You can configure how frequently the query is run, which is independent of how frequently prometheus collects data from this service. Grouping and Group Repeating¶ While more and more panels are being added onto a dashboard, we need a mechanism to group them based on logical or other criterias in order to quick focus on the metrics we care about. prometheus_rule_group_last_duration_seconds < prometheus_rule_group_interval_seconds If the difference is large, it means that rule evaluation took more time than the scheduled interval. Taking the varnish_main_client_req metric as an example: Each distinct metric_name & label combination is called a time-series (often just called a series in the documentation). metric name, as measured over the last 5 minutes: Assuming that the http_requests_total time series all have the labels job To address this, the kube-state-metrics and node-exporter Prometheus exporters publish a number of series that essentially exist to provide reference labels. You can tune these two parameters to be displaying and alerting on the live-est data possible without putting too much load on your SQL Server. Please help improve it by filing issues or pull requests. This PromQL query can return no more samples than the smallest of the input vectors has, whereas the superficially syntactically similar SQL If we add a [1m] range selector we now get this: We get two values for each series because the varnish scrape config specifies that it has a 30 second interval, so if you look at the timestamps after the @ symbol in the value, you can see that they are exactly 30 seconds apart. One or more labels, which are simply key-value pairs that distinguish each metric with the same name (e.g. For instance, varnish_main_client_req{namespace=~".*3.*",namespace!~".*env4. The value of our Fabio job is 3 since it is using the system scheduler type. To fix this, we use the group_left or group_right keywords. http_requests_total. So varnish_main_client_req{namespace="section-9469f9cc28d8d"} * on (pod) group_left() kube_pod_info gives: This gives us back all the labels for the varnish_main_client_req series, however we still don’t have the node label. {app="bar"}) : Indeed, all Prometheus metrics are time based data. To install Prometheus in your Kubernetes cluster with helm just run the following commands: Add the Prometheus charts repository to your helm configuration: helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo add stable https://kubernetes-charts.storage.googleapis.com/ helm repo update Then, you can use PromQL to query this data. However, this is not particularly helpful as the IP address given there is only the internal IP address of the pod as addressed by the service the Prometheus operator has used to identify which endpoints to scrape. Using regular expressions, you could select time series only for jobs whose Sysdig’s native compatibility with Prometheus monitoring makes it possible to use the powerful query language, PromQL, in Sysdig Dashboards & Alerts. Compare the three day view with a [5m] duration to a [30m] duration: Technically, Prometheus doesn’t have the concept of joining series like SQL, for example, has. A given call to the custom metrics API is distilled down to a metric name, a group-resource, and one or more objects of that group-resource. The values for each timestamp will be the values recorded in the time series back in time, taken from the timestamp for the length of time given in the range duration. Label filters support four operators: Label filters go inside the {} after the metric name, so an equality match looks like: varnish_main_client_req{namespace="section-9469f9cc28d8d"}. PromQL can be a difficult language to understand, particularly if you are faced with an empty input field and are having to come up with the formation of queries on your own. Ignoring non matching labels won’t help here because pod is the only matching label. namespace=\"section-b4a199920b24b\"). © Prometheus Authors 2014-2021 | Documentation Distributed under CC-BY-4.0. Amazon Managed Service for Prometheus (AMP) is a Prometheus-compatible monitoring service for container infrastructure and application metrics that makes it easy for customers to securely monitor container environments at scale. syntax. but viewed in the tabular ("Console") view of the expression browser. Provides a functional query language, PromQL, that allows us to select and aggregate time-series data in real-time. In the “Expression” input box at the top of the web page, enter the text: istio_requests_total Then, click the Execute button. Use file-based service discovery to discover scrape targets, Understanding and using the multi-target exporter pattern, Monitoring Linux host metrics with the Node Exporter, Monitoring Docker container metrics using cAdvisor. If the expression continues to take too long to graph ad-hoc, you can pre-cord it using a recording rule. For learning, it might be easier tostart with a couple of examples. Prometheus provides a ready-to-use collection of exporters for different technologies. Metric_name (e.g. Multiple label filters are an “AND” query, so in order to be returned, a metric must match all the label filters. For a list of trademarks of The Linux Foundation, please see our Trademark Usage page. The port is t that which the varnishstat promethues exporter publishes its metrics endpoint to by default. What if we wanted to know how many requests were being made against each node in our Kubernetes cluster? It will take some time to learn the syntax, understand the functions and determine the metrics/labels you have available, but once you do you will quickly understand what metrics/labels you … A range-vector is typically generated in order to then apply a function to it to get an instant-vector, which can be graphed (only instant vectors can be graphed). The reason for this is so that you can join them with other series simply by multiplying without having to change the value of the original series, and still gain access to the new set of labels. This automatic interval is calculated based on the width of the graph. Prometheus has its own language specifically dedicated to queries called PromQL. In addition, you can use Prometheus to monitor other instances of it, since it makes its own metrics available in the same way. In other words, it has an interface similar to that of Prometheus and it handles the Prometheus query API. A series is considered to match if and only if it has exactly the same set of labels. See Range Selectors below for further information on this. that it is not fast enough to fill the rule. In this case the returned instant-vector contains a single series with all matching labels left after removing the labels in the ignoring set. There is a kube_pod_info series for every single pod in the cluster. Regex matches use the RE2 syntax. The approach given below uses some relatively advanced Prometheus query techniques which I hope you will find interesting and useful in future. So a query like. The labels provided will be included from the matching lower cardinality side of the operation. Schema exploration covers queries that are useful for viewing and exploring your schema. A caution: if an operator is applied to two instant-vectors, it will only apply to matching series. Return the per-second rate for all time series with the http_requests_total but still preserve the job dimension: If we have two different metrics with the same dimensional labels, we can apply sum or count) and/or metric name. So a group_left means that multiple series on the left side can match a single series on the right. The instant vector and range vector are two of four types of expression language; the final two are scalar, a simple numeric floating point value, and string, a simple string value. See Data Exploration to learn about time syntax and regular expressions in queries. following for every instance: ...we could get the top 3 CPU users grouped by application (app) and process To solve this, the group_left and group_right keywords allow a label list to be passed in. Get Grafana metrics into Prometheus. Our team of engineers is ready to talk through edge compute solutions to fit your application's needs. It is worth pointing out here that Prometheus also has a number of aggregation operators. For example, a Prometheus query using the interval variable: rate(http_requests_total[$__interval]). The more commonly used functions for working with range-vectors are: Your selection of range duration will determine how granular your chart is. In Kubernetes environments, execute the following command: $ istioctl dashboard prometheus Click Graph to the right of Prometheus in the header. To get a feeling where "all those series" are coming from, you can write PromQL queries to count how many series there are for a given job, instance (target), metric name, or other type of dimensional grouping. This is an example of a nested subquery. We’re very early in the process evaluating VictoriaMetrics but I’m super thrilled it solves this very annoying problem we have with Prometheus query handling. The value for these reference series is always 1. Data exploration covers the query language basics for InfluxQL, including the SELECT statement, GROUP BY clauses, INTO clauses, and more. These are generated by appending a time selector to the instant-vector in square brackets (e.g. Prometheus supports two ways to query annotations. Next, the native query language PromQL allows us to select the required information from the received data set and perform analysis and visualizations with that data using Grafana dashboards. Sometimes graphing a query might overload the server or browser, or lead to a time out because the amount of data is too large. Use Prometheus to query how many jobs are running in our Nomad cluster. PromQL is a built in query-language made for Prometheus. To select all HTTP status codes except 4xx ones, you could run: http_requests_total{status!~"4.."