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  • 来自专栏YashanDB知识库

    YashanDB PERCENTILE_CONT函数

    percentile_cont::= PERCENTILE_CONT "(" percentile ")" WITHIN GROUP "(" ORDER BY expr [DESC|ASC] [NULLS ，给定一个百分比值percentile，返回对应百分比所在的插值。 当给定的percentile为0.5时，返回结果是组内排序键的中位数。不指定OVER关键字语法时，本函数是聚合函数。本函数不支持向量化计算。 percentile百分比值percentile只允许数值类型和可隐式转换为数值类型的其他数据类型。percentile取值范围为[0, 1]，否则报错。 给定的百分比值percentile记为P，组内非空数据行数为N。通过公式RowNum = 1 + P * ( N - 1)计算得到RowNum。

    24500编辑于 2025-06-23
  • 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏

    Google Earth Engine（GEE）——ee.Reducer.percentile使用过程中的注意问题

    我们在获取影像的百分比值使用的函数是ee.Reducer.percentile，但是会存在很多问题有时候会发现我们获取不同百分比值的时候数值会不一样，可能导致结果不同。 函数： ee.Reducer.percentile(percentiles, outputNames, maxBuckets, minBucketWidth, maxRaw) Create a reducer filterBounds(geometry).mosaic().clip(geometry) //original var list1 = image.reduceRegion(ee.Reducer.percentile ([5,25,50,75,95]),geometry,10,null,null,false,1e12) print(list1) // the value of each percentile is same var image_95 = image.reduceRegion({ 'reducer': ee.Reducer.percentile({ 'percentiles

    47610编辑于 2024-02-02
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（8核32G*3，AMD）

    index-append 301.696 ms 90th percentile service time index-append 444.837 ms 99th percentile service percentile service time default 4.45552 ms 99.9th percentile service time default 9.09098 ms 100th percentile ms 99.9th percentile latency term 42.9865 ms 100th percentile latency term 45.2784 ms 50th percentile ms 99.9th percentile latency phrase 22.6104 ms 100th percentile latency phrase 26.7837 ms 50th percentile ms 100th percentile latency scroll 643.218 ms 50th percentile service time scroll 606.853 ms 90th percentile

    9141710编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（16核64G*3 本地NVMe SSD，Intel）

    latency index-stats 3.455013706 ms 90th percentile latency index-stats 3.865102708 ms 99th percentile latency node-stats 3.845604981 ms 90th percentile latency node-stats 4.411485343 ms 99th percentile latency node-stats 7.24903738 ms 99.9th percentile latency node-stats 30.31716143 ms 100th percentile latency term 3.888126492 ms 90th percentile latency term 4.309532285 ms 99th percentile latency term ms 50th percentile service time term 3.183407011 ms 90th percentile service time term 3.321979992 ms

    9941814编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 6.0.0本地单机16核32G压测报告

    latency | index-append | 1668.42 | ms | | 50th percentile latency | index-stats | 10.6119 | ms | | 50th percentile latency | node-stats | 8.49078 | ms | | 50th percentile latency | default | 11.3709 | ms | | 50th percentile latency | term | 4.76169 | ms | | 50th percentile

    2.1K2111发布于 2021-10-20
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（16核64G*3 SSD云盘，AMD）

    latency node-stats 3.296191 ms 90th percentile latency node-stats 3.799131 ms 99th percentile latency 5.385959 ms 99.9th percentile latency default 8.354142 ms 100th percentile latency default 9.191337 ms 50th percentile service time default 3.050713 ms 90th percentile service time default 3.320561 ms ms 99.9th percentile latency term 7.350553 ms 100th percentile latency term 14.43949 ms 50th percentile percentile service time phrase 3.2294 ms 99.9th percentile service time phrase 14.31686 ms 100th percentile

    1.4K1712编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（32核64G*3，Intel）

    10.33466 ms 99.9th percentile latency default 22.50608 ms 100th percentile latency default 26.34883 ms 50th percentile service time default 7.163304 ms 90th percentile service time default 7.841727 ms ms 99.9th percentile latency term 28.44925 ms 100th percentile latency term 30.47515 ms 50th percentile service time term 6.956592 ms 90th percentile service time term 7.521901 ms 99th percentile service percentile service time phrase 8.131189 ms 99.9th percentile service time phrase 28.56503 ms 100th percentile

