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homework-jianmu/source/libs/function/src/detail/tavgfunction.c

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/*
* Copyright (c) 2019 TAOS Data, Inc. <jhtao@taosdata.com>
*
* This program is free software: you can use, redistribute, and/or modify
* it under the terms of the GNU Affero General Public License, version 3
* or later ("AGPL"), as published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "builtinsimpl.h"
#include "function.h"
#include "tdatablock.h"
#include "tfunctionInt.h"
#include "tglobal.h"
#define SET_VAL(_info, numOfElem, res) \
do { \
if ((numOfElem) <= 0) { \
break; \
} \
(_info)->numOfRes = (res); \
} while (0)
#define LIST_AVG_N(sumT, T) \
do { \
T* plist = (T*)pCol->pData; \
for (int32_t i = start; i < numOfRows + pInput->startRowIndex; ++i) { \
if (colDataIsNull_f(pCol->nullbitmap, i)) { \
continue; \
} \
\
numOfElem += 1; \
pAvgRes->count -= 1; \
sumT -= plist[i]; \
} \
} while (0)
// define signed number sum with check overflow
#define CHECK_OVERFLOW_SUM_SIGNED(out, val) \
if (out->sum.overflow) { \
out->sum.dsum += val; \
} else if (out->sum.isum > 0 && val > 0 && INT64_MAX - out->sum.isum <= val || \
out->sum.isum < 0 && val < 0 && INT64_MIN - out->sum.isum >= val) { \
double dsum = (double)out->sum.isum; \
out->sum.overflow = true; \
out->sum.dsum = dsum + val; \
} else { \
out->sum.isum += val; \
}
// val is big than INT64_MAX, val come from merge
#define CHECK_OVERFLOW_SUM_SIGNED_BIG(out, val, big) \
if (out->sum.overflow) { \
out->sum.dsum += val; \
} else if (out->sum.isum > 0 && val > 0 && INT64_MAX - out->sum.isum <= val || \
out->sum.isum < 0 && val < 0 && INT64_MIN - out->sum.isum >= val || \
big) { \
double dsum = (double)out->sum.isum; \
out->sum.overflow = true; \
out->sum.dsum = dsum + val; \
} else { \
out->sum.isum += val; \
}
// define unsigned number sum with check overflow
#define CHECK_OVERFLOW_SUM_UNSIGNED(out, val) \
if (out->sum.overflow) { \
out->sum.dsum += val; \
} else if (UINT64_MAX - out->sum.usum <= val) { \
double dsum = (double)out->sum.usum; \
out->sum.overflow = true; \
out->sum.dsum = dsum + val; \
} else { \
out->sum.usum += val; \
}
// val is big than UINT64_MAX, val come from merge
#define CHECK_OVERFLOW_SUM_UNSIGNED_BIG(out, val, big) \
if (out->sum.overflow) { \
out->sum.dsum += val; \
} else if (UINT64_MAX - out->sum.usum <= val || big) { \
double dsum = (double)out->sum.usum; \
out->sum.overflow = true; \
out->sum.dsum = dsum + val; \
} else { \
out->sum.usum += val; \
}
typedef struct SAvgRes {
double result;
SSumRes sum;
int64_t count;
int16_t type; // store the original input type, used in merge function
} SAvgRes;
static void floatVectorSumAVX(const float* plist, int32_t numOfRows, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth>>3u) / sizeof(float);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
const float* p = plist;
__m256 val;
__m256 sum = _mm256_setzero_ps();
for (int32_t i = 0; i < rounds; ++i) {
val = _mm256_loadu_ps(p);
sum = _mm256_add_ps(sum, val);
p += width;
}
// let sum up the final results
const float* q = (const float*)&sum;
pRes->sum.dsum += q[0] + q[1] + q[2] + q[3] + q[4] + q[5] + q[6] + q[7];
int32_t startIndex = rounds * width;
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.dsum += plist[j + startIndex];
}
#endif
}
static void doubleVectorSumAVX(const double* plist, int32_t numOfRows, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth>>3u) / sizeof(int64_t);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
const double* p = plist;
__m256d val;
__m256d sum = _mm256_setzero_pd();
for (int32_t i = 0; i < rounds; ++i) {
val = _mm256_loadu_pd(p);
sum = _mm256_add_pd(sum, val);
p += width;
}
