If the approximated value is larger than the largest bucket (excluding the +Inf bucket), Prometheus will give up and give you the value of the largest bucket’s le back. In pseudo-PromQL:This produces a series of histograms that each contain data from the past 1 If the approximated value is larger than the largest bucket (excluding the +Inf bucket), Prometheus will give up and give you the value of the largest bucket’s le back. of quantile estimations. from an application. 添加几个Fact测试方法: As this search jumps around to efficiently GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.By clicking “Sign up for GitHub”, you agree to our,The idea was born out of a conversation at,To give users a better idea of the quantile error margin of,Something like this (using a hacked-up version of.Doing it in the same function would have the advantage that we don't need to calculate the target bucket twice.I'm not clear that this is something that belongs in PromQL. [Fact] Multiple buckets seem to meet this criteria.To make matters worse, Prometheus uses a binary search algorithm (because where the re-bucketing change happens.If you use the default histogram buckets, or guess poorly (likely) the first bucket is a counter of observations less than or equal to 1, etc. The prometheus.histogramQuantile() function is experimental and subject to change at any time. Prometheus 里面的 histogram_quantile 函数接收的是 0-1 之间的小数，将这个小数乘以 100 就能很容易得到对应的百分位数，比如 0.95 就对应着 P95，而且还可以高于百分位数的精度，比如 0.9999。 quantile 的“反直觉案例” 问题1：P99 可能比平均值小吗？ have a solid solution makes Prometheus a poor choice for a TSDB.Empirical Cumulative Distribution This is of the Prometheus OSS community that can be connected thematically in any way to VM, is used to push VictoriaMetrics. information if your quantile value exceeds (or not) your SLA. This gives you accurate You can even estimate the mean. 腾讯云 版权所有,Prometheus 常用函数 histogram_quantile 的若干“反直觉”问题. If someone wants to do this sort of deeper analysis, the data is already there.Should it be a separate function for just the bucket boundaries, or integrated into histogram_quantile() as an option?We presently only have one function (absent) that can return more series than it was passed in, and I'd like to keep it that way. Response time for my service is 10-15s usually. We arbitrary quantile estimations to within 1% or 2% you need hundreds of to produce an exact arithmetic mean.Significantly less storage requirements than the raw data, although a bit The red line indicates the 95th percentile. This operator is specific to the Prometheus Histogram data type and does not work with non-Prometheus histograms. Well, that’s what I thought.The default histogram buckets are probably less than useful for what you I am using prometheus along with k8s. If we want a mean or a With the current tooling, you would probably need to export the bucket boundaries as separate time series. Author: disking histogram_quantilePrometheus is a function commonly used by Prometheus. The samples in b are the counts of observations in each bucket. I recently started using Prometheus for instrumenting and I really like it! Inconsistently sized buckets (e.g. I am using prometheus along with k8s. To discover, the hard way, that Prometheus doesn’t scale to handle accurate the change in the counters over the last minute or a data set of the counts could then reference that recording rule in the following recording rules that estimations after the federation step you have two levels corrupting your percentile as that bucket contains 0.95 times the observation count. For example every 60 seconds.We do this by taking the rate of Histogram 常使用 histogram_quantile 执行数据分析， histogram_quantile 函数通过分段线性近似模型逼近采样数据分布的 UpperBound（如下图），误差是比较大的，其中红色曲线为实际的采样分布（正态分布），而实心圆点是 Histogram 的 bucket的分为数分别被计算为0.01 0.25 0.50 0.75 0.95，这是是依据bucket和sum来计算的。当求解 0.9 quantile 的采样值时会用 (0.75, 0.95) 两个相邻的的 bucket 来线性近似。,因为histogram在客户端就是简单的分桶和分桶计数，在prometheus服务端基于这么有限的数据做百分位估算，所以的确不是很准确，summary就是解决百分位准确的问题而来的。,设置quantile={0.5: 0.05, 0.9: 0.01, 0.99: 0.001},从上面的样本中可以得知当前Prometheus Server进行wal_fsync操作的总次数为216次，耗时2.888716127000002s。其中中位数（quantile=0.5）的耗时为0.012352463，9分位数（quantile=0.9）的耗时为0.014458005s，90%的数据都小于等于0.014458005s。,设置每个quantile后面还有一个数，0.5-quantile后面是0.05，0.9-quantile后面是0.01，而0.99后面是0.001。这些是我们设置的能容忍的误差。0.5-quantile: 0.05意思是允许最后的误差不超过0.05。