/*******************************************************************************
 * Copyright 上海赜睿信息科技有限公司(Zilliz) - All Rights Reserved
 * Unauthorized copying of this file, via any medium is strictly prohibited.
 * Proprietary and confidential.
 ******************************************************************************/
#include "DBImpl.h"
#include "DBMetaImpl.h"
#include "Log.h"
#include "EngineFactory.h"
#include "metrics/Metrics.h"
#include "scheduler/TaskScheduler.h"
#include "scheduler/context/SearchContext.h"
#include "scheduler/context/DeleteContext.h"
#include "utils/TimeRecorder.h"

#include <assert.h>
#include <chrono>
#include <thread>
#include <iostream>
#include <cstring>
#include <cache/CpuCacheMgr.h>
#include <boost/filesystem.hpp>

namespace zilliz {
namespace milvus {
namespace engine {

namespace {

static constexpr uint64_t METRIC_ACTION_INTERVAL = 1;
static constexpr uint64_t COMPACT_ACTION_INTERVAL = 1;
static constexpr uint64_t INDEX_ACTION_INTERVAL = 1;

void CollectInsertMetrics(double total_time, size_t n, bool succeed) {
    double avg_time = total_time / n;
    for (int i = 0; i < n; ++i) {
        server::Metrics::GetInstance().AddVectorsDurationHistogramOberve(avg_time);
    }

//    server::Metrics::GetInstance().add_vector_duration_seconds_quantiles().Observe((average_time));
    if (succeed) {
        server::Metrics::GetInstance().AddVectorsSuccessTotalIncrement(n);
        server::Metrics::GetInstance().AddVectorsSuccessGaugeSet(n);
    }
    else {
        server::Metrics::GetInstance().AddVectorsFailTotalIncrement(n);
        server::Metrics::GetInstance().AddVectorsFailGaugeSet(n);
    }
}

void CollectQueryMetrics(double total_time, size_t nq) {
    for (int i = 0; i < nq; ++i) {
        server::Metrics::GetInstance().QueryResponseSummaryObserve(total_time);
    }
    auto average_time = total_time / nq;
    server::Metrics::GetInstance().QueryVectorResponseSummaryObserve(average_time, nq);
    server::Metrics::GetInstance().QueryVectorResponsePerSecondGaugeSet(double (nq) / total_time);
}

void CollectFileMetrics(int file_type, size_t file_size, double total_time) {
    switch(file_type) {
        case meta::TableFileSchema::RAW:
        case meta::TableFileSchema::TO_INDEX: {
            server::Metrics::GetInstance().SearchRawDataDurationSecondsHistogramObserve(total_time);
            server::Metrics::GetInstance().RawFileSizeHistogramObserve(file_size);
            server::Metrics::GetInstance().RawFileSizeTotalIncrement(file_size);
            server::Metrics::GetInstance().RawFileSizeGaugeSet(file_size);
            break;
        }
        default: {
            server::Metrics::GetInstance().SearchIndexDataDurationSecondsHistogramObserve(total_time);
            server::Metrics::GetInstance().IndexFileSizeHistogramObserve(file_size);
            server::Metrics::GetInstance().IndexFileSizeTotalIncrement(file_size);
            server::Metrics::GetInstance().IndexFileSizeGaugeSet(file_size);
            break;
        }
    }
}

void CalcScore(uint64_t vector_count,
               const float *vectors_data,
               uint64_t dimension,
               const SearchContext::ResultSet &result_src,
               SearchContext::ResultSet &result_target) {
    result_target.clear();
    if(result_src.empty()){
        return;
    }

    server::TimeRecorder rc("Calculate Score");
    int vec_index = 0;
    for(auto& result : result_src) {
        const float * vec_data = vectors_data + vec_index*dimension;
        double vec_len = 0;
        for(uint64_t i = 0; i < dimension; i++) {
            vec_len += vec_data[i]*vec_data[i];
        }
        vec_index++;

        double max_score = 0.0;
        for(auto& pair : result) {
            if(max_score < pair.second) {
                max_score = pair.second;
            }
        }

        //makesure socre is less than 100
        if(max_score > vec_len) {
            vec_len = max_score;
        }

