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ClickHouse/src/Storages/MergeTree/MergeTreeDataSelectExecutor.cpp
Amos Bird 211c3d4d49
Better Projection IN
2021-10-29 20:24:36 +08:00

1683 lines
66 KiB
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#include <boost/rational.hpp> /// For calculations related to sampling coefficients.
#include <base/scope_guard_safe.h>
#include <optional>
#include <unordered_set>
#include <Storages/MergeTree/MergeTreeDataSelectExecutor.h>
#include <Storages/MergeTree/MergeTreeReadPool.h>
#include <Storages/MergeTree/MergeTreeIndices.h>
#include <Storages/MergeTree/MergeTreeIndexReader.h>
#include <Storages/MergeTree/KeyCondition.h>
#include <Storages/MergeTree/MergeTreeDataPartUUID.h>
#include <Storages/ReadInOrderOptimizer.h>
#include <Parsers/ASTIdentifier.h>
#include <Parsers/ASTLiteral.h>
#include <Parsers/ASTFunction.h>
#include <Parsers/ASTSampleRatio.h>
#include <Parsers/parseIdentifierOrStringLiteral.h>
#include <Interpreters/ExpressionAnalyzer.h>
#include <Interpreters/Context.h>
#include <Processors/ConcatProcessor.h>
#include <Processors/QueryPlan/QueryPlan.h>
#include <Processors/QueryPlan/CreatingSetsStep.h>
#include <Processors/QueryPlan/FilterStep.h>
#include <Processors/QueryPlan/ExpressionStep.h>
#include <Processors/QueryPlan/ReadFromPreparedSource.h>
#include <Processors/QueryPlan/ReadFromMergeTree.h>
#include <Processors/QueryPlan/UnionStep.h>
#include <Processors/Sources/SourceFromSingleChunk.h>
#include <Core/UUID.h>
#include <DataTypes/DataTypeDate.h>
#include <DataTypes/DataTypeEnum.h>
#include <DataTypes/DataTypeUUID.h>
#include <DataTypes/DataTypeTuple.h>
#include <DataTypes/DataTypesNumber.h>
#include <DataTypes/DataTypeArray.h>
#include <Storages/VirtualColumnUtils.h>
#include <Interpreters/InterpreterSelectQuery.h>
#include <Processors/Transforms/AggregatingTransform.h>
#include <Storages/MergeTree/StorageFromMergeTreeDataPart.h>
#include <IO/WriteBufferFromOStream.h>
namespace ProfileEvents
{
extern const Event SelectedParts;
extern const Event SelectedRanges;
extern const Event SelectedMarks;
}
namespace DB
{
namespace ErrorCodes
{
extern const int LOGICAL_ERROR;
extern const int INDEX_NOT_USED;
extern const int ILLEGAL_TYPE_OF_COLUMN_FOR_FILTER;
extern const int ILLEGAL_COLUMN;
extern const int ARGUMENT_OUT_OF_BOUND;
extern const int TOO_MANY_ROWS;
extern const int CANNOT_PARSE_TEXT;
extern const int TOO_MANY_PARTITIONS;
extern const int DUPLICATED_PART_UUIDS;
extern const int NO_SUCH_COLUMN_IN_TABLE;
extern const int PROJECTION_NOT_USED;
}
MergeTreeDataSelectExecutor::MergeTreeDataSelectExecutor(const MergeTreeData & data_)
: data(data_), log(&Poco::Logger::get(data.getLogName() + " (SelectExecutor)"))
{
}
size_t MergeTreeDataSelectExecutor::getApproximateTotalRowsToRead(
const MergeTreeData::DataPartsVector & parts,
const StorageMetadataPtr & metadata_snapshot,
const KeyCondition & key_condition,
const Settings & settings,
Poco::Logger * log)
{
size_t rows_count = 0;
/// We will find out how many rows we would have read without sampling.
LOG_DEBUG(log, "Preliminary index scan with condition: {}", key_condition.toString());
for (const auto & part : parts)
{
MarkRanges ranges = markRangesFromPKRange(part, metadata_snapshot, key_condition, settings, log);
/** In order to get a lower bound on the number of rows that match the condition on PK,
* consider only guaranteed full marks.
* That is, do not take into account the first and last marks, which may be incomplete.
*/
for (const auto & range : ranges)
if (range.end - range.begin > 2)
rows_count += part->index_granularity.getRowsCountInRange({range.begin + 1, range.end - 1});
}
return rows_count;
}
using RelativeSize = boost::rational<ASTSampleRatio::BigNum>;
static std::string toString(const RelativeSize & x)
{
return ASTSampleRatio::toString(x.numerator()) + "/" + ASTSampleRatio::toString(x.denominator());
}
/// Converts sample size to an approximate number of rows (ex. `SAMPLE 1000000`) to relative value (ex. `SAMPLE 0.1`).
static RelativeSize convertAbsoluteSampleSizeToRelative(const ASTPtr & node, size_t approx_total_rows)
{
if (approx_total_rows == 0)
return 1;
const auto & node_sample = node->as<ASTSampleRatio &>();
auto absolute_sample_size = node_sample.ratio.numerator / node_sample.ratio.denominator;
return std::min(RelativeSize(1), RelativeSize(absolute_sample_size) / RelativeSize(approx_total_rows));
}
QueryPlanPtr MergeTreeDataSelectExecutor::read(
const Names & column_names_to_return,
const StorageMetadataPtr & metadata_snapshot,
const SelectQueryInfo & query_info,
ContextPtr context,
const UInt64 max_block_size,
const unsigned num_streams,
QueryProcessingStage::Enum processed_stage,
std::shared_ptr<PartitionIdToMaxBlock> max_block_numbers_to_read) const
{
if (query_info.merge_tree_empty_result)
return std::make_unique<QueryPlan>();
const auto & settings = context->getSettingsRef();
if (!query_info.projection)
{
auto plan = readFromParts(
query_info.merge_tree_select_result_ptr ? MergeTreeData::DataPartsVector{} : data.getDataPartsVector(),
column_names_to_return,
metadata_snapshot,
metadata_snapshot,
query_info,
context,
max_block_size,
num_streams,
max_block_numbers_to_read,
query_info.merge_tree_select_result_ptr);
if (plan->isInitialized() && settings.allow_experimental_projection_optimization && settings.force_optimize_projection
&& !metadata_snapshot->projections.empty())
throw Exception(
"No projection is used when allow_experimental_projection_optimization = 1 and force_optimize_projection = 1",
ErrorCodes::PROJECTION_NOT_USED);
return plan;
}
LOG_DEBUG(
log,
"Choose {} {} projection {}",
query_info.projection->complete ? "complete" : "incomplete",
query_info.projection->desc->type,
query_info.projection->desc->name);
Pipes pipes;
Pipe projection_pipe;
Pipe ordinary_pipe;
auto projection_plan = std::make_unique<QueryPlan>();
if (query_info.projection->desc->is_minmax_count_projection)
{
Pipe pipe(std::make_shared<SourceFromSingleChunk>(query_info.minmax_count_projection_block));
auto read_from_pipe = std::make_unique<ReadFromPreparedSource>(std::move(pipe));
projection_plan->addStep(std::move(read_from_pipe));
}
else if (query_info.projection->merge_tree_projection_select_result_ptr)
{
LOG_DEBUG(log, "projection required columns: {}", fmt::join(query_info.projection->required_columns, ", "));
projection_plan = readFromParts(
{},
query_info.projection->required_columns,
metadata_snapshot,
query_info.projection->desc->metadata,
query_info,
context,
max_block_size,
num_streams,
max_block_numbers_to_read,
query_info.projection->merge_tree_projection_select_result_ptr);
}
if (projection_plan->isInitialized())
{
if (query_info.projection->before_where)
{
auto where_step = std::make_unique<FilterStep>(
projection_plan->getCurrentDataStream(),
query_info.projection->before_where,
query_info.projection->where_column_name,
query_info.projection->remove_where_filter);
where_step->setStepDescription("WHERE");
projection_plan->addStep(std::move(where_step));
}