} Subquery. want to sum over the rate of all instances, so we get fewer output time series, The result of each expression can be shown either as a graph, viewed as tabular data within Prometheus’ own expression browser, or consumed via external systems via the HTTP API. varnish_main_client_req) 2. making it a range vector: Note that an expression resulting in a range vector cannot be graphed directly, These are series like kube_pod_info & node_uname_info. A portion from a query listing all metrics for an app (i.e. 可以通过在后面添加用大括号包围起来的一组标签键值对来对时序进行过滤。. On the main page, type nomad_nomad_job_summary_running into the query section. When constructing queries over unknown data, it is better to begin building the query in the tabular view of Prometheus’ expression browser until you arrive at a reasonable result set (i.e. If the user zooms out a lot then the interval becomes greater, resulting in a more coarse grained aggregation whereas if the user zooms in then the interval decreases resulting in a more fine grained aggregation. Each series in the instant-vector has the value applied with the operator. The subquery for the deriv function uses the default resolution. Most scrape intervals are 30s. PromQL is designed from scratch and has zero common grounds with other query languages used in time series databases such as SQL in TimescaleDB , InfluxQL or Flux . Return the 5-minute rate of the http_requests_total metric for the past 30 minutes, with a resolution of 1 minute. All rights reserved. It has the usual arithmetic, comparison and logical operators that can be applied to multiple series or to scalar values. So if we really wanted to get the total number of client requests in a namespace, we would never actually do this because the pod names would change over time. job and handler labels: Return a whole range of time (in this case 5 minutes) for the same vector, This article is a primer dedicated to the basics of how to run Prometheus queries. You can also combine an instant-vector with a scalar value. binary operators to them and elements on both sides with the same label set (fanout by job name) and instance (fanout by instance of the job), we might Prometheus has many functions for both instant and range vectors. © 2021 The Linux Foundation. To select all HTTP status codes except 4xx ones, you could run: Return the 5-minute rate of the http_requests_total metric for the past 30 minutes, with a resolution of 1 minute. PromQL, short for Prometheus Querying Language, is the main way to query metrics within Prometheus. The value may or may not have changed, but at every scrape_interval, there will be a new datapoint. Note that using subqueries unnecessarily is unwise. dataSet |> group(columns: ["_time"]) When grouping by _time, all records that share a common _time value are grouped into individual tables. If you select the Graph tab in the Prometheus web UI on a range-vector, you’ll see this message: Error executing query: invalid expression type "range vector" for range query, must be Scalar or instant Vector. So, taking the two series from the previous examples as separate instant-vectors: varnish_main_client_req{endpoint="metrics",instance="10.244.24.68:9131",job="varnish",namespace="section-9469f9cc28d8d",pod="varnish-786d4648bd-lrlrc",service="p8s-varnish"}, varnish_main_client_req{endpoint="metrics",instance="10.244.48.66:9131",job="varnish",namespace="section-9469f9cc28d8d",pod="varnish-786d4648bd-rfnjb",service="p8s-varnish"}. In a previous getting started blog post, we showed how to set up an AMP workspace and ingest metrics from an Amazon Elastic Kubernetes Service […] returns the unused memory in MiB for every instance (on a fictional cluster count the number of running instances per application like this: This documentation is open-source. Each metric will have at least a job label, which corresponds t… Grouping data by the _time column is a good illustration of how grouping changes the structure of your data. which will return only varnish_main_client_req metrics with that exact namespace. So, to get our varnish_main_client_req series with the node label on them, we can do this : varnish_main_client_req{namespace="section-9469f9cc28d8d"} * on (pod) kube_pod_info. 比如下面的表达式：. This is because there are no series returned that have exactly matching labels. As an example, take varnish_main_client_req{namespace="section-9469f9cc28d8d"}. There are four parts to every metric. The left and right indicate the side that has the higher cardinality. The metricsQuery field is a Go template that gets turned into a Prometheus query, using input from a particular call to the custom metrics API. type (proc) like this: Assuming this metric contains one time series per running instance, you could instance="10.244.48.66:9131"). *"} will return all varnish_main_client_req metrics with a 3 in their namespace that don’t also contain env4. The project is written in Go and licensed under the Apache 2 License, with source code available on GitHub, and is a graduated project of the Cloud Native Computing Foundation, along with … Is there a way to group all metrics of an app by metric names? hundreds as opposed to thousands of time series). Prometheus comes with its own query language called PromQL, Understanding PromQL is difficult, let alone the scary syntax — especially if you are supposed to come up with queries on