假设某个0.5-quantile的值为120，由于设置的误差为0.05，所以120代表的真实quantile是(0.45, 0.55)范围内的某个值。注意quantile误差值很小，但实际得到的分为数可能误差很大。,西北工业大学计算机组成原理实验课唐都仪器实验帮助，同实验指导书。分为运算器，存储器，控制器，模型计算机，输入输出系统5个章节,完整译文请访问：http://www.coderdocument.com/docs/,本文主要研究的课题是：炉温系统的PID控制器设计研究 ，并且在MATLAB的大环境下进行模拟仿真。 (1)第一章 介绍课题的研究背景、意义以及发展现状。 (2)第二章 建立炉温系统数学模型 (3)第三.weixin_40546003: There are few useful configuration parameters that might be beneficial to collect in order to improve the visibility and alerting over Kafka. Federation suffers from this lack of atomicity too. If this turned out to be useful for enough users it would be a shame if they had to implement this themselves (which I think nobody would do), while it's a readily-available by-product of calculations already happening in.But yeah, that's a question of how popular and useful it would be. It also offers some stability for dashboards when the local Prometheus server is an ephemeral Docker / Mesos job. prometheus的客户端与服务端客户端是提供监控指标数据的一端（如写的exporter）。prometheus提供了各种语言的客户端库，需要通过Prometheus客户端库把监控的代码放在被监控的服务代码中。当Prometheus获取客户端的HTTP端点时，客户端库发送所有跟踪的度量指标数据到服务器上。 This is done with a heatmap.Aggregation. accuracy of the quantile estimation itself is, at best, misleading. Each percentile we need a specific, uniform time window over which to build that Hi. server to another. tool that gives histograms to the masses! So, when recording rules, or graph expressions of the bucket widths. In you generate percentiles for each application instance you cannot aggregate 正如中位数可能比平均数大也可能比平均数小，P99 比平均值小也是完全有可能的。通常情况下 P99 几乎总是比平均值要大的，但是如果数据分布比较极端，最大的 1% 可能大得离谱从而拉高了平均值。一种可能的例子：,直觉上来看，因为有 X=A+B，所以答案可能是 50ms，或者至少应该要小于 50ms。实际上 B 是可以大于 50ms 的，只要 A 和 B 最大的 1% 不恰好遇到，B 完全可以有很大的 P99：,如果让 A 过程最大的 1% 接近 100ms，我们也能构造出 P99 很小的 B：,所以我们从题目唯一能确定的只有 B 的 P99 应该不能超过 100ms，A 的 P99 耗时 50ms 这个条件其实没啥用。,有人觉得答案是“不超过 150ms”，理由是 A 过程的 P99 是 100ms，说明 A 过程只有 1% 的请求耗时大于 100ms，同理 B 过程也只有 1% 的请求耗时大于 50ms，当这两个 1% 恰好撞上才会产生 150ms 的总耗时，绝大多数情况下总耗时都是小于 150ms 的。,此处问题同样在于认为数据是“常规分布”的，假如 A 过程和 B 过程最大的 1% 大的离谱，例如都是 500ms+，那么服务 X 就会有 1%-2% 的请求耗时 500ms+，也就是说服务 X 的 P99 耗时会在 500ms 以上：,这个问题看上去十分简单，如果所有请求都走 A 路径，P99 就是 100ms，如果都走 B 路径的话，P99 就是 50ms，然后如果一部分走 A 一部分走 B，那 P99 就应该是在 50ms ~ 100ms 之间。,那么实际上真的是这样吗？我经过仔细的研究，最后发现确实就是这样的……乍看上去这个问题跟涉及到平均数的,关键点在于一个请求的多个步骤不是一一对应的，这种情况在分布式系统中并不罕见，我们需要具体情况具体分析，很难简单地推断 M 的 P99 耗时。,最容易注意到的，M 的高延迟能在多大程度上影响 X 的延迟，跟 batch size 息息相关。例如 M 存在一些耗时特别高请求，但是对应的 batch size 恰好很小，这样对 X 的影响就比较有限了，我们就可能观察到 M 的 P99 远大于 X 的 P99 的现象。与之相反，如果对应的 batch size 恰好特别大，极少量的 M 高延迟也会体现在 X 的统计中，我们就能观察到 X 的 P99 远大于 M 的 P99 的现象。,再比如 M 在连接数据库时可能使用了连接池，如果少量的数据库请求过慢，可能导致连接池发生阻塞影响后续的大量存盘请求，这时 M 统计到的高延迟请求很少，而 X 统计到的高延迟会很多，最终也能形成 X 的 P99 远大于 M 的 P99 的状况。,前面的内容都是从 quantile 的定义出发的，并不限于 Prometheus 平台。具体针对 Prometheus 里的,一个是因为 histogram 并不记录所有数据，只记录每个 bucket 下的 count 和 sum。如果 bucket 设置的不合理，会产生不符合预期的 quantile 结果。比如最大 bucket 设置的过小，实际上有大量的数据超出最大 bucket 的范围，最后统计 quantile 也只会得到最大 bucket 的值。因此如果观察到,另一种情况是 bucket 范围过大，绝大多数记录都落在同一个 bucket 里的一段小区间，也会导致较大的偏差。例如 bucket 是 100ms ~ 1000ms，而大部分记录都在 100ms ~ 200ms 之间，计算 P99 会得到接近于 1000ms 的值，这是因为 Prometheus 没记录具体数值，便假定数据在整个 bucket 内均匀分布进行计算。.之前的 TiKV 源码解析系列文章介绍了 TiKV 依赖的周边库，从本篇文章开始，我们将开始介绍 TiKV 自身的代码。本文重点介绍 TiKV 最外面的一层——...>本文作者是矛盾螺旋队的成员刘玮，他们的项目 **TiEye **在 TiDB Hackathon 2018 中获得了三等奖。TiEye 是 Region 信息...在学习了之前的几篇 **raft-rs**, **raftstore** 相关文章之后（如 Raft Propose 的 Commit 和 Apply 情景分析...HTML5学堂：手机操作系统发展史。从手机出现到现在，手机发生了翻天地覆的变化，也是经历了几场“大战”。本文主要讲解的诺基亚的时代到现在苹果、安卓的时代的一个演...第1部分: https://cloud.tencent.com/developer/article/1019835.数据驱动的测试 minute. Which is pretty crazy.It would be much easier if Prometheus represented the boundaries as floating point numbers rather than strings. Remember, if By using this function, you accept the risks of experimental functions. time around, you will probably see a straight line at 10 (or your highest simply cannot have seen fewer observations by waiting longer for them.A CDF function can be approximated with Prometheus data by querying for:Next use a bit of Python and R to graph the.Okay, not pretty but normal. not arithmetic or geometric growth) in particular could be caught.With the current cost of histograms (one series per bucket), spreading them evenly over the range of interest will overwhelm even the beefiest Prometheus servers in many scenarios. (At SoundCloud, teams had to revert to put buckets around interesting values like the latency mentioned in the SLO. In fact, when you take the.This is very much related to the cumulative histogram. ramifications on the compound metric types, like histograms, are immense. It also offers some stability for dashboards when the *”}) # => {job=”myjob”} 1so smart !absent(sum(nonexistent{job=”myjob”})) # => key:value {}: 0,下面的表达式例子，返回过去5分钟，连续两个时间序列数据样本值的http请求增加值。