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596fb6f327ecafa7aeb43132c2d284e34d60dce4
doris/be/src/pipeline/exec/streaming_aggregation_operator.cpp
zhangstar333 596fb6f327 [improve](ub) fix some runtime error of ubsan when downcast (#35343) 
those code could work well, but it will be report some runtime error under UBSAN,
so refactor it to let's ubsan could running happy.
2024-05-27 15:27:43 +08:00

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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
#include "streaming_aggregation_operator.h"
#include <gen_cpp/Metrics_types.h>
#include <utility>
#include "common/compiler_util.h" // IWYU pragma: keep
#include "pipeline/exec/operator.h"
namespace doris {
class RuntimeState;
} // namespace doris
namespace doris::pipeline {
/// The minimum reduction factor (input rows divided by output rows) to grow hash tables
/// in a streaming preaggregation, given that the hash tables are currently the given
/// size or above. The sizes roughly correspond to hash table sizes where the bucket
/// arrays will fit in a cache level. Intuitively, we don't want the working set of the
/// aggregation to expand to the next level of cache unless we're reducing the input
/// enough to outweigh the increased memory latency we'll incur for each hash table
/// lookup.
///
/// Note that the current reduction achieved is not always a good estimate of the
/// final reduction. It may be biased either way depending on the ordering of the
/// input. If the input order is random, we will underestimate the final reduction
/// factor because the probability of a row having the same key as a previous row
/// increases as more input is processed. If the input order is correlated with the
/// key, skew may bias the estimate. If high cardinality keys appear first, we
/// may overestimate and if low cardinality keys appear first, we underestimate.
/// To estimate the eventual reduction achieved, we estimate the final reduction
/// using the planner's estimated input cardinality and the assumption that input
/// is in a random order. This means that we assume that the reduction factor will
/// increase over time.
struct StreamingHtMinReductionEntry {
// Use 'streaming_ht_min_reduction' if the total size of hash table bucket directories in
// bytes is greater than this threshold.
int min_ht_mem;
// The minimum reduction factor to expand the hash tables.
double streaming_ht_min_reduction;
};
// TODO: experimentally tune these values and also programmatically get the cache size
// of the machine that we're running on.
static constexpr StreamingHtMinReductionEntry STREAMING_HT_MIN_REDUCTION[] = {
// Expand up to L2 cache always.
{0, 0.0},
// Expand into L3 cache if we look like we're getting some reduction.
// At present, The L2 cache is generally 1024k or more
{1024 * 1024, 1.1},
// Expand into main memory if we're getting a significant reduction.
// The L3 cache is generally 16MB or more
{16 * 1024 * 1024, 2.0},
};
static constexpr int STREAMING_HT_MIN_REDUCTION_SIZE =
sizeof(STREAMING_HT_MIN_REDUCTION) / sizeof(STREAMING_HT_MIN_REDUCTION[0]);
StreamingAggLocalState::StreamingAggLocalState(RuntimeState* state, OperatorXBase* parent)
: Base(state, parent),
_agg_arena_pool(std::make_unique<vectorized::Arena>()),
_agg_data(std::make_unique<vectorized::AggregatedDataVariants>()),
_agg_profile_arena(std::make_unique<vectorized::Arena>()),
_child_block(vectorized::Block::create_unique()),
_pre_aggregated_block(vectorized::Block::create_unique()) {}
Status StreamingAggLocalState::init(RuntimeState* state, LocalStateInfo& info) {
RETURN_IF_ERROR(Base::init(state, info));
SCOPED_TIMER(Base::exec_time_counter());
SCOPED_TIMER(Base::_init_timer);
_hash_table_memory_usage = ADD_CHILD_COUNTER_WITH_LEVEL(Base::profile(), "HashTable",
TUnit::BYTES, "MemoryUsage", 1);
_serialize_key_arena_memory_usage = Base::profile()->AddHighWaterMarkCounter(
"SerializeKeyArena", TUnit::BYTES, "MemoryUsage", 1);
_build_timer = ADD_TIMER(Base::profile(), "BuildTime");
_build_table_convert_timer = ADD_TIMER(Base::profile(), "BuildConvertToPartitionedTime");
_serialize_key_timer = ADD_TIMER(Base::profile(), "SerializeKeyTime");
_exec_timer = ADD_TIMER(Base::profile(), "ExecTime");
_merge_timer = ADD_TIMER(Base::profile(), "MergeTime");
_expr_timer = ADD_TIMER(Base::profile(), "ExprTime");
_serialize_data_timer = ADD_TIMER(Base::profile(), "SerializeDataTime");
_deserialize_data_timer = ADD_TIMER(Base::profile(), "DeserializeAndMergeTime");
_hash_table_compute_timer = ADD_TIMER(Base::profile(), "HashTableComputeTime");
_hash_table_emplace_timer = ADD_TIMER(Base::profile(), "HashTableEmplaceTime");
_hash_table_input_counter = ADD_COUNTER(Base::profile(), "HashTableInputCount", TUnit::UNIT);
_max_row_size_counter = ADD_COUNTER(Base::profile(), "MaxRowSizeInBytes", TUnit::UNIT);
_hash_table_size_counter = ADD_COUNTER(profile(), "HashTableSize", TUnit::UNIT);
_queue_byte_size_counter = ADD_COUNTER(profile(), "MaxSizeInBlockQueue", TUnit::BYTES);
_queue_size_counter = ADD_COUNTER(profile(), "MaxSizeOfBlockQueue", TUnit::UNIT);
_streaming_agg_timer = ADD_TIMER(profile(), "StreamingAggTime");
_build_timer = ADD_TIMER(profile(), "BuildTime");
_expr_timer = ADD_TIMER(Base::profile(), "ExprTime");
_get_results_timer = ADD_TIMER(profile(), "GetResultsTime");
_serialize_result_timer = ADD_TIMER(profile(), "SerializeResultTime");
_hash_table_iterate_timer = ADD_TIMER(profile(), "HashTableIterateTime");
_insert_keys_to_column_timer = ADD_TIMER(profile(), "InsertKeysToColumnTime");
return Status::OK();
}
Status StreamingAggLocalState::open(RuntimeState* state) {
SCOPED_TIMER(Base::exec_time_counter());
SCOPED_TIMER(Base::_open_timer);
RETURN_IF_ERROR(Base::open(state));
auto& p = Base::_parent->template cast<StreamingAggOperatorX>();
for (auto& evaluator : p._aggregate_evaluators) {
_aggregate_evaluators.push_back(evaluator->clone(state, p._pool));
}
_probe_expr_ctxs.resize(p._probe_expr_ctxs.size());
for (size_t i = 0; i < _probe_expr_ctxs.size(); i++) {
RETURN_IF_ERROR(p._probe_expr_ctxs[i]->clone(state, _probe_expr_ctxs[i]));
}
for (auto& evaluator : _aggregate_evaluators) {
evaluator->set_timer(_merge_timer, _expr_timer);
}
if (_probe_expr_ctxs.empty()) {
_agg_data->without_key = reinterpret_cast<vectorized::AggregateDataPtr>(
_agg_profile_arena->alloc(p._total_size_of_aggregate_states));
if (p._is_merge) {
if (p._needs_finalize) {
_executor = std::make_unique<Executor<true, true, true>>();
} else {
_executor = std::make_unique<Executor<true, true, false>>();
}
} else {
if (p._needs_finalize) {
_executor = std::make_unique<Executor<true, false, true>>();
} else {
_executor = std::make_unique<Executor<true, false, false>>();
}
}
} else {
_init_hash_method(_probe_expr_ctxs);
std::visit(
[&](auto&& agg_method) {
using HashTableType = std::decay_t<decltype(agg_method)>;
using KeyType = typename HashTableType::Key;
/// some aggregate functions (like AVG for decimal) have align issues.