if (query_info.projection->before_aggregation)
{
auto expression_before_aggregation
= std::make_unique<ExpressionStep>(projection_plan->getCurrentDataStream(), query_info.projection->before_aggregation);
expression_before_aggregation->setStepDescription("Before GROUP BY");
projection_plan->addStep(std::move(expression_before_aggregation));
}
projection_pipe = projection_plan->convertToPipe(
QueryPlanOptimizationSettings::fromContext(context), BuildQueryPipelineSettings::fromContext(context));
}
if (query_info.projection->merge_tree_normal_select_result_ptr)
{
auto storage_from_base_parts_of_projection
= StorageFromMergeTreeDataPart::create(data, query_info.projection->merge_tree_normal_select_result_ptr);
auto interpreter = InterpreterSelectQuery(
query_info.query,
context,
storage_from_base_parts_of_projection,
nullptr,
SelectQueryOptions{processed_stage}.projectionQuery());
QueryPlan ordinary_query_plan;
interpreter.buildQueryPlan(ordinary_query_plan);
const auto & expressions = interpreter.getAnalysisResult();
if (processed_stage == QueryProcessingStage::Enum::FetchColumns && expressions.before_where)
{
auto where_step = std::make_unique<FilterStep>(
ordinary_query_plan.getCurrentDataStream(),
expressions.before_where,
expressions.where_column_name,
expressions.remove_where_filter);
where_step->setStepDescription("WHERE");
ordinary_query_plan.addStep(std::move(where_step));
}
ordinary_pipe = ordinary_query_plan.convertToPipe(
QueryPlanOptimizationSettings::fromContext(context), BuildQueryPipelineSettings::fromContext(context));
}
if (query_info.projection->desc->type == ProjectionDescription::Type::Aggregate)
{
/// Here we create shared ManyAggregatedData for both projection and ordinary data.
/// For ordinary data, AggregatedData is filled in a usual way.
/// For projection data, AggregatedData is filled by merging aggregation states.
/// When all AggregatedData is filled, we merge aggregation states together in a usual way.
/// Pipeline will look like:
/// ReadFromProjection -> Aggregating (only merge states) ->
/// ReadFromProjection -> Aggregating (only merge states) ->
/// ... -> Resize -> ConvertingAggregatedToChunks
/// ReadFromOrdinaryPart -> Aggregating (usual) -> (added by last Aggregating)
/// ReadFromOrdinaryPart -> Aggregating (usual) ->
/// ...
auto many_data = std::make_shared<ManyAggregatedData>(projection_pipe.numOutputPorts() + ordinary_pipe.numOutputPorts());
size_t counter = 0;
AggregatorListPtr aggregator_list_ptr = std::make_shared<AggregatorList>();
// TODO apply in_order_optimization here
auto build_aggregate_pipe = [&](Pipe & pipe, bool projection)
{
const auto & header_before_aggregation = pipe.getHeader();
ColumnNumbers keys;
for (const auto & key : query_info.projection->aggregation_keys)
keys.push_back(header_before_aggregation.getPositionByName(key.name));
AggregateDescriptions aggregates = query_info.projection->aggregate_descriptions;
if (!projection)
{
for (auto & descr : aggregates)
if (descr.arguments.empty())
for (const auto & name : descr.argument_names)
descr.arguments.push_back(header_before_aggregation.getPositionByName(name));
}
AggregatingTransformParamsPtr transform_params;
if (projection)
{
Aggregator::Params params(
header_before_aggregation,
keys,
aggregates,
query_info.projection->aggregate_overflow_row,
settings.max_rows_to_group_by,
settings.group_by_overflow_mode,
settings.group_by_two_level_threshold,
settings.group_by_two_level_threshold_bytes,
settings.max_bytes_before_external_group_by,
settings.empty_result_for_aggregation_by_empty_set,
context->getTemporaryVolume(),
settings.max_threads,
settings.min_free_disk_space_for_temporary_data,
settings.compile_expressions,
settings.min_count_to_compile_aggregate_expression,
header_before_aggregation); // The source header is also an intermediate header
transform_params = std::make_shared<AggregatingTransformParams>(
std::move(params), aggregator_list_ptr, query_info.projection->aggregate_final);
/// This part is hacky.
/// We want AggregatingTransform to work with aggregate states instead of normal columns.
/// It is almost the same, just instead of adding new data to aggregation state we merge it with existing.
///
/// It is needed because data in projection:
/// * is not merged completely (we may have states with the same key in different parts)
/// * is not split into buckets (so if we just use MergingAggregated, it will use single thread)
transform_params->only_merge = true;
}
else
{
Aggregator::Params params(
header_before_aggregation,
keys,
aggregates,
query_info.projection->aggregate_overflow_row,
settings.max_rows_to_group_by,
settings.group_by_overflow_mode,
settings.group_by_two_level_threshold,
settings.group_by_two_level_threshold_bytes,
settings.max_bytes_before_external_group_by,
settings.empty_result_for_aggregation_by_empty_set,
context->getTemporaryVolume(),
settings.max_threads,
settings.min_free_disk_space_for_temporary_data,
settings.compile_aggregate_expressions,
settings.min_count_to_compile_aggregate_expression);
transform_params = std::make_shared<AggregatingTransformParams>(
std::move(params), aggregator_list_ptr, query_info.projection->aggregate_final);
}
pipe.resize(pipe.numOutputPorts(), true, true);
auto merge_threads = num_streams;
auto temporary_data_merge_threads = settings.aggregation_memory_efficient_merge_threads
? static_cast<size_t>(settings.aggregation_memory_efficient_merge_threads)
: static_cast<size_t>(settings.max_threads);
pipe.addSimpleTransform([&](const Block & header)
{
return std::make_shared<AggregatingTransform>(
header, transform_params, many_data, counter++, merge_threads, temporary_data_merge_threads);
});
};
if (!projection_pipe.empty())
build_aggregate_pipe(projection_pipe, true);
if (!ordinary_pipe.empty())
build_aggregate_pipe(ordinary_pipe, false);
}
pipes.emplace_back(std::move(projection_pipe));
pipes.emplace_back(std::move(ordinary_pipe));
auto pipe = Pipe::unitePipes(std::move(pipes));
auto plan = std::make_unique<QueryPlan>();
if (pipe.empty())
return plan;
pipe.resize(1);
auto step = std::make_unique<ReadFromStorageStep>(
std::move(pipe),
fmt::format("MergeTree(with {} projection {})", query_info.projection->desc->type, query_info.projection->desc->name));
plan->addStep(std::move(step));
if (query_info.projection->subqueries_for_sets && !query_info.projection->subqueries_for_sets->empty())
{
SizeLimits limits(settings.max_rows_to_transfer, settings.max_bytes_to_transfer, settings.transfer_overflow_mode);
addCreatingSetsStep(*plan, std::move(*query_info.projection->subqueries_for_sets), limits, context);
}
return plan;
}
MergeTreeDataSelectSamplingData MergeTreeDataSelectExecutor::getSampling(
const ASTSelectQuery & select,
NamesAndTypesList available_real_columns,
const MergeTreeData::DataPartsVector & parts,
KeyCondition & key_condition,
const MergeTreeData & data,
const StorageMetadataPtr & metadata_snapshot,
ContextPtr context,
bool sample_factor_column_queried,
Poco::Logger * log)
{
const Settings & settings = context->getSettingsRef();
/// Sampling.
