How do I align things in the following tabular environment? what error message are you getting to show that theres a problem? it works perfectly if one is missing as count() then returns 1 and the rule fires. hackers at Making statements based on opinion; back them up with references or personal experience. A sample is something in between metric and time series - its a time series value for a specific timestamp. Lets create a demo Kubernetes cluster and set up Prometheus to monitor it. count(container_last_seen{environment="prod",name="notification_sender.*",roles=".application-server."}) @rich-youngkin Yes, the general problem is non-existent series. will get matched and propagated to the output. Explanation: Prometheus uses label matching in expressions. There is a maximum of 120 samples each chunk can hold. information which you think might be helpful for someone else to understand To set up Prometheus to monitor app metrics: Download and install Prometheus. Im new at Grafan and Prometheus. These will give you an overall idea about a clusters health. In addition to that in most cases we dont see all possible label values at the same time, its usually a small subset of all possible combinations. These queries will give you insights into node health, Pod health, cluster resource utilization, etc. Is it possible to rotate a window 90 degrees if it has the same length and width? Using regular expressions, you could select time series only for jobs whose Chunks that are a few hours old are written to disk and removed from memory. Our metric will have a single label that stores the request path. A simple request for the count (e.g., rio_dashorigin_memsql_request_fail_duration_millis_count) returns no datapoints). In reality though this is as simple as trying to ensure your application doesnt use too many resources, like CPU or memory - you can achieve this by simply allocating less memory and doing fewer computations. Often it doesnt require any malicious actor to cause cardinality related problems. This patchset consists of two main elements. I made the changes per the recommendation (as I understood it) and defined separate success and fail metrics. Prometheus metrics can have extra dimensions in form of labels. Although, sometimes the values for project_id doesn't exist, but still end up showing up as one. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? name match a certain pattern, in this case, all jobs that end with server: All regular expressions in Prometheus use RE2 We know that each time series will be kept in memory. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? If we let Prometheus consume more memory than it can physically use then it will crash. I'm still out of ideas here. To get rid of such time series Prometheus will run head garbage collection (remember that Head is the structure holding all memSeries) right after writing a block. However when one of the expressions returns no data points found the result of the entire expression is no data points found.In my case there haven't been any failures so rio_dashorigin_serve_manifest_duration_millis_count{Success="Failed"} returns no data points found.Is there a way to write the query so that a . 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 Under which circumstances? This allows Prometheus to scrape and store thousands of samples per second, our biggest instances are appending 550k samples per second, while also allowing us to query all the metrics simultaneously. @zerthimon You might want to use 'bool' with your comparator How to tell which packages are held back due to phased updates. We know that the more labels on a metric, the more time series it can create. Extra fields needed by Prometheus internals. The thing with a metric vector (a metric which has dimensions) is that only the series for it actually get exposed on /metrics which have been explicitly initialized. to your account, What did you do? Even i am facing the same issue Please help me on this. Thanks for contributing an answer to Stack Overflow! Our patched logic will then check if the sample were about to append belongs to a time series thats already stored inside TSDB or is it a new time series that needs to be created. Do new devs get fired if they can't solve a certain bug? This is the last line of defense for us that avoids the risk of the Prometheus server crashing due to lack of memory. If so it seems like this will skew the results of the query (e.g., quantiles). Lets pick client_python for simplicity, but the same concepts will apply regardless of the language you use. Our HTTP response will now show more entries: As we can see we have an entry for each unique combination of labels. It's worth to add that if using Grafana you should set 'Connect null values' proeprty to 'always' in order to get rid of blank spaces in the graph. One or more for historical ranges - these chunks are only for reading, Prometheus wont try to append anything here. I.e., there's no way to coerce no datapoints to 0 (zero)? Then imported a dashboard from " 1 Node Exporter for Prometheus Dashboard EN 20201010 | Grafana Labs ".Below is my Dashboard which is showing empty results.So kindly check and suggest. Sign in Managed Service for Prometheus https://goo.gle/3ZgeGxv Asking for help, clarification, or responding to other answers. This scenario is often described as cardinality explosion - some metric suddenly adds a huge number of distinct label values, creates a huge number of time series, causes Prometheus to run out of memory and you lose all observability as a result. Going back to our metric with error labels we could imagine a scenario where some operation returns a huge error message, or even stack trace with hundreds of lines. what error message are you getting to show that theres a problem? by (geo_region) < bool 4 Every time we add a new label to our metric we risk multiplying the number of time series that will be exported to Prometheus as the result. I then hide the original query. Timestamps here can be explicit or implicit. With any monitoring system its important that youre able to pull out the right data. All regular