_aggregate_data_container.reset(new vectorized::AggregateDataContainer(
sizeof(KeyType),
((p._total_size_of_aggregate_states + p._align_aggregate_states - 1) /
p._align_aggregate_states) *
p._align_aggregate_states));
},
_agg_data->method_variant);
if (p._is_merge) {
if (p._needs_finalize) {
_executor = std::make_unique<Executor<false, true, true>>();
} else {
_executor = std::make_unique<Executor<false, true, false>>();
}
} else {
if (p._needs_finalize) {
_executor = std::make_unique<Executor<false, false, true>>();
} else {
_executor = std::make_unique<Executor<false, false, false>>();
}
}
_should_limit_output = p._limit != -1 && // has limit
(!p._have_conjuncts) && // no having conjunct
p._needs_finalize; // agg's finalize step
}
_opened = true;
return Status::OK();
}
void StreamingAggLocalState::_make_nullable_output_key(vectorized::Block* block) {
if (block->rows() != 0) {
for (auto cid : Base::_parent->cast<StreamingAggOperatorX>()._make_nullable_keys) {
block->get_by_position(cid).column = make_nullable(block->get_by_position(cid).column);
block->get_by_position(cid).type = make_nullable(block->get_by_position(cid).type);
}
}
}
Status StreamingAggLocalState::_execute_without_key(vectorized::Block* block) {
DCHECK(_agg_data->without_key != nullptr);
SCOPED_TIMER(_build_timer);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_single_add(
block,
_agg_data->without_key + Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_agg_arena_pool.get()));
}
return Status::OK();
}
template <bool limit, bool for_spill>
Status StreamingAggLocalState::_merge_with_serialized_key_helper(vectorized::Block* block) {
SCOPED_TIMER(_merge_timer);
size_t key_size = _probe_expr_ctxs.size();
vectorized::ColumnRawPtrs key_columns(key_size);
for (size_t i = 0; i < key_size; ++i) {
if constexpr (for_spill) {
key_columns[i] = block->get_by_position(i).column.get();
} else {
int result_column_id = -1;
RETURN_IF_ERROR(_probe_expr_ctxs[i]->execute(block, &result_column_id));
block->replace_by_position_if_const(result_column_id);
key_columns[i] = block->get_by_position(result_column_id).column.get();
}
}
int rows = block->rows();
if (_places.size() < rows) {
_places.resize(rows);
}
if constexpr (limit) {
_find_in_hash_table(_places.data(), key_columns, rows);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
if (_aggregate_evaluators[i]->is_merge()) {
int col_id = _get_slot_column_id(_aggregate_evaluators[i]);
auto column = block->get_by_position(col_id).column;
if (column->is_nullable()) {
column = ((vectorized::ColumnNullable*)column.get())->get_nested_column_ptr();
}
size_t buffer_size = _aggregate_evaluators[i]->function()->size_of_data() * rows;
if (_deserialize_buffer.size() < buffer_size) {
_deserialize_buffer.resize(buffer_size);
}
{
SCOPED_TIMER(_deserialize_data_timer);
_aggregate_evaluators[i]->function()->deserialize_and_merge_vec_selected(
_places.data(),
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_deserialize_buffer.data(), column.get(), _agg_arena_pool.get(), rows);
}
} else {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_batch_add_selected(
block,
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_places.data(), _agg_arena_pool.get()));
}
}
} else {
_emplace_into_hash_table(_places.data(), key_columns, rows);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
if (_aggregate_evaluators[i]->is_merge() || for_spill) {
int col_id = 0;
if constexpr (for_spill) {
col_id = _probe_expr_ctxs.size() + i;
} else {
col_id = _get_slot_column_id(_aggregate_evaluators[i]);
}
auto column = block->get_by_position(col_id).column;
if (column->is_nullable()) {
column = ((vectorized::ColumnNullable*)column.get())->get_nested_column_ptr();
}
size_t buffer_size = _aggregate_evaluators[i]->function()->size_of_data() * rows;
if (_deserialize_buffer.size() < buffer_size) {
_deserialize_buffer.resize(buffer_size);
}
{
SCOPED_TIMER(_deserialize_data_timer);
_aggregate_evaluators[i]->function()->deserialize_and_merge_vec(
_places.data(),
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_deserialize_buffer.data(), column.get(), _agg_arena_pool.get(), rows);
}
} else {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_batch_add(
block,
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_places.data(), _agg_arena_pool.get()));
}
}
if (_should_limit_output) {
_reach_limit = _get_hash_table_size() >=
Base::_parent->template cast<StreamingAggOperatorX>()._limit;
}
}
return Status::OK();
}
size_t StreamingAggLocalState::_get_hash_table_size() {
return std::visit([&](auto&& agg_method) { return agg_method.hash_table->size(); },
_agg_data->method_variant);
}
Status StreamingAggLocalState::_merge_without_key(vectorized::Block* block) {
