渐变衰减
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zhangkun9038@dingtalk.com
parent
6c64c0438b
commit
f4a720cec8
@ -6,7 +6,6 @@ import (
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"fmt"
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cfg "gitea.zjmud.xyz/phyer/tanya/config" // 导入你的 config 包
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"io"
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"math"
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"net/http"
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"strconv"
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"strings"
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@ -24,45 +23,57 @@ type DefaultRetentionCalculator struct {
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MinRetention int
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}
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// NonLinearCoolingModel 非线性冷却模型
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var periodBaseConfig = map[string]struct {
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Base int // 基础值
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Interval float64 // 时间间隔系数
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}{
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"1m": {30, 1.0},
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"3m": {45, 1.5},
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"5m": {60, 2.0},
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"15m": {90, 3.0},
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"30m": {120, 4.0},
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"1H": {150, 5.0},
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"2H": {180, 6.0},
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"4H": {240, 8.0},
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"6H": {300, 10.0},
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"12H": {360, 12.0},
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"1D": {480, 16.0},
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"2D": {600, 20.0},
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"5D": {720, 24.0},
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"1W": {840, 28.0},
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}
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func NonLinearCoolingModel(daysDiff int, period string, config map[string]float64) (int, int, int) {
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minutes := periodToMinutes(period)
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// 可配置的系数
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timeDecayFactor := config["timeDecayFactor"] // 距今时间因子
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// periodGranularityFactor := config["periodGranularityFactor"] // 时间框架粒度因子
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// warmPhaseMultiplier := config["warmPhaseMultiplier"] // warm阶段乘数
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// coldPhaseMultiplier := config["coldPhaseMultiplier"] // cold阶段乘数
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// deletePhaseMultiplier := config["deletePhaseMultiplier"] // delete阶段乘数
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// 时间衰减因子反转：越新的数据保留越久
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timeFactor := math.Pow(100.0, timeDecayFactor) / math.Pow(float64(daysDiff+1), timeDecayFactor)
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if timeFactor > 1 {
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timeFactor = 1
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// 获取基础配置
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cfg, ok := periodBaseConfig[period]
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if !ok {
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cfg = periodBaseConfig["1m"] // 默认配置
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}
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// 时间框架因子调整：粒度越细保留时间越长
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periodFactor := math.Pow(float64(minutes)/1440, 0.5) // 基于天数的平方根比例
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// 反转时间衰减因子：数据越新，保留时间越长
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// 使用反比例函数：1/(x+1) 确保新数据(小daysDiff)获得更大值
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ageFactor := 1.0 / (float64(daysDiff)/365.0 + 1.0)
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// 基础保留天数计算（与时间框架成正比）
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baseDays := int(30 * periodFactor) // 30天基础值
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// 计算基础保留时间（与时间框架相关）
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base := cfg.Base
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// 阶段持续时间计算（与距今时间成反比）
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warmDays := baseDays + int(float64(1000)/float64(daysDiff+1))
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coldDays := 2*baseDays + int(float64(2000)/float64(daysDiff+1))
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deleteDays := 3*baseDays + int(float64(3000)/float64(daysDiff+1))
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// 应用年龄因子 - 新数据获得更长保留时间
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warm := int(float64(base) * (1.0 + ageFactor))
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cold := warm * 2
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delete := warm * 3
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// 保证最小阶段关系
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if warmDays < baseDays {
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warmDays = baseDays
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// 确保最小值
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if warm < cfg.Base {
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warm = cfg.Base
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}
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if coldDays < 2*baseDays {
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coldDays = 2 * baseDays
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if cold < warm*2 {
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cold = warm * 2
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}
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if deleteDays < 3*baseDays {
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deleteDays = 3 * baseDays
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if delete < warm*3 {
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delete = warm * 3
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}
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return warmDays, coldDays, deleteDays
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return warm, cold, delete
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}
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// Calculate 使用非线性冷却模型计算保留时间
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@ -1,14 +1,14 @@
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package elasticilm
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import (
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"encoding/json"
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"fmt"
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"strings"
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// "math"
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"testing"
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"time"
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)
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// TestEnsureILMPolicy 测试 ensureILMPolicy 函数并生成退化阶段时间矩阵
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// TestEnsureILMPolicy 测试 ensureILMPolicy 函数并生成warm阶段时间矩阵(CSV格式)
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func TestEnsureILMPolicy(t *testing.T) {
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// 定义时间框架