        //avoid divided by zero
        static constexpr double TOLERANCE = std::numeric_limits<float>::epsilon();
        if(vec_len < TOLERANCE) {
            vec_len = TOLERANCE;
        }

        SearchContext::Id2ScoreMap score_array;
        double vec_len_inverse = 1.0/vec_len;
        for(auto& pair : result) {
            score_array.push_back(std::make_pair(pair.first, (1 - pair.second*vec_len_inverse)*100.0));
        }
        result_target.emplace_back(score_array);
    }

    rc.Elapse("totally cost");
}

}


DBImpl::DBImpl(const Options& options)
    : options_(options),
      shutting_down_(false),
      meta_ptr_(new meta::DBMetaImpl(options_.meta)),
      mem_mgr_(new MemManager(meta_ptr_, options_)),
      compact_thread_pool_(1, 1),
      index_thread_pool_(1, 1) {
    StartTimerTasks();
}

Status DBImpl::CreateTable(meta::TableSchema& table_schema) {
    return meta_ptr_->CreateTable(table_schema);
}

Status DBImpl::DeleteTable(const std::string& table_id, const meta::DatesT& dates) {
    //dates partly delete files of the table but currently we don't support

    mem_mgr_->EraseMemVector(table_id); //not allow insert
    meta_ptr_->DeleteTable(table_id); //soft delete table

    //scheduler will determine when to delete table files
    TaskScheduler& scheduler = TaskScheduler::GetInstance();
    DeleteContextPtr context = std::make_shared<DeleteContext>(table_id, meta_ptr_);
    scheduler.Schedule(context);

    return Status::OK();
}

Status DBImpl::DescribeTable(meta::TableSchema& table_schema) {
    return meta_ptr_->DescribeTable(table_schema);
}

Status DBImpl::HasTable(const std::string& table_id, bool& has_or_not) {
    return meta_ptr_->HasTable(table_id, has_or_not);
}

Status DBImpl::AllTables(std::vector<meta::TableSchema>& table_schema_array) {
    return meta_ptr_->AllTables(table_schema_array);
}

Status DBImpl::GetTableRowCount(const std::string& table_id, uint64_t& row_count) {
    return meta_ptr_->Count(table_id, row_count);
}

Status DBImpl::InsertVectors(const std::string& table_id_,
        uint64_t n, const float* vectors, IDNumbers& vector_ids_) {

    auto start_time = METRICS_NOW_TIME;
    Status status = mem_mgr_->InsertVectors(table_id_, n, vectors, vector_ids_);
    auto end_time = METRICS_NOW_TIME;
    double total_time = METRICS_MICROSECONDS(start_time,end_time);
//    std::chrono::microseconds time_span = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
//    double average_time = double(time_span.count()) / n;

    CollectInsertMetrics(total_time, n, status.ok());
    return status;

}

Status DBImpl::Query(const std::string &table_id, uint64_t k, uint64_t nq,
                      const float *vectors, QueryResults &results) {
    auto start_time = METRICS_NOW_TIME;
    meta::DatesT dates = {meta::Meta::GetDate()};
    Status result = Query(table_id, k, nq, vectors, dates, results);
    auto end_time = METRICS_NOW_TIME;
    auto total_time = METRICS_MICROSECONDS(start_time,end_time);

    CollectQueryMetrics(total_time, nq);

    return result;
}

Status DBImpl::Query(const std::string& table_id, uint64_t k, uint64_t nq,
        const float* vectors, const meta::DatesT& dates, QueryResults& results) {
#if 0
    return QuerySync(table_id, k, nq, vectors, dates, results);
#else

    //get all table files from table
    meta::DatePartionedTableFilesSchema files;
    auto status = meta_ptr_->FilesToSearch(table_id, dates, files);
    if (!status.ok()) { return status; }

    meta::TableFilesSchema file_id_array;
    for (auto &day_files : files) {
        for (auto &file : day_files.second) {
            file_id_array.push_back(file);
        }
    }

    return QueryAsync(table_id, file_id_array, k, nq, vectors, dates, results);
#endif
}

