InputMetadata.cpp File Reference

#include "InputMetadata.h"
#include "Execute.h"
#include "../Fragmenter/Fragmenter.h"
#include <tbb/parallel_for.h>
#include <tbb/task_arena.h>
#include <future>

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Classes
union	anonymous_namespace{InputMetadata.cpp}::Number64

Namespaces
	anonymous_namespace{InputMetadata.cpp}

Functions
Fragmenter_Namespace::TableInfo	anonymous_namespace{InputMetadata.cpp}::copy_table_info (const Fragmenter_Namespace::TableInfo &table_info)
Fragmenter_Namespace::TableInfo	build_table_info (const std::vector< const TableDescriptor * > &shard_tables)
bool	anonymous_namespace{InputMetadata.cpp}::uses_int_meta (const SQLTypeInfo &col_ti)
Fragmenter_Namespace::TableInfo	anonymous_namespace{InputMetadata.cpp}::synthesize_table_info (const ResultSetPtr &rows)
void	anonymous_namespace{InputMetadata.cpp}::collect_table_infos (std::vector< InputTableInfo > &table_infos, const std::vector< InputDescriptor > &input_descs, Executor *executor)
template<typename T >
void	compute_table_function_col_chunk_stats (std::shared_ptr< ChunkMetadata > &chunk_metadata, const T *values_buffer, const size_t values_count, const T null_val)
ChunkMetadataMap	synthesize_metadata_table_function (const ResultSet *rows)
ChunkMetadataMap	synthesize_metadata (const ResultSet *rows)
size_t	get_frag_count_of_table (const shared::TableKey &table_key, Executor *executor)
std::vector< InputTableInfo >	get_table_infos (const std::vector< InputDescriptor > &input_descs, Executor *executor)
std::vector< InputTableInfo >	get_table_infos (const RelAlgExecutionUnit &ra_exe_unit, Executor *executor)

Variables
bool	g_enable_data_recycler
bool	g_use_chunk_metadata_cache

Function Documentation

Fragmenter_Namespace::TableInfo build_table_info

(

const std::vector< const TableDescriptor * > &

shard_tables

)

Definition at line 44 of file InputMetadata.cpp.

References CHECK, Fragmenter_Namespace::TableInfo::fragments, and Fragmenter_Namespace::TableInfo::setPhysicalNumTuples().

Referenced by InputTableInfoCache::getTableInfo().

                                                           {
  size_t total_number_of_tuples{0};
  Fragmenter_Namespace::TableInfo table_info_all_shards;
  for (const TableDescriptor* shard_table : shard_tables) {
    CHECK(shard_table->fragmenter);
    const auto& shard_metainfo = shard_table->fragmenter->getFragmentsForQuery();
    total_number_of_tuples += shard_metainfo.getPhysicalNumTuples();
    table_info_all_shards.fragments.reserve(table_info_all_shards.fragments.size() +
                                            shard_metainfo.fragments.size());
    table_info_all_shards.fragments.insert(table_info_all_shards.fragments.end(),
                                           shard_metainfo.fragments.begin(),
                                           shard_metainfo.fragments.end());
  }
  table_info_all_shards.setPhysicalNumTuples(total_number_of_tuples);
  return table_info_all_shards;
}

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template<typename T >

void compute_table_function_col_chunk_stats	(	std::shared_ptr< ChunkMetadata > &	chunk_metadata,
		const T *	values_buffer,
		const size_t	values_count,
		const T	null_val
	)

Definition at line 142 of file InputMetadata.cpp.

References max_inputs_per_thread, threading_serial::parallel_for(), and heavydb.dtypes::T.

Referenced by synthesize_metadata_table_function().