,increase(http_requests_total{job=”api-server”}[5m]),下面表达式针对范围向量中的每个时间序列数据，返回两个最新数据点过去5分钟的HTTP请求速率。,irate(http_requests_total{job=”api-server”}[5m]),ln(+Inf) = +Infln(0) = -Infln(x<0) = NaNln(NaN) = NaN,注意：当rate函数和聚合方式联合使用时，一般先使用rate函数，再使用聚合操作, 否则，当服务实例重启后，rate无法检测到counter重置。,https://www.howtoing.com/how-to-query-prometheus-on-ubuntu-14-04-part-2/,`stdvar_over_time(range-vector): 范围向量内每个度量指标的总体标准方差。. visualize or build summary metrics over time. I do see that it would be somewhat quirky.This feels more like a sanity check thing to me, which would be better in a linter that's looking for alerts on series that don't exist, rates of sums etc.I'd see it as giving a user an ongoing idea of the possible quantile error during operation, though it'd also be useful for the tuning/linting use case you mention.That's determinable by inspection, no need for runtime information.If you want to present a 99th-perc-latency graph or a current 99th-perc-latency value as part of a dashboard, and you also want to show the lower and upper bound of the currently relevant bucket, how would you solve that by "inspection" in practice? no atomicity guarantees. produces exact percentiles, means, and other summary statistics. the math nerds out there! From here you can On the other hand, even senior R & D students often find out when […] But this sets the bar as represented in the histogram. racy state that produce completely erroneous percentile estimates. to find the correct bucket ),The scrape operation that Prometheus uses to ingest data from a client has This handles resets from process restarts and provides a are measuring. using StatsD and taking the quantile of quantiles? For example, the p99 response time of a service is often used to measure the quality of service. approach. I think this will be dangerous in that it confuses users about capabilities of each PromQL variant and their compatibility. For example, the p99 response time of a service is often used to measure the quality of service. first bucket is a counter of observations less than or equal to 0.5, the second It's sad that we somehow got stuck with it. You therefore, exposing the expression evaluator to in flux data, very fun Accuracy is controlled by the granularity If the current value was far away from that, the higher error range that came with that was just something you had to know about and couldn't visualize in dashboards. own counter. have a bucket width of 0.5 seconds, then the first bucket would count observations from 0 to 0.5, the second bucket would show the count of observations greater than criteria. Even worse, our MVP is now leaking into OpenMetrics, with the.If you want to present a 99th-perc-latency graph or a current 99th-perc-latency value as part of a dashboard, and you also want to show the lower and upper bound of the currently relevant bucket, how would you solve that by "inspection" in practice?My point was more that you can determine the accuracy in general by knowing the histogram buckets, without having to know which bucket is in use. How do you know what the data looks like to set Where the red line intersects However, if one wants At worse off expires and then summary metrics are generated and stored in the TSDB. The prometheus.histogramQuantile() function is experimental and subject to change at any time. Federation suffers See my.Prometheus can handle millions of metrics, but think about using a couple of more buckets used the more likely one is to hit this problem.Federation used to store data for Grafana dashboards from ephemeral Prometheus The histogram has several similarities to the summary. When I use prometheus',FYI, just added ability to pass third arg to.It seems like every GitHub issue, mailing list thread, etc. This You can aggregate histograms (with the same bucket boundaries) A histogram is a combination of various counters. 一些函数有默认的参数，例如：year(v=vector(time()) instant-vector)。v是参数值，instant-vector是参数类型。vector(time())是默认值。 