PromQL allows querying historical data and combining / comparing it to the current data. 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. With 1,000 random requests we would end up with 1,000 time series in Prometheus. Prometheus allows us to measure health & performance over time and, if theres anything wrong with any service, let our team know before it becomes a problem. At the moment of writing this post we run 916 Prometheus instances with a total of around 4.9 billion time series. Its not going to get you a quicker or better answer, and some people might I've been using comparison operators in Grafana for a long while. 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. count the number of running instances per application like this: This documentation is open-source. Managing the entire lifecycle of a metric from an engineering perspective is a complex process. Its very easy to keep accumulating time series in Prometheus until you run out of memory. This is one argument for not overusing labels, but often it cannot be avoided. 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. Samples are stored inside chunks using "varbit" encoding which is a lossless compression scheme optimized for time series data. So I still can't use that metric in calculations ( e.g., success / (success + fail) ) as those calculations will return no datapoints. Yeah, absent() is probably the way to go. Simple, clear and working - thanks a lot. I believe it's the logic that it's written, but is there any conditions that can be used if there's no data recieved it returns a 0. what I tried doing is putting a condition or an absent function,but not sure if thats the correct approach. First rule will tell Prometheus to calculate per second rate of all requests and sum it across all instances of our server. These will give you an overall idea about a clusters health. Prometheus is an open-source monitoring and alerting software that can collect metrics from different infrastructure and applications. What this means is that using Prometheus defaults each memSeries should have a single chunk with 120 samples on it for every two hours of data. Each time series stored inside Prometheus (as a memSeries instance) consists of: The amount of memory needed for labels will depend on the number and length of these. There is an open pull request on the Prometheus repository. This is because the only way to stop time series from eating memory is to prevent them from being appended to TSDB. This is a deliberate design decision made by Prometheus developers. Adding labels is very easy and all we need to do is specify their names. 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. Find centralized, trusted content and collaborate around the technologies you use most. privacy statement. 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. 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. Is it possible to rotate a window 90 degrees if it has the same length and width? If instead of beverages we tracked the number of HTTP requests to a web server, and we used the request path as one of the label values, then anyone making a huge number of random requests could force our application to create a huge number of time series. This is the last line of defense for us that avoids the risk of the Prometheus server crashing due to lack of memory. Labels are stored once per each memSeries instance. In this article, you will learn some useful PromQL queries to monitor the performance of Kubernetes-based systems. Just add offset to the query. This helps us avoid a situation where applications are exporting thousands of times series that arent really needed. ***> wrote: You signed in with another tab or window. Now we should pause to make an important distinction between metrics and time series. How to tell which packages are held back due to phased updates. This gives us confidence that we wont overload any Prometheus server after applying changes. I'm not sure what you mean by exposing a metric. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Although, sometimes the values for project_id doesn't exist, but still end up showing up as one. Thanks, I used a Grafana transformation which seems to work. These queries are a good starting point. Making statements based on opinion; back them up with references or personal experience. Names and labels tell us what is being observed, while timestamp & value pairs tell us how that observable property changed over time, allowing us to plot graphs using this data. The below posts may be helpful for you to learn more about Kubernetes and our company. Selecting data from Prometheus's TSDB forms the basis of almost any useful PromQL query before . to get notified when one of them is not mounted anymore. The result is a table of failure reason and its count. Bulk update symbol size units from mm to map units in rule-based symbology. Time series scraped from applications are kept in memory. Once it has a memSeries instance to work with it will append our sample to the Head Chunk. https://grafana.com/grafana/dashboards/2129. 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. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? That way even the most inexperienced engineers can start exporting metrics without constantly wondering Will this cause an incident?. Thats why what our application exports isnt really metrics or time series - its samples. For a list of trademarks of The Linux Foundation, please see our Trademark Usage page. I'm sure there's a proper way to do this, but in the end, I used label_replace to add an arbitrary key-value label to each sub-query that I wished to add to the original values, and then applied an or to each. 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. Our metrics are exposed as a HTTP response. Name the nodes as Kubernetes Master and Kubernetes Worker. Prometheus simply counts how many samples are there in a scrape and if thats more than sample_limit allows it will fail the scrape. will get matched and propagated to the output. Run the following commands in both nodes to install kubelet, kubeadm, and kubectl. 