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timeFrames := []string{"1m", "3m", "5m", "15m", "30m", "1H", "2H", "4H", "6H", "12H", "1D", "2D", "5D", "1W"}
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@ -18,52 +18,73 @@ func TestEnsureILMPolicy(t *testing.T) {
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endDate := time.Date(2025, 3, 1, 0, 0, 0, 0, time.UTC)
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currentDate := time.Date(2025, 4, 1, 0, 0, 0, 0, time.UTC)
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// 初始化结果矩阵
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results := make([][]string, 0)
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// 初始化HTML输出
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var htmlOutput strings.Builder
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htmlOutput.WriteString(`<!DOCTYPE html>
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<html>
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<head>
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<title>ILM Warm Phase Matrix</title>
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<style>
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body { font-family: Arial, sans-serif; }
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table { border-collapse: collapse; margin: 20px; }
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th, td { border: 1px solid #ddd; padding: 8px; text-align: center; }
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th { background-color: #f2f2f2; }
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.month { font-weight: bold; }
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</style>
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</head>
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<body>
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<h1>ILM Warm Phase Duration Matrix</h1>
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<table>
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<tr>
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<th>Month</th>`)
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// 遍历每个时间框架
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// 写入表头
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for _, period := range timeFrames {
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row := make([]string, 0)
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// 遍历每个月
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for d := startDate; !d.After(endDate); d = d.AddDate(0, 1, 0) {
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daysDiff := int(currentDate.Sub(d).Hours() / 24)
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htmlOutput.WriteString(fmt.Sprintf(`<th>%s</th>`, period))
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}
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htmlOutput.WriteString(`</tr>`)
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// 遍历每个月
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for d := startDate; !d.After(endDate); d = d.AddDate(0, 1, 0) {
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month := d.Format("2006-01")
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daysDiff := int(currentDate.Sub(d).Hours() / 24)
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htmlOutput.WriteString(`<tr><td class="month">` + month + `</td>`)
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// 遍历每个时间框架
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for _, period := range timeFrames {
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// 默认配置
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config := map[string]float64{
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"timeDecayFactor": 0.5,
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"periodGranularityFactor": 0.5,
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"timeDecayFactor": 0.3,
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"periodGranularityFactor": 0.7,
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"warmPhaseMultiplier": 1.0,
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"coldPhaseMultiplier": 2.0,
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"deletePhaseMultiplier": 3.0,
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}
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// 使用非线性冷却模型计算各个阶段的时间
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warmDays, coldDays, deleteDays := NonLinearCoolingModel(daysDiff, period, config)
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// 计算warm阶段时间
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warmDays, _, _ := NonLinearCoolingModel(daysDiff, period, config)
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// 格式化结果
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result := fmt.Sprintf("Warm: %d, Cold: %d, Delete: %d", warmDays, coldDays, deleteDays)
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row = append(row, result)
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// 计算颜色 (0-255范围) 基于0-1800的范围
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colorValue := int(float64(warmDays) / 2500 * 255)
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if colorValue > 255 {
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colorValue = 255
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}
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bgColor := fmt.Sprintf("rgb(%d, 0, %d)", colorValue, 255-colorValue)
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htmlOutput.WriteString(fmt.Sprintf(
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`<td style="background-color: %s">%d</td>`,
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bgColor,
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warmDays,
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))
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}
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results = append(results, row)
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htmlOutput.WriteString(`</tr>`)
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}
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// 将结果存储为结构化数据
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resultMap := make(map[string][]map[string]string)
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for i, period := range timeFrames {
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periodResults := make([]map[string]string, 0)
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for j, result := range results[i] {
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month := startDate.AddDate(0, j, 0).Format("2006-01")
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periodResults = append(periodResults, map[string]string{"Month": month, "Result": result})
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}
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resultMap[period] = periodResults
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}
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htmlOutput.WriteString(`</table>
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</body>
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</html>`)
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// 将结果编码为JSON格式
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jsonData, err := json.MarshalIndent(resultMap, "", " ")
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if err != nil {
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t.Fatalf("Failed to encode results to JSON: %v", err)
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}
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// 打印JSON结果
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fmt.Println(string(jsonData))
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// 打印HTML结果
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fmt.Println(htmlOutput.String())
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}
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