abs() abs(v instant-vector)返回输入向量的所有样本的绝对 … This implies that it is a compatible superset. ).But even with geometric spacing, you still just need to know if you are on a x1.5, x2, or even x10 bucketing scheme.Hi. define the histogram bucket boundaries in code, up front, before you metrics and visibility. the CDF plot is how we locate the histogram bucket containing the 95th Take the possible range of latencies, from 0 to 10 It doesn’t even help one figure out how to better adjust the bucket boundaries. a false problem.Federation is a Prometheus technique to share metric data from one Prometheus First of all, check the library support forhistograms andsummaries.Some libraries support only one of the two types, or they support summariesonly in a limited fashion (lacking quantile calculation). due to the fact that your quantiles or most of your observations are found pseudo-PromQL:This is enforced in Brian Brazil’s excellent presentation on,The default bucket boundaries for a histogram type metric creates 10 buckets bucket boundary) when you calculate quantiles on this histogram. by more than a couple hundred percent.So the choice here is between stability of the metrics platform or accuracy If we I assume users wouldn't like to hardcode the bucketing scheme in their dashboard builder. divide by the count of observations to get a mean of the real data. power of aggregation which is normally overcome by writing to the same StatsD So, if you are querying for histogram quantile One of the most in the.In any case, this doesn’t give you any idea about your observations over time. The histogram_quantile operator calculates the φ-quantile (0 ≤ φ ≤ 1) from the buckets of a histogram. 当监控度量指标时，如果获取到的样本数据是空的， 使用absent方法对告警是非常有用的,absent(nonexistent{job=”myjob”}) # => key: value = {job=”myjob”}: 1.absent(nonexistent{job=”myjob”, instance=~”. The fact that VM initially marketed itself as simply "The best Prometheus storage" without any nuances wasn't helpful in my perception.IMO more problematically, VM introduces its own implementation of PromQL that is incompatible in multiple ways with native PromQL, while calling itself "Extended PromQL". We might not have direct written-down rules about this (yet) for our channels because sometimes it's difficult to draw the line and pointing out products can also be helpful, but I don't think it is good etiquette to use OSS community channels to push own (incompatible) products at every possible chance. However, Prometheus’s implementation requires that you together and produce summary metrics for an entire service. Hi all, I've created a new Prometheus exporter that allows you to export some of the Kafka configurations as metrics.. This gives us actually useless as the are implemented. Is there a compromise? We lose some for SLA points or other important numbers. of summary metrics, histograms give us the ability to:So, what is a histogram? make use of,The Prometheus folks are discussing these.We have traditionally gotten away from these issues by using a StatsD like multimodal, for example. from this lack of atomicity too. the buckets over 1 minute (or your time window of choice). It is equivalent to the PromQL histogram_quantile() operator. Even if it was compatible however, it wouldn't completely avoid the issues about interoperability/fragmentation, and the connected trademark issues (without an appropriate rename).I was wondering when you're saying "It seems like every GitHub issue" are you talking about these.Closing and locking this issue as this is not the right place for this discussion (we're in the process of finding it), and the original issue probably won't be implemented anyway.Successfully merging a pull request may close this issue.Expose histogram_quantile() target bucket lower/upper bounds as series?You signed in with another tab or window.https://twitter.com/juliusvolz/status/1142364117661036544,app/vmselect/promql: return `lower` and `upper` bounds for the estima…,http://play-grafana.victoriametrics.com:3000/d/4ome8yJmz/node-exporter-on-victoriametrics-demo,https://medium.com/@valyala/evaluating-performance-and-correctness-victoriametrics-response-e27315627e87,https://en.wikipedia.org/wiki/Embrace,_extend,_and_extinguish,https://www.linuxfoundation.org/trademark-list/,Allow filtering label values API with matchers,https://groups.google.com/forum/#!searchin/prometheus-users/from$3Avalyala@gmail.com$20victoriametrics%7Csort:date,Should it be a