SCOPED_TIMER(_merge_timer);
DCHECK(_agg_data->without_key != nullptr);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
if (_aggregate_evaluators[i]->is_merge()) {
int col_id = _get_slot_column_id(_aggregate_evaluators[i]);
auto column = block->get_by_position(col_id).column;
if (column->is_nullable()) {
column = ((vectorized::ColumnNullable*)column.get())->get_nested_column_ptr();
}
SCOPED_TIMER(_deserialize_data_timer);
_aggregate_evaluators[i]->function()->deserialize_and_merge_from_column(
_agg_data->without_key + Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
*column, _agg_arena_pool.get());
} else {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_single_add(
block,
_agg_data->without_key + Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_agg_arena_pool.get()));
}
}
return Status::OK();
}
void StreamingAggLocalState::_update_memusage_without_key() {
auto arena_memory_usage = _agg_arena_pool->size() - _mem_usage_record.used_in_arena;
Base::_mem_tracker->consume(arena_memory_usage);
_serialize_key_arena_memory_usage->add(arena_memory_usage);
_mem_usage_record.used_in_arena = _agg_arena_pool->size();
}
Status StreamingAggLocalState::_execute_with_serialized_key(vectorized::Block* block) {
if (_reach_limit) {
return _execute_with_serialized_key_helper<true>(block);
} else {
return _execute_with_serialized_key_helper<false>(block);
}
}
void StreamingAggLocalState::_update_memusage_with_serialized_key() {
std::visit(
[&](auto&& agg_method) -> void {
auto& data = *agg_method.hash_table;
auto arena_memory_usage = _agg_arena_pool->size() +
_aggregate_data_container->memory_usage() -
_mem_usage_record.used_in_arena;
Base::_mem_tracker->consume(arena_memory_usage);
Base::_mem_tracker->consume(data.get_buffer_size_in_bytes() -
_mem_usage_record.used_in_state);
_serialize_key_arena_memory_usage->add(arena_memory_usage);
COUNTER_UPDATE(_hash_table_memory_usage,
data.get_buffer_size_in_bytes() - _mem_usage_record.used_in_state);
_mem_usage_record.used_in_state = data.get_buffer_size_in_bytes();
_mem_usage_record.used_in_arena =
_agg_arena_pool->size() + _aggregate_data_container->memory_usage();
},
_agg_data->method_variant);
}
template <bool limit>
Status StreamingAggLocalState::_execute_with_serialized_key_helper(vectorized::Block* block) {
SCOPED_TIMER(_build_timer);
DCHECK(!_probe_expr_ctxs.empty());
size_t key_size = _probe_expr_ctxs.size();
vectorized::ColumnRawPtrs key_columns(key_size);
{
SCOPED_TIMER(_expr_timer);
for (size_t i = 0; i < key_size; ++i) {
int result_column_id = -1;
RETURN_IF_ERROR(_probe_expr_ctxs[i]->execute(block, &result_column_id));
block->get_by_position(result_column_id).column =
block->get_by_position(result_column_id)
.column->convert_to_full_column_if_const();
key_columns[i] = block->get_by_position(result_column_id).column.get();
}
}
int rows = block->rows();
if (_places.size() < rows) {
_places.resize(rows);
}
if constexpr (limit) {
_find_in_hash_table(_places.data(), key_columns, rows);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_batch_add_selected(
block,
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_places.data(), _agg_arena_pool.get()));
}
} else {
_emplace_into_hash_table(_places.data(), key_columns, rows);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_batch_add(
block,
Base::_parent->template cast<StreamingAggOperatorX>()
._offsets_of_aggregate_states[i],
_places.data(), _agg_arena_pool.get()));
}
if (_should_limit_output) {
_reach_limit = _get_hash_table_size() >=
Base::_parent->template cast<StreamingAggOperatorX>()._limit;
if (_reach_limit &&
Base::_parent->template cast<StreamingAggOperatorX>()._can_short_circuit) {
Base::_dependency->set_ready_to_read();
return Status::Error<ErrorCode::END_OF_FILE>("");
}
}
}
return Status::OK();
}
// We should call this function only at 1st phase.
// 1st phase: is_merge=true, only have one SlotRef.
// 2nd phase: is_merge=false, maybe have multiple exprs.
int StreamingAggLocalState::_get_slot_column_id(const vectorized::AggFnEvaluator* evaluator) {
auto ctxs = evaluator->input_exprs_ctxs();
CHECK(ctxs.size() == 1 && ctxs[0]->root()->is_slot_ref())
<< "input_exprs_ctxs is invalid, input_exprs_ctx[0]="
<< ctxs[0]->root()->debug_string();
return ((vectorized::VSlotRef*)ctxs[0]->root().get())->column_id();
}
void StreamingAggLocalState::_find_in_hash_table(vectorized::AggregateDataPtr* places,
vectorized::ColumnRawPtrs& key_columns,
size_t num_rows) {
std::visit(
[&](auto&& agg_method) -> void {
using HashMethodType = std::decay_t<decltype(agg_method)>;
using AggState = typename HashMethodType::State;
AggState state(key_columns);
agg_method.init_serialized_keys(key_columns, num_rows);
/// For all rows.