MergeTreeDataSelectSamplingData sampling;
RelativeSize relative_sample_size = 0;
RelativeSize relative_sample_offset = 0;
auto select_sample_size = select.sampleSize();
auto select_sample_offset = select.sampleOffset();
if (select_sample_size)
{
relative_sample_size.assign(
select_sample_size->as<ASTSampleRatio &>().ratio.numerator,
select_sample_size->as<ASTSampleRatio &>().ratio.denominator);
if (relative_sample_size < 0)
throw Exception("Negative sample size", ErrorCodes::ARGUMENT_OUT_OF_BOUND);
relative_sample_offset = 0;
if (select_sample_offset)
relative_sample_offset.assign(
select_sample_offset->as<ASTSampleRatio &>().ratio.numerator,
select_sample_offset->as<ASTSampleRatio &>().ratio.denominator);
if (relative_sample_offset < 0)
throw Exception("Negative sample offset", ErrorCodes::ARGUMENT_OUT_OF_BOUND);
/// Convert absolute value of the sampling (in form `SAMPLE 1000000` - how many rows to
/// read) into the relative `SAMPLE 0.1` (how much data to read).
size_t approx_total_rows = 0;
if (relative_sample_size > 1 || relative_sample_offset > 1)
approx_total_rows = getApproximateTotalRowsToRead(parts, metadata_snapshot, key_condition, settings, log);
if (relative_sample_size > 1)
{
relative_sample_size = convertAbsoluteSampleSizeToRelative(select_sample_size, approx_total_rows);
LOG_DEBUG(log, "Selected relative sample size: {}", toString(relative_sample_size));
}
/// SAMPLE 1 is the same as the absence of SAMPLE.
if (relative_sample_size == RelativeSize(1))
relative_sample_size = 0;
if (relative_sample_offset > 0 && RelativeSize(0) == relative_sample_size)
throw Exception("Sampling offset is incorrect because no sampling", ErrorCodes::ARGUMENT_OUT_OF_BOUND);
if (relative_sample_offset > 1)
{
relative_sample_offset = convertAbsoluteSampleSizeToRelative(select_sample_offset, approx_total_rows);
LOG_DEBUG(log, "Selected relative sample offset: {}", toString(relative_sample_offset));
}
}
/** Which range of sampling key values do I need to read?
* First, in the whole range ("universe") we select the interval
* of relative `relative_sample_size` size, offset from the beginning by `relative_sample_offset`.
*
* Example: SAMPLE 0.4 OFFSET 0.3
*
* [------********------]
* ^ - offset
* <------> - size
*
* If the interval passes through the end of the universe, then cut its right side.
*
* Example: SAMPLE 0.4 OFFSET 0.8
*
* [----------------****]
* ^ - offset
* <------> - size
*
* Next, if the `parallel_replicas_count`, `parallel_replica_offset` settings are set,
* then it is necessary to break the received interval into pieces of the number `parallel_replicas_count`,
* and select a piece with the number `parallel_replica_offset` (from zero).
*
* Example: SAMPLE 0.4 OFFSET 0.3, parallel_replicas_count = 2, parallel_replica_offset = 1
*
* [----------****------]
* ^ - offset
* <------> - size
* <--><--> - pieces for different `parallel_replica_offset`, select the second one.
*
* It is very important that the intervals for different `parallel_replica_offset` cover the entire range without gaps and overlaps.
* It is also important that the entire universe can be covered using SAMPLE 0.1 OFFSET 0, ... OFFSET 0.9 and similar decimals.
*/
/// Parallel replicas has been requested but there is no way to sample data.
/// Select all data from first replica and no data from other replicas.
if (settings.parallel_replicas_count > 1 && !data.supportsSampling() && settings.parallel_replica_offset > 0)
{
LOG_DEBUG(log, "Will use no data on this replica because parallel replicas processing has been requested"
" (the setting 'max_parallel_replicas') but the table does not support sampling and this replica is not the first.");
sampling.read_nothing = true;
return sampling;
}
sampling.use_sampling = relative_sample_size > 0 || (settings.parallel_replicas_count > 1 && data.supportsSampling());
bool no_data = false; /// There is nothing left after sampling.
if (sampling.use_sampling)
{
if (sample_factor_column_queried && relative_sample_size != RelativeSize(0))
sampling.used_sample_factor = 1.0 / boost::rational_cast<Float64>(relative_sample_size);
RelativeSize size_of_universum = 0;
const auto & sampling_key = metadata_snapshot->getSamplingKey();
DataTypePtr sampling_column_type = sampling_key.data_types[0];
if (sampling_key.data_types.size() == 1)
{
if (typeid_cast<const DataTypeUInt64 *>(sampling_column_type.get()))
size_of_universum = RelativeSize(std::numeric_limits<UInt64>::max()) + RelativeSize(1);
else if (typeid_cast<const DataTypeUInt32 *>(sampling_column_type.get()))
size_of_universum = RelativeSize(std::numeric_limits<UInt32>::max()) + RelativeSize(1);
else if (typeid_cast<const DataTypeUInt16 *>(sampling_column_type.get()))
size_of_universum = RelativeSize(std::numeric_limits<UInt16>::max()) + RelativeSize(1);
else if (typeid_cast<const DataTypeUInt8 *>(sampling_column_type.get()))
size_of_universum = RelativeSize(std::numeric_limits<UInt8>::max()) + RelativeSize(1);
}
if (size_of_universum == RelativeSize(0))
throw Exception(
"Invalid sampling column type in storage parameters: " + sampling_column_type->getName()
+ ". Must be one unsigned integer type",
ErrorCodes::ILLEGAL_TYPE_OF_COLUMN_FOR_FILTER);
if (settings.parallel_replicas_count > 1)
{
if (relative_sample_size == RelativeSize(0))
relative_sample_size = 1;
relative_sample_size /= settings.parallel_replicas_count.value;
relative_sample_offset += relative_sample_size * RelativeSize(settings.parallel_replica_offset.value);
}
if (relative_sample_offset >= RelativeSize(1))
no_data = true;
/// Calculate the half-interval of `[lower, upper)` column values.
bool has_lower_limit = false;
bool has_upper_limit = false;
RelativeSize lower_limit_rational = relative_sample_offset * size_of_universum;
RelativeSize upper_limit_rational = (relative_sample_offset + relative_sample_size) * size_of_universum;
UInt64 lower = boost::rational_cast<ASTSampleRatio::BigNum>(lower_limit_rational);
UInt64 upper = boost::rational_cast<ASTSampleRatio::BigNum>(upper_limit_rational);
if (lower > 0)
has_lower_limit = true;
if (upper_limit_rational < size_of_universum)
has_upper_limit = true;
/*std::cerr << std::fixed << std::setprecision(100)
<< "relative_sample_size: " << relative_sample_size << "\n"
<< "relative_sample_offset: " << relative_sample_offset << "\n"
<< "lower_limit_float: " << lower_limit_rational << "\n"
<< "upper_limit_float: " << upper_limit_rational << "\n"
<< "lower: " << lower << "\n"
<< "upper: " << upper << "\n";*/
if ((has_upper_limit && upper == 0)
|| (has_lower_limit && has_upper_limit && lower == upper))
no_data = true;
if (no_data || (!has_lower_limit && !has_upper_limit))
{
sampling.use_sampling = false;
}
else
{
/// Let's add the conditions to cut off something else when the index is scanned again and when the request is processed.
std::shared_ptr<ASTFunction> lower_function;
std::shared_ptr<ASTFunction> upper_function;
/// If sample and final are used together no need to calculate sampling expression twice.