// let sum up the final results
const double* q = (const double*)&sum;
pRes->sum.dsum += q[0] + q[1] + q[2] + q[3];
int32_t startIndex = rounds * width;
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.dsum += plist[j + startIndex];
}
#endif
}
static void i8VectorSumAVX2(const int8_t* plist, int32_t numOfRows, int32_t type, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX2__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth>>3u) / sizeof(int64_t);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
__m256i sum = _mm256_setzero_si256();
if (type == TSDB_DATA_TYPE_TINYINT) {
const int8_t* p = plist;
for (int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepi8_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const int64_t* q = (const int64_t*)&sum;
pRes->sum.isum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.isum += plist[j + rounds * width];
}
} else {
const uint8_t* p = (const uint8_t*)plist;
for(int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepu8_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const uint64_t* q = (const uint64_t*)&sum;
pRes->sum.usum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.usum += (uint8_t)plist[j + rounds * width];
}
}
#endif
}
static void i16VectorSumAVX2(const int16_t* plist, int32_t numOfRows, int32_t type, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX2__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth>>3u) / sizeof(int64_t);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
__m256i sum = _mm256_setzero_si256();
if (type == TSDB_DATA_TYPE_SMALLINT) {
const int16_t* p = plist;
for (int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepi16_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const int64_t* q = (const int64_t*)&sum;
pRes->sum.isum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.isum += plist[j + rounds * width];
}
} else {
const uint16_t* p = (const uint16_t*)plist;
for(int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepu16_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const uint64_t* q = (const uint64_t*)&sum;
pRes->sum.usum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.usum += (uint16_t)plist[j + rounds * width];
}
}
#endif
}
static void i32VectorSumAVX2(const int32_t* plist, int32_t numOfRows, int32_t type, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX2__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth>>3u) / sizeof(int64_t);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
__m256i sum = _mm256_setzero_si256();
if (type == TSDB_DATA_TYPE_INT) {
const int32_t* p = plist;
for (int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepi32_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const int64_t* q = (const int64_t*)&sum;
pRes->sum.isum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.isum += plist[j + rounds * width];
}
} else {
const uint32_t* p = (const uint32_t*)plist;
for(int32_t i = 0; i < rounds; ++i) {
__m128i val = _mm_lddqu_si128((__m128i*)p);
__m256i extVal = _mm256_cvtepu32_epi64(val); // only four items will be converted into __m256i
sum = _mm256_add_epi64(sum, extVal);
p += width;
}
// let sum up the final results
const uint64_t* q = (const uint64_t*)&sum;
pRes->sum.usum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.usum += (uint32_t)plist[j + rounds * width];
}
}
#endif
}
static void i64VectorSumAVX2(const int64_t* plist, int32_t numOfRows, SAvgRes* pRes) {
const int32_t bitWidth = 256;
#if __AVX2__
// find the start position that are aligned to 32bytes address in memory
int32_t width = (bitWidth >> 3u) / sizeof(int64_t);
int32_t remainder = numOfRows % width;
int32_t rounds = numOfRows / width;
__m256i sum = _mm256_setzero_si256();
const int64_t* p = plist;
for (int32_t i = 0; i < rounds; ++i) {
