package core

import (
    "log"
    "math"
    "sort"
    "sync"

    "github.com/huichen/wukong/types"
    "github.com/huichen/wukong/utils"
)

// 索引器
type Indexer struct {
    // 从搜索键到文档列表的反向索引
    // 加了读写锁以保证读写安全
    tableLock struct {
        sync.RWMutex
        table     map[string]*KeywordIndices
        docsState map[uint64]int // nil: 表示无状态记录，0: 存在于索引中，1: 等待删除，2: 等待加入
    }
    addCacheLock struct {
        sync.RWMutex
        addCachePointer int
        addCache        types.DocumentsIndex
    }
    removeCacheLock struct {
        sync.RWMutex
        removeCachePointer int
        removeCache        types.DocumentsId
    }

    initOptions types.IndexerInitOptions
    initialized bool

    // 这实际上是总文档数的一个近似
    numDocuments uint64

    // 所有被索引文本的总关键词数
    totalTokenLength float32

    // 每个文档的关键词长度
    docTokenLengths map[uint64]float32
}

// 反向索引表的一行，收集了一个搜索键出现的所有文档，按照DocId从小到大排序。
type KeywordIndices struct {
    // 下面的切片是否为空，取决于初始化时IndexType的值
    docIds      []uint64  // 全部类型都有
    frequencies []float32 // IndexType == FrequenciesIndex
    locations   [][]int   // IndexType == LocationsIndex
}

// 初始化索引器
func (indexer *Indexer) Init(options types.IndexerInitOptions) {
    if indexer.initialized == true {
        log.Fatal("索引器不能初始化两次")
    }
    options.Init()
    indexer.initOptions = options
    indexer.initialized = true

    indexer.tableLock.table = make(map[string]*KeywordIndices)
    indexer.tableLock.docsState = make(map[uint64]int)
    indexer.addCacheLock.addCache = make([]*types.DocumentIndex, indexer.initOptions.DocCacheSize)
    indexer.removeCacheLock.removeCache = make([]uint64, indexer.initOptions.DocCacheSize*2)
    indexer.docTokenLengths = make(map[uint64]float32)
}

// 从KeywordIndices中得到第i个文档的DocId
func (indexer *Indexer) getDocId(ti *KeywordIndices, i int) uint64 {
    return ti.docIds[i]
}

// 得到KeywordIndices中文档总数
func (indexer *Indexer) getIndexLength(ti *KeywordIndices) int {
    return len(ti.docIds)
}

// 向 ADDCACHE 中加入一个文档
func (indexer *Indexer) AddDocumentToCache(document *types.DocumentIndex, forceUpdate bool) {
    if indexer.initialized == false {
        log.Fatal("索引器尚未初始化")
    }

    indexer.addCacheLock.Lock()
    if document != nil {
        indexer.addCacheLock.addCache[indexer.addCacheLock.addCachePointer] = document
        indexer.addCacheLock.addCachePointer++
    }
    if indexer.addCacheLock.addCachePointer >= indexer.initOptions.DocCacheSize || forceUpdate {
        indexer.tableLock.Lock()
        position := 0
        for i := 0; i < indexer.addCacheLock.addCachePointer; i++ {
            docIndex := indexer.addCacheLock.addCache[i]
            if docState, ok := indexer.tableLock.docsState[docIndex.DocId]; ok && docState <= 1 {
                // ok && docState == 0 表示存在于索引中，需先删除再添加
                // ok && docState == 1 表示不一定存在于索引中，等待删除，需先删除再添加
                if position != i {
                    indexer.addCacheLock.addCache[position], indexer.addCacheLock.addCache[i] =
                        indexer.addCacheLock.addCache[i], indexer.addCacheLock.addCache[position]
                }
                if docState == 0 {
                    indexer.removeCacheLock.Lock()
                    indexer.removeCacheLock.removeCache[indexer.removeCacheLock.removeCachePointer] =
                        docIndex.DocId
                    indexer.removeCacheLock.removeCachePointer++
                    indexer.removeCacheLock.Unlock()
                    indexer.tableLock.docsState[docIndex.DocId] = 1
                    indexer.numDocuments--
                }
                position++
            } else if !ok {
                indexer.tableLock.docsState[docIndex.DocId] = 2
            }
        }