/// The first time it was calculated for final, because sample key is a part of the PK.
/// So, assume that we already have calculated column.
ASTPtr sampling_key_ast = metadata_snapshot->getSamplingKeyAST();
if (select.final())
{
sampling_key_ast = std::make_shared<ASTIdentifier>(sampling_key.column_names[0]);
/// We do spoil available_real_columns here, but it is not used later.
available_real_columns.emplace_back(sampling_key.column_names[0], std::move(sampling_column_type));
}
if (has_lower_limit)
{
if (!key_condition.addCondition(sampling_key.column_names[0], Range::createLeftBounded(lower, true)))
throw Exception("Sampling column not in primary key", ErrorCodes::ILLEGAL_COLUMN);
ASTPtr args = std::make_shared<ASTExpressionList>();
args->children.push_back(sampling_key_ast);
args->children.push_back(std::make_shared<ASTLiteral>(lower));
lower_function = std::make_shared<ASTFunction>();
lower_function->name = "greaterOrEquals";
lower_function->arguments = args;
lower_function->children.push_back(lower_function->arguments);
sampling.filter_function = lower_function;
}
if (has_upper_limit)
{
if (!key_condition.addCondition(sampling_key.column_names[0], Range::createRightBounded(upper, false)))
throw Exception("Sampling column not in primary key", ErrorCodes::ILLEGAL_COLUMN);
ASTPtr args = std::make_shared<ASTExpressionList>();
args->children.push_back(sampling_key_ast);
args->children.push_back(std::make_shared<ASTLiteral>(upper));
upper_function = std::make_shared<ASTFunction>();
upper_function->name = "less";
upper_function->arguments = args;
upper_function->children.push_back(upper_function->arguments);
sampling.filter_function = upper_function;
}
if (has_lower_limit && has_upper_limit)
{
ASTPtr args = std::make_shared<ASTExpressionList>();
args->children.push_back(lower_function);
args->children.push_back(upper_function);
sampling.filter_function = std::make_shared<ASTFunction>();
sampling.filter_function->name = "and";
sampling.filter_function->arguments = args;
sampling.filter_function->children.push_back(sampling.filter_function->arguments);
}
ASTPtr query = sampling.filter_function;
auto syntax_result = TreeRewriter(context).analyze(query, available_real_columns);
sampling.filter_expression = ExpressionAnalyzer(sampling.filter_function, syntax_result, context).getActionsDAG(false);
}
}
if (no_data)
{
LOG_DEBUG(log, "Sampling yields no data.");
sampling.read_nothing = true;
}
return sampling;
}
std::optional<std::unordered_set<String>> MergeTreeDataSelectExecutor::filterPartsByVirtualColumns(
const MergeTreeData & data,
const MergeTreeData::DataPartsVector & parts,
const ASTPtr & query,
ContextPtr context)
{
std::unordered_set<String> part_values;
ASTPtr expression_ast;
auto virtual_columns_block = data.getBlockWithVirtualPartColumns(parts, true /* one_part */);
// Generate valid expressions for filtering
VirtualColumnUtils::prepareFilterBlockWithQuery(query, context, virtual_columns_block, expression_ast);
// If there is still something left, fill the virtual block and do the filtering.
if (expression_ast)
{
virtual_columns_block = data.getBlockWithVirtualPartColumns(parts, false /* one_part */);
VirtualColumnUtils::filterBlockWithQuery(query, virtual_columns_block, context, expression_ast);
return VirtualColumnUtils::extractSingleValueFromBlock<String>(virtual_columns_block, "_part");
}
return {};
}
void MergeTreeDataSelectExecutor::filterPartsByPartition(
MergeTreeData::DataPartsVector & parts,
const std::optional<std::unordered_set<String>> & part_values,
const StorageMetadataPtr & metadata_snapshot,
const MergeTreeData & data,
const SelectQueryInfo & query_info,
const ContextPtr & context,
const PartitionIdToMaxBlock * max_block_numbers_to_read,
Poco::Logger * log,
ReadFromMergeTree::IndexStats & index_stats)
{
const Settings & settings = context->getSettingsRef();
std::optional<PartitionPruner> partition_pruner;
std::optional<KeyCondition> minmax_idx_condition;
DataTypes minmax_columns_types;
if (metadata_snapshot->hasPartitionKey())
{
const auto & partition_key = metadata_snapshot->getPartitionKey();
auto minmax_columns_names = data.getMinMaxColumnsNames(partition_key);
minmax_columns_types = data.getMinMaxColumnsTypes(partition_key);
minmax_idx_condition.emplace(
query_info, context, minmax_columns_names, data.getMinMaxExpr(partition_key, ExpressionActionsSettings::fromContext(context)));
partition_pruner.emplace(metadata_snapshot, query_info, context, false /* strict */);
if (settings.force_index_by_date && (minmax_idx_condition->alwaysUnknownOrTrue() && partition_pruner->isUseless()))
{
String msg = "Neither MinMax index by columns (";
bool first = true;
for (const String & col : minmax_columns_names)
{
if (first)
first = false;
else
msg += ", ";
msg += col;
}
msg += ") nor partition expr is used and setting 'force_index_by_date' is set";
throw Exception(msg, ErrorCodes::INDEX_NOT_USED);
}
}
auto query_context = context->hasQueryContext() ? context->getQueryContext() : context;
PartFilterCounters part_filter_counters;
if (query_context->getSettingsRef().allow_experimental_query_deduplication)
selectPartsToReadWithUUIDFilter(
parts,
part_values,
data.getPinnedPartUUIDs(),
minmax_idx_condition,
minmax_columns_types,
partition_pruner,
max_block_numbers_to_read,
query_context,
part_filter_counters,
log);
else
selectPartsToRead(
parts,
part_values,
minmax_idx_condition,
minmax_columns_types,
partition_pruner,
max_block_numbers_to_read,
part_filter_counters);
index_stats.emplace_back(ReadFromMergeTree::IndexStat{
.type = ReadFromMergeTree::IndexType::None,
.num_parts_after = part_filter_counters.num_initial_selected_parts,
.num_granules_after = part_filter_counters.num_initial_selected_granules});
if (minmax_idx_condition)
{
auto description = minmax_idx_condition->getDescription();
index_stats.emplace_back(ReadFromMergeTree::IndexStat{
.type = ReadFromMergeTree::IndexType::MinMax,
.condition = std::move(description.condition),
.used_keys = std::move(description.used_keys),
.num_parts_after = part_filter_counters.num_parts_after_minmax,
.num_granules_after = part_filter_counters.num_granules_after_minmax});
LOG_DEBUG(log, "MinMax index condition: {}", minmax_idx_condition->toString());