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. Run the following commands on the master node, only copy the kubeconfig and set up Flannel CNI. Prometheus Authors 2014-2023 | Documentation Distributed under CC-BY-4.0. Has 90% of ice around Antarctica disappeared in less than a decade? This also has the benefit of allowing us to self-serve capacity management - theres no need for a team that signs off on your allocations, if CI checks are passing then we have the capacity you need for your applications. A common class of mistakes is to have an error label on your metrics and pass raw error objects as values. The more labels you have, or the longer the names and values are, the more memory it will use. The advantage of doing this is that memory-mapped chunks dont use memory unless TSDB needs to read them. But you cant keep everything in memory forever, even with memory-mapping parts of data. Variable of the type Query allows you to query Prometheus for a list of metrics, labels, or label values. 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. A sample is something in between metric and time series - its a time series value for a specific timestamp. A metric can be anything that you can express as a number, for example: To create metrics inside our application we can use one of many Prometheus client libraries. Internally all time series are stored inside a map on a structure called Head. Play with bool 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. Run the following commands in both nodes to configure the Kubernetes repository. 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. That response will have a list of, When Prometheus collects all the samples from our HTTP response it adds the timestamp of that collection and with all this information together we have a. 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. There is no equivalent functionality in a standard build of Prometheus, if any scrape produces some samples they will be appended to time series inside TSDB, creating new time series if needed. Chunks will consume more memory as they slowly fill with more samples, after each scrape, and so the memory usage here will follow a cycle - we start with low memory usage when the first sample is appended, then memory usage slowly goes up until a new chunk is created and we start again. We can use these to add more information to our metrics so that we can better understand whats going on. 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. Chunks that are a few hours old are written to disk and removed from memory. Please help improve it by filing issues or pull requests. Theres only one chunk that we can append to, its called the Head Chunk. Has 90% of ice around Antarctica disappeared in less than a decade? I know prometheus has comparison operators but I wasn't able to apply them. By clicking Sign up for GitHub, you agree to our terms of service and 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. The containers are named with a specific pattern: I need an alert when the number of container of the same pattern (eg. In our example case its a Counter class object. 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. Prometheus is an open-source monitoring and alerting software that can collect metrics from different infrastructure and applications. 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. The main motivation seems to be that dealing with partially scraped metrics is difficult and youre better off treating failed scrapes as incidents. Please open a new issue for related bugs. I then hide the original query. Neither of these solutions seem to retain the other dimensional information, they simply produce a scaler 0. The number of time series depends purely on the number of labels and the number of all possible values these labels can take. Better to simply ask under the single best category you think fits and see VictoriaMetrics has other advantages compared to Prometheus, ranging from massively parallel operation for scalability, better performance, and better data compression, though what we focus on for this blog post is a rate () function handling. This makes a bit more sense with your explanation. Finally getting back to this. The Graph tab allows you to graph a query expression over a specified range of time. In the following steps, you will create a two-node Kubernetes cluster (one master and one worker) in AWS. Use it to get a rough idea of how much memory is used per time series and dont assume its that exact number. Under which circumstances? Thirdly Prometheus is written in Golang which is a language with garbage collection. 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. This process is also aligned with the wall clock but shifted by one hour. 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. Returns a list of label names. 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. Both patches give us two levels of protection. Prometheus will keep each block on disk for the configured retention period. As we mentioned before a time series is generated from metrics. What happens when somebody wants to export more time series or use longer labels? Every two hours Prometheus will persist chunks from memory onto the disk. Making statements based on opinion; back them up with references or personal experience. It saves these metrics as time-series data, which is used to create visualizations and alerts for IT teams. 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. 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. what does the Query Inspector show for the query you have a problem with? These flags are only exposed for testing and might have a negative impact on other parts of Prometheus server. Now comes the fun stuff. 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. Internally time series names are just another label called __name__, so there is no practical distinction between name and labels. 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. accelerate any Are you not exposing the fail metric when there hasn't been a failure yet? This selector is just a metric name.