    8811913编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（8核32G*3，Intel）

    latency index-append 356.13 ms 90th percentile latency index-append 1044.3 ms 99th percentile latency index-append 2036.47 ms 100th percentile latency index-append 2761.67 ms 50th percentile latency index-stats 3.76232 ms 90th percentile latency index-stats 4.31119 ms 99th percentile latency index-stats 25.0359 ms 99.9th percentile latency index-stats 87.6608 ms 100th percentile ms 99.9th percentile latency term 12.8783 ms 100th percentile latency term 19.7935 ms 50th percentile

    1.2K1610编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（32核64G*3，AMD）

    latency node-stats 3.69659 ms 90th percentile latency node-stats 4.27771 ms 99th percentile latency percentile service time default 3.60439 ms 99.9th percentile service time default 5.87795 ms 100th percentile 99.9th percentile latency term 6.03389 ms 100th percentile latency term 7.04569 ms 50th percentile service ms 99.9th percentile latency phrase 14.8293 ms 100th percentile latency phrase 20.7531 ms 50th percentile ms 100th percentile latency scroll 639.222 ms 50th percentile service time scroll 615.78 ms 90th percentile

    7191911编辑于 2022-05-16
  • 来自专栏Elastic Stack专栏

    Elastic Stack最佳实践：7.10.1与7.14.2的性能比较

    2.03125 ms -21.17% 99th percentile latency default 15.3999 15.3726 -0.02733 ms -0.18% 100th percentile ms -14.21% 99th percentile latency term 22.8338 16.6021 -6.23172 ms -27.29% 100th percentile latency ms +13.01% 99th percentile latency range 25.3066 27.5328 2.22616 ms +8.80% 100th percentile latency +42.44% 90th percentile service time range 17.3298 18.4377 1.10789 ms +6.39% 99th percentile service ms -4.99% 99th percentile latency scroll 478.402 508.923 30.5212 ms +6.38% 100th percentile latency

    2K61编辑于 2022-03-20
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（4核16G*3，AMD）

    273.913 ms 99th percentile service time country_agg_uncached 297.431 ms 100th percentile service time 2.41103 ms 99.9th percentile service time country_agg_cached 6.71132 ms 100th percentile service time ms 100th percentile latency scroll 629.479 ms 50th percentile service time scroll 601.664 ms 90th percentile latency expression 480.244 ms 90th percentile latency expression 497.217 ms 99th percentile latency 478.77 ms 90th percentile service time expression 496.211 ms 99th percentile service time expression

    2.3K2510编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch压测之Esrally压测标准

    越小越好 100th percentile latency node-stats 提交请求和收到完整回复之间的时间段 越小越好 50th percentile service time node-stats 越小越好 90th percentile service time default 请求处理开始和接收完整响应之间的时间段 越小越好 99th percentile service time default 越小越好 99.9th percentile service time term 请求处理开始和接收完整响应之间的时间段 越小越好 100th percentile service time term 越小越好 90th percentile service time phrase 请求处理开始和接收完整响应之间的时间段 越小越好 99th percentile service time phrase percentile latency decay_geo_gauss_script_score 提交请求和收到完整回复之间的时间段 越小越好 50th percentile service time

    4.5K2214编辑于 2022-05-16
  • 来自专栏大数据生态

    Elasticsearch 8.8 原生向量检索性能测试

    percentile: 13.10 ms 95 percentile: 13.70 ms 98 percentile: 14.20 ms 99 percentile: 14.60 ms 99.5 percentile: 15.10 ms 99.6 percentile: 14.70 ms 99 percentile: 15.00 ms 99.5 percentile: 15.50 ms 99.6 percentile: 15.20 ms 99 percentile: 15.70 ms 99.5 percentile: 17.00 ms 99.6 percentile: 17.00 ms 99 percentile: 20.00 ms 99.5 percentile: 22.20 ms 99.6 percentile:

    2.7K104编辑于 2023-09-06
  • 来自专栏大数据生态

    Elasticsearch 7.10.1集群压测报告（4核16G*3，Intel）

    latency index-append 757.069 ms 90th percentile latency index-append 1558.87 ms 99th percentile latency index-append 3131.2 ms 99.9th percentile latency index-append 4159.98 ms 100th percentile latency index-stats 3.74153 ms 90th percentile latency index-stats 4.26026 ms 99th percentile latency index-stats 5.06623 ms 99.9th percentile latency index-stats 10.9303 ms 100th percentile ms 99.9th percentile latency term 6.21686 ms 100th percentile latency term 7.67519 ms 50th percentile

    1.2K2010编辑于 2022-05-16
  • 来自专栏数据库相关

    通过 esrally 压测elasticsearch

    latency |           index-append |     4678.18 |      ms | |                                   50th percentile latency |            index-stats |     88.8076 |      ms | |                                   50th percentile latency |             node-stats |      98.339 |      ms | |                                   50th percentile latency |                default |     22.3396 |      ms | |                                   50th percentile latency |                   term |     16.8316 |      ms | |                                   50th percentile

    1.4K10发布于 2019-12-10
  • 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏

    Google Earth Engine ——澳大利亚土壤和景观网格 (SLGA) 是澳大利亚土壤属性的综合数据集（CSIRO/SLGA）

    confidence limit at depth 0-5cm % AWC_000_005_95 The soil attribute's 95th percentile confidence limit confidence limit at depth 60-100cm % AWC_060_100_95 The soil attribute's 95th percentile confidence confidence limit at depth 100-200cm % AWC_100_200_95 The soil attribute's 95th percentile confidence confidence limit at depth 0-5cm g/cm^3 BDW_000_005_95 The soil attribute's 95th percentile confidence confidence limit at depth 60-100cm % CLY_060_100_95 The soil attribute's 95th percentile confidence

    48410编辑于 2024-02-02
  • 来自专栏数据仓库技术

    Hive基础知识07-求取中位数

    ()计算 percentile(col, p) OVER ([PARTITION BY ...] | 5.5| +------+ 6.海量数据时可以使用percentile_approx 近似计算 如果数据集非常大，排序可能会非常耗时。 在这种情况下，可以使用percentile_approx函数，它提供了一个近似的百分位数计算，通常比percentile函数更快。 和percentile 的结果不一样呢？ 这个和percentile_approx 的计算方式有关。

    2.2K10编辑于 2024-03-06
  • 来自专栏数据社

    你知道Hive中的中位数吗

    和percentile_approx。 NOTE: A true percentile can only be computed for integer  values. Use PERCENTILE_APPROX if your input is non-integral. Use PERCENTILE_APPROX if your input is non-integral. 也就是说，真正的中位数只能用percentile来计算，输入需要为整数类型，使用percentile_approx（输入为浮点型）计算得到的并不是真正的中位数，也就是所说的近似中位数，经过大量数据验证，

    2.1K20发布于 2020-05-25
  • 来自专栏小工匠聊架构

    白话Elasticsearch49-深入聚合数据分析之 Percentile Ranks Aggregation-percentiles rank以及网站访问时延SLA统计

    更多请参考官网 ---- 案例 需求：在200ms以内的，有百分之多少，在1000毫秒以内的有百分之多少 , 那就要用到 percentile ranks metric 啦 。 group_by_province": { "terms": { "field": "province" }, "aggs": { "latency_percentile_ranks ": { "percentile_ranks": { "field": "latency", "values": [ 0, "buckets": [ { "key": "新疆", "doc_count": 6, "latency_percentile_ranks } }, { "key": "江苏", "doc_count": 6, "latency_percentile_ranks

    70620发布于 2021-08-17
  • 来自专栏R语言数据分析指南

    ggplot2绘制嵌套圆图

    client == "mobile") %>% select(measure, date, p50, p90) %>% pivot_longer(c(p50, p90), names_to = "percentile ") %>% mutate(year = str_sub(date, 1, 4)) %>% mutate(name = paste(percentile, year, sep = "_")) %>% mutate(x = c(0.45, 1, -0.57, -1)) %>% mutate(label = case_when(percentile == "p50" ~ paste0 ("Peso\nmediano:\n", round(value), " KB"), percentile == "p90" ~ paste0("Percentil 90:\n", round( aes(x = x, size = value, color = year), y = 0, alpha = 0.5, data = filter(mobile_bytes, percentile

    85010编辑于 2022-12-20
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