}
if (partition_pruner)
{
auto description = partition_pruner->getKeyCondition().getDescription();
index_stats.emplace_back(ReadFromMergeTree::IndexStat{
.type = ReadFromMergeTree::IndexType::Partition,
.condition = std::move(description.condition),
.used_keys = std::move(description.used_keys),
.num_parts_after = part_filter_counters.num_parts_after_partition_pruner,
.num_granules_after = part_filter_counters.num_granules_after_partition_pruner});
}
}
RangesInDataParts MergeTreeDataSelectExecutor::filterPartsByPrimaryKeyAndSkipIndexes(
MergeTreeData::DataPartsVector && parts,
StorageMetadataPtr metadata_snapshot,
const SelectQueryInfo & query_info,
const ContextPtr & context,
const KeyCondition & key_condition,
const MergeTreeReaderSettings & reader_settings,
Poco::Logger * log,
size_t num_streams,
ReadFromMergeTree::IndexStats & index_stats,
bool use_skip_indexes)
{
RangesInDataParts parts_with_ranges(parts.size());
const Settings & settings = context->getSettingsRef();
/// Let's start analyzing all useful indices
struct DataSkippingIndexAndCondition
{
MergeTreeIndexPtr index;
MergeTreeIndexConditionPtr condition;
std::atomic<size_t> total_granules{0};
std::atomic<size_t> granules_dropped{0};
std::atomic<size_t> total_parts{0};
std::atomic<size_t> parts_dropped{0};
DataSkippingIndexAndCondition(MergeTreeIndexPtr index_, MergeTreeIndexConditionPtr condition_)
: index(index_), condition(condition_)
{
}
};
std::list<DataSkippingIndexAndCondition> useful_indices;
if (use_skip_indexes)
{
for (const auto & index : metadata_snapshot->getSecondaryIndices())
{
auto index_helper = MergeTreeIndexFactory::instance().get(index);
auto condition = index_helper->createIndexCondition(query_info, context);
if (!condition->alwaysUnknownOrTrue())
useful_indices.emplace_back(index_helper, condition);
}
}
if (use_skip_indexes && settings.force_data_skipping_indices.changed)
{
const auto & indices = settings.force_data_skipping_indices.toString();
Strings forced_indices;
{
Tokens tokens(&indices[0], &indices[indices.size()], settings.max_query_size);
IParser::Pos pos(tokens, settings.max_parser_depth);
Expected expected;
if (!parseIdentifiersOrStringLiterals(pos, expected, forced_indices))
throw Exception(ErrorCodes::CANNOT_PARSE_TEXT, "Cannot parse force_data_skipping_indices ('{}')", indices);
}
if (forced_indices.empty())
throw Exception(ErrorCodes::CANNOT_PARSE_TEXT, "No indices parsed from force_data_skipping_indices ('{}')", indices);
std::unordered_set<std::string> useful_indices_names;
for (const auto & useful_index : useful_indices)
useful_indices_names.insert(useful_index.index->index.name);
for (const auto & index_name : forced_indices)
{
if (!useful_indices_names.count(index_name))
{
throw Exception(
ErrorCodes::INDEX_NOT_USED,
"Index {} is not used and setting 'force_data_skipping_indices' contains it",
backQuote(index_name));
}
}
}
std::atomic<size_t> sum_marks_pk = 0;
std::atomic<size_t> sum_parts_pk = 0;
/// Let's find what range to read from each part.
{
std::atomic<size_t> total_rows{0};
SizeLimits limits;
if (settings.read_overflow_mode == OverflowMode::THROW && settings.max_rows_to_read)
limits = SizeLimits(settings.max_rows_to_read, 0, settings.read_overflow_mode);
SizeLimits leaf_limits;
if (settings.read_overflow_mode_leaf == OverflowMode::THROW && settings.max_rows_to_read_leaf)
leaf_limits = SizeLimits(settings.max_rows_to_read_leaf, 0, settings.read_overflow_mode_leaf);
auto mark_cache = context->getIndexMarkCache();
auto uncompressed_cache = context->getIndexUncompressedCache();
auto process_part = [&](size_t part_index)
{
auto & part = parts[part_index];
RangesInDataPart ranges(part, part_index);
size_t total_marks_count = part->index_granularity.getMarksCountWithoutFinal();
if (metadata_snapshot->hasPrimaryKey())
ranges.ranges = markRangesFromPKRange(part, metadata_snapshot, key_condition, settings, log);
else if (total_marks_count)
ranges.ranges = MarkRanges{MarkRange{0, total_marks_count}};
sum_marks_pk.fetch_add(ranges.getMarksCount(), std::memory_order_relaxed);
if (!ranges.ranges.empty())
sum_parts_pk.fetch_add(1, std::memory_order_relaxed);
for (auto & index_and_condition : useful_indices)
{
if (ranges.ranges.empty())
break;
index_and_condition.total_parts.fetch_add(1, std::memory_order_relaxed);
size_t total_granules = 0;
size_t granules_dropped = 0;
ranges.ranges = filterMarksUsingIndex(
index_and_condition.index,
index_and_condition.condition,
part,
ranges.ranges,
settings,
reader_settings,
total_granules,
granules_dropped,
mark_cache.get(),
uncompressed_cache.get(),
log);
index_and_condition.total_granules.fetch_add(total_granules, std::memory_order_relaxed);
index_and_condition.granules_dropped.fetch_add(granules_dropped, std::memory_order_relaxed);
if (ranges.ranges.empty())
index_and_condition.parts_dropped.fetch_add(1, std::memory_order_relaxed);
}
if (!ranges.ranges.empty())
{
if (limits.max_rows || leaf_limits.max_rows)
{
/// Fail fast if estimated number of rows to read exceeds the limit
auto current_rows_estimate = ranges.getRowsCount();
size_t prev_total_rows_estimate = total_rows.fetch_add(current_rows_estimate);
size_t total_rows_estimate = current_rows_estimate + prev_total_rows_estimate;
limits.check(total_rows_estimate, 0, "rows (controlled by 'max_rows_to_read' setting)", ErrorCodes::TOO_MANY_ROWS);
leaf_limits.check(
total_rows_estimate, 0, "rows (controlled by 'max_rows_to_read_leaf' setting)", ErrorCodes::TOO_MANY_ROWS);
}
parts_with_ranges[part_index] = std::move(ranges);
}
};
size_t num_threads = std::min(size_t(num_streams), parts.size());
if (num_threads <= 1)
{
for (size_t part_index = 0; part_index < parts.size(); ++part_index)
process_part(part_index);
}
else
{
/// Parallel loading of data parts.
ThreadPool pool(num_threads);
for (size_t part_index = 0; part_index < parts.size(); ++part_index)
pool.scheduleOrThrowOnError([&, part_index, thread_group = CurrentThread::getGroup()]
{
SCOPE_EXIT_SAFE(if (thread_group) CurrentThread::detachQueryIfNotDetached(););
if (thread_group)
CurrentThread::attachTo(thread_group);
process_part(part_index);
});
pool.wait();
}
/// Skip empty ranges.