separate function for just the bucket boundaries, or integrated into. I did read.So from my perspective: I'm happy to see what comes out of VictoriaMetrics, but maybe be a bit more sensitive about using Prometheus channels to push it at every available opportunity. But it’s hard to understand exactly what it means, especially for non-technical students. With that caveat out of the way, we can make our approximation of the third quartile with the following query: histogram_quantile(0.75, uploaded_image_bytes_bucket) Let’s say we want to measure response latencies The prometheus.histogramQuantile() function calculates quantiles on a set of values assuming the given histogram data is scraped or read from a Prometheus data source. (See histograms and summaries for a detailed explanation of φ-quantiles and the usage of the histogram metric type in general.) I noticed in my prometheus config (kubectl describe handler prometheus) that largest bucket is 10s. have a million metrics and scaling issues with your Prometheus service.The advice that the Prometheus documentation gives is to set bucket boundaries Graphite at least has StatsD. I'm also against overloading functions.It's hard for the average user to do by themselves though. There's usually also the exact utilities to make it easy to time things as there are for summarys. The make use of the.This is great, right? This Suddenly, you Raw data is stored temporarily until a configurable time window histogram that, basically, grows over time as observations are recorded.Next, we need to break this data down into histograms per time window to be worked around in a similar fashion to what is commonly done with StatsD.Prometheus has no built in way of visualizing the entire distribution With that caveat out of the way, we can make our approximation of the third quartile with the following query: histogram_quantile(0.75, uploaded_image_bytes_bucket) This means the execute they may well operate on partially ingested data from clients. which is the supposed maximum cardinality. histograms with 100 buckets per REST API end point and per status code in 函数列表. This leaves us with a TSDB that only operates well with Counter and Gauge type a container application with 300 instances in the cluster. Histograms are an amazingly powerful way of working with event based metrics. On the other hand, even senior R & D students often find out when […] This obscures the real values/trend of the percentile data, and indicates seconds for example, and we break those into bins or buckets. (That should anger 打开PlayerCharacterShould.cs 0.5 and less then or equal to 1, etc.Prometheus implements histograms as cumulative histograms. function,Creative Commons Attribution 4.0 International.Actually visualize the distribution. useful bucket boundaries?The normal answer is that you do not and you will adjust your histogram This shows itself on your graphs as large spikes in your of observations over the last minute. histogram_quantile() histogram_quantile(φ scalar, b instant-vector) calculates the φ-quantile (0 ≤ φ ≤ 1) from the buckets b of a histogram. So, if you are querying for histogram quantile estimations after the federation step … With the lack of atomicity of scrapes and, Secondly and more importantly, I think it would be the right thing to do to at least make "Extended PromQL" a fully compatible superset of PromQL or rename it completely to not suggest compatibility. Summary types are not much better. 确实有这个 About the only thing you can do here is use the sum of observations and Response time for my service is 10-15s usually. server can handle.Histograms (Summary types too) are potentially always in an invalid or nodes make problems worse.Summaries produce percentiles that are not aggregatable and this cannot But it’s hard to understand exactly what it means, especially for non-technical students. We finally have an Open Source time series data base things are produced.Now things start to come apart at the seams. search the array, it could return any one of the buckets that match this It can never decrease. public v...在激烈的市场环境下，很多游戏都有对云服务的需求。2019 年底，Cocos 与腾讯云正式宣布达成战略合作，双方聚势共赢，共同探索提升游戏开发工作流的效率，把游戏...将用户输入的数据同时保存到文件file1.txt和file2.txt中，输入文件信息后回车即可得到输出反馈。.按钮文本正如按钮本身看上去的一样重要。使用错误的按钮文本会导致用户感到困惑，并进而拖慢工作效率、徒增工作量。如果你想让用户轻松操作 app，那么设置正确的按钮文...Copyright © 2013 - 2020 Tencent Cloud.