your own. It is designed for building powerful yet simple queries for graphs, alerts or derived time series (aka recording rules ). The setting for when the intervals should occur is specified by the scrape_interval in the prometheus.yaml config. Section supports many open source projects including: Prometheus server port forwarded from the local connection, A data visualization and monitoring tool, either within Prometheus or an external one, such as Grafana. If each series only has a single value for each timestamp, as in the above example, the collection of series returned from a query is called an instant-vector. In the meanwhile, we also need the functionality to … [5m] for five minutes). If we filter it down to the two pods we are interested in knowing more about, we can see what kind of information it provides: From this, we can see the name of the node, the IP address of the nodes and which ReplicaSet created the pods. Only Instant Vectors can be graphed. Through query building, you will end up with a graph per CPU by the deployment. It records real-time metrics in a time series database built using a HTTP pull model, with flexible queries and real-time alerting. You can also use multiple label filters, separated by a comma. http_requests_total{job="prometheus", group="canary"} 匹配标签值时可以是 等于 ，也可以 使用正则表 … Overview . I wonder if this is a problem for you as well, and if you too find VictoriaMetrics behavior more user-friendly or if Prometheus’ behavior is preferred in your environment. Schema exploration. Also note the value. Which means that you can also access it when Prometheus is operating to see which queries are in flight: $ cat data/queries.active [{"query":"{job=~\".+\"}","timestamp_sec":1583790553}, The second query log will log every query that is run by the engine into a file. This monitor scrapes Prmoetheus server’s own internal collector metrics from a Prometheus exporter and sends them to SignalFx. Switch to graph mode only once you have sufficiently aggregated or filtered your data. 比如下面的表达式筛选出了 job 为 prometheus ，并且 group 为 canary 的时序：. Basics Instant Vectors. However, series can be combined in Prometheus by using an operator on them. scheduler exposing these metrics about the instances it runs): The same expression, but summed by application, could be written like this: If the same fictional cluster scheduler exposed CPU usage metrics like the If you graphed these series without the range selector and inspected the value of the lines at those timestamps, it would show these values. As a Prometheus server administrator, the total number of time series that a server needs to keep track of is one of the main memory and scaling bottlenecks to watch out for. Next, you can filter the query using labels. If each series has multiple values, it is referred to as a range-vector. Grafana exposes metrics for Prometheus on the /metrics endpoint Prometheus stores each time series identified by its metric name and key-value pairs (labels): name match a certain pattern, in this case, all jobs that end with server: All regular expressions in Prometheus use RE2 Additionally, it might have the visualization’s query, group by, or interval and mathematical actions using the metric. A [1m] duration, for instance, will give a very spiky chart, making it difficult to visualize a trend, looking something like this: For a one hour view, [5m] would show a decent view: For longer time-spans, you may want to set a longer range duration to help smooth out spikes and achieve more of a long-term trend view. A regular metric query; A Prometheus query for pending and firing alerts (for details see Inspecting alerts during runtime) The step option is useful to limit the number of events returned from your query. However, if we use the on keyword to specify that we only want to match on the namespace label, we get: Note that the new instant-vector contains a single series with only the label(s) specified in the on keyword. Indeed, all Prometheus metrics are time based data. These keywords convert the match into a many-to-one or one-to-many matching respectively. a * b. is vector matching between two instant vectors and thus a form of join. Return all time series with the metric http_requests_total: Return all time series with the metric http_requests_total and the given All of these metrics are scraped from exporters. The ignoring keyword can also be used as an inverse of that to specify which labels should be ignored when trying to match. We can see this in the varnish_main_client_req series by looking at the instance label (e.g. The core part of any query in PromQL are the metric names of a time-series. Open the Prometheus UI. So each output table represents a single point in time. Prometheus is a free software application used for event monitoring and alerting. We’re going to deal with counters for this analysis, as it’s the most common metric type. rate(http_requests_total[5m])[30m:1m] This is an example of a nested subquery. Taking the varnish_main_client_req metric as an example:The parts are: 1. All regular expressions in Prometheus use RE2 syntax. Query instances will randomly pick any two store instances1 from the same group and use the first response returned. For example, this expression Prometheus provides a web interface to … Ensures simple reconfiguration. Also, expressions that aggregate over multiple time series will generate load on the server even when the output is only a small amount of time series. For example: varnish_main_client_req{namespace="section-9469f9cc28d8d"} * 10. results in each value for each series in the instant-vector being multiplied by ten. 