Status DBImpl::Query(const std::string& table_id, const std::vector<std::string>& file_ids,
        uint64_t k, uint64_t nq, const float* vectors,
        const meta::DatesT& dates, QueryResults& results) {
    //get specified files
    std::vector<size_t> ids;
    for (auto &id : file_ids) {
        meta::TableFileSchema table_file;
        table_file.table_id_ = table_id;
        std::string::size_type sz;
        ids.push_back(std::stol(id, &sz));
    }

    meta::TableFilesSchema files_array;
    auto status = meta_ptr_->GetTableFiles(table_id, ids, files_array);
    if (!status.ok()) {
        return status;
    }

    if(files_array.empty()) {
        return Status::Error("Invalid file id");
    }

    return QueryAsync(table_id, files_array, k, nq, vectors, dates, results);
}

Status DBImpl::QuerySync(const std::string& table_id, uint64_t k, uint64_t nq,
                 const float* vectors, const meta::DatesT& dates, QueryResults& results) {
    meta::DatePartionedTableFilesSchema files;
    auto status = meta_ptr_->FilesToSearch(table_id, dates, files);
    if (!status.ok()) { return status; }

    ENGINE_LOG_DEBUG << "Search DateT Size = " << files.size();

    meta::TableFilesSchema index_files;
    meta::TableFilesSchema raw_files;
    for (auto &day_files : files) {
        for (auto &file : day_files.second) {
            file.file_type_ == meta::TableFileSchema::INDEX ?
            index_files.push_back(file) : raw_files.push_back(file);
        }
    }

    int dim = 0;
    if (!index_files.empty()) {
        dim = index_files[0].dimension_;
    } else if (!raw_files.empty()) {
        dim = raw_files[0].dimension_;
    } else {
        ENGINE_LOG_DEBUG << "no files to search";
        return Status::OK();
    }

    {
        // [{ids, distence}, ...]
        using SearchResult = std::pair<std::vector<long>, std::vector<float>>;
        std::vector<SearchResult> batchresult(nq); // allocate nq cells.

        auto cluster = [&](long *nns, float *dis, const int& k) -> void {
            for (int i = 0; i < nq; ++i) {
                auto f_begin = batchresult[i].first.cbegin();
                auto s_begin = batchresult[i].second.cbegin();
                batchresult[i].first.insert(f_begin, nns + i * k, nns + i * k + k);
                batchresult[i].second.insert(s_begin, dis + i * k, dis + i * k + k);
            }
        };

        // Allocate Memory
        float *output_distence;
        long *output_ids;
        output_distence = (float *) malloc(k * nq * sizeof(float));
        output_ids = (long *) malloc(k * nq * sizeof(long));
        memset(output_distence, 0, k * nq * sizeof(float));
        memset(output_ids, 0, k * nq * sizeof(long));

        long search_set_size = 0;

        auto search_in_index = [&](meta::TableFilesSchema& file_vec) -> void {
            for (auto &file : file_vec) {

                ExecutionEnginePtr index = EngineFactory::Build(file.dimension_, file.location_, (EngineType)file.engine_type_);
                index->Load();
                auto file_size = index->PhysicalSize();
                search_set_size += file_size;

                ENGINE_LOG_DEBUG << "Search file_type " << file.file_type_ << " Of Size: "
                    << file_size/(1024*1024) << " M";

                int inner_k = index->Count() < k ? index->Count() : k;
                auto start_time = METRICS_NOW_TIME;
                index->Search(nq, vectors, inner_k, output_distence, output_ids);
                auto end_time = METRICS_NOW_TIME;
                auto total_time = METRICS_MICROSECONDS(start_time, end_time);
                CollectFileMetrics(file.file_type_, file_size, total_time);
                cluster(output_ids, output_distence, inner_k); // cluster to each query
                memset(output_distence, 0, k * nq * sizeof(float));
                memset(output_ids, 0, k * nq * sizeof(long));
            }
        };