        indexer.tableLock.Unlock()
        if indexer.RemoveDocumentToCache(0, forceUpdate) {
            // 只有当存在于索引表中的文档已被删除，其才可以重新加入到索引表中
            position = 0
        }

        addCachedDocuments := indexer.addCacheLock.addCache[position:indexer.addCacheLock.addCachePointer]
        indexer.addCacheLock.addCachePointer = position
        indexer.addCacheLock.Unlock()
        sort.Sort(addCachedDocuments)
        indexer.AddDocuments(&addCachedDocuments)
    } else {
        indexer.addCacheLock.Unlock()
    }
}

// 向反向索引表中加入 ADDCACHE 中所有文档
func (indexer *Indexer) AddDocuments(documents *types.DocumentsIndex) {
    if indexer.initialized == false {
        log.Fatal("索引器尚未初始化")
    }

    indexer.tableLock.Lock()
    defer indexer.tableLock.Unlock()
    indexPointers := make(map[string]int, len(indexer.tableLock.table))

    // DocId 递增顺序遍历插入文档保证索引移动次数最少
    for i, document := range *documents {
        if i < len(*documents)-1 && (*documents)[i].DocId == (*documents)[i+1].DocId {
            // 如果有重复文档加入，因为稳定排序，只加入最后一个
            continue
        }
        if docState, ok := indexer.tableLock.docsState[document.DocId]; ok && docState == 1 {
            // 如果此时 docState 仍为 1，说明该文档需被删除
            // docState 合法状态为 nil & 2，保证一定不会插入已经在索引表中的文档
            continue
        }

        // 更新文档关键词总长度
        if document.TokenLength != 0 {
            indexer.docTokenLengths[document.DocId] = float32(document.TokenLength)
            indexer.totalTokenLength += document.TokenLength
        }

        docIdIsNew := true
        for _, keyword := range document.Keywords {
            indices, foundKeyword := indexer.tableLock.table[keyword.Text]
            if !foundKeyword {
                // 如果没找到该搜索键则加入
                ti := KeywordIndices{}
                switch indexer.initOptions.IndexType {
                case types.LocationsIndex:
                    ti.locations = [][]int{keyword.Starts}
                case types.FrequenciesIndex:
                    ti.frequencies = []float32{keyword.Frequency}
                }
                ti.docIds = []uint64{document.DocId}
                indexer.tableLock.table[keyword.Text] = &ti
                continue
            }

            // 查找应该插入的位置，且索引一定不存在
            position, _ := indexer.searchIndex(
                indices, indexPointers[keyword.Text], indexer.getIndexLength(indices)-1, document.DocId)
            indexPointers[keyword.Text] = position
            switch indexer.initOptions.IndexType {
            case types.LocationsIndex:
                indices.locations = append(indices.locations, []int{})
                copy(indices.locations[position+1:], indices.locations[position:])
                indices.locations[position] = keyword.Starts
            case types.FrequenciesIndex:
                indices.frequencies = append(indices.frequencies, float32(0))
                copy(indices.frequencies[position+1:], indices.frequencies[position:])
                indices.frequencies[position] = keyword.Frequency
            }
            indices.docIds = append(indices.docIds, 0)
            copy(indices.docIds[position+1:], indices.docIds[position:])
            indices.docIds[position] = document.DocId
        }

        // 更新文章状态和总数
        if docIdIsNew {
            indexer.tableLock.docsState[document.DocId] = 0
            indexer.numDocuments++
        }
    }
}

// 向 REMOVECACHE 中加入一个待删除文档
// 返回值表示文档是否在索引表中被删除
func (indexer *Indexer) RemoveDocumentToCache(docId uint64, forceUpdate bool) bool {
    if indexer.initialized == false {
        log.Fatal("索引器尚未初始化")
    }

    indexer.removeCacheLock.Lock()
    if docId != 0 {
        indexer.tableLock.Lock()
        if docState, ok := indexer.tableLock.docsState[docId]; ok && docState == 0 {
            indexer.removeCacheLock.removeCache[indexer.removeCacheLock.removeCachePointer] = docId
            indexer.removeCacheLock.removeCachePointer++
            indexer.tableLock.docsState[docId] = 1
            indexer.numDocuments--
        } else if ok && docState == 2 {
            // 删除一个等待加入的文档
            indexer.tableLock.docsState[docId] = 1
        } else if !ok {
            // 若文档不存在，则无法判断其是否在 addCache 中，需避免这样的操作
        }
        indexer.tableLock.Unlock()
    }