                      {
  T min_val{std::numeric_limits<T>::max()};
  T max_val{std::numeric_limits<T>::lowest()};
  bool has_nulls{false};
  constexpr size_t parallel_stats_compute_threshold = 20000UL;
  if (values_count < parallel_stats_compute_threshold) {
    for (size_t row_idx = 0; row_idx < values_count; ++row_idx) {
      const T cell_val = values_buffer[row_idx];
      if (cell_val == null_val) {
        has_nulls = true;
        continue;
      }
      if (cell_val < min_val) {
        min_val = cell_val;
      }
      if (cell_val > max_val) {
        max_val = cell_val;
      }
    }
  } else {
    const size_t max_thread_count = std::thread::hardware_concurrency();
    const size_t max_inputs_per_thread = 20000;
    const size_t min_grain_size = max_inputs_per_thread / 2;
    const size_t num_threads =
        std::min(max_thread_count,
                 ((values_count + max_inputs_per_thread - 1) / max_inputs_per_thread));

    std::vector<T> threads_local_mins(num_threads, std::numeric_limits<T>::max());
    std::vector<T> threads_local_maxes(num_threads, std::numeric_limits<T>::lowest());
    std::vector<bool> threads_local_has_nulls(num_threads, false);
    tbb::task_arena limited_arena(num_threads);

    limited_arena.execute([&] {
      tbb::parallel_for(
          tbb::blocked_range<size_t>(0, values_count, min_grain_size),
          [&](const tbb::blocked_range<size_t>& r) {
            const size_t start_idx = r.begin();
            const size_t end_idx = r.end();
            T local_min_val = std::numeric_limits<T>::max();
            T local_max_val = std::numeric_limits<T>::lowest();
            bool local_has_nulls = false;
            for (size_t row_idx = start_idx; row_idx < end_idx; ++row_idx) {
              const T cell_val = values_buffer[row_idx];
              if (cell_val == null_val) {
                local_has_nulls = true;
                continue;
              }
              if (cell_val < local_min_val) {
                local_min_val = cell_val;
              }
              if (cell_val > local_max_val) {
                local_max_val = cell_val;
              }
            }
            size_t thread_idx = tbb::this_task_arena::current_thread_index();
            if (local_min_val < threads_local_mins[thread_idx]) {
              threads_local_mins[thread_idx] = local_min_val;
            }
            if (local_max_val > threads_local_maxes[thread_idx]) {
              threads_local_maxes[thread_idx] = local_max_val;
            }
            if (local_has_nulls) {
              threads_local_has_nulls[thread_idx] = true;
            }
          },
          tbb::simple_partitioner());
    });

    for (size_t thread_idx = 0; thread_idx < num_threads; ++thread_idx) {
      if (threads_local_mins[thread_idx] < min_val) {
        min_val = threads_local_mins[thread_idx];
      }
      if (threads_local_maxes[thread_idx] > max_val) {
        max_val = threads_local_maxes[thread_idx];
      }
      has_nulls |= threads_local_has_nulls[thread_idx];
    }
  }
  chunk_metadata->fillChunkStats(min_val, max_val, has_nulls);
}

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size_t get_frag_count_of_table	(	const shared::TableKey &	table_key,
		Executor *	executor
	)

Definition at line 500 of file InputMetadata.cpp.

References CHECK, CHECK_GE, and shared::TableKey::table_id.

Referenced by RelAlgExecutor::getOuterFragmentCount().

                                                                                    {
  const auto temporary_tables = executor->getTemporaryTables();
  CHECK(temporary_tables);
  auto it = temporary_tables->find(table_key.table_id);
  if (it != temporary_tables->end()) {
    CHECK_GE(int(0), table_key.table_id);
    return size_t(1);
  } else {
    const auto table_info = executor->getTableInfo(table_key);
    return table_info.fragments.size();
  }
}

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std::vector<InputTableInfo> get_table_infos	(	const std::vector< InputDescriptor > &	input_descs,
		Executor *	executor
	)

Definition at line 513 of file InputMetadata.cpp.

References anonymous_namespace{InputMetadata.cpp}::collect_table_infos().

Referenced by RelAlgExecutor::computeWindow(), RelAlgExecutor::createAggregateWorkUnit(), RelAlgExecutor::createCompoundWorkUnit(), RelAlgExecutor::createFilterWorkUnit(), RelAlgExecutor::createProjectWorkUnit(), RelAlgExecutor::createTableFunctionWorkUnit(), RelAlgExecutor::createUnionWorkUnit(), RelAlgExecutor::executeDelete(), RelAlgExecutor::executeTableFunction(), RelAlgExecutor::executeUpdate(), RelAlgExecutor::executeWorkUnit(), TableOptimizer::getDeletedColumnStats(), RelAlgExecutor::getFilteredCountAll(), RelAlgExecutor::getFilterSelectivity(), RelAlgExecutor::getNDVEstimation(), RelAlgExecutor::handleOutOfMemoryRetry(), TableOptimizer::recomputeColumnMetadata(), and RelAlgExecutor::selectFiltersToBePushedDown().