        auto topk_cpu = [](const std::vector<float> &input_data,
                           const int &k,
                           float *output_distence,
                           long *output_ids) -> void {
            std::map<float, std::vector<int>> inverted_table;
            for (int i = 0; i < input_data.size(); ++i) {
                if (inverted_table.count(input_data[i]) == 1) {
                    auto& ori_vec = inverted_table[input_data[i]];
                    ori_vec.push_back(i);
                }
                else {
                    inverted_table[input_data[i]] = std::vector<int>{i};
                }
            }

            int count = 0;
            for (auto &item : inverted_table){
                if (count == k) break;
                for (auto &id : item.second){
                    output_distence[count] = item.first;
                    output_ids[count] = id;
                    if (++count == k) break;
                }
            }
        };
        auto cluster_topk = [&]() -> void {
            QueryResult res;
            for (auto &result_pair : batchresult) {
                auto &dis = result_pair.second;
                auto &nns = result_pair.first;

                topk_cpu(dis, k, output_distence, output_ids);

                int inner_k = dis.size() < k ? dis.size() : k;
                for (int i = 0; i < inner_k; ++i) {
                    res.emplace_back(std::make_pair(nns[output_ids[i]], output_distence[i])); // mapping
                }
                results.push_back(res); // append to result list
                res.clear();
                memset(output_distence, 0, k * nq * sizeof(float));
                memset(output_ids, 0, k * nq * sizeof(long));
            }
        };

        search_in_index(raw_files);
        search_in_index(index_files);

        ENGINE_LOG_DEBUG << "Search Overall Set Size = " << search_set_size << " M";
        cluster_topk();

        free(output_distence);
        free(output_ids);
    }

    if (results.empty()) {
        return Status::NotFound("Group " + table_id + ", search result not found!");
    }

    QueryResults temp_results;
    CalcScore(nq, vectors, dim, results, temp_results);
    results.swap(temp_results);

    return Status::OK();
}

Status DBImpl::QueryAsync(const std::string& table_id, const meta::TableFilesSchema& files,
                          uint64_t k, uint64_t nq, const float* vectors,
                          const meta::DatesT& dates, QueryResults& results) {

    //step 1: get files to search
    ENGINE_LOG_DEBUG << "Search DateT Size=" << files.size();
    SearchContextPtr context = std::make_shared<SearchContext>(k, nq, vectors);
    for (auto &file : files) {
        TableFileSchemaPtr file_ptr = std::make_shared<meta::TableFileSchema>(file);
        context->AddIndexFile(file_ptr);
    }

    //step 2: put search task to scheduler
    TaskScheduler& scheduler = TaskScheduler::GetInstance();
    scheduler.Schedule(context);

    context->WaitResult();

    //step 3: construct results, calculate score between 0 ~ 100
    auto& context_result = context->GetResult();
    meta::TableSchema table_schema;
    table_schema.table_id_ = table_id;
    meta_ptr_->DescribeTable(table_schema);

    CalcScore(context->nq(), context->vectors(), table_schema.dimension_, context_result, results);

    return Status::OK();
}

void DBImpl::StartTimerTasks() {
    bg_timer_thread_ = std::thread(&DBImpl::BackgroundTimerTask, this);
}

void DBImpl::BackgroundTimerTask() {
    Status status;
    server::SystemInfo::GetInstance().Init();
    while (true) {
        if (!bg_error_.ok()) break;
        if (shutting_down_.load(std::memory_order_acquire)){
            for(auto& iter : compact_thread_results_) {
                iter.wait();
            }
            for(auto& iter : index_thread_results_) {
                iter.wait();
            }
            break;
        }

        std::this_thread::sleep_for(std::chrono::seconds(1));

