Influxdb Error Group By Requires At Least One Aggregate Function

To define your query, select the field that you want to create a check on. The mean is calculated from the window period matches the Schedule Every configuration option. Aggregate functions are applied to your data by default for two reasons. First, users prefer to alert on trends in their data rather than raw time series data. Second, this default aggregation interval ensures that your check run schedule aligns with your aggregated data output.

You can configure the window period for which the aggregation is applied by changing the Schedule Every configuration option, described below. Notification rules in InfluxDB are a type of task, similar to a check. Like statuses, notifications are written to the "_monitoring" bucket and are the output of a Flux task. While statuses are the output of a check, notifications are the output of a notification rule. A notification endpoint is metadata in a notification rule that enables notification messages to be sent to a third party service like HTTP, Slack, or PagerDuty. To summarize, it might be helpful to think of notification rules as being synonymous with alerts.

However, these tasks are referred to as notification rules in the InfluxDB API and InfluxDB UI. InfluxDB's Checks and Notifications system is highly customizable and enables users to take action on their time series data. All checks and notifications are Flux tasks under the hood. The main difference between checks and notifications and other tasks is that checks and notifications read and write data to and from the "_monitoring" bucket. They also use specialized Flux packages like the Flux InfluxDB Monitor Package and notification endpoint packages.

The check parameters are defined in the subsequent line. The monitor.check() function checks the input data, assigns a level to the output, and writes the output to the "_monitoring" bucket. Finally our queried data, data, undergoes one final transformation with the v1.fieldsAsCols function before being passed to the monitor.check() function. The fields will become tag keys, and the values returned for fields become the correspond tag values.

If an empty string is given, then the result set will have a single series and will have an empty tagset . These keys become terms filters for the date histogram. Technically, you could use Flux to write custom statuses with custom levels other than "CRIT", "WARN", "OK" and "INFO" by using the map() function. However, we strongly warn against this approach because you would need to incorporate a lot of schema transformations so that your output matches that of the "_monitoring" bucket.

Additionally, many useful functions in the monitoring package require default levels of "CRIT", "WARN", "INFO", and "OK" in your status to function. If you need to define more than 4 custom levels for your check, we recommend making multiple checks and implementing helpful check naming, custom tags, and messages. A task is a Flux query that runs on a schedule or defined frequency. Flux is the functional data scripting language for InfluxDB.

Flux allows you to query, transform, and analyze your time series data in almost any way you need. Checks query data and apply a status or level to each data point based on specified condition. The output of the check is a status which is written to the " _monitoring" bucket. The "_monitoring" bucket is a default internal bucket. To write and query data or use the API in any way, you'll need to first create a user, credentials, organization and bucket. Everything in InfluxDB is organized under a concept of an organization.

Buckets represent where you store time series data. They're synonymous with what was previously in InfluxDB 1.x a database and retention policy. Where possible, it writes the value 1000 for time intervals with no results.

As part of the About information configuration, you must name your notification rule, specify the Schedule Every interval, and specify the Offset interval. In the example above, we name our notification rule "CPU Notification Rule" and Schedule Every interval of 10 minutes. For the notification Conditions, you must define the types of statuses that you want to create a notification and send a notification message for. In the example above, we're creating a notification only when the level of our status is equal to "CRIT".

We also add a tag to this notification rule; the tag scopes this notification rule to a single check — "CPU check". If it wasn't added, then our notification rule would send alerts for every status with a "_level" of "CRIT". Especially if you're using that data in a check, you could reduce the retention policy to 2 or 3 times your Schedule Every interval. This design recommendation also applies to notification rules, discussed below. Adding tags to the output of your check can facilitate further data processing. For example, you might decide to implement corrective action or program a response only when multiple fields meet certain check conditions.

When a Prometheus query is made all time series in the response do not have to have the same set of tag keys. For example, when making a PromQL request that has group by results may be included in the response that contain only host, only interface, or no tag keys at all. Bosun requires that the tag keys be consistent for each series within a seriesSet. Therefore, these results are removed from the responses when using functions like prom, promrate, promm, and promratem.

Aggregation becomes even more important for frequently changed data, e.g. There are a bunch of another functions you may use instead of mean in your queries. If those terms just going over your head, I would recommend to refreshing InfluxDB terminology given in the part one. The mapFn then builds the object used to generate the POST request.

For example, let's say you want to create a threshold check on the difference between two fields from separate measurements. Then create a simple threshold check that queries the new data. Isolating data processing work in a separate task provides you with more visibility into your system and reduces the complexity of your check. Azmulti queries a metric for multiple resources and returns them as a single series set. The arguments metric, tagKeysCSV, agType, interval, startDuration, and endDuration all behave the same as in the az function.

Also like the az functions the result will be tagged with rsg, name, and any dimensions from tagKeysCSV. Package stdlib represents the Flux standard library. The Flux standard library is a collection of built-in packages that may be imported by any Flux script. Each package in the standard library exports a collection of values most interesting of which are the exported funtion values. These valeus are callable in the flux query processor.

Another reason for implementing a built-in function is to provide a function that is broadly applicable for many users (e.g., sum() or max()). When no rows are selected, aggregate functions will return their initial value. This can occur when filtering results in no matches while aggregating values across an entire table without a grouping, or, when using filtered aggregations within a grouping. What this value is exactly varies per aggregator, but COUNT, and the various approximate count distinct sketch functions, will always return 0. It is essential to understand that the data points with the same timestamp will be stored as one point in case if they have equal tags or tags are missing. More specifically, the point which came later will overwrite previous points due to the time series database model.

The registration in this phase creates two lookups. First, it creates a named lookup in a similar fashion as for OperationSpecs in the query phase. One feature to note is that the registration takes a list of flux.OperationSpec values. This is because several user-facing query functions may map to the same internal procedure.