                        {
  std::vector<InputTableInfo> table_infos;
  collect_table_infos(table_infos, input_descs, executor);
  return table_infos;
}

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std::vector<InputTableInfo> get_table_infos	(	const RelAlgExecutionUnit &	ra_exe_unit,
		Executor *	executor
	)

Definition at line 521 of file InputMetadata.cpp.

References anonymous_namespace{InputMetadata.cpp}::collect_table_infos(), and RelAlgExecutionUnit::input_descs.

                                                                {
  std::vector<InputTableInfo> table_infos;
  collect_table_infos(table_infos, ra_exe_unit.input_descs, executor);
  return table_infos;
}

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ChunkMetadataMap synthesize_metadata

(

const ResultSet *

rows

)

Definition at line 368 of file InputMetadata.cpp.

References CHECK, CHECK_LT, cpu_threads(), Encoder::Create(), DEBUG_TIMER, inline_fp_null_val(), inline_int_null_val(), kDOUBLE, kFLOAT, threading_serial::parallel_for(), report::rows, synthesize_metadata_table_function(), heavyai::TableFunction, result_set::use_parallel_algorithms(), and anonymous_namespace{InputMetadata.cpp}::uses_int_meta().

Referenced by Fragmenter_Namespace::FragmentInfo::getChunkMetadataMap().

                                                            {
  auto timer = DEBUG_TIMER(__func__);
  ChunkMetadataMap metadata_map;

  // If the ResultSet has no rows, fill with dummy metadata and return early.
  if (rows->definitelyHasNoRows()) {
    // resultset has no valid storage, so we fill dummy metadata and return early
    std::vector<std::unique_ptr<Encoder>> decoders;
    for (size_t i = 0; i < rows->colCount(); ++i) {
      decoders.emplace_back(Encoder::Create(nullptr, rows->getColType(i)));
      const auto it_ok =
          metadata_map.emplace(i, decoders.back()->getMetadata(rows->getColType(i)));
      CHECK(it_ok.second);
    }
    return metadata_map;
  }

  // Create a vector of Encoder vectors for each worker.
  std::vector<std::vector<std::unique_ptr<Encoder>>> dummy_encoders;
  const size_t worker_count =
      result_set::use_parallel_algorithms(*rows) ? cpu_threads() : 1;
  for (size_t worker_idx = 0; worker_idx < worker_count; ++worker_idx) {
    dummy_encoders.emplace_back();
    for (size_t i = 0; i < rows->colCount(); ++i) {
      const auto& col_ti = rows->getColType(i);
      dummy_encoders.back().emplace_back(Encoder::Create(nullptr, col_ti));
    }
  }

  // For TableFunctions, call the optimized function we have for this format.
  if (rows->getQueryMemDesc().getQueryDescriptionType() ==
      QueryDescriptionType::TableFunction) {
    return synthesize_metadata_table_function(rows);
  }
  rows->moveToBegin();

  std::vector<SQLTypeInfo> row_col_ti;
  std::vector<Number64> col_null_vals(rows->colCount());
  for (size_t i = 0; i < rows->colCount(); i++) {
    auto const col_ti = rows->getColType(i);
    row_col_ti.push_back(col_ti);
    if (uses_int_meta(col_ti)) {
      col_null_vals[i].as_int64 = inline_int_null_val(col_ti);
    } else if (col_ti.is_fp()) {
      col_null_vals[i].as_double = inline_fp_null_val(col_ti);
    } else {
      throw std::runtime_error(col_ti.get_type_name() +
                               " is not supported in temporary table.");
    }
  }