size_t next_part = 0;
for (size_t part_index = 0; part_index < parts.size(); ++part_index)
{
auto & part = parts_with_ranges[part_index];
if (!part.data_part)
continue;
if (next_part != part_index)
std::swap(parts_with_ranges[next_part], part);
++next_part;
}
parts_with_ranges.resize(next_part);
}
if (metadata_snapshot->hasPrimaryKey())
{
auto description = key_condition.getDescription();
index_stats.emplace_back(ReadFromMergeTree::IndexStat{
.type = ReadFromMergeTree::IndexType::PrimaryKey,
.condition = std::move(description.condition),
.used_keys = std::move(description.used_keys),
.num_parts_after = sum_parts_pk.load(std::memory_order_relaxed),
.num_granules_after = sum_marks_pk.load(std::memory_order_relaxed)});
}
for (const auto & index_and_condition : useful_indices)
{
const auto & index_name = index_and_condition.index->index.name;
LOG_DEBUG(
log,
"Index {} has dropped {}/{} granules.",
backQuote(index_name),
index_and_condition.granules_dropped,
index_and_condition.total_granules);
std::string description
= index_and_condition.index->index.type + " GRANULARITY " + std::to_string(index_and_condition.index->index.granularity);
index_stats.emplace_back(ReadFromMergeTree::IndexStat{
.type = ReadFromMergeTree::IndexType::Skip,
.name = index_name,
.description = std::move(description), //-V1030
.num_parts_after = index_and_condition.total_parts - index_and_condition.parts_dropped,
.num_granules_after = index_and_condition.total_granules - index_and_condition.granules_dropped});
}
return parts_with_ranges;
}
std::shared_ptr<QueryIdHolder> MergeTreeDataSelectExecutor::checkLimits(
const MergeTreeData & data,
const ReadFromMergeTree::AnalysisResult & result,
const ContextPtr & context)
{
const auto & settings = context->getSettingsRef();
const auto data_settings = data.getSettings();
auto max_partitions_to_read
= settings.max_partitions_to_read.changed ? settings.max_partitions_to_read : data_settings->max_partitions_to_read;
if (max_partitions_to_read > 0)
{
std::set<String> partitions;
for (const auto & part_with_ranges : result.parts_with_ranges)
partitions.insert(part_with_ranges.data_part->info.partition_id);
if (partitions.size() > size_t(max_partitions_to_read))
throw Exception(
ErrorCodes::TOO_MANY_PARTITIONS,
"Too many partitions to read. Current {}, max {}",
partitions.size(),
max_partitions_to_read);
}
if (data_settings->max_concurrent_queries > 0 && data_settings->min_marks_to_honor_max_concurrent_queries > 0
&& result.selected_marks >= data_settings->min_marks_to_honor_max_concurrent_queries)
{
auto query_id = context->getCurrentQueryId();
if (!query_id.empty())
{
auto lock = data.getQueryIdSetLock();
if (data.insertQueryIdOrThrowNoLock(query_id, data_settings->max_concurrent_queries, lock))
{
try
{
return std::make_shared<QueryIdHolder>(query_id, data);
}
catch (...)
{
/// If we fail to construct the holder, remove query_id explicitly to avoid leak.
data.removeQueryIdNoLock(query_id, lock);
throw;
}
}
}
}
return nullptr;
}
static void selectColumnNames(
const Names & column_names_to_return,
const MergeTreeData & data,
Names & real_column_names,
Names & virt_column_names,
bool & sample_factor_column_queried)
{
sample_factor_column_queried = false;
for (const String & name : column_names_to_return)
{
if (name == "_part")
{
virt_column_names.push_back(name);
}
else if (name == "_part_index")
{
virt_column_names.push_back(name);
}
else if (name == "_partition_id")
{
virt_column_names.push_back(name);
}
else if (name == "_part_uuid")
{
virt_column_names.push_back(name);
}
else if (name == "_partition_value")
{
if (!typeid_cast<const DataTypeTuple *>(data.getPartitionValueType().get()))
{
throw Exception(
ErrorCodes::NO_SUCH_COLUMN_IN_TABLE,
"Missing column `_partition_value` because there is no partition column in table {}",
data.getStorageID().getTableName());
}
virt_column_names.push_back(name);
}
else if (name == "_sample_factor")
{
sample_factor_column_queried = true;
virt_column_names.push_back(name);
}
else
{
real_column_names.push_back(name);
}
}
}
MergeTreeDataSelectAnalysisResultPtr MergeTreeDataSelectExecutor::estimateNumMarksToRead(
MergeTreeData::DataPartsVector parts,
const Names & column_names_to_return,
const StorageMetadataPtr & metadata_snapshot_base,
const StorageMetadataPtr & metadata_snapshot,
const SelectQueryInfo & query_info,
ContextPtr context,
unsigned num_streams,
std::shared_ptr<PartitionIdToMaxBlock> max_block_numbers_to_read) const
{
size_t total_parts = parts.size();
if (total_parts == 0)
return std::make_shared<MergeTreeDataSelectAnalysisResult>(
MergeTreeDataSelectAnalysisResult{.result = ReadFromMergeTree::AnalysisResult()});
Names real_column_names;
Names virt_column_names;
/// If query contains restrictions on the virtual column `_part` or `_part_index`, select only parts suitable for it.
/// The virtual column `_sample_factor` (which is equal to 1 / used sample rate) can be requested in the query.
bool sample_factor_column_queried = false;
selectColumnNames(column_names_to_return, data, real_column_names, virt_column_names, sample_factor_column_queried);
return ReadFromMergeTree::selectRangesToRead(
std::move(parts),
metadata_snapshot_base,
metadata_snapshot,
query_info,
context,
num_streams,
max_block_numbers_to_read,
data,
real_column_names,
sample_factor_column_queried,
log);
}
QueryPlanPtr MergeTreeDataSelectExecutor::readFromParts(
MergeTreeData::DataPartsVector parts,
const Names & column_names_to_return,
const StorageMetadataPtr & metadata_snapshot_base,
const StorageMetadataPtr & metadata_snapshot,
const SelectQueryInfo & query_info,
ContextPtr context,
const UInt64 max_block_size,
const unsigned num_streams,
std::shared_ptr<PartitionIdToMaxBlock> max_block_numbers_to_read,
MergeTreeDataSelectAnalysisResultPtr merge_tree_select_result_ptr) const
{
/// If merge_tree_select_result_ptr != nullptr, we use analyzed result so parts will always be empty.
if (merge_tree_select_result_ptr)
{
if (merge_tree_select_result_ptr->marks() == 0)
return std::make_unique<QueryPlan>();
}
else if (parts.empty())
return std::make_unique<QueryPlan>();
Names real_column_names;
Names virt_column_names;
/// If query contains restrictions on the virtual column `_part` or `_part_index`, select only parts suitable for it.
/// The virtual column `_sample_factor` (which is equal to 1 / used sample rate) can be requested in the query.
bool sample_factor_column_queried = false;
selectColumnNames(column_names_to_return, data, real_column_names, virt_column_names, sample_factor_column_queried);
auto read_from_merge_tree = std::make_unique<ReadFromMergeTree>(
std::move(parts),
real_column_names,
virt_column_names,
data,
query_info,
metadata_snapshot,
metadata_snapshot_base,
context,
max_block_size,
num_streams,
sample_factor_column_queried,
max_block_numbers_to_read,
log,
merge_tree_select_result_ptr
);
QueryPlanPtr plan = std::make_unique<QueryPlan>();
plan->addStep(std::move(read_from_merge_tree));
return plan;
}
/// Marks are placed whenever threshold on rows or bytes is met.
/// So we have to return the number of marks on whatever estimate is higher - by rows or by bytes.
size_t MergeTreeDataSelectExecutor::roundRowsOrBytesToMarks(
size_t rows_setting,
size_t bytes_setting,
size_t rows_granularity,
size_t bytes_granularity)
{
size_t res = (rows_setting + rows_granularity - 1) / rows_granularity;
if (bytes_granularity == 0)
return res;
else
return std::max(res, (bytes_setting + bytes_granularity - 1) / bytes_granularity);
}
/// Same as roundRowsOrBytesToMarks() but do not return more then max_marks
size_t MergeTreeDataSelectExecutor::minMarksForConcurrentRead(
size_t rows_setting,
size_t bytes_setting,
size_t rows_granularity,
size_t bytes_granularity,
size_t max_marks)
{
size_t marks = 1;
if (rows_setting + rows_granularity <= rows_setting) /// overflow
marks = max_marks;
else if (rows_setting)
marks = (rows_setting + rows_granularity - 1) / rows_granularity;
if (bytes_granularity == 0)
return marks;
else
{
/// Overflow
if (bytes_setting + bytes_granularity <= bytes_setting) /// overflow
return max_marks;
if (bytes_setting)
return std::max(marks, (bytes_setting + bytes_granularity - 1) / bytes_granularity);
else
return marks;
}
}
/// Calculates a set of mark ranges, that could possibly contain keys, required by condition.