for (size_t i = 0; i < num_rows; ++i) {
auto find_result = agg_method.find(state, i);
if (find_result.is_found()) {
places[i] = find_result.get_mapped();
} else {
places[i] = nullptr;
}
}
},
_agg_data->method_variant);
}
Status StreamingAggLocalState::_merge_with_serialized_key(vectorized::Block* block) {
if (_reach_limit) {
return _merge_with_serialized_key_helper<true, false>(block);
} else {
return _merge_with_serialized_key_helper<false, false>(block);
}
}
void StreamingAggLocalState::_init_hash_method(const vectorized::VExprContextSPtrs& probe_exprs) {
DCHECK(probe_exprs.size() >= 1);
using Type = vectorized::AggregatedDataVariants::Type;
Type t(Type::serialized);
if (probe_exprs.size() == 1) {
auto is_nullable = probe_exprs[0]->root()->is_nullable();
PrimitiveType type = probe_exprs[0]->root()->result_type();
switch (type) {
case TYPE_TINYINT:
case TYPE_BOOLEAN:
case TYPE_SMALLINT:
case TYPE_INT:
case TYPE_FLOAT:
case TYPE_DATEV2:
case TYPE_BIGINT:
case TYPE_DOUBLE:
case TYPE_DATE:
case TYPE_DATETIME:
case TYPE_DATETIMEV2:
case TYPE_LARGEINT:
case TYPE_DECIMALV2:
case TYPE_DECIMAL32:
case TYPE_DECIMAL64:
case TYPE_DECIMAL128I: {
size_t size = get_primitive_type_size(type);
if (size == 1) {
t = Type::int8_key;
} else if (size == 2) {
t = Type::int16_key;
} else if (size == 4) {
t = Type::int32_key;
} else if (size == 8) {
t = Type::int64_key;
} else if (size == 16) {
t = Type::int128_key;
} else {
throw Exception(ErrorCode::INTERNAL_ERROR,
"meet invalid type size, size={}, type={}", size,
type_to_string(type));
}
break;
}
case TYPE_CHAR:
case TYPE_VARCHAR:
case TYPE_STRING: {
t = Type::string_key;
break;
}
default:
t = Type::serialized;
}
_agg_data->init(
get_hash_key_type_with_phase(
t, !Base::_parent->template cast<StreamingAggOperatorX>()._is_first_phase),
is_nullable);
} else {
if (!try_get_hash_map_context_fixed<PHNormalHashMap, HashCRC32,
vectorized::AggregateDataPtr>(_agg_data->method_variant,
probe_exprs)) {
_agg_data->init(Type::serialized);
}
}
}
Status StreamingAggLocalState::do_pre_agg(vectorized::Block* input_block,
vectorized::Block* output_block) {
RETURN_IF_ERROR(_pre_agg_with_serialized_key(input_block, output_block));
// pre stream agg need use _num_row_return to decide whether to do pre stream agg
_cur_num_rows_returned += output_block->rows();
_make_nullable_output_key(output_block);
// COUNTER_SET(_rows_returned_counter, _num_rows_returned);
_executor->update_memusage(this);
return Status::OK();
}
bool StreamingAggLocalState::_should_expand_preagg_hash_tables() {
if (!_should_expand_hash_table) {
return false;
}
return std::visit(
[&](auto&& agg_method) -> bool {
auto& hash_tbl = *agg_method.hash_table;
auto [ht_mem, ht_rows] =
std::pair {hash_tbl.get_buffer_size_in_bytes(), hash_tbl.size()};
// Need some rows in tables to have valid statistics.
if (ht_rows == 0) {
return true;
}
// Find the appropriate reduction factor in our table for the current hash table sizes.
int cache_level = 0;
while (cache_level + 1 < STREAMING_HT_MIN_REDUCTION_SIZE &&
ht_mem >= STREAMING_HT_MIN_REDUCTION[cache_level + 1].min_ht_mem) {
++cache_level;
}
// Compare the number of rows in the hash table with the number of input rows that
// were aggregated into it. Exclude passed through rows from this calculation since
// they were not in hash tables.
const int64_t input_rows = _input_num_rows;
const int64_t aggregated_input_rows = input_rows - _cur_num_rows_returned;
// TODO chenhao
// const int64_t expected_input_rows = estimated_input_cardinality_ - num_rows_returned_;
double current_reduction = static_cast<double>(aggregated_input_rows) / ht_rows;
// TODO: workaround for IMPALA-2490: subplan node rows_returned counter may be
// inaccurate, which could lead to a divide by zero below.
if (aggregated_input_rows <= 0) {
return true;
}
// Extrapolate the current reduction factor (r) using the formula
// R = 1 + (N / n) * (r - 1), where R is the reduction factor over the full input data
// set, N is the number of input rows, excluding passed-through rows, and n is the
// number of rows inserted or merged into the hash tables. This is a very rough
// approximation but is good enough to be useful.
// TODO: consider collecting more statistics to better estimate reduction.
// double estimated_reduction = aggregated_input_rows >= expected_input_rows
// ? current_reduction
// : 1 + (expected_input_rows / aggregated_input_rows) * (current_reduction - 1);
double min_reduction =
STREAMING_HT_MIN_REDUCTION[cache_level].streaming_ht_min_reduction;
// COUNTER_SET(preagg_estimated_reduction_, estimated_reduction);
// COUNTER_SET(preagg_streaming_ht_min_reduction_, min_reduction);
// return estimated_reduction > min_reduction;
_should_expand_hash_table = current_reduction > min_reduction;
return _should_expand_hash_table;
},
_agg_data->method_variant);
}
size_t StreamingAggLocalState::_memory_usage() const {
size_t usage = 0;
if (_agg_arena_pool) {
usage += _agg_arena_pool->size();
}
if (_aggregate_data_container) {
usage += _aggregate_data_container->memory_usage();
}
std::visit(
[&](auto&& agg_method) { usage += agg_method.hash_table->get_buffer_size_in_bytes(); },
_agg_data->method_variant);
return usage;
}
Status StreamingAggLocalState::_pre_agg_with_serialized_key(doris::vectorized::Block* in_block,
doris::vectorized::Block* out_block) {
SCOPED_TIMER(_build_timer);
DCHECK(!_probe_expr_ctxs.empty());
auto& p = Base::_parent->template cast<StreamingAggOperatorX>();
size_t key_size = _probe_expr_ctxs.size();
vectorized::ColumnRawPtrs key_columns(key_size);
{
SCOPED_TIMER(_expr_timer);
for (size_t i = 0; i < key_size; ++i) {
int result_column_id = -1;
RETURN_IF_ERROR(_probe_expr_ctxs[i]->execute(in_block, &result_column_id));
in_block->get_by_position(result_column_id).column =
in_block->get_by_position(result_column_id)
.column->convert_to_full_column_if_const();
key_columns[i] = in_block->get_by_position(result_column_id).column.get();
}
}
int rows = in_block->rows();
_places.resize(rows);
// Stop expanding hash tables if we're not reducing the input sufficiently. As our
// hash tables expand out of each level of cache hierarchy, every hash table lookup
// will take longer. We also may not be able to expand hash tables because of memory
// pressure. In either case we should always use the remaining space in the hash table
// to avoid wasting memory.
