// 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.

#pragma once

#include <gen_cpp/parquet_types.h>

#include <cstddef>

#include <cstdint>

#include <map>

#include <memory>

#include <optional>

#include <unordered_map>

#include <utility>

#include <vector>

#include "common/status.h"

#include "core/column/column.h"

#include "format_v2/file_reader.h"

#include "format_v2/parquet/parquet_profile.h"

#include "format_v2/parquet/parquet_statistics.h"

#include "format_v2/parquet/reader/column_reader.h"

#include "format_v2/parquet/selection_vector.h"

#include "runtime/runtime_profile.h"

#include "storage/segment/condition_cache.h"

namespace cctz {

class time_zone;

} // namespace cctz

namespace doris {

class Block;

class RuntimeState;

namespace format {

struct FileScanRequest;

} // namespace format

} // namespace doris

namespace doris::format::parquet {

struct ParquetFileContext;

struct ParquetColumnSchema;

struct ParquetPageCacheRange;

struct ParquetScanRange;

class NativeParquetMetadata;

namespace detail {

struct PredicateConjunctSchedule {

    std::map<size_t, VExprContextSPtrs> single_column_conjuncts;

    VExprContextSPtrs remaining_conjuncts;

};

struct AdaptivePredicateStats {

    double cost_per_input_row_ns = 0;

    double survival_ratio = 1;

    size_t samples = 0;

};

std::vector<size_t> order_adaptive_predicates(

        const std::vector<size_t>& positions,

        const std::unordered_map<size_t, AdaptivePredicateStats>& stats);

std::vector<size_t> adaptive_prefetch_prefix(

        const std::vector<size_t>& ordered_positions,

        const std::unordered_map<size_t, AdaptivePredicateStats>& stats,

        double minimum_reach_probability);

bool should_sample_adaptive_predicate(size_t samples, size_t batch_sequence);

Status build_native_prefetch_ranges(

        const tparquet::FileMetaData& metadata,

        const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

        const std::vector<format::LocalColumnIndex>& scan_columns, int row_group_idx,

        size_t file_size, bool parquet_816_padding, std::vector<ParquetPageCacheRange>* ranges);

Status select_native_row_groups_by_scan_range(const tparquet::FileMetaData& metadata,

                                              const ParquetScanRange& scan_range,

                                              std::vector<int64_t>* row_group_first_rows,

                                              std::vector<int>* selected_row_groups);

} // namespace detail

// ============================================================================

// ============================================================================

struct ParquetScanRange {

    int64_t start_offset = 0;

    int64_t size = -1;      // -1 means read the whole file

    int64_t file_size = -1; // -1 means unknown

};

struct RowGroupReadPlan {

    int row_group_id = -1;                 // row group id

    int64_t first_file_row = 0;            // first file row for this row group (0-based)

    int64_t row_group_rows = 0;            // row count of this row group

    std::vector<RowRange> selected_ranges; // row ranges to read after page-index pruning

    std::map<int, ParquetPageSkipPlan>

            page_skip_plans; // leaf_column_id -> data pages that can be skipped completely

    // Deferred planning transfers parsed indexes to execution so narrowed scans never issue the

    // same remote index reads a second time while opening the row group.

    std::unordered_map<int, tparquet::OffsetIndex> offset_indexes;

    // Footer statistics are cheap and eager. Remote dictionary/Bloom/page-index probes fill the

    // remaining fields only when this row group reaches the scheduler.

    bool expensive_pruning_pending = false;

};

struct RowGroupScanPlan {

    std::vector<RowGroupReadPlan> row_groups; // row groups selected after pruning

    ParquetPruningStats pruning_stats;        // pruning statistics

    bool enable_bloom_filter = false;

};

// ============================================================================

// ============================================================================

Status plan_parquet_row_groups(const NativeParquetMetadata& metadata,

                               const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

                               const format::FileScanRequest& request,

                               const ParquetScanRange& scan_range, bool enable_bloom_filter,

                               RowGroupScanPlan* plan, const cctz::time_zone* timezone = nullptr,

                               const RuntimeState* runtime_state = nullptr,

                               ParquetFileContext* file_context = nullptr,

                               const ParquetColumnReaderProfile& column_reader_profile = {});

Status finalize_parquet_row_group_plans(

        const NativeParquetMetadata& metadata,

        const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

        const format::FileScanRequest& request, bool enable_bloom_filter, RowGroupScanPlan* plan,

        const cctz::time_zone* timezone, const RuntimeState* runtime_state,

        ParquetFileContext* file_context, const ParquetColumnReaderProfile& column_reader_profile,

        const ParquetProfile* parquet_profile = nullptr);