/// In other words, it removes subranges from whole range, that definitely could not contain required keys.
MarkRanges MergeTreeDataSelectExecutor::markRangesFromPKRange(
const MergeTreeData::DataPartPtr & part,
const StorageMetadataPtr & metadata_snapshot,
const KeyCondition & key_condition,
const Settings & settings,
Poco::Logger * log)
{
MarkRanges res;
size_t marks_count = part->index_granularity.getMarksCount();
const auto & index = part->index;
if (marks_count == 0)
return res;
bool has_final_mark = part->index_granularity.hasFinalMark();
/// If index is not used.
if (key_condition.alwaysUnknownOrTrue())
{
if (has_final_mark)
res.push_back(MarkRange(0, marks_count - 1));
else
res.push_back(MarkRange(0, marks_count));
return res;
}
size_t used_key_size = key_condition.getMaxKeyColumn() + 1;
std::function<void(size_t, size_t, FieldRef &)> create_field_ref;
/// If there are no monotonic functions, there is no need to save block reference.
/// Passing explicit field to FieldRef allows to optimize ranges and shows better performance.
const auto & primary_key = metadata_snapshot->getPrimaryKey();
if (key_condition.hasMonotonicFunctionsChain())
{
auto index_columns = std::make_shared<ColumnsWithTypeAndName>();
for (size_t i = 0; i < used_key_size; ++i)
index_columns->emplace_back(ColumnWithTypeAndName{index[i], primary_key.data_types[i], primary_key.column_names[i]});
create_field_ref = [index_columns](size_t row, size_t column, FieldRef & field)
{
field = {index_columns.get(), row, column};
// NULL_LAST
if (field.isNull())
field = POSITIVE_INFINITY;
};
}
else
{
create_field_ref = [&index](size_t row, size_t column, FieldRef & field)
{
index[column]->get(row, field);
// NULL_LAST
if (field.isNull())
field = POSITIVE_INFINITY;
};
}
/// NOTE Creating temporary Field objects to pass to KeyCondition.
std::vector<FieldRef> index_left(used_key_size);
std::vector<FieldRef> index_right(used_key_size);
auto may_be_true_in_range = [&](MarkRange & range)
{
if (range.end == marks_count && !has_final_mark)
{
for (size_t i = 0; i < used_key_size; ++i)
{
create_field_ref(range.begin, i, index_left[i]);
index_right[i] = POSITIVE_INFINITY;
}
}
else
{
if (has_final_mark && range.end == marks_count)
range.end -= 1; /// Remove final empty mark. It's useful only for primary key condition.
for (size_t i = 0; i < used_key_size; ++i)
{
create_field_ref(range.begin, i, index_left[i]);
create_field_ref(range.end, i, index_right[i]);
}
}
return key_condition.mayBeTrueInRange(
used_key_size, index_left.data(), index_right.data(), primary_key.data_types);
};
if (!key_condition.matchesExactContinuousRange())
{
// Do exclusion search, where we drop ranges that do not match
size_t min_marks_for_seek = roundRowsOrBytesToMarks(
settings.merge_tree_min_rows_for_seek,
settings.merge_tree_min_bytes_for_seek,
part->index_granularity_info.fixed_index_granularity,
part->index_granularity_info.index_granularity_bytes);
/** There will always be disjoint suspicious segments on the stack, the leftmost one at the top (back).
* At each step, take the left segment and check if it fits.
* If fits, split it into smaller ones and put them on the stack. If not, discard it.
* If the segment is already of one mark length, add it to response and discard it.
*/
std::vector<MarkRange> ranges_stack = { {0, marks_count} };
size_t steps = 0;
while (!ranges_stack.empty())
{
MarkRange range = ranges_stack.back();
ranges_stack.pop_back();
steps++;
if (!may_be_true_in_range(range))
continue;
if (range.end == range.begin + 1)
{
/// We saw a useful gap between neighboring marks. Either add it to the last range, or start a new range.
if (res.empty() || range.begin - res.back().end > min_marks_for_seek)
res.push_back(range);
else
res.back().end = range.end;
}
else
{
/// Break the segment and put the result on the stack from right to left.
size_t step = (range.end - range.begin - 1) / settings.merge_tree_coarse_index_granularity + 1;
size_t end;
for (end = range.end; end > range.begin + step; end -= step)
ranges_stack.emplace_back(end - step, end);
ranges_stack.emplace_back(range.begin, end);
}
}
LOG_TRACE(log, "Used generic exclusion search over index for part {} with {} steps", part->name, steps);
}
else
{
/// In case when SELECT's predicate defines a single continuous interval of keys,
/// we can use binary search algorithm to find the left and right endpoint key marks of such interval.
/// The returned value is the minimum range of marks, containing all keys for which KeyCondition holds
LOG_TRACE(log, "Running binary search on index range for part {} ({} marks)", part->name, marks_count);
size_t steps = 0;
MarkRange result_range;
size_t searched_left = 0;
size_t searched_right = marks_count;
while (searched_left + 1 < searched_right)
{
const size_t middle = (searched_left + searched_right) / 2;
MarkRange range(0, middle);
if (may_be_true_in_range(range))
searched_right = middle;
else
searched_left = middle;
++steps;
}
result_range.begin = searched_left;
LOG_TRACE(log, "Found (LEFT) boundary mark: {}", searched_left);
searched_right = marks_count;
while (searched_left + 1 < searched_right)
{
const size_t middle = (searched_left + searched_right) / 2;
MarkRange range(middle, marks_count);
if (may_be_true_in_range(range))
searched_left = middle;
else
searched_right = middle;
++steps;
}
result_range.end = searched_right;
LOG_TRACE(log, "Found (RIGHT) boundary mark: {}", searched_right);
if (result_range.begin < result_range.end && may_be_true_in_range(result_range))
res.emplace_back(std::move(result_range));
LOG_TRACE(log, "Found {} range in {} steps", res.empty() ? "empty" : "continuous", steps);
}
return res;
}
MarkRanges MergeTreeDataSelectExecutor::filterMarksUsingIndex(
MergeTreeIndexPtr index_helper,
MergeTreeIndexConditionPtr condition,
MergeTreeData::DataPartPtr part,
const MarkRanges & ranges,
const Settings & settings,
const MergeTreeReaderSettings & reader_settings,
size_t & total_granules,
size_t & granules_dropped,
MarkCache * mark_cache,
UncompressedCache * uncompressed_cache,
Poco::Logger * log)
{
const std::string & path_prefix = part->getFullRelativePath() + index_helper->getFileName();
if (!index_helper->getDeserializedFormat(part->volume->getDisk(), path_prefix))
{
LOG_DEBUG(log, "File for index {} does not exist ({}.*). Skipping it.", backQuote(index_helper->index.name), path_prefix);
return ranges;
}
auto index_granularity = index_helper->index.granularity;
const size_t min_marks_for_seek = roundRowsOrBytesToMarks(
settings.merge_tree_min_rows_for_seek,
settings.merge_tree_min_bytes_for_seek,
part->index_granularity_info.fixed_index_granularity,
part->index_granularity_info.index_granularity_bytes);
size_t marks_count = part->getMarksCount();
size_t final_mark = part->index_granularity.hasFinalMark();
size_t index_marks_count = (marks_count - final_mark + index_granularity - 1) / index_granularity;
MergeTreeIndexReader reader(
index_helper, part,
index_marks_count,
ranges,
mark_cache,
uncompressed_cache,
reader_settings);
MarkRanges res;
/// Some granules can cover two or more ranges,
/// this variable is stored to avoid reading the same granule twice.
