Grafana increase over time26.10.2020
In a presentation I gave at IIUG titled Making system monitoring better I showed, without much detail, how Grafana is a powerful tool for visualising what is happening within your Informix server. Performance metrics from your database server are collected at regular usually 10 second intervals and stored in a time-series database which can be used as the source for dashboards containing dynamic graphs and other ways of presenting the data.
For the DBA the benefits are legion:. C ulture A utomation M easurement S haring. This has the added benefit of more eyes and others can learn to spot database problems, or when they are probably not database problems, by referring to these. The rest are popular open source tools which other teams in your organisation are probably already using. Some of the tools listed above can also produce events or alerts when a trigger condition occurs and automatically pass this up a stack to PagerDuty or another call-out system.
There are a lot of ways of implementing a full monitoring stack with choices to make about data collection, storing, visualisation, analysis and alerting.
For a fuller discussion of the benefits of the three open source technologies mentioned above I highly recommend reading this blog post from Loom Systems written in JunePrometheus vs. Grafana vs. Graphite — A Feature Comparison. The solution would be richer if Graphite was used as the data source and Grafana for visualisation only. This would provide more aggregation functions and allows you to do things like subtract one time series from another.
The open observability platform
As an example of what this might provide, I have a dashboard not covered in this blog post displaying the buffer turnover ratio and buffer waits ratio over an arbitrary moving window irrespective of when onstat -z was last run. It is easy to confuse Graphite and Grafana, especially as both can be used independently, or Graphite can be a data source for Grafana. Potentially anything we can put a value to every ten seconds can be collected and stored in InfluxDB which is a statement you can make about time series collections in general.
For Linux operating system metrics there is a well-established collection daemon called collectd and, if I had better C programming skills, I could a collectd plugin for Informix. For Informix systems the most obvious source is the system monitoring interface SMI which is the presentation of information held in shared memory through pseudo-tables in the sysmaster database. This covers the vast majority of what can be collected using onstat but is easier to handle in a programming language.
Doing it this way means we can also collect real table data in the same manner. That is they only increase over time unless they get so large they run out of bits and wrap or a DBA runs onstat -z. Some things you might collect are automatically suitable for graphing because they are gauges : an example of this is the number of threads in your ready queue at any given moment. Nothing better than an example you can try at home or work! What this demonstration is going to build will look like the above.
A collector script will collect metrics from Informix at a regular interval and post the results to InfluxDB. You will be able to use your usual web browser to connect to Grafana and visualise the data. Sounds simple? In a terminal run:. Your terminal should now be inside the Docker container and logged in as root. Run these commands to install some extra packages and then InfluxDB:. Log in with the user name admin and the password secret.
Once logged in click Add data source and fill in the settings as follows some of them are case-sensitive :. All being well you should see Data source is working in a big green box.
Now we are going to set up the Informix container to monitor. On your workstation in another terminal run:.Learn about Grafana the monitoring solution for every database. Open Source is at the heart of what we do at Grafana Labs. This tutorial uses a sample application to demonstrate some of the features in Grafana. To complete the exercises in this tutorial, you need to download the files to your local machine. Clone the github. No errors means it is running.
If you get an error, then start Docker and then run the command again. The first time you run docker-compose up -dDocker downloads all the necessary resources for the tutorial.
This might take a few minutes, depending on your internet connection. Note: If you already have Grafana, Loki, or Prometheus running on your system, then you might see errors because the Docker image is trying to use ports that your local installations are already using. Stop the services, then run the command again. Grafana is an open-source platform for monitoring and observability that lets you visualize and explore the state of your systems.
To the far left you can see the sidebara set of quick access icons for navigating Grafana. The sample application exposes metrics which are stored in Prometheusa popular time series database TSDB. To be able to visualize the metrics from Prometheus, you first need to add it as a data source in Grafana.World's Fastest Internet - 1.6 TERABITS per Second
Grafana Explore is a workflow for troubleshooting and data exploration. Ad-hoc queries are queries that are made interactively, with the purpose of exploring data. An ad-hoc query is commonly followed by another, more specific query. You just made your first PromQL query! PromQL is a powerful query language that lets you select and aggregate time series data stored in Prometheus. Rather than visualizing the actual value, you can use counters to calculate the rate of changei. Add the rate function to your query to visualize the rate of requests per second.
Enter the following in the Query editor and then press Enter. This area is called the legend. Go back to the sample application and generate some traffic by adding new links, voting, or just refresh the browser. In the upper right corner, click the time pickerand select Last 5 minutes.