最简单的情况就是指定一个度量指标，选择出所有属于该度量指标的时序的当前采样值。. The structure of a basic Prometheus query looks very much like a metric. There are four parts to every metric. In PromQL JSON, each query has its own “mini section.” This section includes the function (performed on the metric,e.g. All vector matching is done between instant vectors using binary operators, such as multiplication. I used prometheus-sql to periodically query SQL Server. Configuration can be changed via the command line. Using these operators on series achieves a one-to-one matching when each series from the left side of the expression exactly matches one series on the right side. If we apply an addition operator to these two to try and get a total number of requests in that namespace, nothing will be returned. If you’re familiar with PCRE, it will look much the same, but it doesn’t support backreferences (which really shouldn’t matter here anyway). Prometheus is a powerful tool with a powerful query language. This is particularly relevant to PromQL when a bare metric name selector such as api_http_requests_total can easily expand to thousands of time series each with a different label. Execute a Prometheus query. The Linux Foundation has registered trademarks and uses trademarks. Prometheus scrapes these metrics at regular intervals. You can display an expression’s return either as a graph or export it using the HTTP API.PromQL uses three data types: scalars, range vectors, and instant vectors. If you just query varnish_main_client_req, every one of those metrics for every varnish pod in every namespace will get returned. So by doing: varnish_main_client_req{namespace="section-9469f9cc28d8d"} * on (pod) group_left(node) kube_pod_info. It gathers data from the underlying Store APIs (Sidecars and Stores) to … Now, range-vectors can’t be graphed because they have multiple values for each timestamp. After the server has collected the metrics, it saves it in a time-series database. The result of this is that the returned instant-vector contains all of the labels from the side with the higher cardinality, even if they don’t match any label on the right. We can then take this over to Grafana to make a dashboard and chart, add this data to a graph panel, and clearly view it all. The sum operator is much simpler: sum(varnish_main_client_req{namespace="section-9469f9cc28d8d"}) by (namespace). The core part of any query in PromQL are the metric names of a time-series. PromQL is a query language for Prometheus monitoring system. It can query metrics by leveraging advanced functions, operators, and boolean logic. will get matched and propagated to the output. It can filter and group by labels, and use regular expressions for improved matching and filtering. It can apply subqueries, functions, and operators. Unfortunately because we needed to use the on keyword to match, that is also the only label we get back. You start with a metric name. This document is meant as a reference. The queriers implements the Prometheus HTTP API to query data in a Thanos cluster via PromQL. Prometheus: grouping metrics by metric names. It is a powerful functional expression language, which lets you filter with Prometheus’ multi-dimensional time-series labels. By appending a range duration to a query, we get multiple values for each timestamp. The active query log is a file called queries.active in the data directory. Yes, you can you use label replace to group all the misc together: sum by (new_group) ( label_replace( label_replace(my_metric, "new_group", "$1", "group", ".+"), "new_group", "misc", "group", "misc group.+" ) ) These get turned into the following fields in the template: This can be useful for calculating ratios & percentages. Otherwise, for the SOURCE peer type, query instances will wait for all instances within the same group to respond (subject to existing timeouts) before returning a response, consistent with the current behaviour. You can also select the query from the drop-down list. If you do this in Grafana, you risk crashing the browser tab as it tries to render so many data points simultaneously. It is a wrapper around the prometheus-exporter monitor that provides a restricted but expandable set of metrics. This means that every 30s, there will be a new data point with a new timestamp. This is referred to as a range-vector. It can indicate that your query backend (e.g Querier) takes too much time to evaluate the query, i.e. Here at Timber we've found Prometheus to be awesome, but PromQL difficult to wrap our heads around.This is our attempt to change that.
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