    if indexer.removeCacheLock.removeCachePointer > 0 &&
        (indexer.removeCacheLock.removeCachePointer >= indexer.initOptions.DocCacheSize ||
            forceUpdate) {
        removeCachedDocuments := indexer.removeCacheLock.removeCache[:indexer.removeCacheLock.removeCachePointer]
        indexer.removeCacheLock.removeCachePointer = 0
        indexer.removeCacheLock.Unlock()
        sort.Sort(removeCachedDocuments)
        indexer.RemoveDocuments(&removeCachedDocuments)
        return true
    }
    indexer.removeCacheLock.Unlock()
    return false
}

// 向反向索引表中删除 REMOVECACHE 中所有文档
func (indexer *Indexer) RemoveDocuments(documents *types.DocumentsId) {
    if indexer.initialized == false {
        log.Fatal("索引器尚未初始化")
    }

    indexer.tableLock.Lock()
    defer indexer.tableLock.Unlock()

    // 更新文档关键词总长度，删除文档状态
    for _, docId := range *documents {
        indexer.totalTokenLength -= indexer.docTokenLengths[docId]
        delete(indexer.docTokenLengths, docId)
        delete(indexer.tableLock.docsState, docId)
    }

    for keyword, indices := range indexer.tableLock.table {
        indicesTop, indicesPointer := 0, 0
        documentsPointer := sort.Search(
            len(*documents), func(i int) bool { return (*documents)[i] >= indices.docIds[0] })
        // 双指针扫描，进行批量删除操作
        for documentsPointer < len(*documents) && indicesPointer < indexer.getIndexLength(indices) {
            if indices.docIds[indicesPointer] < (*documents)[documentsPointer] {
                if indicesTop != indicesPointer {
                    switch indexer.initOptions.IndexType {
                    case types.LocationsIndex:
                        indices.locations[indicesTop] = indices.locations[indicesPointer]
                    case types.FrequenciesIndex:
                        indices.frequencies[indicesTop] = indices.frequencies[indicesPointer]
                    }
                    indices.docIds[indicesTop] = indices.docIds[indicesPointer]
                }
                indicesTop++
                indicesPointer++
            } else if indices.docIds[indicesPointer] == (*documents)[documentsPointer] {
                indicesPointer++
                documentsPointer++
            } else {
                documentsPointer++
            }
        }
        if indicesTop != indicesPointer {
            switch indexer.initOptions.IndexType {
            case types.LocationsIndex:
                indices.locations = append(
                    indices.locations[:indicesTop], indices.locations[indicesPointer:]...)
            case types.FrequenciesIndex:
                indices.frequencies = append(
                    indices.frequencies[:indicesTop], indices.frequencies[indicesPointer:]...)
            }
            indices.docIds = append(
                indices.docIds[:indicesTop], indices.docIds[indicesPointer:]...)
        }
        if len(indices.docIds) == 0 {
            delete(indexer.tableLock.table, keyword)
        }
    }
}

// 查找包含全部搜索键(AND操作)的文档
// 当docIds不为nil时仅从docIds指定的文档中查找
func (indexer *Indexer) Lookup(
    tokens []string, labels []string, docIds map[uint64]bool, countDocsOnly bool) (docs []types.IndexedDocument, numDocs int) {
    if indexer.initialized == false {
        log.Fatal("索引器尚未初始化")
    }

    if indexer.numDocuments == 0 {
        return
    }
    numDocs = 0

    // 合并关键词和标签为搜索键
    keywords := make([]string, len(tokens)+len(labels))
    copy(keywords, tokens)
    copy(keywords[len(tokens):], labels)

    indexer.tableLock.RLock()
    defer indexer.tableLock.RUnlock()
    table := make([]*KeywordIndices, len(keywords))
    for i, keyword := range keywords {
        indices, found := indexer.tableLock.table[keyword]
        if !found {
            // 当反向索引表中无此搜索键时直接返回
            return
        } else {
            // 否则加入反向表中
            table[i] = indices
        }
    }