Depending on your use case, you might want to group on other labels. Grafana supports log data sources, like Loki. Just like for metrics, you first need to add your data source to Grafana. Grafana Explore not only lets you make ad-hoc queries for metrics, but lets you explore your logs as well.
Grafana displays all logs within the log file of the sample application. The height of each bar encodes the number of logs that were generated at that time. Grafana only shows logs within the current time interval.Learn about Grafana the monitoring solution for every database. Open Source is at the heart of what we do at Grafana Labs.
The main panel in Grafana is simply named Graph. It provides a very rich set of graphing options. Repeat a panel for each value of a variable.
Repeating panels are described in more detail here. The metrics tab defines what series data and sources to render. Each data source provides different options. The default option is Time and means the x-axis represents time and that the data is grouped by time for example, by hour or by minute. The Series option means that the data is grouped by series and not by time. The y-axis still represents the value. The Histogram option converts the graph into a histogram.
A Histogram is a kind of bar chart that groups numbers into ranges, often called buckets or bins. Taller bars show that more data falls in that range. Histograms and buckets are described in more detail here. The legend values are calculated client side by Grafana and depend on what type of aggregation or point consolidation your metric query is using.
All the above legend values cannot be correct at the same time. It is just the sum of all data points received by Grafana. The section allows a series to be rendered differently from the others. There is an option under Series overrides to draw lines as dashes.
Set Dashes to the value True to override the line draw setting for a specific series. Thresholds allow you to add arbitrary lines or sections to the graph to make it easier to see when the graph crosses a particular threshold. The time range tab allows you to override the dashboard time range and specify a panel specific time.
Either through a relative from now time option or through a timeshift. Panel time overrides and timeshift are described in more detail here. Data links allow you add dynamic URL links to your visualizations, read more on data links.
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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. This is a bit of a bugbear for me in Kibana. It would be great to support having per-panel time periods that can be, or can not be, overridden by the dashboard time period at will.
In our use case, we have some graph chage with the time picker, and a graph show a fixed long time for example 2d or 1week to see the trends. So much to do! A PR is always welcomed :. I have spent a few hours working on this issue. So far it only works on the graph panel, need some feedback for how it should work on the singlestat panel.
There is now a time range tab in the graph panel edit view. As you can see in the first screenshot the relative time override is displayed in the top right corner of the graph, as is the time shift if present.
The way this works is that if the dashboard time is relative for example Last 6hthen the relative time override will be applied. If the dashboard time is absolute, for example you have zoomed in. Then the relative time override will not be applied. That way the zoom works as expected even when you use it on a panel with a different time range.
If you change back the dashboard time to a relative time the panel override will be turned on again. The timeshift will still be active when an absolute time is used for example when you zoom in. The problem with this is that if you select a region to zoom in on a timeshifted graph, the time range will not be what you expected as the zoomed in region will set to the dashboard time, but the graph time shift will still be applied.
This could perhaps be fixed by disabling the timeshift if the zoom in happens on a panel where time shift is enabled. Another issue is how to implement this on the singlestat panel. The only problem is how to visualize the relative time override or the time shift. Singlestat panels can be quite small, and some even do not have a title, so not sure where to put the info that the panel is not using the dashboard time range. At the moment we use netcat echo. Assuming you want to display how long API call took time, not just a count.
Dichotomia because the panel time range tab allows for different time periods for each panel. I can't set absolute fixed time that is static and different than the time represented by other panels in the same dashboard.
Dichotomia that is true, there is a limitation. Hi, as far as my understanding goes, grafana can support different time range for each panel using the time range option. But it is a relative time to the time range that i select at top of the app. Is there any development that is being done for giving specific time range for each panel.Takes each timeseries and consolidate its points fallen in the given interval into one point using functionwhich can be one of: avgminmaxmedian.
Converts absolute values to delta. This function just calculate difference between values. For the per-second calculation use rate. Calculates the per-second rate of increase of the time series.
Resistant to counter reset. Suitable for converting of growing counters into the per-sercond rate. Graphs the moving average of a metric over a fixed number of past points, specified by windowSize param. In order to do it, plugin should fetch previous N points first and calculate simple moving average for it.
To avoid it, plugin uses this hack: assume, previous N points have the same average values as first N windowSize. So you should keep this fact in mind and don't rely on first N points interval. Takes a series of values and a window size and consolidate all its points fallen in the given interval into one point by Nth percentile.
