ClickHouse/dbms/src/Common/HashTable/Hash.h

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#pragma once
#include <Core/Types.h>
#include <Common/UInt128.h>
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#include <type_traits>
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/** Hash functions that are better than the trivial function std::hash.
*
* Example: when we do aggregation by the visitor ID, the performance increase is more than 5 times.
* This is because of following reasons:
* - in Yandex, visitor identifier is an integer that has timestamp with seconds resolution in lower bits;
* - in typical implementation of standard library, hash function for integers is trivial and just use lower bits;
* - traffic is non-uniformly distributed across a day;
* - we are using open-addressing linear probing hash tables that are most critical to hash function quality,
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* and trivial hash function gives disastrous results.
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*/
/** Taken from MurmurHash. This is Murmur finalizer.
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* Faster than intHash32 when inserting into the hash table UInt64 -> UInt64, where the key is the visitor ID.
*/
inline DB::UInt64 intHash64(DB::UInt64 x)
{
x ^= x >> 33;
x *= 0xff51afd7ed558ccdULL;
x ^= x >> 33;
x *= 0xc4ceb9fe1a85ec53ULL;
x ^= x >> 33;
return x;
}
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/** CRC32C is not very high-quality as a hash function,
* according to avalanche and bit independence tests (see SMHasher software), as well as a small number of bits,
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* but can behave well when used in hash tables,
* due to high speed (latency 3 + 1 clock cycle, throughput 1 clock cycle).
* Works only with SSE 4.2 support.
*/
#ifdef __SSE4_2__
#include <nmmintrin.h>
#endif
#if defined(__aarch64__) && defined(__ARM_FEATURE_CRC32)
#include <arm_acle.h>
#include <arm_neon.h>
#endif
inline DB::UInt64 intHashCRC32(DB::UInt64 x)
{
#ifdef __SSE4_2__
return _mm_crc32_u64(-1ULL, x);
#elif defined(__aarch64__) && defined(__ARM_FEATURE_CRC32)
return __crc32cd(-1U, x);
#else
/// On other platforms we do not have CRC32. NOTE This can be confusing.
return intHash64(x);
#endif
}
inline DB::UInt64 intHashCRC32(DB::UInt64 x, DB::UInt64 updated_value)
{
#ifdef __SSE4_2__
return _mm_crc32_u64(updated_value, x);
#elif defined(__aarch64__) && defined(__ARM_FEATURE_CRC32)
return __crc32cd(updated_value, x);
#else
/// On other platforms we do not have CRC32. NOTE This can be confusing.
return intHash64(x) ^ updated_value;
#endif
}
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template <typename T>
inline size_t DefaultHash64(T key)
{
union
{
T in;
DB::UInt64 out;
} u;
u.out = 0;
u.in = key;
return intHash64(u.out);
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}
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template <typename T, typename Enable = void>
struct DefaultHash;
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template <typename T>
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struct DefaultHash<T, std::enable_if_t<is_arithmetic_v<T>>>
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{
size_t operator() (T key) const
{
return DefaultHash64<T>(key);
}
};
template <typename T>
struct DefaultHash<T, std::enable_if_t<DB::IsDecimalNumber<T> && sizeof(T) <= 8>>
{
size_t operator() (T key) const
{
return DefaultHash64<typename T::NativeType>(key);
}
};
template <typename T>
struct DefaultHash<T, std::enable_if_t<DB::IsDecimalNumber<T> && sizeof(T) == 16>>
{
size_t operator() (T key) const
{
return DefaultHash64<Int64>(key >> 64) ^ DefaultHash64<Int64>(key);
}
};
template <typename T> struct HashCRC32;
template <typename T>
inline size_t hashCRC32(T key)
{
union
{
T in;
DB::UInt64 out;
} u;
u.out = 0;
u.in = key;
return intHashCRC32(u.out);
}
#define DEFINE_HASH(T) \
template <> struct HashCRC32<T>\
{\
size_t operator() (T key) const\
{\
return hashCRC32<T>(key);\
}\
};
DEFINE_HASH(DB::UInt8)
DEFINE_HASH(DB::UInt16)
DEFINE_HASH(DB::UInt32)
DEFINE_HASH(DB::UInt64)
DEFINE_HASH(DB::UInt128)
DEFINE_HASH(DB::Int8)
DEFINE_HASH(DB::Int16)
DEFINE_HASH(DB::Int32)
DEFINE_HASH(DB::Int64)
DEFINE_HASH(DB::Float32)
DEFINE_HASH(DB::Float64)
#undef DEFINE_HASH
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/// It is reasonable to use for UInt8, UInt16 with sufficient hash table size.
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struct TrivialHash
{
template <typename T>
size_t operator() (T key) const
{
return key;
}
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};
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/** A relatively good non-cryptographic hash function from UInt64 to UInt32.
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* But worse (both in quality and speed) than just cutting intHash64.
* Taken from here: http://www.concentric.net/~ttwang/tech/inthash.htm
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*
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* Slightly changed compared to the function by link: shifts to the right are accidentally replaced by a cyclic shift to the right.
* This change did not affect the smhasher test results.
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*
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* It is recommended to use different salt for different tasks.
* That was the case that in the database values were sorted by hash (for low-quality pseudo-random spread),
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* and in another place, in the aggregate function, the same hash was used in the hash table,
* as a result, this aggregate function was monstrously slowed due to collisions.
*
* NOTE Salting is far from perfect, because it commutes with first steps of calculation.
*
* NOTE As mentioned, this function is slower than intHash64.
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* But occasionally, it is faster, when written in a loop and loop is vectorized.
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*/
template <DB::UInt64 salt>
inline DB::UInt32 intHash32(DB::UInt64 key)
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{
key ^= salt;
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key = (~key) + (key << 18);
key = key ^ ((key >> 31) | (key << 33));
key = key * 21;
key = key ^ ((key >> 11) | (key << 53));
key = key + (key << 6);
key = key ^ ((key >> 22) | (key << 42));
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return key;
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}
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/// For containers.
template <typename T, DB::UInt64 salt = 0>
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struct IntHash32
{
size_t operator() (const T & key) const
{
return intHash32<salt>(key);
}
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};