// But for fixed hash map, it never need to expand
bool ret_flag = false;
const auto spill_streaming_agg_mem_limit =
_parent->cast<StreamingAggOperatorX>()._spill_streaming_agg_mem_limit;
const bool used_too_much_memory =
spill_streaming_agg_mem_limit > 0 && _memory_usage() > spill_streaming_agg_mem_limit;
RETURN_IF_ERROR(std::visit(
[&](auto&& agg_method) -> Status {
auto& hash_tbl = *agg_method.hash_table;
/// If too much memory is used during the pre-aggregation stage,
/// it is better to output the data directly without performing further aggregation.
// do not try to do agg, just init and serialize directly return the out_block
if (used_too_much_memory || (hash_tbl.add_elem_size_overflow(rows) &&
!_should_expand_preagg_hash_tables())) {
SCOPED_TIMER(_streaming_agg_timer);
ret_flag = true;
// will serialize value data to string column.
// non-nullable column(id in `_make_nullable_keys`)
// will be converted to nullable.
bool mem_reuse = p._make_nullable_keys.empty() && out_block->mem_reuse();
std::vector<vectorized::DataTypePtr> data_types;
vectorized::MutableColumns value_columns;
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
auto data_type =
_aggregate_evaluators[i]->function()->get_serialized_type();
if (mem_reuse) {
value_columns.emplace_back(
std::move(*out_block->get_by_position(i + key_size).column)
.mutate());
} else {
// slot type of value it should always be string type
value_columns.emplace_back(_aggregate_evaluators[i]
->function()
->create_serialize_column());
}
data_types.emplace_back(data_type);
}
for (int i = 0; i != _aggregate_evaluators.size(); ++i) {
SCOPED_TIMER(_serialize_data_timer);
RETURN_IF_ERROR(_aggregate_evaluators[i]->streaming_agg_serialize_to_column(
in_block, value_columns[i], rows, _agg_arena_pool.get()));
}
if (!mem_reuse) {
vectorized::ColumnsWithTypeAndName columns_with_schema;
for (int i = 0; i < key_size; ++i) {
columns_with_schema.emplace_back(
key_columns[i]->clone_resized(rows),
_probe_expr_ctxs[i]->root()->data_type(),
_probe_expr_ctxs[i]->root()->expr_name());
}
for (int i = 0; i < value_columns.size(); ++i) {
columns_with_schema.emplace_back(std::move(value_columns[i]),
data_types[i], "");
}
out_block->swap(vectorized::Block(columns_with_schema));
} else {
for (int i = 0; i < key_size; ++i) {
std::move(*out_block->get_by_position(i).column)
.mutate()
->insert_range_from(*key_columns[i], 0, rows);
}
}
}
return Status::OK();
},
_agg_data->method_variant));
if (!ret_flag) {
RETURN_IF_CATCH_EXCEPTION(_emplace_into_hash_table(_places.data(), key_columns, rows));
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
RETURN_IF_ERROR(_aggregate_evaluators[i]->execute_batch_add(
in_block, p._offsets_of_aggregate_states[i], _places.data(),
_agg_arena_pool.get(), _should_expand_hash_table));
}
}
return Status::OK();
}
Status StreamingAggLocalState::_create_agg_status(vectorized::AggregateDataPtr data) {
auto& p = Base::_parent->template cast<StreamingAggOperatorX>();
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
try {
_aggregate_evaluators[i]->create(data + p._offsets_of_aggregate_states[i]);
} catch (...) {
for (int j = 0; j < i; ++j) {
_aggregate_evaluators[j]->destroy(data + p._offsets_of_aggregate_states[j]);
}
throw;
}
}
return Status::OK();
}
Status StreamingAggLocalState::_get_with_serialized_key_result(RuntimeState* state,
vectorized::Block* block,
bool* eos) {
auto& p = _parent->cast<StreamingAggOperatorX>();
// non-nullable column(id in `_make_nullable_keys`) will be converted to nullable.
bool mem_reuse = p._make_nullable_keys.empty() && block->mem_reuse();
auto columns_with_schema =
vectorized::VectorizedUtils::create_columns_with_type_and_name(p._row_descriptor);
int key_size = _probe_expr_ctxs.size();
vectorized::MutableColumns key_columns;
for (int i = 0; i < key_size; ++i) {
if (!mem_reuse) {
key_columns.emplace_back(columns_with_schema[i].type->create_column());
} else {
key_columns.emplace_back(std::move(*block->get_by_position(i).column).mutate());
}
}
vectorized::MutableColumns value_columns;
for (int i = key_size; i < columns_with_schema.size(); ++i) {
if (!mem_reuse) {
value_columns.emplace_back(columns_with_schema[i].type->create_column());
} else {
value_columns.emplace_back(std::move(*block->get_by_position(i).column).mutate());
}
}
SCOPED_TIMER(_get_results_timer);
std::visit(
[&](auto&& agg_method) -> void {
auto& data = *agg_method.hash_table;
agg_method.init_iterator();
const auto size = std::min(data.size(), size_t(state->batch_size()));
using KeyType = std::decay_t<decltype(agg_method.iterator->get_first())>;
std::vector<KeyType> keys(size);
if (_values.size() < size) {
_values.resize(size);
}
size_t num_rows = 0;
_aggregate_data_container->init_once();
auto& iter = _aggregate_data_container->iterator;
{
SCOPED_TIMER(_hash_table_iterate_timer);
while (iter != _aggregate_data_container->end() &&
num_rows < state->batch_size()) {
keys[num_rows] = iter.template get_key<KeyType>();
_values[num_rows] = iter.get_aggregate_data();
++iter;
++num_rows;
}
}
{
SCOPED_TIMER(_insert_keys_to_column_timer);
agg_method.insert_keys_into_columns(keys, key_columns, num_rows);
}
for (size_t i = 0; i < _aggregate_evaluators.size(); ++i) {
_aggregate_evaluators[i]->insert_result_info_vec(
_values, p._offsets_of_aggregate_states[i], value_columns[i].get(),
num_rows);
}
if (iter == _aggregate_data_container->end()) {
if (agg_method.hash_table->has_null_key_data()) {
// only one key of group by support wrap null key
// here need additional processing logic on the null key / value
DCHECK(key_columns.size() == 1);
DCHECK(key_columns[0]->is_nullable());