  // Code in the do_work lambda runs for and processes each row.
  const auto do_work = [rows, &row_col_ti, &col_null_vals](
                           const std::vector<TargetValue>& crt_row,
                           std::vector<std::unique_ptr<Encoder>>& dummy_encoders) {
    for (size_t i = 0; i < rows->colCount(); ++i) {
      const auto& col_ti = row_col_ti[i];
      const auto& col_val = crt_row[i];
      const auto scalar_col_val = boost::get<ScalarTargetValue>(&col_val);
      CHECK(scalar_col_val);
      if (uses_int_meta(col_ti)) {
        const auto i64_p = boost::get<int64_t>(scalar_col_val);
        CHECK(i64_p);
        dummy_encoders[i]->updateStats(*i64_p, *i64_p == col_null_vals[i].as_int64);
      } else {
        CHECK(col_ti.is_fp());
        switch (col_ti.get_type()) {
          case kFLOAT: {
            const auto float_p = boost::get<float>(scalar_col_val);
            CHECK(float_p);
            dummy_encoders[i]->updateStats(*float_p,
                                           *float_p == col_null_vals[i].as_double);
            break;
          }
          case kDOUBLE: {
            const auto double_p = boost::get<double>(scalar_col_val);
            CHECK(double_p);
            dummy_encoders[i]->updateStats(*double_p,
                                           *double_p == col_null_vals[i].as_double);
            break;
          }
          default:
            CHECK(false);
        }
      }
    }
  };

  // Parallelize the processing using TBB if parallel algorithms are enabled.
  if (result_set::use_parallel_algorithms(*rows)) {
    const size_t entry_count = rows->entryCount();
    tbb::parallel_for(
        tbb::blocked_range<size_t>(0, entry_count),
        [&do_work, &rows, &dummy_encoders](const tbb::blocked_range<size_t>& range) {
          const size_t worker_idx = tbb::this_task_arena::current_thread_index();
          for (size_t i = range.begin(); i < range.end(); ++i) {
            const auto crt_row = rows->getRowAtNoTranslations(i);
            if (!crt_row.empty()) {
              do_work(crt_row, dummy_encoders[worker_idx]);
            }
          }
        });

  } else {
    // If parallel algorithms are not enabled, process the rows sequentially.
    while (true) {
      auto crt_row = rows->getNextRow(false, false);
      if (crt_row.empty()) {
        break;
      }
      do_work(crt_row, dummy_encoders[0]);
    }
  }
  rows->moveToBegin();

  // Reduce the results from each worker.
  for (size_t worker_idx = 1; worker_idx < worker_count; ++worker_idx) {
    CHECK_LT(worker_idx, dummy_encoders.size());
    const auto& worker_encoders = dummy_encoders[worker_idx];
    for (size_t i = 0; i < rows->colCount(); ++i) {
      dummy_encoders[0][i]->reduceStats(*worker_encoders[i]);
    }
  }
  // Add each column's results to the metadata map.
  for (size_t i = 0; i < rows->colCount(); ++i) {
    const auto it_ok =
        metadata_map.emplace(i, dummy_encoders[0][i]->getMetadata(rows->getColType(i)));
    CHECK(it_ok.second);
  }
  return metadata_map;
}

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ChunkMetadataMap synthesize_metadata_table_function

(

const ResultSet *

rows

)

Definition at line 227 of file InputMetadata.cpp.

References CHECK, CHECK_EQ, compute_table_function_col_chunk_stats(), FlatBufferManager::getBufferSize(), inline_fixed_encoding_null_val(), inline_fp_null_value< double >(), inline_fp_null_value< float >(), FlatBufferManager::isFlatBuffer(), kBIGINT, kBOOLEAN, kDOUBLE, kENCODING_DICT, kENCODING_NONE, kFLOAT, kINT, kLINESTRING, kMULTILINESTRING, kMULTIPOLYGON, kPOINT, kPOLYGON, kSMALLINT, kTEXT, kTIMESTAMP, kTINYINT, heavyai::TableFunction, and UNREACHABLE.

Referenced by synthesize_metadata().

                                                                           {
  CHECK(rows->getQueryMemDesc().getQueryDescriptionType() ==
        QueryDescriptionType::TableFunction);
  CHECK(rows->didOutputColumnar());
  CHECK(!(rows->areAnyColumnsLazyFetched()));
  const size_t col_count = rows->colCount();
  const auto row_count = rows->entryCount();

  ChunkMetadataMap chunk_metadata_map;

  for (size_t col_idx = 0; col_idx < col_count; ++col_idx) {
    std::shared_ptr<ChunkMetadata> chunk_metadata = std::make_shared<ChunkMetadata>();
    const int8_t* columnar_buffer = const_cast<int8_t*>(rows->getColumnarBuffer(col_idx));
    const auto col_sql_type_info = rows->getColType(col_idx);
    // Here, min/max of a column of arrays, col, is defined as
    // min/max(unnest(col)). That is, if is_array is true, the
    // metadata is supposed to be syntesized for a query like `SELECT
    // UNNEST(col_of_arrays) ... GROUP BY ...`. How can we verify that
    // here?