MergeTreeIndexGranulePtr granule = nullptr;
size_t last_index_mark = 0;
for (const auto & range : ranges)
{
MarkRange index_range(
range.begin / index_granularity,
(range.end + index_granularity - 1) / index_granularity);
if (last_index_mark != index_range.begin || !granule)
reader.seek(index_range.begin);
total_granules += index_range.end - index_range.begin;
for (size_t index_mark = index_range.begin; index_mark < index_range.end; ++index_mark)
{
if (index_mark != index_range.begin || !granule || last_index_mark != index_range.begin)
granule = reader.read();
MarkRange data_range(
std::max(range.begin, index_mark * index_granularity),
std::min(range.end, (index_mark + 1) * index_granularity));
if (!condition->mayBeTrueOnGranule(granule))
{
++granules_dropped;
continue;
}
if (res.empty() || res.back().end - data_range.begin > min_marks_for_seek)
res.push_back(data_range);
else
res.back().end = data_range.end;
}
last_index_mark = index_range.end - 1;
}
return res;
}
void MergeTreeDataSelectExecutor::selectPartsToRead(
MergeTreeData::DataPartsVector & parts,
const std::optional<std::unordered_set<String>> & part_values,
const std::optional<KeyCondition> & minmax_idx_condition,
const DataTypes & minmax_columns_types,
std::optional<PartitionPruner> & partition_pruner,
const PartitionIdToMaxBlock * max_block_numbers_to_read,
PartFilterCounters & counters)
{
MergeTreeData::DataPartsVector prev_parts;
std::swap(prev_parts, parts);
for (const auto & part_or_projection : prev_parts)
{
const auto * part = part_or_projection->isProjectionPart() ? part_or_projection->getParentPart() : part_or_projection.get();
if (part_values && part_values->find(part->name) == part_values->end())
continue;
if (part->isEmpty())
continue;
if (max_block_numbers_to_read)
{
auto blocks_iterator = max_block_numbers_to_read->find(part->info.partition_id);
if (blocks_iterator == max_block_numbers_to_read->end() || part->info.max_block > blocks_iterator->second)
continue;
}
size_t num_granules = part->getMarksCount();
if (num_granules && part->index_granularity.hasFinalMark())
--num_granules;
counters.num_initial_selected_parts += 1;
counters.num_initial_selected_granules += num_granules;
if (minmax_idx_condition && !minmax_idx_condition->checkInHyperrectangle(
part->minmax_idx->hyperrectangle, minmax_columns_types).can_be_true)
continue;
counters.num_parts_after_minmax += 1;
counters.num_granules_after_minmax += num_granules;
if (partition_pruner)
{
if (partition_pruner->canBePruned(*part))
continue;
}
counters.num_parts_after_partition_pruner += 1;
counters.num_granules_after_partition_pruner += num_granules;
parts.push_back(part_or_projection);
}
}
void MergeTreeDataSelectExecutor::selectPartsToReadWithUUIDFilter(
MergeTreeData::DataPartsVector & parts,
const std::optional<std::unordered_set<String>> & part_values,
MergeTreeData::PinnedPartUUIDsPtr pinned_part_uuids,
const std::optional<KeyCondition> & minmax_idx_condition,
const DataTypes & minmax_columns_types,
std::optional<PartitionPruner> & partition_pruner,
const PartitionIdToMaxBlock * max_block_numbers_to_read,
ContextPtr query_context,
PartFilterCounters & counters,
Poco::Logger * log)
{
const Settings & settings = query_context->getSettings();
/// process_parts prepare parts that have to be read for the query,
/// returns false if duplicated parts' UUID have been met
auto select_parts = [&] (MergeTreeData::DataPartsVector & selected_parts) -> bool
{
auto ignored_part_uuids = query_context->getIgnoredPartUUIDs();
std::unordered_set<UUID> temp_part_uuids;
MergeTreeData::DataPartsVector prev_parts;
std::swap(prev_parts, selected_parts);
for (const auto & part_or_projection : prev_parts)
{
const auto * part = part_or_projection->isProjectionPart() ? part_or_projection->getParentPart() : part_or_projection.get();
if (part_values && part_values->find(part->name) == part_values->end())
continue;
if (part->isEmpty())
continue;
if (max_block_numbers_to_read)
{
auto blocks_iterator = max_block_numbers_to_read->find(part->info.partition_id);
if (blocks_iterator == max_block_numbers_to_read->end() || part->info.max_block > blocks_iterator->second)
continue;
}
/// Skip the part if its uuid is meant to be excluded
if (part->uuid != UUIDHelpers::Nil && ignored_part_uuids->has(part->uuid))
continue;
size_t num_granules = part->getMarksCount();
if (num_granules && part->index_granularity.hasFinalMark())
--num_granules;
counters.num_initial_selected_parts += 1;
counters.num_initial_selected_granules += num_granules;
if (minmax_idx_condition
&& !minmax_idx_condition->checkInHyperrectangle(part->minmax_idx->hyperrectangle, minmax_columns_types)
.can_be_true)
continue;
counters.num_parts_after_minmax += 1;
counters.num_granules_after_minmax += num_granules;
if (partition_pruner)
{
if (partition_pruner->canBePruned(*part))
continue;
}
counters.num_parts_after_partition_pruner += 1;
counters.num_granules_after_partition_pruner += num_granules;
/// populate UUIDs and exclude ignored parts if enabled
if (part->uuid != UUIDHelpers::Nil)
{
if (settings.experimental_query_deduplication_send_all_part_uuids || pinned_part_uuids->contains(part->uuid))
{
auto result = temp_part_uuids.insert(part->uuid);
if (!result.second)
throw Exception("Found a part with the same UUID on the same replica.", ErrorCodes::LOGICAL_ERROR);
}
}
selected_parts.push_back(part_or_projection);
}
if (!temp_part_uuids.empty())
{
auto duplicates = query_context->getPartUUIDs()->add(std::vector<UUID>{temp_part_uuids.begin(), temp_part_uuids.end()});
if (!duplicates.empty())
{
/// on a local replica with prefer_localhost_replica=1 if any duplicates appeared during the first pass,
/// adding them to the exclusion, so they will be skipped on second pass
query_context->getIgnoredPartUUIDs()->add(duplicates);
return false;
}
}
return true;
};
/// Process parts that have to be read for a query.
auto needs_retry = !select_parts(parts);
/// If any duplicated part UUIDs met during the first step, try to ignore them in second pass.
/// This may happen when `prefer_localhost_replica` is set and "distributed" stage runs in the same process with "remote" stage.
if (needs_retry)
{
LOG_DEBUG(log, "Found duplicate uuids locally, will retry part selection without them");
counters = PartFilterCounters();
/// Second attempt didn't help, throw an exception
if (!select_parts(parts))
throw Exception("Found duplicate UUIDs while processing query.", ErrorCodes::DUPLICATED_PART_UUIDS);
}
}
}
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