if (key_columns[0]->size() < state->batch_size()) {
key_columns[0]->insert_data(nullptr, 0);
auto mapped = agg_method.hash_table->template get_null_key_data<
vectorized::AggregateDataPtr>();
for (size_t i = 0; i < _aggregate_evaluators.size(); ++i)
_aggregate_evaluators[i]->insert_result_info(
mapped + p._offsets_of_aggregate_states[i],
value_columns[i].get());
*eos = true;
}
} else {
*eos = true;
}
}
},
_agg_data->method_variant);
if (!mem_reuse) {
*block = columns_with_schema;
vectorized::MutableColumns columns(block->columns());
for (int i = 0; i < block->columns(); ++i) {
if (i < key_size) {
columns[i] = std::move(key_columns[i]);
} else {
columns[i] = std::move(value_columns[i - key_size]);
}
}
block->set_columns(std::move(columns));
}
return Status::OK();
}
Status StreamingAggLocalState::_serialize_without_key(RuntimeState* state, vectorized::Block* block,
bool* eos) {
// 1. `child(0)->rows_returned() == 0` mean not data from child
// in level two aggregation node should return NULL result
// level one aggregation node set `eos = true` return directly
SCOPED_TIMER(_serialize_result_timer);
if (UNLIKELY(_input_num_rows == 0)) {
*eos = true;
return Status::OK();
}
block->clear();
DCHECK(_agg_data->without_key != nullptr);
int agg_size = _aggregate_evaluators.size();
vectorized::MutableColumns value_columns(agg_size);
std::vector<vectorized::DataTypePtr> data_types(agg_size);
// will serialize data to string column
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
data_types[i] = _aggregate_evaluators[i]->function()->get_serialized_type();
value_columns[i] = _aggregate_evaluators[i]->function()->create_serialize_column();
}
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
_aggregate_evaluators[i]->function()->serialize_without_key_to_column(
_agg_data->without_key +
_parent->cast<StreamingAggOperatorX>()._offsets_of_aggregate_states[i],
*value_columns[i]);
}
{
vectorized::ColumnsWithTypeAndName data_with_schema;
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
vectorized::ColumnWithTypeAndName column_with_schema = {nullptr, data_types[i], ""};
data_with_schema.push_back(std::move(column_with_schema));
}
*block = vectorized::Block(data_with_schema);
}
block->set_columns(std::move(value_columns));
*eos = true;
return Status::OK();
}
Status StreamingAggLocalState::_serialize_with_serialized_key_result(RuntimeState* state,
vectorized::Block* block,
bool* eos) {
SCOPED_TIMER(_serialize_result_timer);
auto& p = _parent->cast<StreamingAggOperatorX>();
int key_size = _probe_expr_ctxs.size();
int agg_size = _aggregate_evaluators.size();
vectorized::MutableColumns value_columns(agg_size);
vectorized::DataTypes value_data_types(agg_size);
// non-nullable column(id in `_make_nullable_keys`) will be converted to nullable.
bool mem_reuse = p._make_nullable_keys.empty() && block->mem_reuse();
vectorized::MutableColumns key_columns;
for (int i = 0; i < key_size; ++i) {
if (mem_reuse) {
key_columns.emplace_back(std::move(*block->get_by_position(i).column).mutate());
} else {
key_columns.emplace_back(_probe_expr_ctxs[i]->root()->data_type()->create_column());
}
}
SCOPED_TIMER(_get_results_timer);
std::visit(
[&](auto&& agg_method) -> void {
agg_method.init_iterator();
auto& data = *agg_method.hash_table;
const auto size = std::min(data.size(), size_t(state->batch_size()));
using KeyType = std::decay_t<decltype(agg_method.iterator->get_first())>;
std::vector<KeyType> keys(size);
if (_values.size() < size + 1) {
_values.resize(size + 1);
}
size_t num_rows = 0;
_aggregate_data_container->init_once();
auto& iter = _aggregate_data_container->iterator;
{
SCOPED_TIMER(_hash_table_iterate_timer);
while (iter != _aggregate_data_container->end() &&
num_rows < state->batch_size()) {
keys[num_rows] = iter.template get_key<KeyType>();
_values[num_rows] = iter.get_aggregate_data();
++iter;
++num_rows;
}
}
{
SCOPED_TIMER(_insert_keys_to_column_timer);
agg_method.insert_keys_into_columns(keys, key_columns, num_rows);
}
if (iter == _aggregate_data_container->end()) {
if (agg_method.hash_table->has_null_key_data()) {
// only one key of group by support wrap null key
// here need additional processing logic on the null key / value
DCHECK(key_columns.size() == 1);
DCHECK(key_columns[0]->is_nullable());
if (agg_method.hash_table->has_null_key_data()) {
key_columns[0]->insert_data(nullptr, 0);
_values[num_rows] = agg_method.hash_table->template get_null_key_data<
vectorized::AggregateDataPtr>();
++num_rows;
*eos = true;
}
} else {
*eos = true;
}
}
{
SCOPED_TIMER(_serialize_data_timer);
for (size_t i = 0; i < _aggregate_evaluators.size(); ++i) {
value_data_types[i] =
_aggregate_evaluators[i]->function()->get_serialized_type();
if (mem_reuse) {
value_columns[i] =
std::move(*block->get_by_position(i + key_size).column)
.mutate();
} else {
value_columns[i] =
_aggregate_evaluators[i]->function()->create_serialize_column();
}
_aggregate_evaluators[i]->function()->serialize_to_column(
_values, p._offsets_of_aggregate_states[i], value_columns[i],
num_rows);
}
}
},
_agg_data->method_variant);
if (!mem_reuse) {
vectorized::ColumnsWithTypeAndName columns_with_schema;
for (int i = 0; i < key_size; ++i) {
columns_with_schema.emplace_back(std::move(key_columns[i]),
_probe_expr_ctxs[i]->root()->data_type(),
_probe_expr_ctxs[i]->root()->expr_name());
}
for (int i = 0; i < agg_size; ++i) {
columns_with_schema.emplace_back(std::move(value_columns[i]), value_data_types[i], "");
}
*block = vectorized::Block(columns_with_schema);
}
return Status::OK();
}
void StreamingAggLocalState::make_nullable_output_key(vectorized::Block* block) {
if (block->rows() != 0) {
