prometheus query return 0 if no data prometheus query return 0 if no data

The most basic layer of protection that we deploy are scrape limits, which we enforce on all configured scrapes. At the same time our patch gives us graceful degradation by capping time series from each scrape to a certain level, rather than failing hard and dropping all time series from affected scrape, which would mean losing all observability of affected applications. Internally time series names are just another label called __name__, so there is no practical distinction between name and labels. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. PromQL allows querying historical data and combining / comparing it to the current data. If this query also returns a positive value, then our cluster has overcommitted the memory. We protect Its not going to get you a quicker or better answer, and some people might Those limits are there to catch accidents and also to make sure that if any application is exporting a high number of time series (more than 200) the team responsible for it knows about it. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. 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. However when one of the expressions returns no data points found the result of the entire expression is no data points found. name match a certain pattern, in this case, all jobs that end with server: All regular expressions in Prometheus use RE2 Inside the Prometheus configuration file we define a scrape config that tells Prometheus where to send the HTTP request, how often and, optionally, to apply extra processing to both requests and responses. There is a maximum of 120 samples each chunk can hold. For instance, the following query would return week-old data for all the time series with node_network_receive_bytes_total name: node_network_receive_bytes_total offset 7d To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Finally getting back to this. Making statements based on opinion; back them up with references or personal experience. 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. Once TSDB knows if it has to insert new time series or update existing ones it can start the real work. That's the query ( Counter metric): sum (increase (check_fail {app="monitor"} [20m])) by (reason) The result is a table of failure reason and its count. I'm displaying Prometheus query on a Grafana table. One of the most important layers of protection is a set of patches we maintain on top of Prometheus. This means that looking at how many time series an application could potentially export, and how many it actually exports, gives us two completely different numbers, which makes capacity planning a lot harder. I am always registering the metric as defined (in the Go client library) by prometheus.MustRegister(). Is a PhD visitor considered as a visiting scholar? A metric is an observable property with some defined dimensions (labels). notification_sender-. You can calculate how much memory is needed for your time series by running this query on your Prometheus server: Note that your Prometheus server must be configured to scrape itself for this to work. All they have to do is set it explicitly in their scrape configuration. In general, having more labels on your metrics allows you to gain more insight, and so the more complicated the application you're trying to monitor, the more need for extra labels. This holds true for a lot of labels that we see are being used by engineers. vishnur5217 May 31, 2020, 3:44am 1. Now we should pause to make an important distinction between metrics and time series. The advantage of doing this is that memory-mapped chunks dont use memory unless TSDB needs to read them. We have hundreds of data centers spread across the world, each with dedicated Prometheus servers responsible for scraping all metrics. The next layer of protection is checks that run in CI (Continuous Integration) when someone makes a pull request to add new or modify existing scrape configuration for their application. However, if i create a new panel manually with a basic commands then i can see the data on the dashboard. Since we know that the more labels we have the more time series we end up with, you can see when this can become a problem. 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. Using a query that returns "no data points found" in an expression. 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. Cardinality is the number of unique combinations of all labels. 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. Selecting data from Prometheus's TSDB forms the basis of almost any useful PromQL query before . Of course, this article is not a primer on PromQL; you can browse through the PromQL documentation for more in-depth knowledge. Prometheus and PromQL (Prometheus Query Language) are conceptually very simple, but this means that all the complexity is hidden in the interactions between different elements of the whole metrics pipeline. If your expression returns anything with labels, it won't match the time series generated by vector(0). prometheus promql Share Follow edited Nov 12, 2020 at 12:27 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. No error message, it is just not showing the data while using the JSON file from that website. Under which circumstances? Basically our labels hash is used as a primary key inside TSDB. new career direction, check out our open Samples are stored inside chunks using "varbit" encoding which is a lossless compression scheme optimized for time series data. This is because the Prometheus server itself is responsible for timestamps. which outputs 0 for an empty input vector, but that outputs a scalar I believe it's the logic that it's written, but is there any . count the number of running instances per