Takes all timeseries and consolidate all its points fallen in the given interval into one point using functionwhich can be one of: avgminmaxmedian. This will add metrics together and return the sum at each datapoint. This method required interpolation of each timeseries so it may cause high CPU load. Try to combine it with groupBy function to reduce load. Takes all timeseries and consolidate all its points fallen in the given interval into one point by Nth percentile. Returns top N series, sorted by valuewhich can be one of: avgminmaxmedian.
Returns bottom N series, sorted by valuewhich can be one of: avgminmaxmedian. Draws the selected metrics shifted in time. If no sign is given, a minus sign - is implied which will shift the metric back in time. Following template variables available for using in setAlias and replaceAlias functions:.Data gives us insight into the world around us — and allows us to visualize and prepare for the spread of the COVID pandemic. Not only are we a globally distributed team, some Timescale team family members see fears and concerns first-hand while working in our emergency rooms.
First and foremost, our thoughts are with those who've been affected in any way.
We join the global community in hoping for the best in the coming weeks and months, especially in containing transmission and reducing the virus' spread. We were recently made aware of a time-series dataset from Johns Hopkins University, containing daily case reports from around the world. In this post, we'll walkthrough how to load the dataset into TimescaleDB and use Grafana to visualize queries.
You can follow these instructions to choose your installation method. First, you will want to clone the GitHub repository Joel created:. Setup your database and ingest the data according to the instructions in the GitHub repository.
Once the data is fully ingested, you should be able to login to your database from psql :. At this point, your time-series database is properly configured and you've loaded the COVID dataset.
Note: the Johns Hopkins University data is a running total, not a per-day total. So, the data for February 23rd for any given location represents a cumulative tally of all cases in that location as of February 23rd.
Cleaning public datasets is fairly common, and fortunately, this dataset is easy to prepare for the rest of our tutorial. In psql enter the following commands:. As of the publication date of this post, northern Italy has the highest concentration of COVID cases, outside of China, and it continues to intensify.
This requires that we make use of the latitude and longitude information stored in our database. One caveat: The Johns Hopkins University dataset has limited information about specific locations. This is available out-of-the-box on Timescale Cloudor you can manually install for self-hosted versions. You should see the PostGIS extension in your extension list, as noted below:.
Seattle is located at lat, long Our database is setup, so we're ready to run a geospatial query that returns the number of confirmed COVID cases within 75km of Seattle, each day, since the start of our dataset. We have the dataset, we have our queries, and, now, let's take the COVID dataset and visualize the total number of confirmed cases by geographic location.
In Grafana, create a new dashboard and add a new visualization. In the visualization menu, search for and select the WorldMap panel type. As mentioned earlier, the Johns Hopkins dataset includes cumulative data. For example, an entry for March 2, contains all cases that have been confirmed at that point in time. Suppose we wanted to learn the rate at which cases have been identified near a specific location.
TimescaleDB includes a feature called continuous aggregates. A continuous aggregate recomputes a query automatically at user-specified time intervals and maintains the results into a table.
Thus, instead of everyone running an aggregation query each time, the database can run a common aggregation periodically in the background, and users can query the results of the aggregation. Continuous aggregates should improve database performance and query speed for common calculations.
In our case, we want to maintain a continuous aggregation for the daily change in confirmed cases. Let's look at this continuous aggregation query:. In our table, we'll create a yesterday and today column, as well as a change column that represents the delta between the two. Under normal circumstances with large amounts of data, TimescaleDB calculates continuous aggregates in the background, but if you want to see the result immediately, you can force it:.
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I have an application that increments a Prometheus counter when it receives a particular HTTP request. The application runs in Kubernetes, has multiple instances and redeploys multiple times a day.
I would like to create a Grafana graph that shows the cumulative frequency of requests received over the last 7 days. My first thought was use increase However, resulting graph isn't quite what was asked for because the component increase How would I go about creating a graph that shows the cumulative sum of the increase in these metrics over the passed 7 days?
For example, given the simplified following data. Learn more. Simple cumulative increase in Prometheus Ask Question. Asked 8 months ago. Active 8 months ago.
Viewed times. For example, given the simplified following data Day Requests 1 10 2 5 3 15 4 10 5 20 6 5 7 5 8 10 If I was to view a graph of day 2 to day 8 I would like the graph to render a line as follows, Day Cumulative Requests d0 0 d1 5 d2 20 d3 30 d4 50 d5 55 d6 60 d7 70 Where d0 represents the initial value in the graph Thanks.
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