IColumn::Filter selection_to_filter(const SelectionVector& selection, uint16_t selected_rows,

                                    int64_t batch_rows);

uint16_t apply_compact_filter_to_selection(const IColumn::Filter& filter,

                                           SelectionVector* selection, uint16_t selected_rows);

Status execute_batch_filters(const format::FileScanRequest& request, int64_t batch_rows,

                             Block* file_block, SelectionVector* selection, uint16_t* selected_rows,

                             int64_t* conjunct_filtered_rows = nullptr);

// ============================================================================

// ============================================================================

//   while true:

//     3. read_current_row_group_batch(batch_rows)

// ============================================================================

class ParquetScanScheduler {

public:

    static constexpr int64_t DEFAULT_READ_BATCH_SIZE = 4096;

    void set_plan(RowGroupScanPlan plan);

    void set_page_skip_profile(ParquetPageSkipProfile page_skip_profile) {

        _page_skip_profile = page_skip_profile;

    }

    void set_scan_profile(ParquetScanProfile scan_profile) { _scan_profile = scan_profile; }

    void set_pruning_profile(const ParquetProfile* parquet_profile) {

        _parquet_profile = parquet_profile;

    }

    void set_merge_read_options(RuntimeProfile* profile, int64_t merge_read_slice_size) {

        _profile = profile;

        _merge_read_slice_size = merge_read_slice_size;

    }

    void set_global_rowid_context(std::optional<format::GlobalRowIdContext> context) {

        _global_rowid_context = context;

    }

    void set_condition_cache_context(std::shared_ptr<ConditionCacheContext> ctx);

    void set_timezone(const cctz::time_zone* timezone) { _timezone = timezone; }

    void set_enable_strict_mode(bool enable_strict_mode) {

        _enable_strict_mode = enable_strict_mode;

    }

    void set_runtime_state(RuntimeState* runtime_state) { _runtime_state = runtime_state; }

    // Release row-group readers before the owning RuntimeProfile is reported. Native readers

    // publish their accumulated page/decode statistics from their destructor.

    void close() { reset_current_row_group(); }

    // Upper scanner owns adaptive memory feedback; scheduler only applies the current row cap when

    // splitting selected row ranges into physical read batches.

    void set_batch_size(size_t batch_size) {

        _batch_size = batch_size == 0 ? 1 : static_cast<int64_t>(batch_size);

    }

    void reset();

    bool empty() const { return _row_group_plans.empty(); }

    int64_t condition_cache_filtered_rows() const { return _condition_cache_filtered_rows; }

    int64_t predicate_filtered_rows() const { return _predicate_filtered_rows; }

    int64_t raw_rows_read() const { return _raw_rows_read; }

    Status read_next_batch(ParquetFileContext& file_context,

                           const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

                           const format::FileScanRequest& request, Block* file_block, size_t* rows,

                           bool* eof);

private:

    static constexpr size_t PROFILE_FLUSH_BATCH_INTERVAL = 16;

    void reset_current_row_group();

    void flush_current_reader_profiles();

    bool finish_current_reader_batch_profiles();

    const detail::PredicateConjunctSchedule& predicate_conjunct_schedule(

            const format::FileScanRequest& request);

    std::vector<format::LocalColumnIndex> adaptive_predicate_prefetch_columns(

            const format::FileScanRequest& request) const;

    Status open_next_row_group(ParquetFileContext& file_context,

                               const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

                               const format::FileScanRequest& request, bool* has_row_group);

    Status skip_current_row_group_rows(int64_t rows);

    Status flush_pending_non_predicate_skip_rows();

    Status read_filter_columns(int64_t batch_rows, const format::FileScanRequest& request,

                               Block* file_block, SelectionVector* selection,

                               uint16_t* selected_rows, int64_t* conjunct_filtered_rows,

                               bool* predicate_columns_filtered);

    Status prepare_current_dictionary_filters(

            ParquetFileContext& file_context,

            const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

            const format::FileScanRequest& request, int row_group_idx,

            const tparquet::RowGroup& row_group_metadata);

    Status prefetch_current_row_group_columns(

            ParquetFileContext& file_context,

            const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

            const std::vector<format::LocalColumnIndex>& scan_columns, bool* prefetched);

    Status read_current_row_group_batch(

            ParquetFileContext& file_context,

            const std::vector<std::unique_ptr<ParquetColumnSchema>>& file_schema,

            int64_t batch_rows, const format::FileScanRequest& request,

            int64_t batch_first_file_row, Block* file_block, size_t* rows);

    Status materialize_pending_predicate_batch(const format::FileScanRequest& request,

                                               Block* file_block, size_t* rows);

    void mark_condition_cache_granules(const SelectionVector& selection, uint16_t selected_rows,

