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What kind of condition types can I use (like avg()…) in case of setting up an alert?


This calculates the average (mean) value of the data points within a given time window. For example, if you have temperature readings every minute, you could use avg() to find the average temperature over the last hour.


This gives you the smallest value in the data series during the specified time window. If you’re monitoring server response times, min() would give you the lowest response time within a certain period.


On the other hand, this function gives you the largest value in the data series within the time window. For instance, you could use max() to find the highest CPU usage over the past day.


This function adds up all the values in the data series within the specified time range. If you’re tracking the total data transferred by a network device, sum() would give you the cumulative data transfer in a specific time period.


This simply counts the number of data points within the chosen time frame. It’s useful if you want to know how many events occurred within a certain time period, like the number of logins in the last 15 minutes.


This gives you the value of the most recent data point in the selected time range. If you’re monitoring a sensor that reports its status, last() would provide the latest status update.


The median is the middle value in a sorted dataset. It’s useful when you want to find the central tendency of your data while being less affected by extreme values, unlike the mean. For instance, you might use median() to find the median response time of a web server.


This calculates the difference between consecutive data points in the time series. It’s helpful for tracking how much values change from one point to the next. If you’re measuring the number of new user sign-ups, diff() could tell you how many new sign-ups happened in each time interval.


Similar to diff(), this calculates the absolute difference between consecutive data points. It’s like taking the positive value of the difference, so negative changes become positive. Useful for tracking changes regardless of direction.


This calculates the percentage difference between consecutive data points. It’s a relative measure of change and can help you understand the magnitude of changes in your data.


Like percent_diff(), this calculates the absolute percentage difference between consecutive data points. It’s useful for understanding relative changes without considering the direction.


This counts the number of non-null (non-missing) data points within the chosen time range. It’s useful if you want to know how complete your data is during a specific period.