application like this: This documentation is open-source. This is in contrast to a metric without any dimensions, which always gets exposed as exactly one present series and is initialized to 0. To learn more, see our tips on writing great answers. If we have a scrape with sample_limit set to 200 and the application exposes 201 time series, then all except one final time series will be accepted. Visit 1.1.1.1 from any device to get started with You saw how PromQL basic expressions can return important metrics, which can be further processed with operators and functions. If you're looking for a bay, Of course there are many types of queries you can write, and other useful queries are freely available. Is a PhD visitor considered as a visiting scholar? Short story taking place on a toroidal planet or moon involving flying, How to handle a hobby that makes income in US, Doubling the cube, field extensions and minimal polynoms, Follow Up: struct sockaddr storage initialization by network format-string. Has 90% of ice around Antarctica disappeared in less than a decade? This is one argument for not overusing labels, but often it cannot be avoided. Already on GitHub? Is what you did above (failures.WithLabelValues) an example of "exposing"? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What happens when somebody wants to export more time series or use longer labels? These will give you an overall idea about a clusters health. 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. What am I doing wrong here in the PlotLegends specification? So perhaps the behavior I'm running into applies to any metric with a label, whereas a metric without any labels would behave as @brian-brazil indicated? You can query Prometheus metrics directly with its own query language: PromQL. There will be traps and room for mistakes at all stages of this process. If we make a single request using the curl command: We should see these time series in our application: But what happens if an evil hacker decides to send a bunch of random requests to our application? Thanks, This is true both for client libraries and Prometheus server, but its more of an issue for Prometheus itself, since a single Prometheus server usually collects metrics from many applications, while an application only keeps its own metrics. Run the following commands in both nodes to disable SELinux and swapping: Also, change SELINUX=enforcing to SELINUX=permissive in the /etc/selinux/config file. Do new devs get fired if they can't solve a certain bug? These queries are a good starting point. This pod wont be able to run because we dont have a node that has the label disktype: ssd. You must define your metrics in your application, with names and labels that will allow you to work with resulting time series easily. Adding labels is very easy and all we need to do is specify their names. The speed at which a vehicle is traveling. Being able to answer How do I X? yourself without having to wait for a subject matter expert allows everyone to be more productive and move faster, while also avoiding Prometheus experts from answering the same questions over and over again. Those memSeries objects are storing all the time series information. 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. We use Prometheus to gain insight into all the different pieces of hardware and software that make up our global network. Thanks for contributing an answer to Stack Overflow! Here are two examples of instant vectors: You can also use range vectors to select a particular time range. Run the following commands on the master node to set up Prometheus on the Kubernetes cluster: Next, run this command on the master node to check the Pods status: Once all the Pods are up and running, you can access the Prometheus console using kubernetes port forwarding. If we were to continuously scrape a lot of time series that only exist for a very brief period then we would be slowly accumulating a lot of memSeries in memory until the next garbage collection. @rich-youngkin Yes, the general problem is non-existent series. After sending a request it will parse the response looking for all the samples exposed there. To this end, I set up the query to instant so that the very last data point is returned but, when the query does not return a value - say because the server is down and/or no scraping took place - the stat panel produces no data. attacks, keep This patchset consists of two main elements. Connect and share knowledge within a single location that is structured and easy to search. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. If the total number of stored time series is below the configured limit then we append the sample as usual. The only exception are memory-mapped chunks which are offloaded to disk, but will be read into memory if needed by queries. Please help improve it by filing issues or pull requests. Here is the extract of the relevant options from Prometheus documentation: Setting all the label length related limits allows you to avoid a situation where extremely long label names or values end up taking too much memory. At the moment of writing this post we run 916 Prometheus instances with a total of around 4.9 billion time series. count(ALERTS) or (1-absent(ALERTS)), Alternatively, count(ALERTS) or vector(0). accelerate any Since labels are copied around when Prometheus is handling queries this could cause significant memory usage increase. Not the answer you're looking for? The second patch modifies how Prometheus handles sample_limit - with our patch instead of failing the entire scrape it simply ignores excess time series. Is it a bug? How is Jesus " " (Luke 1:32 NAS28) different from a prophet (, Luke 1:76 NAS28)? All chunks must be aligned to those two hour slots of wall clock time, so if TSDB was building a chunk for 10:00-11:59 and it was already full at 11:30 then it would create an extra chunk for the 11:30-11:59 time range. 