                                       int64_t batch_first_file_row);

    std::vector<RowGroupReadPlan> _row_group_plans; // row group queue to scan

    size_t _next_row_group_plan_idx = 0;            // index of the next row group to process

    bool _has_current_row_group = false;

    // Readers retain pointers into this immutable row-group map, so it must outlive both maps below.

    std::unordered_map<int, tparquet::OffsetIndex> _current_offset_indexes;

    // File-local ids are signed because virtual columns use reserved negative values. Keeping the

    // typed id as the map key prevents GLOBAL_ROWID_COLUMN_ID from wrapping to a storage ColumnId.

    std::map<format::LocalColumnId, std::unique_ptr<ParquetColumnReader>>

            _current_predicate_columns; // predicate ColumnReaders

    std::map<format::LocalColumnId, std::unique_ptr<ParquetColumnReader>>

            _current_non_predicate_columns; // non-predicate ColumnReaders

    std::map<format::LocalColumnId, IColumn::Filter>

            _current_dictionary_filters; // local id -> dict entry bitmap

    std::map<format::LocalColumnId, std::vector<std::pair<VExprContextSPtr, VExprSPtr>>>

            _current_dictionary_residual_conjuncts; // local id -> row-level residual conjuncts

    int64_t _current_row_group_rows = 0;            // current row group row count

    int _current_row_group_id = -1;                 // current row group id in parquet metadata

    int64_t _current_row_group_rows_read = 0;       // rows read in the current row group (cursor)

    int64_t _current_row_group_first_row = 0;       // first file row of the current row group

    std::vector<RowRange>

            _current_selected_ranges; // selected ranges for the current row group after page-index pruning

    size_t _current_range_idx = 0;        // current selected_range index

    int64_t _current_range_rows_read = 0; // rows read in the current range

    // Predicate readers move immediately because they decide which rows survive. Non-predicate

    // readers can lag behind across fully filtered batches and range gaps; the lag is flushed once

    // before the next surviving batch is materialized, or discarded with the row group.

    int64_t _pending_non_predicate_skip_rows = 0;

    // Empty predicate batches may widen their physical probe. If the first non-empty probe finds

    // more rows than the caller's cap, keep its narrow predicate result here and materialize lazy

    // columns in capped physical slices on subsequent calls.

    int64_t _pending_predicate_batch_rows = 0;

    int64_t _pending_predicate_batch_rows_consumed = 0;

    size_t _pending_predicate_selected_offset = 0;

    std::vector<SelectionVector::Index> _pending_predicate_selection;

    std::map<size_t, ColumnPtr> _pending_predicate_columns;

    SelectionVector _pending_output_selection;

    bool _current_predicate_prefetched = false;

    bool _current_non_predicate_prefetched = false;

    bool _current_merge_range_active = false;

    ParquetPageSkipProfile _page_skip_profile;

    ParquetScanProfile _scan_profile;

    const ParquetProfile* _parquet_profile = nullptr;

    RuntimeProfile* _profile = nullptr;

    int64_t _merge_read_slice_size = -1;

    std::optional<format::GlobalRowIdContext> _global_rowid_context;

    const cctz::time_zone* _timezone = nullptr;

    bool _enable_strict_mode = false;

    bool _enable_bloom_filter = false;

    RuntimeState* _runtime_state = nullptr;

    int64_t _batch_size = DEFAULT_READ_BATCH_SIZE;

    // Batch control scratch is scheduler-owned so adaptive row caps change logical sizes without

    // reallocating selection indices, dense filter bytes, or compacted-column positions.

    SelectionVector _selection;

    std::vector<uint32_t> _read_column_positions_scratch;

    const format::FileScanRequest* _predicate_schedule_request = nullptr;

    detail::PredicateConjunctSchedule _predicate_schedule;

    std::vector<size_t> _predicate_positions_scratch;

    std::unordered_map<size_t, size_t> _predicate_indices_by_position_scratch;

    std::vector<size_t> _ordered_predicate_positions_scratch;

    std::unordered_map<uint32_t, std::vector<SelectionVector::Index>>

            _predicate_column_selection_scratch;

    IColumn::Filter _predicate_compaction_filter_scratch;

    size_t _predicate_batch_sequence = 0;

    size_t _batches_since_profile_flush = 0;

    std::unordered_map<size_t, detail::AdaptivePredicateStats> _predicate_runtime_stats;

    double _predicate_survival_ratio = -1;

    std::shared_ptr<ConditionCacheContext> _condition_cache_ctx;

    int64_t _condition_cache_filtered_rows = 0;

    int64_t _predicate_filtered_rows = 0;

    int64_t _raw_rows_read = 0;

};

} // namespace doris::format::parquet