for (auto cid : _parent->cast<StreamingAggOperatorX>()._make_nullable_keys) {
block->get_by_position(cid).column = make_nullable(block->get_by_position(cid).column);
block->get_by_position(cid).type = make_nullable(block->get_by_position(cid).type);
}
}
}
Status StreamingAggLocalState::_get_without_key_result(RuntimeState* state,
vectorized::Block* block, bool* eos) {
DCHECK(_agg_data->without_key != nullptr);
block->clear();
auto& p = _parent->cast<StreamingAggOperatorX>();
*block = vectorized::VectorizedUtils::create_empty_columnswithtypename(p._row_descriptor);
int agg_size = _aggregate_evaluators.size();
vectorized::MutableColumns columns(agg_size);
std::vector<vectorized::DataTypePtr> data_types(agg_size);
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
data_types[i] = _aggregate_evaluators[i]->function()->get_return_type();
columns[i] = data_types[i]->create_column();
}
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
auto column = columns[i].get();
_aggregate_evaluators[i]->insert_result_info(
_agg_data->without_key + p._offsets_of_aggregate_states[i], column);
}
const auto& block_schema = block->get_columns_with_type_and_name();
DCHECK_EQ(block_schema.size(), columns.size());
for (int i = 0; i < block_schema.size(); ++i) {
const auto column_type = block_schema[i].type;
if (!column_type->equals(*data_types[i])) {
if (!vectorized::is_array(remove_nullable(column_type))) {
if (!column_type->is_nullable() || data_types[i]->is_nullable() ||
!remove_nullable(column_type)->equals(*data_types[i])) {
return Status::InternalError(
"node id = {}, column_type not match data_types, column_type={}, "
"data_types={}",
_parent->node_id(), column_type->get_name(), data_types[i]->get_name());
}
}
if (column_type->is_nullable() && !data_types[i]->is_nullable()) {
vectorized::ColumnPtr ptr = std::move(columns[i]);
// unless `count`, other aggregate function dispose empty set should be null
// so here check the children row return
ptr = make_nullable(ptr, _input_num_rows == 0);
columns[i] = ptr->assume_mutable();
}
}
}
block->set_columns(std::move(columns));
*eos = true;
return Status::OK();
}
void StreamingAggLocalState::_destroy_agg_status(vectorized::AggregateDataPtr data) {
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
_aggregate_evaluators[i]->function()->destroy(
data + _parent->cast<StreamingAggOperatorX>()._offsets_of_aggregate_states[i]);
}
}
void StreamingAggLocalState::_emplace_into_hash_table(vectorized::AggregateDataPtr* places,
vectorized::ColumnRawPtrs& key_columns,
const size_t num_rows) {
std::visit(
[&](auto&& agg_method) -> void {
SCOPED_TIMER(_hash_table_compute_timer);
using HashMethodType = std::decay_t<decltype(agg_method)>;
using AggState = typename HashMethodType::State;
AggState state(key_columns);
agg_method.init_serialized_keys(key_columns, num_rows);
auto creator = [this](const auto& ctor, auto& key, auto& origin) {
HashMethodType::try_presis_key_and_origin(key, origin, *_agg_arena_pool);
auto mapped = _aggregate_data_container->append_data(origin);
auto st = _create_agg_status(mapped);
if (!st) {
throw Exception(st.code(), st.to_string());
}
ctor(key, mapped);
};
auto creator_for_null_key = [&](auto& mapped) {
mapped = _agg_arena_pool->aligned_alloc(
Base::_parent->template cast<StreamingAggOperatorX>()
._total_size_of_aggregate_states,
Base::_parent->template cast<StreamingAggOperatorX>()
._align_aggregate_states);
auto st = _create_agg_status(mapped);
if (!st) {
throw Exception(st.code(), st.to_string());
}
};
SCOPED_TIMER(_hash_table_emplace_timer);
for (size_t i = 0; i < num_rows; ++i) {
places[i] = agg_method.lazy_emplace(state, i, creator, creator_for_null_key);
}
COUNTER_UPDATE(_hash_table_input_counter, num_rows);
},
_agg_data->method_variant);
}
StreamingAggOperatorX::StreamingAggOperatorX(ObjectPool* pool, int operator_id,
const TPlanNode& tnode, const DescriptorTbl& descs)
: StatefulOperatorX<StreamingAggLocalState>(pool, tnode, operator_id, descs),
_intermediate_tuple_id(tnode.agg_node.intermediate_tuple_id),
_intermediate_tuple_desc(nullptr),
_output_tuple_id(tnode.agg_node.output_tuple_id),
_output_tuple_desc(nullptr),
_needs_finalize(tnode.agg_node.need_finalize),
_is_merge(false),
_is_first_phase(tnode.agg_node.__isset.is_first_phase && tnode.agg_node.is_first_phase),
_have_conjuncts(tnode.__isset.vconjunct && !tnode.vconjunct.nodes.empty()),
_agg_fn_output_row_descriptor(descs, tnode.row_tuples, tnode.nullable_tuples) {}
Status StreamingAggOperatorX::init(const TPlanNode& tnode, RuntimeState* state) {
RETURN_IF_ERROR(StatefulOperatorX<StreamingAggLocalState>::init(tnode, state));
// ignore return status for now , so we need to introduce ExecNode::init()
RETURN_IF_ERROR(
vectorized::VExpr::create_expr_trees(tnode.agg_node.grouping_exprs, _probe_expr_ctxs));
// init aggregate functions
_aggregate_evaluators.reserve(tnode.agg_node.aggregate_functions.size());
// In case of : `select * from (select GoodEvent from hits union select CounterID from hits) as h limit 10;`
// only union with limit: we can short circuit query the pipeline exec engine.
_can_short_circuit =
tnode.agg_node.aggregate_functions.empty() && state->enable_pipeline_exec();
TSortInfo dummy;
for (int i = 0; i < tnode.agg_node.aggregate_functions.size(); ++i) {
vectorized::AggFnEvaluator* evaluator = nullptr;
RETURN_IF_ERROR(vectorized::AggFnEvaluator::create(
_pool, tnode.agg_node.aggregate_functions[i],
tnode.agg_node.__isset.agg_sort_infos ? tnode.agg_node.agg_sort_infos[i] : dummy,
&evaluator));
_aggregate_evaluators.push_back(evaluator);
}
if (state->enable_agg_spill()) {
// If spill enabled, the streaming agg should not occupy too much memory.