    // 当没有找到时直接返回
    if len(table) == 0 {
        return
    }

    // 归并查找各个搜索键出现文档的交集
    // 从后向前查保证先输出DocId较大文档
    indexPointers := make([]int, len(table))
    for iTable := 0; iTable < len(table); iTable++ {
        indexPointers[iTable] = indexer.getIndexLength(table[iTable]) - 1
    }
    // 平均文本关键词长度，用于计算BM25
    avgDocLength := indexer.totalTokenLength / float32(indexer.numDocuments)
    for ; indexPointers[0] >= 0; indexPointers[0]-- {
        // 以第一个搜索键出现的文档作为基准，并遍历其他搜索键搜索同一文档
        baseDocId := indexer.getDocId(table[0], indexPointers[0])
        if docIds != nil {
            if _, found := docIds[baseDocId]; !found {
                continue
            }
        }
        iTable := 1
        found := true
        for ; iTable < len(table); iTable++ {
            // 二分法比简单的顺序归并效率高，也有更高效率的算法，
            // 但顺序归并也许是更好的选择，考虑到将来需要用链表重新实现
            // 以避免反向表添加新文档时的写锁。
            // TODO: 进一步研究不同求交集算法的速度和可扩展性。
            position, foundBaseDocId := indexer.searchIndex(table[iTable],
                0, indexPointers[iTable], baseDocId)
            if foundBaseDocId {
                indexPointers[iTable] = position
            } else {
                if position == 0 {
                    // 该搜索键中所有的文档ID都比baseDocId大，因此已经没有
                    // 继续查找的必要。
                    return
                } else {
                    // 继续下一indexPointers[0]的查找
                    indexPointers[iTable] = position - 1
                    found = false
                    break
                }
            }
        }

        if found {
            if docState, ok := indexer.tableLock.docsState[baseDocId]; !ok || docState != 0 {
                continue
            }
            indexedDoc := types.IndexedDocument{}

            // 当为LocationsIndex时计算关键词紧邻距离
            if indexer.initOptions.IndexType == types.LocationsIndex {
                // 计算有多少关键词是带有距离信息的
                numTokensWithLocations := 0
                for i, t := range table[:len(tokens)] {
                    if len(t.locations[indexPointers[i]]) > 0 {
                        numTokensWithLocations++
                    }
                }
                if numTokensWithLocations != len(tokens) {
                    if !countDocsOnly {
                        docs = append(docs, types.IndexedDocument{
                            DocId: baseDocId,
                        })
                    }
                    numDocs++
                    //当某个关键字对应多个文档且有lable关键字存在时，若直接break,将会丢失相当一部分搜索结果
                    continue
                }

                // 计算搜索键在文档中的紧邻距离
                tokenProximity, tokenLocations := computeTokenProximity(table[:len(tokens)], indexPointers, tokens)
                indexedDoc.TokenProximity = int32(tokenProximity)
                indexedDoc.TokenSnippetLocations = tokenLocations

                // 添加TokenLocations
                indexedDoc.TokenLocations = make([][]int, len(tokens))
                for i, t := range table[:len(tokens)] {
                    indexedDoc.TokenLocations[i] = t.locations[indexPointers[i]]
                }
            }

            // 当为LocationsIndex或者FrequenciesIndex时计算BM25
            if indexer.initOptions.IndexType == types.LocationsIndex ||
                indexer.initOptions.IndexType == types.FrequenciesIndex {
                bm25 := float32(0)
                d := indexer.docTokenLengths[baseDocId]
                for i, t := range table[:len(tokens)] {
                    var frequency float32
                    if indexer.initOptions.IndexType == types.LocationsIndex {
                        frequency = float32(len(t.locations[indexPointers[i]]))
                    } else {
                        frequency = t.frequencies[indexPointers[i]]
                    }