expressions in Prometheus use RE2 syntax. At this point we should know a few things about Prometheus: With all of that in mind we can now see the problem - a metric with high cardinality, especially one with label values that come from the outside world, can easily create a huge number of time series in a very short time, causing cardinality explosion. We can use these to add more information to our metrics so that we can better understand whats going on. Making statements based on opinion; back them up with references or personal experience. (pseudocode): summary = 0 + sum (warning alerts) + 2*sum (alerts (critical alerts)) This gives the same single value series, or no data if there are no alerts. If so I'll need to figure out a way to pre-initialize the metric which may be difficult since the label values may not be known a priori. These are the sane defaults that 99% of application exporting metrics would never exceed. Thank you for subscribing! I'm not sure what you mean by exposing a metric. Second rule does the same but only sums time series with status labels equal to "500". One Head Chunk - containing up to two hours of the last two hour wall clock slot. This single sample (data point) will create a time series instance that will stay in memory for over two and a half hours using resources, just so that we have a single timestamp & value pair. So lets start by looking at what cardinality means from Prometheus' perspective, when it can be a problem and some of the ways to deal with it. Using a query that returns "no data points found" in an expression. Also, providing a reasonable amount of information about where youre starting Please dont post the same question under multiple topics / subjects. The subquery for the deriv function uses the default resolution. website Please open a new issue for related bugs. As we mentioned before a time series is generated from metrics. A time series that was only scraped once is guaranteed to live in Prometheus for one to three hours, depending on the exact time of that scrape. The Head Chunk is never memory-mapped, its always stored in memory. Finally we do, by default, set sample_limit to 200 - so each application can export up to 200 time series without any action. without any dimensional information. I have just used the JSON file that is available in below website It would be easier if we could do this in the original query though. Each chunk represents a series of samples for a specific time range. Object, url:api/datasources/proxy/2/api/v1/query_range?query=wmi_logical_disk_free_bytes%7Binstance%3D~%22%22%2C%20volume%20!~%22HarddiskVolume.%2B%22%7D&start=1593750660&end=1593761460&step=20&timeout=60s, Powered by Discourse, best viewed with JavaScript enabled, 1 Node Exporter for Prometheus Dashboard EN 20201010 | Grafana Labs, https://grafana.com/grafana/dashboards/2129. That way even the most inexperienced engineers can start exporting metrics without constantly wondering Will this cause an incident?. If all the label values are controlled by your application you will be able to count the number of all possible label combinations. What happens when somebody wants to export more time series or use longer labels? Blocks will eventually be compacted, which means that Prometheus will take multiple blocks and merge them together to form a single block that covers a bigger time range. With this simple code Prometheus client library will create a single metric. After a chunk was written into a block and removed from memSeries we might end up with an instance of memSeries that has no chunks. By default we allow up to 64 labels on each time series, which is way more than most metrics would use. what does the Query Inspector show for the query you have a problem with? Both of the representations below are different ways of exporting the same time series: Since everything is a label Prometheus can simply hash all labels using sha256 or any other algorithm to come up with a single ID that is unique for each time series. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, How Intuit democratizes AI development across teams through reusability. 1 Like. For example, /api/v1/query?query=http_response_ok [24h]&time=t would return raw samples on the time range (t-24h . notification_sender-. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. Cadvisors on every server provide container names. For example our errors_total metric, which we used in example before, might not be present at all until we start seeing some errors, and even then it might be just one or two errors that will be recorded. Every two hours Prometheus will persist chunks from memory onto the disk. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. In this query, you will find nodes that are intermittently switching between Ready" and NotReady" status continuously. prometheus-promql query based on label value, Select largest label value in Prometheus query, Prometheus Query Overall average under a time interval, Prometheus endpoint of all available metrics. Run the following commands in both nodes to configure the Kubernetes repository. The process of sending HTTP requests from Prometheus to our application is called scraping. Youll be executing all these queries in the Prometheus expression browser, so lets get started. Making statements based on opinion; back them up with references or personal experience. Good to know, thanks for the quick response! This means that our memSeries still consumes some memory (mostly labels) but doesnt really do anything. to your account. Having good internal documentation that covers all of the basics specific for our environment and most common tasks is very important. You set up a Kubernetes cluster, installed Prometheus on it ,and ran some queries to check the clusters health. Play with bool This had the effect of merging the series without overwriting any values. Once we appended sample_limit number of samples we start to be selective. but viewed in the tabular ("Console") view of the expression browser. count(container_last_seen{name="container_that_doesn't_exist"}), What did you see instead? Finally, please remember that some people read these postings as an email If you need to obtain raw samples, then a range query must be sent to /api/v1/query. When using Prometheus defaults and assuming we have a single chunk for each two hours of wall clock we would see this: Once a chunk is written into a block it is removed from memSeries and thus from memory. The Graph tab allows you to graph a query expression over a specified range of time. Monitoring Docker container metrics using cAdvisor, 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. There is a single time series for each unique combination of metrics labels. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Instead we count time series as we append them to TSDB. group by returns a value of 1, so we subtract 1 to get 0 for each deployment and I now wish to add to this the number of alerts that are applicable to each deployment. Does a summoned creature play immediately after being summoned by a ready action? Run the following commands on the master node, only copy the kubeconfig and set up Flannel CNI. Basically our labels hash is used as a primary key inside TSDB. Perhaps I misunderstood, but it looks like any defined metrics that hasn't yet recorded any values can be used in a larger expression. The below posts may be helpful for you to learn more about Kubernetes and our company. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Prometheus promQL query is not showing 0 when metric data does not exists, PromQL - how to get an interval between result values, PromQL delta for each elment in values array, Trigger alerts according to the environment in alertmanger, Prometheus alertmanager includes resolved alerts in a new alert. prometheus promql Share Follow edited Nov 12, 2020 at 12:27 To get a better idea of this problem lets adjust our example metric to track HTTP requests. I cant see how absent() may help me here @juliusv yeah, I tried count_scalar() but I can't use aggregation with it. This helps Prometheus query data faster since all it needs to do is first locate the memSeries instance with labels matching our query and then find the chunks responsible for time range of the query. The only exception are memory-mapped chunks which are offloaded to disk, but will be read into memory if needed by queries. The region and polygon don't match. Is it plausible for constructed languages to be used to affect thought and control or mold people towards desired outcomes? If you're looking for a Secondly this calculation is based on all memory used by Prometheus, not only time series data, so its just an approximation. rate (http_requests_total [5m]) [30m:1m] I used a Grafana transformation which seems to work. notification_sender-. For example, the following query will show the total amount of CPU time spent over the last two minutes: And the query below will show the total number of HTTP requests received in the last five minutes: There are different ways to filter, combine, and manipulate Prometheus data using operators and further processing using built-in functions. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Show or hide query result depending on variable value in Grafana, Understanding the CPU Busy Prometheus query, Group Label value prefixes by Delimiter in Prometheus, Why time duration needs double dot for Prometheus but not for Victoria metrics, Using a Grafana Histogram with Prometheus Buckets. In this blog post well cover some of the issues one might encounter when trying to collect many millions of time series per Prometheus instance. Will this approach record 0 durations on every success? However when one of the expressions returns no data points found the result of the entire expression is no data points found. Its not going to get you a quicker or better answer, and some people might Can airtags be tracked from an iMac desktop, with no iPhone? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Visit 1.1.1.1 from any device to get started with The Linux Foundation has registered trademarks and uses trademarks. Have a question about this project? or something like that. Find centralized, trusted content and collaborate around the technologies you use most. When Prometheus sends an HTTP request to our application it will receive this response: This format and underlying data model are both covered extensively in Prometheus' own documentation. To better handle problems with cardinality its best if we first get a better understanding of how Prometheus works and how time series consume memory. This works well if errors that need to be handled are generic, for example Permission Denied: But if the error string contains some task specific information, for example the name of the file that our application didnt have access to, or a TCP connection error, then we might easily end up with high cardinality metrics this way: Once scraped all those time series will stay in memory for a minimum of one hour. Monitor the health of your cluster and troubleshoot issues faster with pre-built dashboards that just work. These flags are only exposed for testing and might have a negative impact on other parts of Prometheus server. This works fine when there are data points for all queries in the expression. For Prometheus to collect this metric we need our application to run an HTTP server and expose our metrics there. In the same blog post we also mention one of the tools we use to help our engineers write valid Prometheus alerting rules. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Another reason is that trying to stay on top of your usage can be a challenging task. For that reason we do tolerate some percentage of short lived time series even if they are not a perfect fit for Prometheus and cost us more memory. Next, create a Security Group to allow access to the instances. Thanks, We covered some of the most basic pitfalls in our previous blog post on Prometheus - Monitoring our monitoring. Run the following commands in both nodes to install kubelet, kubeadm, and kubectl. Passing sample_limit is the ultimate protection from high cardinality. Its the chunk responsible for the most recent time range, including the time of our scrape. Cadvisors on every server provide container names. We had a fair share of problems with overloaded Prometheus instances in the past and developed a number of tools that help us deal with them, including custom patches. Sign up and get Kubernetes tips delivered straight to your inbox. Run the following command on the master node: Once the command runs successfully, youll see joining instructions to add the worker node to the cluster. Up until now all time series are stored entirely in memory and the more time series you have, the higher Prometheus memory usage youll see. Once it has a memSeries instance to work with it will append our sample to the Head Chunk. About an argument in Famine, Affluence and Morality. t]. If the time series doesnt exist yet and our append would create it (a new memSeries instance would be created) then we skip this sample. In this article, you will learn some useful PromQL queries to monitor the performance of Kubernetes-based systems. new career direction, check out our open Is a PhD visitor considered as a visiting scholar? Finally we maintain a set of internal documentation pages that try to guide engineers through the process of scraping and working with metrics, with a lot of information thats specific to our environment. Today, let's look a bit closer at the two ways of selecting data in PromQL: instant vector selectors and range vector selectors. Comparing current data with historical data. count the number of running instances per application like this: This documentation is open-source. This is the modified flow with our patch: By running go_memstats_alloc_bytes / prometheus_tsdb_head_series query we know how much memory we need per single time series (on average), we also know how much physical memory we have available for Prometheus on each server, which means that we can easily calculate the rough number of time series we can store inside Prometheus, taking into account the fact the theres garbage collection overhead since Prometheus is written in Go: memory available to Prometheus / bytes per time series = our capacity. We have hundreds of data centers spread across the world, each with dedicated Prometheus servers responsible for scraping all metrics. Internally all time series are stored inside a map on a structure called Head. (pseudocode): This gives the same single value series, or no data if there are no alerts. The result is a table of failure reason and its count. Simple, clear and working - thanks a lot. Just add offset to the query. Or maybe we want to know if it was a cold drink or a hot one? Return all time series with the metric http_requests_total: Return all time series with the metric http_requests_total and the given So I still can't use that metric in calculations ( e.g., success / (success + fail) ) as those calculations will return no datapoints. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? Both patches give us two levels of protection. See these docs for details on how Prometheus calculates the returned results. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Each time series will cost us resources since it needs to be kept in memory, so the more time series we have, the more resources metrics will consume. If you do that, the line will eventually be redrawn, many times over. This process helps to reduce disk usage since each block has an index taking a good chunk of disk space. He has a Bachelor of Technology in Computer Science & Engineering from SRMS. This makes a bit more sense with your explanation. I'm displaying Prometheus query on a Grafana table. In our example we have two labels, content and temperature, and both of them can have two different values. Heres a screenshot that shows exact numbers: Thats an average of around 5 million time series per instance, but in reality we have a mixture of very tiny and very large instances, with the biggest instances storing around 30 million time series each. What sort of strategies would a medieval military use against a fantasy giant? Selecting data from Prometheus's TSDB forms the basis of almost any useful PromQL query before . By default Prometheus will create a chunk per each two hours of wall clock. If you look at the HTTP response of our example metric youll see that none of the returned entries have timestamps. SSH into both servers and run the following commands to install Docker. Even Prometheus' own client libraries had bugs that could expose you to problems like this. Minimising the environmental effects of my dyson brain. The text was updated successfully, but these errors were encountered: This is correct. PromQL queries the time series data and returns all elements that match the metric name, along with their values for a particular point in time (when the query runs). The downside of all these limits is that breaching any of them will cause an error for the entire scrape. While the sample_limit patch stops individual scrapes from using too much Prometheus capacity, which could lead to creating too many time series in total and exhausting total Prometheus capacity (enforced by the first patch), which would in turn affect all other scrapes since some new time series would have to be ignored. The second patch modifies how Prometheus handles sample_limit - with our patch instead of failing the entire scrape it simply ignores excess time series. Thirdly Prometheus is written in Golang which is a language with garbage collection. I've been using comparison operators in Grafana for a long while. This is because once we have more than 120 samples on a chunk efficiency of varbit encoding drops. Your needs or your customers' needs will evolve over time and so you cant just draw a line on how many bytes or cpu cycles it can consume. Our metrics are exposed as a HTTP response. Time arrow with "current position" evolving with overlay number. For example, this expression If a sample lacks any explicit timestamp then it means that the sample represents the most recent value - its the current value of a given time series, and the timestamp is simply the time you make your observation at.

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