        StartMetricTask();
        StartCompactionTask();
        StartBuildIndexTask();
    }
}

void DBImpl::StartMetricTask() {
    static uint64_t metric_clock_tick = 0;
    metric_clock_tick++;
    if(metric_clock_tick%METRIC_ACTION_INTERVAL != 0) {
        return;
    }

    server::Metrics::GetInstance().KeepingAliveCounterIncrement(METRIC_ACTION_INTERVAL);
    int64_t cache_usage = cache::CpuCacheMgr::GetInstance()->CacheUsage();
    int64_t cache_total = cache::CpuCacheMgr::GetInstance()->CacheCapacity();
    server::Metrics::GetInstance().CacheUsageGaugeSet(cache_usage*100/cache_total);
    uint64_t size;
    Size(size);
    server::Metrics::GetInstance().DataFileSizeGaugeSet(size);
    server::Metrics::GetInstance().CPUUsagePercentSet();
    server::Metrics::GetInstance().RAMUsagePercentSet();
    server::Metrics::GetInstance().GPUPercentGaugeSet();
    server::Metrics::GetInstance().GPUMemoryUsageGaugeSet();
    server::Metrics::GetInstance().OctetsSet();
}

void DBImpl::StartCompactionTask() {
    static uint64_t compact_clock_tick = 0;
    compact_clock_tick++;
    if(compact_clock_tick%COMPACT_ACTION_INTERVAL != 0) {
        return;
    }

    //serialize memory data
    std::vector<std::string> temp_table_ids;
    mem_mgr_->Serialize(temp_table_ids);
    for(auto& id : temp_table_ids) {
        compact_table_ids_.insert(id);
    }

    //compactiong has been finished?
    if(!compact_thread_results_.empty()) {
        std::chrono::milliseconds span(10);
        if (compact_thread_results_.back().wait_for(span) == std::future_status::ready) {
            compact_thread_results_.pop_back();
        }
    }

    //add new compaction task
    if(compact_thread_results_.empty()) {
        compact_thread_results_.push_back(
                compact_thread_pool_.enqueue(&DBImpl::BackgroundCompaction, this, compact_table_ids_));
        compact_table_ids_.clear();
    }
}

Status DBImpl::MergeFiles(const std::string& table_id, const meta::DateT& date,
        const meta::TableFilesSchema& files) {
    meta::TableFileSchema table_file;
    table_file.table_id_ = table_id;
    table_file.date_ = date;
    Status status = meta_ptr_->CreateTableFile(table_file);

    if (!status.ok()) {
        ENGINE_LOG_INFO << status.ToString() << std::endl;
        return status;
    }

    ExecutionEnginePtr index =
            EngineFactory::Build(table_file.dimension_, table_file.location_, (EngineType)table_file.engine_type_);

    meta::TableFilesSchema updated;
    long  index_size = 0;

    for (auto& file : files) {

        auto start_time = METRICS_NOW_TIME;
        index->Merge(file.location_);
        auto file_schema = file;
        auto end_time = METRICS_NOW_TIME;
        auto total_time = METRICS_MICROSECONDS(start_time,end_time);
        server::Metrics::GetInstance().MemTableMergeDurationSecondsHistogramObserve(total_time);

        file_schema.file_type_ = meta::TableFileSchema::TO_DELETE;
        updated.push_back(file_schema);
        ENGINE_LOG_DEBUG << "Merging file " << file_schema.file_id_;
        index_size = index->Size();

        if (index_size >= options_.index_trigger_size) break;
    }


    index->Serialize();

    if (index_size >= options_.index_trigger_size) {
        table_file.file_type_ = meta::TableFileSchema::TO_INDEX;
    } else {
        table_file.file_type_ = meta::TableFileSchema::RAW;
    }
    table_file.size_ = index_size;
    updated.push_back(table_file);
    status = meta_ptr_->UpdateTableFiles(updated);
    ENGINE_LOG_DEBUG << "New merged file " << table_file.file_id_ <<
        " of size=" << index->PhysicalSize()/(1024*1024) << " M";

    index->Cache();

    return status;
}

Status DBImpl::BackgroundMergeFiles(const std::string& table_id) {
    meta::DatePartionedTableFilesSchema raw_files;
    auto status = meta_ptr_->FilesToMerge(table_id, raw_files);
    if (!status.ok()) {
        return status;
    }

    bool has_merge = false;
    for (auto& kv : raw_files) {
        auto files = kv.second;
        if (files.size() <= options_.merge_trigger_number) {
            continue;
        }
        has_merge = true;
        MergeFiles(table_id, kv.first, kv.second);

        if (shutting_down_.load(std::memory_order_acquire)){
            break;
        }
    }

    return Status::OK();
}

void DBImpl::BackgroundCompaction(std::set<std::string> table_ids) {
    Status status;
    for (auto table_id : table_ids) {
        status = BackgroundMergeFiles(table_id);
        if (!status.ok()) {
            bg_error_ = status;
            return;
        }
    }

    meta_ptr_->Archive();
    meta_ptr_->CleanUpFilesWithTTL(1);
}

void DBImpl::StartBuildIndexTask() {
    static uint64_t index_clock_tick = 0;
    index_clock_tick++;
    if(index_clock_tick%INDEX_ACTION_INTERVAL != 0) {
        return;
    }