    // min/max of a column of a geotype is defined as the min/max of
    // all x and y coordinate values
    bool is_array = col_sql_type_info.is_array();
    bool is_geometry = col_sql_type_info.is_geometry();
    const auto col_type =
        (is_array ? col_sql_type_info.get_subtype()
                  : (is_geometry ? col_sql_type_info.get_elem_type().get_type()
                                 : col_sql_type_info.get_type()));
    const auto col_type_info =
        ((is_array || is_geometry) ? col_sql_type_info.get_elem_type()
                                   : col_sql_type_info);

    chunk_metadata->sqlType = col_type_info;
    chunk_metadata->numElements = row_count;

    const int8_t* values_buffer{nullptr};
    size_t values_count{0};
    if (FlatBufferManager::isFlatBuffer(columnar_buffer)) {
      CHECK(FlatBufferManager::isFlatBuffer(columnar_buffer));
      FlatBufferManager m{const_cast<int8_t*>(columnar_buffer)};
      chunk_metadata->numBytes = m.getBufferSize();
      if (is_geometry) {
        switch (col_sql_type_info.get_type()) {
          case kPOINT:
            // a geometry value is a pair of coordinates but its element
            // type value is a int or double, hence multiplication by 2:
            values_count = row_count * 2;
            values_buffer = m.get_values();
            break;
          case kLINESTRING:
          case kPOLYGON:
          case kMULTILINESTRING:
          case kMULTIPOLYGON: {
            values_count = m.getValuesCount();
            values_buffer = m.getValuesBuffer();
          } break;
          default:
            UNREACHABLE();
        }
      } else {
        CHECK(is_array);
        CHECK(m.isNestedArray());
        values_count = m.getValuesCount();
        values_buffer = m.getValuesBuffer();
      }
    } else {
      chunk_metadata->numBytes = row_count * col_type_info.get_size();
      values_count = row_count;
      values_buffer = columnar_buffer;
    }

    if (col_type != kTEXT) {
      CHECK(col_type_info.get_compression() == kENCODING_NONE);
    } else {
      CHECK(col_type_info.get_compression() == kENCODING_DICT);
      CHECK_EQ(col_type_info.get_size(), sizeof(int32_t));
    }

    switch (col_type) {
      case kBOOLEAN:
      case kTINYINT:
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            values_buffer,
            values_count,
            static_cast<int8_t>(inline_fixed_encoding_null_val(col_type_info)));
        break;
      case kSMALLINT:
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            reinterpret_cast<const int16_t*>(values_buffer),
            values_count,
            static_cast<int16_t>(inline_fixed_encoding_null_val(col_type_info)));
        break;
      case kINT:
      case kTEXT:
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            reinterpret_cast<const int32_t*>(values_buffer),
            values_count,
            static_cast<int32_t>(inline_fixed_encoding_null_val(col_type_info)));
        break;
      case kBIGINT:
      case kTIMESTAMP:
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            reinterpret_cast<const int64_t*>(values_buffer),
            values_count,
            static_cast<int64_t>(inline_fixed_encoding_null_val(col_type_info)));
        break;
      case kFLOAT:
        // For float use the typed null accessor as the generic one converts to double,
        // and do not want to risk loss of precision
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            reinterpret_cast<const float*>(values_buffer),
            values_count,
            inline_fp_null_value<float>());
        break;
      case kDOUBLE:
        compute_table_function_col_chunk_stats(
            chunk_metadata,
            reinterpret_cast<const double*>(values_buffer),
            values_count,
            inline_fp_null_value<double>());
        break;
      default:
        UNREACHABLE();
    }
    chunk_metadata_map.emplace(col_idx, chunk_metadata);
  }
  return chunk_metadata_map;
}

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Variable Documentation

bool g_enable_data_recycler

Definition at line 158 of file Execute.cpp.

bool g_use_chunk_metadata_cache

Definition at line 161 of file Execute.cpp.