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More granular column selection in ORC reader (#9496)
Closes #8848 - Allows caller to specify nested column paths, so that the fields not listed in the `columns` parameter are excluded. - The order of fields/columns in the output table is consistent with the order of paths/names in the `columns` parameter. - Moved `aggregate_orc_metadata` implementation to a separate file (can be `.cpp`!) - Add tests to cover different cases with a mix of nested and parent columns selection. - changed a few fields from `uint32_t` to `int32_t` to avoid unsigned arithmetic. Authors: - Vukasin Milovanovic (https://github.com/vuule) Approvers: - Robert Maynard (https://github.com/robertmaynard) - Vyas Ramasubramani (https://github.com/vyasr) - Ram (Ramakrishna Prabhu) (https://github.com/rgsl888prabhu) URL: #9496
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/* | ||
* Copyright (c) 2021, NVIDIA CORPORATION. | ||
* | ||
* Licensed 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. | ||
*/ | ||
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#include "aggregate_orc_metadata.hpp" | ||
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#include <algorithm> | ||
#include <numeric> | ||
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namespace cudf::io::orc::detail { | ||
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column_hierarchy::column_hierarchy(nesting_map child_map) : children{std::move(child_map)} | ||
{ | ||
// Sort columns by nesting levels | ||
std::function<void(size_type, int32_t)> levelize = [&](size_type id, int32_t level) { | ||
if (static_cast<int32_t>(levels.size()) == level) levels.emplace_back(); | ||
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levels[level].push_back({id, static_cast<int32_t>(children[id].size())}); | ||
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for (auto child_id : children[id]) { | ||
levelize(child_id, level + 1); | ||
} | ||
}; | ||
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std::for_each( | ||
children[0].cbegin(), children[0].cend(), [&](auto col_id) { levelize(col_id, 0); }); | ||
} | ||
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namespace { | ||
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/** | ||
* @brief Goes up to the root to include the column with the given id and its parents. | ||
*/ | ||
void update_parent_mapping(std::map<size_type, std::vector<size_type>>& selected_columns, | ||
metadata const& metadata, | ||
size_type id) | ||
{ | ||
auto current_id = id; | ||
while (metadata.column_has_parent(current_id)) { | ||
auto parent_id = metadata.parent_id(current_id); | ||
if (std::find(selected_columns[parent_id].cbegin(), | ||
selected_columns[parent_id].cend(), | ||
current_id) == selected_columns[parent_id].end()) { | ||
selected_columns[parent_id].push_back(current_id); | ||
} | ||
current_id = parent_id; | ||
} | ||
} | ||
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/** | ||
* @brief Adds all columns nested under the column with the given id to the nesting map. | ||
*/ | ||
void add_nested_columns(std::map<size_type, std::vector<size_type>>& selected_columns, | ||
std::vector<SchemaType> const& types, | ||
size_type id) | ||
{ | ||
for (auto child_id : types[id].subtypes) { | ||
if (std::find(selected_columns[id].cbegin(), selected_columns[id].cend(), child_id) == | ||
selected_columns[id].end()) { | ||
selected_columns[id].push_back(child_id); | ||
} | ||
add_nested_columns(selected_columns, types, child_id); | ||
} | ||
} | ||
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/** | ||
* @brief Adds the column with the given id to the mapping | ||
* | ||
* All nested columns and direct ancestors of column `id` are included. | ||
* Columns that are not on the direct path are excluded, which may result in prunning. | ||
*/ | ||
void add_column_to_mapping(std::map<size_type, std::vector<size_type>>& selected_columns, | ||
metadata const& metadata, | ||
size_type id) | ||
{ | ||
update_parent_mapping(selected_columns, metadata, id); | ||
add_nested_columns(selected_columns, metadata.ff.types, id); | ||
} | ||
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/** | ||