    //build index has been finished?
    if(!index_thread_results_.empty()) {
        std::chrono::milliseconds span(10);
        if (index_thread_results_.back().wait_for(span) == std::future_status::ready) {
            index_thread_results_.pop_back();
        }
    }

    //add new build index task
    if(index_thread_results_.empty()) {
        index_thread_results_.push_back(
                index_thread_pool_.enqueue(&DBImpl::BackgroundBuildIndex, this));
    }
}

Status DBImpl::BuildIndex(const meta::TableFileSchema& file) {
    ExecutionEnginePtr to_index = EngineFactory::Build(file.dimension_, file.location_, (EngineType)file.engine_type_);
    if(to_index == nullptr) {
        return Status::Error("Invalid engine type");
    }

    try {
        //step 1: load index
        to_index->Load();

        //step 2: create table file
        meta::TableFileSchema table_file;
        table_file.table_id_ = file.table_id_;
        table_file.date_ = file.date_;
        Status status = meta_ptr_->CreateTableFile(table_file);
        if (!status.ok()) {
            return status;
        }

        //step 3: build index
        auto start_time = METRICS_NOW_TIME;
        auto index = to_index->BuildIndex(table_file.location_);
        auto end_time = METRICS_NOW_TIME;
        auto total_time = METRICS_MICROSECONDS(start_time, end_time);
        server::Metrics::GetInstance().BuildIndexDurationSecondsHistogramObserve(total_time);

        //step 4: if table has been deleted, dont save index file
        bool has_table = false;
        meta_ptr_->HasTable(file.table_id_, has_table);
        if(!has_table) {
            meta_ptr_->DeleteTableFiles(file.table_id_);
            return Status::OK();
        }

        //step 5: save index file
        index->Serialize();

        //step 6: update meta
        table_file.file_type_ = meta::TableFileSchema::INDEX;
        table_file.size_ = index->Size();

        auto to_remove = file;
        to_remove.file_type_ = meta::TableFileSchema::TO_DELETE;

        meta::TableFilesSchema update_files = {to_remove, table_file};
        meta_ptr_->UpdateTableFiles(update_files);

        ENGINE_LOG_DEBUG << "New index file " << table_file.file_id_ << " of size "
                   << index->PhysicalSize()/(1024*1024) << " M"
                   << " from file " << to_remove.file_id_;

        index->Cache();

    } catch (std::exception& ex) {
        return Status::Error("Build index encounter exception", ex.what());
    }

    return Status::OK();
}

void DBImpl::BackgroundBuildIndex() {
    meta::TableFilesSchema to_index_files;
    meta_ptr_->FilesToIndex(to_index_files);
    Status status;
    for (auto& file : to_index_files) {
        /* ENGINE_LOG_DEBUG << "Buiding index for " << file.location; */
        status = BuildIndex(file);
        if (!status.ok()) {
            bg_error_ = status;
            return;
        }

        if (shutting_down_.load(std::memory_order_acquire)){
            break;
        }
    }
    /* ENGINE_LOG_DEBUG << "All Buiding index Done"; */
}

Status DBImpl::DropAll() {
    return meta_ptr_->DropAll();
}

Status DBImpl::Size(uint64_t& result) {
    return  meta_ptr_->Size(result);
}

DBImpl::~DBImpl() {
    shutting_down_.store(true, std::memory_order_release);
    bg_timer_thread_.join();
    std::vector<std::string> ids;
    mem_mgr_->Serialize(ids);
}

} // namespace engine
} // namespace milvus
} // namespace zilliz