__m256i val = _mm256_lddqu_si256((__m256i*)p);
sum = _mm256_add_epi64(sum, val);
p += width;
}
// let sum up the final results
const int64_t* q = (const int64_t*)&sum;
pRes->sum.isum += q[0] + q[1] + q[2] + q[3];
for (int32_t j = 0; j < remainder; ++j) {
pRes->sum.isum += plist[j + rounds * width];
}
#endif
}
int32_t getAvgInfoSize() { return (int32_t)sizeof(SAvgRes); }
bool getAvgFuncEnv(SFunctionNode* UNUSED_PARAM(pFunc), SFuncExecEnv* pEnv) {
pEnv->calcMemSize = sizeof(SAvgRes);
return true;
}
bool avgFunctionSetup(SqlFunctionCtx* pCtx, SResultRowEntryInfo* pResultInfo) {
if (!functionSetup(pCtx, pResultInfo)) {
return false;
}
SAvgRes* pRes = GET_ROWCELL_INTERBUF(pResultInfo);
memset(pRes, 0, sizeof(SAvgRes));
return true;
}
static int32_t calculateAvgBySMAInfo(SAvgRes* pRes, int32_t numOfRows, int32_t type, const SColumnDataAgg* pAgg) {
int32_t numOfElem = numOfRows - pAgg->numOfNull;
pRes->count += numOfElem;
if (IS_SIGNED_NUMERIC_TYPE(type)) {
CHECK_OVERFLOW_SUM_SIGNED(pRes, pAgg->sum);
} else if (IS_UNSIGNED_NUMERIC_TYPE(type)) {
CHECK_OVERFLOW_SUM_UNSIGNED(pRes, pAgg->sum);
} else if (IS_FLOAT_TYPE(type)) {
pRes->sum.dsum += GET_DOUBLE_VAL((const char*)&(pAgg->sum));
}
return numOfElem;
}
static int32_t doAddNumericVector(SColumnInfoData* pCol, int32_t type, SInputColumnInfoData *pInput, SAvgRes* pRes) {
int32_t start = pInput->startRowIndex;
int32_t numOfRows = pInput->numOfRows;
int32_t numOfElems = 0;
switch (type) {
case TSDB_DATA_TYPE_TINYINT: {
int8_t* plist = (int8_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_SIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_SMALLINT: {
int16_t* plist = (int16_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_SIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_INT: {
int32_t* plist = (int32_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_SIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_BIGINT: {
int64_t* plist = (int64_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_SIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_UTINYINT: {
uint8_t* plist = (uint8_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_UNSIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_USMALLINT: {
uint16_t* plist = (uint16_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_UNSIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_UINT: {
uint32_t* plist = (uint32_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_UNSIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_UBIGINT: {
uint64_t* plist = (uint64_t*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
CHECK_OVERFLOW_SUM_UNSIGNED(pRes, plist[i])
}
break;
}
case TSDB_DATA_TYPE_FLOAT: {
float* plist = (float*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
pRes->sum.dsum += plist[i];
}
break;
}
case TSDB_DATA_TYPE_DOUBLE: {
double* plist = (double*)pCol->pData;
for (int32_t i = start; i < numOfRows + start; ++i) {
if (colDataIsNull_f(pCol->nullbitmap, i)) {
continue;
}
numOfElems += 1;
pRes->count += 1;
pRes->sum.dsum += plist[i];
}
break;
}
default:
break;
}
return numOfElems;
}
int32_t avgFunction(SqlFunctionCtx* pCtx) {
int32_t numOfElem = 0;
const int32_t THRESHOLD_SIZE = 8;
SInputColumnInfoData* pInput = &pCtx->input;
SColumnDataAgg* pAgg = pInput->pColumnDataAgg[0];
int32_t type = pInput->pData[0]->info.type;
SAvgRes* pAvgRes = GET_ROWCELL_INTERBUF(GET_RES_INFO(pCtx));
// computing based on the true data block
SColumnInfoData* pCol = pInput->pData[0];
int32_t start = pInput->startRowIndex;
int32_t numOfRows = pInput->numOfRows;
if (IS_NULL_TYPE(type)) {
goto _over;
}
pAvgRes->type = type;
if (pInput->colDataSMAIsSet) { // try to use SMA if available
numOfElem = calculateAvgBySMAInfo(pAvgRes, numOfRows, type, pAgg);