                    // 计算BM25
                    if len(t.docIds) > 0 && frequency > 0 && indexer.initOptions.BM25Parameters != nil && avgDocLength != 0 {
                        // 带平滑的idf
                        idf := float32(math.Log2(float64(indexer.numDocuments)/float64(len(t.docIds)) + 1))
                        k1 := indexer.initOptions.BM25Parameters.K1
                        b := indexer.initOptions.BM25Parameters.B
                        bm25 += idf * frequency * (k1 + 1) / (frequency + k1*(1-b+b*d/avgDocLength))
                    }
                }
                indexedDoc.BM25 = float32(bm25)
            }

            indexedDoc.DocId = baseDocId
            if !countDocsOnly {
                docs = append(docs, indexedDoc)
            }
            numDocs++
        }
    }
    return
}

// 二分法查找indices中某文档的索引项
// 第一个返回参数为找到的位置或需要插入的位置
// 第二个返回参数标明是否找到
func (indexer *Indexer) searchIndex(
    indices *KeywordIndices, start int, end int, docId uint64) (int, bool) {
    // 特殊情况
    if indexer.getIndexLength(indices) == start {
        return start, false
    }
    if docId < indexer.getDocId(indices, start) {
        return start, false
    } else if docId == indexer.getDocId(indices, start) {
        return start, true
    }
    if docId > indexer.getDocId(indices, end) {
        return end + 1, false
    } else if docId == indexer.getDocId(indices, end) {
        return end, true
    }

    // 二分
    var middle int
    for end-start > 1 {
        middle = (start + end) / 2
        if docId == indexer.getDocId(indices, middle) {
            return middle, true
        } else if docId > indexer.getDocId(indices, middle) {
            start = middle
        } else {
            end = middle
        }
    }
    return end, false
}

// 计算搜索键在文本中的紧邻距离
//
// 假定第 i 个搜索键首字节出现在文本中的位置为 P_i，长度 L_i
// 紧邻距离计算公式为
//
//     ArgMin(Sum(Abs(P_(i+1) - P_i - L_i)))
//
// 具体由动态规划实现，依次计算前 i 个 token 在每个出现位置的最优值。
// 选定的 P_i 通过 tokenLocations 参数传回。
func computeTokenProximity(table []*KeywordIndices, indexPointers []int, tokens []string) (
    minTokenProximity int, tokenLocations []int) {
    minTokenProximity = -1
    tokenLocations = make([]int, len(tokens))

    var (
        currentLocations, nextLocations []int
        currentMinValues, nextMinValues []int
        path                            [][]int
    )

    // 初始化路径数组
    path = make([][]int, len(tokens))
    for i := 1; i < len(path); i++ {
        path[i] = make([]int, len(table[i].locations[indexPointers[i]]))
    }

    // 动态规划
    currentLocations = table[0].locations[indexPointers[0]]
    currentMinValues = make([]int, len(currentLocations))
    for i := 1; i < len(tokens); i++ {
        nextLocations = table[i].locations[indexPointers[i]]
        nextMinValues = make([]int, len(nextLocations))
        for j, _ := range nextMinValues {
            nextMinValues[j] = -1
        }

        var iNext int
        for iCurrent, currentLocation := range currentLocations {
            if currentMinValues[iCurrent] == -1 {
                continue
            }
            for iNext+1 < len(nextLocations) && nextLocations[iNext+1] < currentLocation {
                iNext++
            }

            update := func(from int, to int) {
                if to >= len(nextLocations) {
                    return
                }
                value := currentMinValues[from] + utils.AbsInt(nextLocations[to]-currentLocations[from]-len(tokens[i-1]))
                if nextMinValues[to] == -1 || value < nextMinValues[to] {
                    nextMinValues[to] = value
                    path[i][to] = from
                }
            }

            // 最优解的状态转移只发生在左右最接近的位置
            update(iCurrent, iNext)
            update(iCurrent, iNext+1)
        }

        currentLocations = nextLocations
        currentMinValues = nextMinValues
    }

    // 找出最优解
    var cursor int
    for i, value := range currentMinValues {
        if value == -1 {
            continue
        }
        if minTokenProximity == -1 || value < minTokenProximity {
            minTokenProximity = value
            cursor = i
        }
    }

    // 从路径倒推出最优解的位置
    for i := len(tokens) - 1; i >= 0; i-- {
        if i != len(tokens)-1 {
            cursor = path[i+1][cursor]
        }
        tokenLocations[i] = table[i].locations[indexPointers[i]][cursor]
    }
    return
}

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