next_pos;
++i;
}
}
/// Tail, in which there can be no substring.
if (i < res.size())
memset(&res[i], negate, (res.size() - i) * sizeof(res[0]));
}
}
static void vectorVector(
const ColumnString::Chars & haystack_data,
const ColumnString::Offsets & haystack_offsets,
const ColumnString::Chars & needle_data,
const ColumnString::Offsets & needle_offset,
[[maybe_unused]] const ColumnPtr & start_pos_,
PaddedPODArray<UInt8> & res)
{
const size_t haystack_size = haystack_offsets.size();
assert(haystack_size == needle_offset.size());
assert(haystack_size == res.size());
assert(start_pos_ == nullptr);
if (haystack_offsets.empty())
return;
String required_substr;
bool is_trivial;
bool required_substring_is_prefix; /// for `anchored` execution of the regexp.
size_t prev_haystack_offset = 0;
size_t prev_needle_offset = 0;
Measure and rework internal re2 caching 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.
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Regexps::LocalCacheTable cache;
Regexps::RegexpPtr regexp;
for (size_t i = 0; i < haystack_size; ++i)
{
const auto * const cur_haystack_data = &haystack_data[prev_haystack_offset];
const size_t cur_haystack_length = haystack_offsets[i] - prev_haystack_offset - 1;
const auto * const cur_needle_data = &needle_data[prev_needle_offset];
const size_t cur_needle_length = needle_offset[i] - prev_needle_offset - 1;
const auto & needle = String(
reinterpret_cast<const char *>(cur_needle_data),
cur_needle_length);
if (is_like && impl::likePatternIsSubstring(needle, required_substr))
{
if (required_substr.size() > cur_haystack_length)
res[i] = negate;
else
{
Searcher searcher(required_substr.data(), required_substr.size(), cur_haystack_length);
const auto * match = searcher.search(cur_haystack_data, cur_haystack_length);
res[i] = negate ^ (match != cur_haystack_data + cur_haystack_length);
}
}
else
{
regexp = cache.getOrSet<is_like, /*no_capture*/ true, case_insensitive>(needle);
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
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regexp->getAnalyzeResult(required_substr, is_trivial, required_substring_is_prefix);
if (required_substr.empty())
{
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
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if (!regexp->getRE2()) /// An empty regexp. Always matches.
res[i] = !negate;
else
{
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
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const bool match = regexp->getRE2()->Match(
{reinterpret_cast<const char *>(cur_haystack_data), cur_haystack_length},
0,
cur_haystack_length,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
}
}
else
{
Searcher searcher(required_substr.data(), required_substr.size(), cur_haystack_length);
const auto * match = searcher.search(cur_haystack_data, cur_haystack_length);
if (match == cur_haystack_data + cur_haystack_length)
res[i] = negate; // no match
else
{
if (is_trivial)
res[i] = !negate; // no wildcards in pattern
else
{
const size_t start_pos = (required_substring_is_prefix) ? (match - cur_haystack_data) : 0;
const size_t end_pos = cur_haystack_length;
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
2022-05-25 19:22:45 +00:00
const bool match2 = regexp->getRE2()->Match(
{reinterpret_cast<const char *>(cur_haystack_data), cur_haystack_length},
start_pos,
end_pos,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match2;
}
}
}
}
prev_haystack_offset = haystack_offsets[i];
prev_needle_offset = needle_offset[i];
}
}
static void vectorFixedVector(
const ColumnString::Chars & haystack,
size_t N,
const ColumnString::Chars & needle_data,
const ColumnString::Offsets & needle_offset,
[[maybe_unused]] const ColumnPtr & start_pos_,
PaddedPODArray<UInt8> & res)
{
const size_t haystack_size = haystack.size()/N;
assert(haystack_size == needle_offset.size());
assert(haystack_size == res.size());
assert(start_pos_ == nullptr);
if (haystack.empty())
return;
String required_substr;
bool is_trivial;
bool required_substring_is_prefix; // for `anchored` execution of the regexp.
size_t prev_haystack_offset = 0;
size_t prev_needle_offset = 0;
Measure and rework internal re2 caching 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.