* @brief Create a metadata object from each element in the source vector | ||
*/ | ||
auto metadatas_from_sources(std::vector<std::unique_ptr<datasource>> const& sources) | ||
{ | ||
std::vector<metadata> metadatas; | ||
std::transform( | ||
sources.cbegin(), sources.cend(), std::back_inserter(metadatas), [](auto const& source) { | ||
return metadata(source.get()); | ||
}); | ||
return metadatas; | ||
} | ||
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} // namespace | ||
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size_type aggregate_orc_metadata::calc_num_rows() const | ||
{ | ||
return std::accumulate( | ||
per_file_metadata.begin(), per_file_metadata.end(), 0, [](auto const& sum, auto const& pfm) { | ||
return sum + pfm.get_total_rows(); | ||
}); | ||
} | ||
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size_type aggregate_orc_metadata::calc_num_stripes() const | ||
{ | ||
return std::accumulate( | ||
per_file_metadata.begin(), per_file_metadata.end(), 0, [](auto const& sum, auto const& pfm) { | ||
return sum + pfm.get_num_stripes(); | ||
}); | ||
} | ||
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aggregate_orc_metadata::aggregate_orc_metadata( | ||
std::vector<std::unique_ptr<datasource>> const& sources) | ||
: per_file_metadata(metadatas_from_sources(sources)), | ||
num_rows(calc_num_rows()), | ||
num_stripes(calc_num_stripes()) | ||
{ | ||
// Verify that the input files have the same number of columns, | ||
// as well as matching types, compression, and names | ||
for (auto const& pfm : per_file_metadata) { | ||
CUDF_EXPECTS(per_file_metadata[0].get_num_columns() == pfm.get_num_columns(), | ||
"All sources must have the same number of columns"); | ||
CUDF_EXPECTS(per_file_metadata[0].ps.compression == pfm.ps.compression, | ||
"All sources must have the same compression type"); | ||
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// Check the types, column names, and decimal scale | ||
for (size_t i = 0; i < pfm.ff.types.size(); i++) { | ||
CUDF_EXPECTS(pfm.ff.types[i].kind == per_file_metadata[0].ff.types[i].kind, | ||
"Column types across all input sources must be the same"); | ||
CUDF_EXPECTS(std::equal(pfm.ff.types[i].fieldNames.begin(), | ||
pfm.ff.types[i].fieldNames.end(), | ||
per_file_metadata[0].ff.types[i].fieldNames.begin()), | ||
"All source column names must be the same"); | ||
CUDF_EXPECTS( | ||
pfm.ff.types[i].scale.value_or(0) == per_file_metadata[0].ff.types[i].scale.value_or(0), | ||
"All scale values must be the same"); | ||
} | ||
} | ||
} | ||
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std::vector<metadata::stripe_source_mapping> aggregate_orc_metadata::select_stripes( | ||
std::vector<std::vector<size_type>> const& user_specified_stripes, | ||
size_type& row_start, | ||
size_type& row_count) | ||
{ | ||
std::vector<metadata::stripe_source_mapping> selected_stripes_mapping; | ||
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if (!user_specified_stripes.empty()) { | ||
CUDF_EXPECTS(user_specified_stripes.size() == per_file_metadata.size(), | ||
"Must specify stripes for each source"); | ||
// row_start is 0 if stripes are set. If this is not true anymore, then | ||
// row_start needs to be subtracted to get the correct row_count | ||
CUDF_EXPECTS(row_start == 0, "Start row index should be 0"); | ||
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row_count = 0; | ||
// Each vector entry represents a source file; each nested vector represents the | ||
// user_defined_stripes to get from that source file | ||
for (size_t src_file_idx = 0; src_file_idx < user_specified_stripes.size(); ++src_file_idx) { | ||
std::vector<OrcStripeInfo> stripe_infos; | ||
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// Coalesce stripe info at the source file later since that makes downstream processing much | ||
// easier in impl::read | ||
for (const size_t& stripe_idx : user_specified_stripes[src_file_idx]) { | ||
CUDF_EXPECTS(stripe_idx < per_file_metadata[src_file_idx].ff.stripes.size(), | ||
"Invalid stripe index"); | ||
stripe_infos.push_back( | ||
std::make_pair(&per_file_metadata[src_file_idx].ff.stripes[stripe_idx], nullptr)); | ||
row_count += per_file_metadata[src_file_idx].ff.stripes[stripe_idx].numberOfRows; | ||