} else if (!pCol->hasNull) { // try to employ the simd instructions to speed up the loop
numOfElem = pInput->numOfRows;
pAvgRes->count += pInput->numOfRows;
bool simdAvailable = tsAVXEnable && tsSIMDEnable && (numOfRows > THRESHOLD_SIZE);
switch(type) {
case TSDB_DATA_TYPE_UTINYINT:
case TSDB_DATA_TYPE_TINYINT: {
const int8_t* plist = (const int8_t*) pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable) {
i8VectorSumAVX2(plist, numOfRows, type, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
if (type == TSDB_DATA_TYPE_TINYINT) {
CHECK_OVERFLOW_SUM_SIGNED(pAvgRes, plist[i])
} else {
CHECK_OVERFLOW_SUM_UNSIGNED(pAvgRes, (uint8_t)plist[i])
}
}
}
break;
}
case TSDB_DATA_TYPE_USMALLINT:
case TSDB_DATA_TYPE_SMALLINT: {
const int16_t* plist = (const int16_t*)pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable) {
i16VectorSumAVX2(plist, numOfRows, type, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
if (type == TSDB_DATA_TYPE_SMALLINT) {
CHECK_OVERFLOW_SUM_SIGNED(pAvgRes, plist[i])
} else {
CHECK_OVERFLOW_SUM_UNSIGNED(pAvgRes, (uint16_t)plist[i])
}
}
}
break;
}
case TSDB_DATA_TYPE_UINT:
case TSDB_DATA_TYPE_INT: {
const int32_t* plist = (const int32_t*) pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable) {
i32VectorSumAVX2(plist, numOfRows, type, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
if (type == TSDB_DATA_TYPE_INT) {
CHECK_OVERFLOW_SUM_SIGNED(pAvgRes, plist[i])
} else {
CHECK_OVERFLOW_SUM_UNSIGNED(pAvgRes, (uint32_t)plist[i])
}
}
}
break;
}
case TSDB_DATA_TYPE_UBIGINT:
case TSDB_DATA_TYPE_BIGINT: {
const int64_t* plist = (const int64_t*) pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable && type == TSDB_DATA_TYPE_BIGINT) {
i64VectorSumAVX2(plist, numOfRows, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
if (type == TSDB_DATA_TYPE_BIGINT) {
CHECK_OVERFLOW_SUM_SIGNED(pAvgRes, plist[i])
} else {
CHECK_OVERFLOW_SUM_UNSIGNED(pAvgRes, (uint64_t)plist[i])
}
}
}
break;
}
case TSDB_DATA_TYPE_FLOAT: {
const float* plist = (const float*) pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable) {
floatVectorSumAVX(plist, numOfRows, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
pAvgRes->sum.dsum += plist[i];
}
}
break;
}
case TSDB_DATA_TYPE_DOUBLE: {
const double* plist = (const double*)pCol->pData;
// 1. If the CPU supports AVX, let's employ AVX instructions to speedup this loop
if (simdAvailable) {
doubleVectorSumAVX(plist, numOfRows, pAvgRes);
} else {
for (int32_t i = pInput->startRowIndex; i < pInput->numOfRows + pInput->startRowIndex; ++i) {
pAvgRes->sum.dsum += plist[i];
}
}
break;
}
default:
return TSDB_CODE_FUNC_FUNTION_PARA_TYPE;
}
} else {
numOfElem = doAddNumericVector(pCol, type, pInput, pAvgRes);
}
_over:
// data in the check operation are all null, not output
SET_VAL(GET_RES_INFO(pCtx), numOfElem, 1);
return TSDB_CODE_SUCCESS;
}
static void avgTransferInfo(SAvgRes* pInput, SAvgRes* pOutput) {
if (IS_NULL_TYPE(pInput->type)) {
return;
}
pOutput->type = pInput->type;
if (IS_SIGNED_NUMERIC_TYPE(pOutput->type)) {
bool overflow = pInput->sum.overflow;
CHECK_OVERFLOW_SUM_SIGNED_BIG(pOutput, (overflow ? pInput->sum.dsum : pInput->sum.isum), overflow);
} else if (IS_UNSIGNED_NUMERIC_TYPE(pOutput->type)) {
bool overflow = pInput->sum.overflow;
CHECK_OVERFLOW_SUM_UNSIGNED_BIG(pOutput, (overflow ? pInput->sum.dsum : pInput->sum.usum), overflow);
} else {
pOutput->sum.dsum += pInput->sum.dsum;
}
pOutput->count += pInput->count;
}
int32_t avgFunctionMerge(SqlFunctionCtx* pCtx) {
SInputColumnInfoData* pInput = &pCtx->input;
SColumnInfoData* pCol = pInput->pData[0];
if (pCol->info.type != TSDB_DATA_TYPE_BINARY) {
return TSDB_CODE_FUNC_FUNTION_PARA_TYPE;
}
SAvgRes* pInfo = GET_ROWCELL_INTERBUF(GET_RES_INFO(pCtx));