Explore time series data using InfluxData's SQLlike query language. Understand how to use the SELECT statement to query data from measurements tags. Cq_advanced_every executes at 30-minute intervals, the same interval as the EVERY interval.

Every 30 minutes, cq_advanced_every runs a single query that covers the time range for the current time bucket, that is, the one-hour time bucket that intersects with now(). Cq_basic_offsetcalculates the average hourly number of passengers from the bus_data measurement and stores the results in the average_passengers measurement. Cq_basic calculates the average hourly number of passengers from the bus_data measurement and stores the results in the average_passengers measurement in the transportation database. UNION ALL can be used to query multiple tables at the same time.

In this case, it must appear in a subquery in the FROM clause, and the lower-level subqueries that are inputs to the UNION ALL operator must be simple table SELECTs. Features like expressions, column aliasing, JOIN, GROUP BY, ORDER BY, and so on cannot be used. The query will run natively using a union datasource. The GROUP BY clause refers to columns in the FROM table. Using GROUP BY, DISTINCT, or any aggregation functions will trigger an aggregation query using one of Druid's three native aggregation query types.

GROUP BY can refer to an expression or a select clause ordinal position . For our custom notification rule, we'll use the Telegram Flux Package to send a notification to Telegram instead of Slack. When writing a custom notification rule, make sure to import the correct notification endpoint package, import "contrib/sranka/telegram". The telegram.endpoint() function requires that you specify your Telegram bot token. In this custom notification rule, we'll send a notification any time our status level is "CRIT" or "WARN". This additional filtering is assigned to the critOrWarn variable.

Finally, make sure to include a _notification_rule_id and a _notification_endpoint_id. These ID's must be 16 characters long and alphanumeric. You can either create a notification rule through the UI, or you can write a custom notification rule with Flux. Therefore a notification rule generated through the UI will be just as performant as a custom notification rule. Therein lies the power of InfluxDB and Flux — the developer truly has the ability to customize their time series workloads as they see fit. The first thing to notice is that the Flux script uses a less commonly used Flux syntax — square brackets instead of dots to represent member expressions.

Group By Requires At Least One Aggregate Function Regardless, we can see how the steps to generate a threshold check in the UI are defined by this corresponding Flux script. The data that we want to create a check on is assigned to the data variable. An aggregation is automatically applied with the aggregate Window() function. The fact that a check is really just a specialized type of task is highlighted by the task options which are defined in the line following the aggregation. The check properties include the Schedule Every, the interval to run your check, and the Offset, the interval at which to delay the execution of a task.

Including an Offset can help you avoid read/write conflicts and assist in successful metrics buffering. Finally, the Tags property allows you to add a custom tag to your check output. This check property will add a new column with your specified tag key as the column name and the tag value for the rows. Leftjoin takes multiple numberSets and joins them to the first numberSet to form a table.

TagsCSV is a string that is comma delimited, and should match tags from query that you want to display (i.e., "host,disk"). DataCSV is a list of column names for each numberset, so it should have the same number of labels as there are numberSets. Elasitc replaces the deprecated logstash functions.

It is meant to be able to work with any elastic documents that have a time field and not just logstash. It introduces two new types to allow for greater flexibility in querying. The ESIndexer type generates index names to query . There are now different functions to generate indexers for people with different configurations. The ESQuery type is generates elastic queries so you can filter your results.

By making these new types, new Indexers and Elastic queries can be added over time. InfluxDB OSS is an open source time series database designed to handle high write and query loads. Learn how to use and leverage InfluxDB in use cases such. Cq_advanced_for_fill executes at one-hour intervals, the same interval as the GROUP BY time() interval. Cq_advanced_every_for executes at one-hour intervals, the same interval as the EVERY interval. Cq_advanced_for executes at 30-minute intervals, the same interval as the GROUP BY time() interval.

The basic CQ runs a single query that covers the time range between now() and now() minus the GROUP BY time() interval. See the advanced syntax for how to configure the query's time range. Cq_basic_rp executes at one-hour intervals, the same interval as the GROUP BY time() interval. Cq_basic executes at one-hour intervals, the same interval as the GROUP BY time() interval. The LIMIT clause limits the number of rows returned. In some situations Druid will push down this limit to data servers, which boosts performance.

Limits are always pushed down for queries that run with the native Scan or TopN query types. With the native GroupBy query type, it is pushed down when ordering on a column that you are grouping by. If you notice that adding a limit doesn't change performance very much, then it's possible that Druid wasn't able to push down the limit for your query. The InfluxDB UI makes creating simple notification rules very easy, but it falls short in its ability to highlight the sophistication of the notification system within InfluxDB. Flux allows you to transform your data in almost any way you see fit. The steps to create a custom notification rule are similar to the steps as defined by the UI.

This same logic can be applied towards creating your own custom notification rule. While the UI enables you to configure notification endpoints for HTTP, Slack, and PagerDuty, Flux has many more notification endpoint packages to take advantage of. Promm is like the prom function, except that it queries multiple Prometheus TSDBs and combines the result into a single seriesSet. A tag key of bosun_prefix with the tag value set to the prefix is added to the results to ensure that series are unique in the result. Also using the historical testing feature to query multiple intervals of time could quickly eat through your request limit.

To express the above LSQL Streaming supports windowed aggregations, by adding a WINDOW BY clause to the query. Given their semantics, tables cannot be aggregated using a window, because it would not make sense. A table represents the latest_ state of a set of pairs, not a series of events interspersed over a time-continuum. Thus trying to window them is not a sensible operation. The master branch on this repo now represents the latest InfluxDB, which now includes functionality for Kapacitor and Chronograf all in a single binary. Cq_basic_br executes at 30 minutes intervals, the same interval as the GROUP BY time() interval.