} | ||
selected_stripes_mapping.push_back({static_cast<int>(src_file_idx), stripe_infos}); | ||
} | ||
} else { | ||
row_start = std::max(row_start, 0); | ||
if (row_count < 0) { | ||
row_count = static_cast<size_type>( | ||
std::min<int64_t>(get_num_rows(), std::numeric_limits<size_type>::max())); | ||
} | ||
row_count = std::min(row_count, get_num_rows() - row_start); | ||
CUDF_EXPECTS(row_count >= 0, "Invalid row count"); | ||
CUDF_EXPECTS(row_start <= get_num_rows(), "Invalid row start"); | ||
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size_type count = 0; | ||
size_type stripe_skip_rows = 0; | ||
// Iterate all source files, each source file has corelating metadata | ||
for (size_t src_file_idx = 0; | ||
src_file_idx < per_file_metadata.size() && count < row_start + row_count; | ||
++src_file_idx) { | ||
std::vector<OrcStripeInfo> stripe_infos; | ||
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for (size_t stripe_idx = 0; stripe_idx < per_file_metadata[src_file_idx].ff.stripes.size() && | ||
count < row_start + row_count; | ||
++stripe_idx) { | ||
count += per_file_metadata[src_file_idx].ff.stripes[stripe_idx].numberOfRows; | ||
if (count > row_start || count == 0) { | ||
stripe_infos.push_back( | ||
std::make_pair(&per_file_metadata[src_file_idx].ff.stripes[stripe_idx], nullptr)); | ||
} else { | ||
stripe_skip_rows = count; | ||
} | ||
} | ||
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selected_stripes_mapping.push_back({static_cast<int>(src_file_idx), stripe_infos}); | ||
} | ||
// Need to remove skipped rows from the stripes which are not selected. | ||
row_start -= stripe_skip_rows; | ||
} | ||
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// Read each stripe's stripefooter metadata | ||
if (not selected_stripes_mapping.empty()) { | ||
for (auto& mapping : selected_stripes_mapping) { | ||
// Resize to all stripe_info for the source level | ||
per_file_metadata[mapping.source_idx].stripefooters.resize(mapping.stripe_info.size()); | ||
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for (size_t i = 0; i < mapping.stripe_info.size(); i++) { | ||
const auto stripe = mapping.stripe_info[i].first; | ||
const auto sf_comp_offset = stripe->offset + stripe->indexLength + stripe->dataLength; | ||
const auto sf_comp_length = stripe->footerLength; | ||
CUDF_EXPECTS( | ||
sf_comp_offset + sf_comp_length < per_file_metadata[mapping.source_idx].source->size(), | ||
"Invalid stripe information"); | ||
const auto buffer = | ||
per_file_metadata[mapping.source_idx].source->host_read(sf_comp_offset, sf_comp_length); | ||
size_t sf_length = 0; | ||
auto sf_data = per_file_metadata[mapping.source_idx].decompressor->Decompress( | ||
buffer->data(), sf_comp_length, &sf_length); | ||
ProtobufReader(sf_data, sf_length) | ||
.read(per_file_metadata[mapping.source_idx].stripefooters[i]); | ||
mapping.stripe_info[i].second = &per_file_metadata[mapping.source_idx].stripefooters[i]; | ||
if (stripe->indexLength == 0) { row_grp_idx_present = false; } | ||
} | ||
} | ||
} | ||
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return selected_stripes_mapping; | ||
} | ||
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column_hierarchy aggregate_orc_metadata::select_columns( | ||
std::vector<std::string> const& column_paths) | ||
{ | ||
auto const& pfm = per_file_metadata[0]; | ||
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column_hierarchy::nesting_map selected_columns; | ||
if (column_paths.empty()) { | ||
for (auto const& col_id : pfm.ff.types[0].subtypes) { | ||
add_column_to_mapping(selected_columns, pfm, col_id); | ||
} | ||
} else { | ||
for (const auto& path : column_paths) { | ||
bool name_found = false; | ||
for (auto col_id = 1; col_id < pfm.get_num_columns(); ++col_id) { | ||
if (pfm.column_path(col_id) == path) { | ||
name_found = true; | ||
add_column_to_mapping(selected_columns, pfm, col_id); | ||
break; | ||
} | ||
} | ||
CUDF_EXPECTS(name_found, "Unknown column name: " + std::string(path)); | ||
} | ||
} | ||
return {std::move(selected_columns)}; | ||
} | ||
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} // namespace cudf::io::orc::detail |
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