int32_t start = pInput->startRowIndex;
for (int32_t i = start; i < start + pInput->numOfRows; ++i) {
char* data = colDataGetData(pCol, i);
SAvgRes* pInputInfo = (SAvgRes*)varDataVal(data);
avgTransferInfo(pInputInfo, pInfo);
}
SET_VAL(GET_RES_INFO(pCtx), 1, 1);
return TSDB_CODE_SUCCESS;
}
int32_t avgInvertFunction(SqlFunctionCtx* pCtx) {
int32_t numOfElem = 0;
// Only the pre-computing information loaded and actual data does not loaded
SInputColumnInfoData* pInput = &pCtx->input;
SAvgRes* pAvgRes = GET_ROWCELL_INTERBUF(GET_RES_INFO(pCtx));
// computing based on the true data block
SColumnInfoData* pCol = pInput->pData[0];
int32_t start = pInput->startRowIndex;
int32_t numOfRows = pInput->numOfRows;
switch (pCol->info.type) {
case TSDB_DATA_TYPE_TINYINT: {
LIST_AVG_N(pAvgRes->sum.isum, int8_t);
break;
}
case TSDB_DATA_TYPE_SMALLINT: {
LIST_AVG_N(pAvgRes->sum.isum, int16_t);
break;
}
case TSDB_DATA_TYPE_INT: {
LIST_AVG_N(pAvgRes->sum.isum, int32_t);
break;
}
case TSDB_DATA_TYPE_BIGINT: {
LIST_AVG_N(pAvgRes->sum.isum, int64_t);
break;
}
case TSDB_DATA_TYPE_UTINYINT: {
LIST_AVG_N(pAvgRes->sum.usum, uint8_t);
break;
}
case TSDB_DATA_TYPE_USMALLINT: {
LIST_AVG_N(pAvgRes->sum.usum, uint16_t);
break;
}
case TSDB_DATA_TYPE_UINT: {
LIST_AVG_N(pAvgRes->sum.usum, uint32_t);
break;
}
case TSDB_DATA_TYPE_UBIGINT: {
LIST_AVG_N(pAvgRes->sum.usum, uint64_t);
break;
}
case TSDB_DATA_TYPE_FLOAT: {
LIST_AVG_N(pAvgRes->sum.dsum, float);
break;
}
case TSDB_DATA_TYPE_DOUBLE: {
LIST_AVG_N(pAvgRes->sum.dsum, double);
break;
}
default:
break;
}
// data in the check operation are all null, not output
SET_VAL(GET_RES_INFO(pCtx), numOfElem, 1);
return TSDB_CODE_SUCCESS;
}
int32_t avgCombine(SqlFunctionCtx* pDestCtx, SqlFunctionCtx* pSourceCtx) {
SResultRowEntryInfo* pDResInfo = GET_RES_INFO(pDestCtx);
SAvgRes* pDBuf = GET_ROWCELL_INTERBUF(pDResInfo);
SResultRowEntryInfo* pSResInfo = GET_RES_INFO(pSourceCtx);
SAvgRes* pSBuf = GET_ROWCELL_INTERBUF(pSResInfo);
int16_t type = pDBuf->type == TSDB_DATA_TYPE_NULL ? pSBuf->type : pDBuf->type;
if (IS_SIGNED_NUMERIC_TYPE(type)) {
CHECK_OVERFLOW_SUM_SIGNED(pDBuf, pSBuf->sum.isum)
} else if (IS_UNSIGNED_NUMERIC_TYPE(type)) {
CHECK_OVERFLOW_SUM_UNSIGNED(pDBuf, pSBuf->sum.usum)
} else {
pDBuf->sum.dsum += pSBuf->sum.dsum;
}
pDBuf->count += pSBuf->count;
return TSDB_CODE_SUCCESS;
}
int32_t avgFinalize(SqlFunctionCtx* pCtx, SSDataBlock* pBlock) {
SResultRowEntryInfo* pEntryInfo = GET_RES_INFO(pCtx);
SAvgRes* pRes = GET_ROWCELL_INTERBUF(pEntryInfo);
int32_t type = pRes->type;
if (pRes->count > 0) {
if(pRes->sum.overflow) {
// overflow flag set , use dsum
pRes->result = pRes->sum.dsum / ((double)pRes->count);
}else if (IS_SIGNED_NUMERIC_TYPE(type)) {
pRes->result = pRes->sum.isum / ((double)pRes->count);
} else if (IS_UNSIGNED_NUMERIC_TYPE(type)) {
pRes->result = pRes->sum.usum / ((double)pRes->count);
} else {
pRes->result = pRes->sum.dsum / ((double)pRes->count);
}
}
if (pRes->count == 0 || isinf(pRes->result) || isnan(pRes->result)) {
pEntryInfo->numOfRes = 0;
} else {
pEntryInfo->numOfRes = 1;
}
return functionFinalize(pCtx, pBlock);
}
int32_t avgPartialFinalize(SqlFunctionCtx* pCtx, SSDataBlock* pBlock) {
SResultRowEntryInfo* pResInfo = GET_RES_INFO(pCtx);
SAvgRes* pInfo = GET_ROWCELL_INTERBUF(GET_RES_INFO(pCtx));
int32_t resultBytes = getAvgInfoSize();
char* res = taosMemoryCalloc(resultBytes + VARSTR_HEADER_SIZE, sizeof(char));
memcpy(varDataVal(res), pInfo, resultBytes);
varDataSetLen(res, resultBytes);
int32_t slotId = pCtx->pExpr->base.resSchema.slotId;
SColumnInfoData* pCol = taosArrayGet(pBlock->pDataBlock, slotId);
colDataSetVal(pCol, pBlock->info.rows, res, false);
taosMemoryFree(res);
return pResInfo->numOfRes;
}
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