If you’re familiar with Telegraf, you know that you can easily configure this lightweight collection agent with a single TOML configuration file to gather metrics from over 180 inputs and write data to a wide variety of different outputs and/or platforms. You might also know that Telegraf can act as a processor, aggregator, parser, and serializer. For example, you might even be familiar with the Starlark Processor Plugin that gives you the ability to perform various mathematical operations in Telegraf.

Needless to say, Telegraf is pretty awesome. But did you know that you can also use Telegraf to perform forecasting…

With InfluxDB you can use Tasks to process data on a schedule. You can also use tasks to write custom alerts. However, sometimes your task will fail. In this TLDR, we’ll learn how to debug your task with the InfluxDB UI and the InfluxDB CLI.

Debugging tasks with the InfluxDB UI

The InfluxDB UI is full of information about your tasks to help you understand more about the tasks you’re running.

Here are some of the UI features of the Task page you should familiarize yourself with:

• Task run status toggle: It enables you to toggle a Task on/off or active/inactive. I toggle my tasks to…

Are you interested in performing time series forecasting or anomaly detection, but you don’t know where to start? If so, you’re not alone. There is an overwhelming variety of libraries, algorithms, and workflow recommendations for these tasks. As a Developer Advocate at InfluxDB, the leading time series database, I’ve researched time series data science methodologies and best practices for forecasting and anomaly detection. Today I want to summarize some important concepts about time series as well as share resources to get you started on your time series data science journey.

Why should a beginner interested in data science start learning about time series?

If you’re interested in becoming a data scientist, learning about…

In this post, we learn about monitoring states with InfluxDB. This TL;DR assumes that you already know how to create a check. If you are new to checks, please read this “TL;DR — Using Tasks and Checks for Monitoring with InfluxDB”.

Q: What ways can I monitor states with InfluxDB?
A: There are several ways to monitor states with InfluxDB. Some Flux functions for monitoring states include:

• stateDuration(): allows you to determine how long a state persists.
• stateCount(): allows you to count the number of consecutive states.
• monitor.stateChanges(): allows you to detect changes in the _level column from one level…

In this post, we learn how to use tasks in combination with checks for monitoring with InfluxDB.

Q: What is the monitoring workflow for InfluxDB?
A: According to the documentation, the monitoring workflow involves the following steps:

1. A check in InfluxDB queries data and assigns a status with a _level based on specific conditions.
2. InfluxDB stores the output of a check in the statuses measurement in the _monitoring system bucket.
3. Notification rules check data in the statuses measurement, and based on conditions set in the notification rule, send a message to a notification endpoint.
4. InfluxDB stores notifications in the notifications…

In this post, we share how to use the array.from() function to construct a table from an array of records with Flux. Flux is InfluxData’s scripting and functional query language.

This TL;DR assumes that you have either registered for a InfluxDB Cloud account — registering for a free account is the easiest way to get started with InfluxDB — or installed InfluxDB 2.0 OSS.

Q: How can I construct and write data to InfluxDB?
A: Users have the following options for construction and writing temporary data to InfluxDB:

• Generate line protocol and write it manually to InfluxDB through the UI…

In this post, we’ll review some common InfluxDB Cloud 2.0 errors for beginners. We’ll discuss probable causes as well as recommended fixes. This blog uses the Telegraf System Configuration and data as an example to illustrate the various errors you may encounter. Having some familiarity with this dataset is useful in understanding the issue and the resolution. However, you may run into these issues with your own time series data, and hopefully, these examples highlight how you can address them!

Error 1: specified column does not exist in table: _time

Possible causes: A likely cause of this error is using an aggregator Flux function. Many aggregators — including count(), mean()…

In this post, we learn how to use the reduce(), findColumn(), and findRecord() Flux functions to perform custom aggregations with InfluxDB.

This TL;DR assumes that you have either registered for an InfluxDB Cloud account — registering for a free account is the easiest way to get started with InfluxDB — or installed InfluxDB 2.0 OSS.

In order to easily demonstrate how these functions work, let’s use the array.from() function to build an ad hoc table to use in the query:

import "experimental/array" data = array.from(rows: [{_time: 2020-01-01T00:00:00Z, mytag: "t0", _field: "f0", _value: "1.0"}, {_time: 2020-01-02T00:00:00Z, mytag: "t1", _field: "f1", _value…