_spill_streaming_agg_mem_limit =
state->query_options().__isset.spill_streaming_agg_mem_limit
? state->query_options().spill_streaming_agg_mem_limit
: 0;
} else {
_spill_streaming_agg_mem_limit = 0;
}
const auto& agg_functions = tnode.agg_node.aggregate_functions;
_is_merge = std::any_of(agg_functions.cbegin(), agg_functions.cend(),
[](const auto& e) { return e.nodes[0].agg_expr.is_merge_agg; });
_op_name = "STREAMING_AGGREGATION_OPERATOR";
return Status::OK();
}
Status StreamingAggOperatorX::prepare(RuntimeState* state) {
RETURN_IF_ERROR(StatefulOperatorX<StreamingAggLocalState>::prepare(state));
_intermediate_tuple_desc = state->desc_tbl().get_tuple_descriptor(_intermediate_tuple_id);
_output_tuple_desc = state->desc_tbl().get_tuple_descriptor(_output_tuple_id);
DCHECK_EQ(_intermediate_tuple_desc->slots().size(), _output_tuple_desc->slots().size());
RETURN_IF_ERROR(vectorized::VExpr::prepare(_probe_expr_ctxs, state, _child_x->row_desc()));
int j = _probe_expr_ctxs.size();
for (int i = 0; i < j; ++i) {
auto nullable_output = _output_tuple_desc->slots()[i]->is_nullable();
auto nullable_input = _probe_expr_ctxs[i]->root()->is_nullable();
if (nullable_output != nullable_input) {
DCHECK(nullable_output);
_make_nullable_keys.emplace_back(i);
}
}
for (int i = 0; i < _aggregate_evaluators.size(); ++i, ++j) {
SlotDescriptor* intermediate_slot_desc = _intermediate_tuple_desc->slots()[j];
SlotDescriptor* output_slot_desc = _output_tuple_desc->slots()[j];
RETURN_IF_ERROR(_aggregate_evaluators[i]->prepare(
state, _child_x->row_desc(), intermediate_slot_desc, output_slot_desc));
}
_offsets_of_aggregate_states.resize(_aggregate_evaluators.size());
for (size_t i = 0; i < _aggregate_evaluators.size(); ++i) {
_offsets_of_aggregate_states[i] = _total_size_of_aggregate_states;
const auto& agg_function = _aggregate_evaluators[i]->function();
// aggreate states are aligned based on maximum requirement
_align_aggregate_states = std::max(_align_aggregate_states, agg_function->align_of_data());
_total_size_of_aggregate_states += agg_function->size_of_data();
// If not the last aggregate_state, we need pad it so that next aggregate_state will be aligned.
if (i + 1 < _aggregate_evaluators.size()) {
size_t alignment_of_next_state =
_aggregate_evaluators[i + 1]->function()->align_of_data();
if ((alignment_of_next_state & (alignment_of_next_state - 1)) != 0) {
return Status::RuntimeError("Logical error: align_of_data is not 2^N");
}
/// Extend total_size to next alignment requirement
/// Add padding by rounding up 'total_size_of_aggregate_states' to be a multiplier of alignment_of_next_state.
_total_size_of_aggregate_states =
(_total_size_of_aggregate_states + alignment_of_next_state - 1) /
alignment_of_next_state * alignment_of_next_state;
}
}
// check output type
if (_needs_finalize) {
RETURN_IF_ERROR(vectorized::AggFnEvaluator::check_agg_fn_output(
_probe_expr_ctxs.size(), _aggregate_evaluators, _agg_fn_output_row_descriptor));
}
return Status::OK();
}
Status StreamingAggOperatorX::open(RuntimeState* state) {
RETURN_IF_ERROR(StatefulOperatorX<StreamingAggLocalState>::open(state));
RETURN_IF_ERROR(vectorized::VExpr::open(_probe_expr_ctxs, state));
for (int i = 0; i < _aggregate_evaluators.size(); ++i) {
RETURN_IF_ERROR(_aggregate_evaluators[i]->open(state));
_aggregate_evaluators[i]->set_version(state->be_exec_version());
}
return Status::OK();
}
Status StreamingAggLocalState::close(RuntimeState* state) {
if (!_opened || _closed) {
return Status::OK();
}
SCOPED_TIMER(Base::exec_time_counter());
SCOPED_TIMER(Base::_close_timer);
if (Base::_closed) {
return Status::OK();
}
_pre_aggregated_block->clear();
vectorized::PODArray<vectorized::AggregateDataPtr> tmp_places;
_places.swap(tmp_places);
std::vector<char> tmp_deserialize_buffer;
_deserialize_buffer.swap(tmp_deserialize_buffer);
Base::_mem_tracker->release(_mem_usage_record.used_in_state + _mem_usage_record.used_in_arena);
/// _hash_table_size_counter may be null if prepare failed.
if (_hash_table_size_counter) {
std::visit(
[&](auto&& agg_method) {
COUNTER_SET(_hash_table_size_counter, int64_t(agg_method.hash_table->size()));
},
_agg_data->method_variant);
}
_close_with_serialized_key();
return Base::close(state);
}
Status StreamingAggOperatorX::pull(RuntimeState* state, vectorized::Block* block, bool* eos) const {
auto& local_state = get_local_state(state);
if (!local_state._pre_aggregated_block->empty()) {
local_state._pre_aggregated_block->swap(*block);
} else {
RETURN_IF_ERROR(local_state._executor->get_result(&local_state, state, block, eos));
local_state.make_nullable_output_key(block);
// dispose the having clause, should not be execute in prestreaming agg
RETURN_IF_ERROR(
vectorized::VExprContext::filter_block(_conjuncts, block, block->columns()));
}
local_state.reached_limit(block, eos);
return Status::OK();
}
Status StreamingAggOperatorX::push(RuntimeState* state, vectorized::Block* in_block,
bool eos) const {
auto& local_state = get_local_state(state);
local_state._input_num_rows += in_block->rows();
if (in_block->rows() > 0) {
RETURN_IF_ERROR(local_state.do_pre_agg(in_block, local_state._pre_aggregated_block.get()));
}
in_block->clear_column_data(_child_x->row_desc().num_materialized_slots());
return Status::OK();
}
bool StreamingAggOperatorX::need_more_input_data(RuntimeState* state) const {
auto& local_state = get_local_state(state);
return local_state._pre_aggregated_block->empty() && !local_state._child_eos;
}
} // namespace doris::pipeline
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