2022-05-27 10:40:53 +00:00
Regexps::LocalCacheTable cache;
Regexps::RegexpPtr regexp;
for (size_t i = 0; i < haystack_size; ++i)
{
const auto * const cur_haystack_data = &haystack[prev_haystack_offset];
const size_t cur_haystack_length = N;
const auto * const cur_needle_data = &needle_data[prev_needle_offset];
const size_t cur_needle_length = needle_offset[i] - prev_needle_offset - 1;
const auto & needle = String(
reinterpret_cast<const char *>(cur_needle_data),
cur_needle_length);
if (is_like && impl::likePatternIsSubstring(needle, required_substr))
{
if (required_substr.size() > cur_haystack_length)
res[i] = negate;
else
{
Searcher searcher(required_substr.data(), required_substr.size(), cur_haystack_length);
const auto * match = searcher.search(cur_haystack_data, cur_haystack_length);
res[i] = negate ^ (match != cur_haystack_data + cur_haystack_length);
}
}
else
{
regexp = cache.getOrSet<is_like, /*no_capture*/ true, case_insensitive>(needle);
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
2022-05-25 19:22:45 +00:00
regexp->getAnalyzeResult(required_substr, is_trivial, required_substring_is_prefix);
if (required_substr.empty())
{
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
2022-05-25 19:22:45 +00:00
if (!regexp->getRE2()) /// An empty regexp. Always matches.
res[i] = !negate;
else
{
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
2022-05-25 19:22:45 +00:00
const bool match = regexp->getRE2()->Match(
{reinterpret_cast<const char *>(cur_haystack_data), cur_haystack_length},
0,
cur_haystack_length,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match;
}
}
else
{
Searcher searcher(required_substr.data(), required_substr.size(), cur_haystack_length);
const auto * match = searcher.search(cur_haystack_data, cur_haystack_length);
if (match == cur_haystack_data + cur_haystack_length)
res[i] = negate; // no match
else
{
if (is_trivial)
res[i] = !negate; // no wildcards in pattern
else
{
const size_t start_pos = (required_substring_is_prefix) ? (match - cur_haystack_data) : 0;
const size_t end_pos = cur_haystack_length;
Cache compiled regexps when evaluating non-const needles Needles in a (non-const) needle column may repeat and this commit allows to skip compilation for known needles. Out of the different design alternatives (see below, if someone is interested), we now maintain - one global pattern cache, - with a fixed size of 42k elements currently, - and use LRU as eviction strategy. ------------------------------------------------------------------------ (sorry for the wall of text, dumping it here not for reading but just for reference) Write-up about considered design alternatives: 1. Keep the current global cache of const needles. For non-const needles, probe the cache but don't store values in it. Pros: need to maintain just a single cache, no problem with cache pollution assuming there are few distinct constant needles Cons: only useful if a non-const needle occurred as already as a const needle --> overall too simplistic 2. Keep the current global cache for const needles. For non-const needles, create a local (e.g. per-query) cache Pros: unlike (1.), non-const needles can be skipped even if they did not occur yet, no pollution of the const pattern cache when there are very many non-const needles (e.g. large / highly distinct needle columns). Cons: caches may explode "horizontally", i.e. we'll end up with the const cache + caches for Q1, Q2, ... QN, this makes it harder to control the overall space consumption, also patterns residing in different caches cannot be reused between queries, another difficulty is that the concept of "query" does not really exist at matching level - there are only column chunks and we'd potentially end up with 1 cache / chunk 3. Queries with const and non-const needles insert into the same global cache. Pros: the advantages of (2.) + allows to reuse compiled patterns accross parallel queries Cons: needs an eviction strategy to control cache size and pollution (and btw. (2.) also needs eviction strategies for the individual caches) 4. Queries with const needle use global cache, queries with non-const needle use a different global cache --> Overall similar to (3) but ignores the (likely) edge case that const and non-const needles overlap. In sum, (3.) seems the simplest and most beneficial approach. Eviction strategies: 0. Don't ever evict --> cache may grow infinitely and eventually make the system unusable (may even pose a DoS risk) 1. Flush the cache after a certain threshold is exceeded --> very simple but may lead to peridic performance drops 2. Use LRU --> more graceful performance degradation at threshold but comes with a (constant) performance overhead to maintain the LRU queue In sum, given that the pattern compilation in RE2 should be quite costly (pattern-to-DFA/NFA), LRU may be acceptable.
2022-05-25 19:22:45 +00:00
const bool match2 = regexp->getRE2()->Match(
{reinterpret_cast<const char *>(cur_haystack_data), cur_haystack_length},
start_pos,
end_pos,
re2_st::RE2::UNANCHORED,
nullptr,
0);
res[i] = negate ^ match2;
}
}
}
}
prev_haystack_offset += N;
prev_needle_offset = needle_offset[i];
}
}
template <typename... Args>
static void constantVector(Args &&...)
{
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "Function '{}' doesn't support search with non-constant needles in constant haystack", name);
}
};
}