1 Like. The below posts may be helpful for you to learn more about Kubernetes and our company. hackers at If we configure a sample_limit of 100 and our metrics response contains 101 samples, then Prometheus wont scrape anything at all. Cadvisors on every server provide container names. If you look at the HTTP response of our example metric youll see that none of the returned entries have timestamps. I don't know how you tried to apply the comparison operators, but if I use this very similar query: I get a result of zero for all jobs that have not restarted over the past day and a non-zero result for jobs that have had instances restart. Has 90% of ice around Antarctica disappeared in less than a decade? Often it doesnt require any malicious actor to cause cardinality related problems. Our HTTP response will now show more entries: As we can see we have an entry for each unique combination of labels. I can't work out how to add the alerts to the deployments whilst retaining the deployments for which there were no alerts returned: If I use sum with or, then I get this, depending on the order of the arguments to or: If I reverse the order of the parameters to or, I get what I am after: But I'm stuck now if I want to do something like apply a weight to alerts of a different severity level, e.g. SSH into both servers and run the following commands to install Docker. You set up a Kubernetes cluster, installed Prometheus on it ,and ran some queries to check the clusters health. Prometheus is a great and reliable tool, but dealing with high cardinality issues, especially in an environment where a lot of different applications are scraped by the same Prometheus server, can be challenging. When you add dimensionality (via labels to a metric), you either have to pre-initialize all the possible label combinations, which is not always possible, or live with missing metrics (then your PromQL computations become more cumbersome). Prometheus simply counts how many samples are there in a scrape and if thats more than sample_limit allows it will fail the scrape. t]. 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. 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. what error message are you getting to show that theres a problem? Hmmm, upon further reflection, I'm wondering if this will throw the metrics off. Use Prometheus to monitor app performance metrics. node_cpu_seconds_total: This returns the total amount of CPU time. This works fine when there are data points for all queries in the expression. These are the sane defaults that 99% of application exporting metrics would never exceed. (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. Im new at Grafan and Prometheus. For example, this expression If, on the other hand, we want to visualize the type of data that Prometheus is the least efficient when dealing with, well end up with this instead: Here we have single data points, each for a different property that we measure. That's the query (Counter metric): sum(increase(check_fail{app="monitor"}[20m])) by (reason). from and what youve done will help people to understand your problem. The process of sending HTTP requests from Prometheus to our application is called scraping. So the maximum number of time series we can end up creating is four (2*2). what error message are you getting to show that theres a problem? The main motivation seems to be that dealing with partially scraped metrics is difficult and youre better off treating failed scrapes as incidents. See these docs for details on how Prometheus calculates the returned results. Even Prometheus' own client libraries had bugs that could expose you to problems like this. help customers build Perhaps I misunderstood, but it looks like any defined metrics that hasn't yet recorded any values can be used in a larger expression. 02:00 - create a new chunk for 02:00 - 03:59 time range, 04:00 - create a new chunk for 04:00 - 05:59 time range, 22:00 - create a new chunk for 22:00 - 23:59 time range. If we try to visualize how the perfect type of data Prometheus was designed for looks like well end up with this: A few continuous lines describing some observed properties. Once configured, your instances should be ready for access. are going to make it By default we allow up to 64 labels on each time series, which is way more than most metrics would use. 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. But you cant keep everything in memory forever, even with memory-mapping parts of data. 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 Internet-scale applications efficiently, Extra metrics exported by Prometheus itself tell us if any scrape is exceeding the limit and if that happens we alert the team responsible for it. 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. notification_sender-. Sign in Thanks for contributing an answer to Stack Overflow! The idea is that if done as @brian-brazil mentioned, there would always be a fail and success metric, because they are not distinguished by a label, but always are exposed. Have a question about this project? Return all time series with the metric http_requests_total: Return all time series with the metric http_requests_total and the given How to tell which packages are held back due to phased updates. will get matched and propagated to the output. Time series scraped from applications are kept in memory. I'd expect to have also: Please use the prometheus-users mailing list for questions. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This article covered a lot of ground. Already on GitHub? to your account, What did you do? For example, /api/v1/query?query=http_response_ok [24h]&time=t would return raw samples on the time range (t-24h . Better to simply ask under the single best category you think fits and see TSDB used in Prometheus is a special kind of database that was highly optimized for a very specific workload: This means that Prometheus is most efficient when continuously scraping the same time series over and over again. Once we do that we need to pass label values (in the same order as label names were specified) when incrementing our counter to pass this extra information. In my case there haven't been any failures so rio_dashorigin_serve_manifest_duration_millis_count{Success="Failed"} returns no data points found. I've added a data source (prometheus) in Grafana. The difference with standard Prometheus starts when a new sample is about to be appended, but TSDB already stores the maximum number of time series its allowed to have. The result is a table of failure reason and its count. Its not difficult to accidentally cause cardinality problems and in the past weve dealt with a fair number of issues relating to it. ncdu: What's going on with this second size column? Making statements based on opinion; back them up with references or personal experience. Is it plausible for constructed languages to be used to affect thought and control or mold people towards desired outcomes? an EC2 regions with application servers running docker containers. How do you get out of a corner when plotting yourself into a corner, Partner is not responding when their writing is needed in European project application. Run the following commands in both nodes to configure the Kubernetes repository. There are a number of options you can set in your scrape configuration block. binary operators to them and elements on both sides with the same label set With 1,000 random requests we would end up with 1,000 time series in Prometheus. With any monitoring system its important that youre able to pull out the right data. 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. But before that, lets talk about the main components of Prometheus. To get a better idea of this problem lets adjust our example metric to track HTTP requests. Finally you will want to create a dashboard to visualize all your metrics and be able to spot trends. I then hide the original query. Does Counterspell prevent from any further spells being cast on a given turn? The more labels we have or the more distinct values they can have the more time series as a result. Since the default Prometheus scrape interval is one minute it would take two hours to reach 120 samples. Can airtags be tracked from an iMac desktop, with no iPhone? job and handler labels: Return a whole range of time (in this case 5 minutes up to the query time) privacy statement. Prometheus will keep each block on disk for the configured retention period. Well occasionally send you account related emails. Just add offset to the query. Looking to learn more? 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. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Passing sample_limit is the ultimate protection from high cardinality. This selector is just a metric name. https://github.com/notifications/unsubscribe-auth/AAg1mPXncyVis81Rx1mIWiXRDe0E1Dpcks5rIXe6gaJpZM4LOTeb. If so it seems like this will skew the results of the query (e.g., quantiles). Finally we do, by default, set sample_limit to 200 - so each application can export up to 200 time series without any action. I'm displaying Prometheus query on a Grafana table. This is what i can see on Query Inspector. When Prometheus collects metrics it records the time it started each collection and then it will use it to write timestamp & value pairs for each time series. The struct definition for memSeries is fairly big, but all we really need to know is that it has a copy of all the time series labels and chunks that hold all the samples (timestamp & value pairs). Or do you have some other label on it, so that the metric still only gets exposed when you record the first failued request it? Is a PhD visitor considered as a visiting scholar? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. To do that, run the following command on the master node: Next, create an SSH tunnel between your local workstation and the master node by running the following command on your local machine: If everything is okay at this point, you can access the Prometheus console at http://localhost:9090. Managed Service for Prometheus Cloud Monitoring Prometheus # ! 11 Queries | Kubernetes Metric Data with PromQL, wide variety of applications, infrastructure, APIs, databases, and other sources. Looking at memory usage of such Prometheus server we would see this pattern repeating over time: The important information here is that short lived time series are expensive. This helps us avoid a situation where applications are exporting thousands of times series that arent really needed. Asking for help, clarification, or responding to other answers. Sign in Instead we count time series as we append them to TSDB. Please open a new issue for related bugs. Will this approach record 0 durations on every success? A simple request for the count (e.g., rio_dashorigin_memsql_request_fail_duration_millis_count) returns no datapoints). 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. Each chunk represents a series of samples for a specific time range. To learn more about our mission to help build a better Internet, start here. One Head Chunk - containing up to two hours of the last two hour wall clock slot. Setting label_limit provides some cardinality protection, but even with just one label name and huge number of values we can see high cardinality. I used a Grafana transformation which seems to work. 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. 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.