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

A simple request for the count (e.g., rio_dashorigin_memsql_request_fail_duration_millis_count) returns no datapoints). Before that, Vinayak worked as a Senior Systems Engineer at Singapore Airlines. Do new devs get fired if they can't solve a certain bug? The more labels you have and the more values each label can take, the more unique combinations you can create and the higher the cardinality. privacy statement. 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. If our metric had more labels and all of them were set based on the request payload (HTTP method name, IPs, headers, etc) we could easily end up with millions of time series. How to react to a students panic attack in an oral exam? Return all time series with the metric http_requests_total: Return all time series with the metric http_requests_total and the given If such a stack trace ended up as a label value it would take a lot more memory than other time series, potentially even megabytes. Visit 1.1.1.1 from any device to get started with hackers at We have hundreds of data centers spread across the world, each with dedicated Prometheus servers responsible for scraping all metrics. For operations between two instant vectors, the matching behavior can be modified. Next, create a Security Group to allow access to the instances. Each Prometheus is scraping a few hundred different applications, each running on a few hundred servers. Its the chunk responsible for the most recent time range, including the time of our scrape. So when TSDB is asked to append a new sample by any scrape, it will first check how many time series are already present. Basically our labels hash is used as a primary key inside TSDB. A time series is an instance of that metric, with a unique combination of all the dimensions (labels), plus a series of timestamp & value pairs - hence the name time series. I've added a data source (prometheus) in Grafana. Now we should pause to make an important distinction between metrics and time series. 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. We use Prometheus to gain insight into all the different pieces of hardware and software that make up our global network. Note that using subqueries unnecessarily is unwise. In this query, you will find nodes that are intermittently switching between Ready" and NotReady" status continuously. With this simple code Prometheus client library will create a single metric. Lets say we have an application which we want to instrument, which means add some observable properties in the form of metrics that Prometheus can read from our application. You can use these queries in the expression browser, Prometheus HTTP API, or visualization tools like Grafana. Lets adjust the example code to do this. 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. The Head Chunk is never memory-mapped, its always stored in memory. I made the changes per the recommendation (as I understood it) and defined separate success and fail metrics. To make things more complicated you may also hear about samples when reading Prometheus documentation. 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? One of the first problems youre likely to hear about when you start running your own Prometheus instances is cardinality, with the most dramatic cases of this problem being referred to as cardinality explosion. Our CI would check that all Prometheus servers have spare capacity for at least 15,000 time series before the pull request is allowed to be merged. Hello, I'm new at Grafan and Prometheus. After a few hours of Prometheus running and scraping metrics we will likely have more than one chunk on our time series: Since all these chunks are stored in memory Prometheus will try to reduce memory usage by writing them to disk and memory-mapping. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. And then there is Grafana, which comes with a lot of built-in dashboards for Kubernetes monitoring. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, How Intuit democratizes AI development across teams through reusability. Its very easy to keep accumulating time series in Prometheus until you run out of memory. 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. By setting this limit on all our Prometheus servers we know that it will never scrape more time series than we have memory for. job and handler labels: Return a whole range of time (in this case 5 minutes up to the query time) This means that our memSeries still consumes some memory (mostly labels) but doesnt really do anything. Extra fields needed by Prometheus internals. So it seems like I'm back to square one. But I'm stuck now if I want to do something like apply a weight to alerts of a different severity level, e.g. Where does this (supposedly) Gibson quote come from? This had the effect of merging the series without overwriting any values. I was then able to perform a final sum by over the resulting series to reduce the results down to a single result, dropping the ad-hoc labels in the process. 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. I'm still out of ideas here. 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. Yeah, absent() is probably the way to go. as text instead of as an image, more people will be able to read it and help. Selecting data from Prometheus's TSDB forms the basis of almost any useful PromQL query before . information which you think might be helpful for someone else to understand Also the link to the mailing list doesn't work for me. You saw how PromQL basic expressions can return important metrics, which can be further processed with operators and functions. Bulk update symbol size units from mm to map units in rule-based symbology. What sort of strategies would a medieval military use against a fantasy giant? How to show that an expression of a finite type must be one of the finitely many possible values? Lets see what happens if we start our application at 00:25, allow Prometheus to scrape it once while it exports: And then immediately after the first scrape we upgrade our application to a new version: At 00:25 Prometheus will create our memSeries, but we will have to wait until Prometheus writes a block that contains data for 00:00-01:59 and runs garbage collection before that memSeries is removed from memory, which will happen at 03:00. There are a number of options you can set in your scrape configuration block. result of a count() on a query that returns nothing should be 0 ? 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 But before doing that it needs to first check which of the samples belong to the time series that are already present inside TSDB and which are for completely new time series. But you cant keep everything in memory forever, even with memory-mapping parts of data. The more labels you have, or the longer the names and values are, the more memory it will use. When time series disappear from applications and are no longer scraped they still stay in memory until all chunks are written to disk and garbage collection removes them. The more any application does for you, the more useful it is, the more resources it might need. Prometheus does offer some options for dealing with high cardinality problems. 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. So just calling WithLabelValues() should make a metric appear, but only at its initial value (0 for normal counters and histogram bucket counters, NaN for summary quantiles). 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. Has 90% of ice around Antarctica disappeared in less than a decade? This article covered a lot of ground. TSDB will try to estimate when a given chunk will reach 120 samples and it will set the maximum allowed time for current Head Chunk accordingly. Return the per-second rate for all time series with the http_requests_total Select the query and do + 0. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. more difficult for those people to help. (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. I'm displaying Prometheus query on a Grafana table. By clicking Sign up for GitHub, you agree to our terms of service and I've created an expression that is intended to display percent-success for a given metric. The below posts may be helpful for you to learn more about Kubernetes and our company. Vinayak is an experienced cloud consultant with a knack of automation, currently working with Cognizant Singapore. Prometheus will keep each block on disk for the configured retention period. We know that the more labels on a metric, the more time series it can create. Our HTTP response will now show more entries: As we can see we have an entry for each unique combination of labels. 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. Since labels are copied around when Prometheus is handling queries this could cause significant memory usage increase. or Internet application, We will examine their use cases, the reasoning behind them, and some implementation details you should be aware of. Time arrow with "current position" evolving with overlay number. To learn more, see our tips on writing great answers. First rule will tell Prometheus to calculate per second rate of all requests and sum it across all instances of our server. Is that correct? Prometheus metrics can have extra dimensions in form of labels. If all the label values are controlled by your application you will be able to count the number of all possible label combinations. But the key to tackling high cardinality was better understanding how Prometheus works and what kind of usage patterns will be problematic. For a list of trademarks of The Linux Foundation, please see our Trademark Usage page. To set up Prometheus to monitor app metrics: Download and install Prometheus. In my case there haven't been any failures so rio_dashorigin_serve_manifest_duration_millis_count{Success="Failed"} returns no data points found. In AWS, create two t2.medium instances running CentOS. Ive added a data source(prometheus) in Grafana. After running the query, a table will show the current value of each result time series (one table row per output series). Thanks for contributing an answer to Stack Overflow! Prometheus Authors 2014-2023 | Documentation Distributed under CC-BY-4.0. There will be traps and room for mistakes at all stages of this process. Is it a bug? To get a better idea of this problem lets adjust our example metric to track HTTP requests. Neither of these solutions seem to retain the other dimensional information, they simply produce a scaler 0.