Get started with InfluxDB Cloud

After you’ve signed up for InfluxDB Cloud, you’re ready to get started:

  1. Do one of the following:
  2. Create a notebook:
    1. Click Notebooks in the navigation menu on the left.

    2. Select metrics

    3. Visualize data

    4. (Optional) Process data

    5. (Optional) Monitor data

    6. (Optional) Output to a new bucket and export as a task

Write and query data using the programming language of your choice

Follow the steps to write data and execute a simple query in the selected programming language:

Click one of the following:

Add sample data

The examples below use the NOAA NDBC sample data, but we provide other sample data sets as well.

Use sample data to quickly populate InfluxDB with sample time series data. Sample data sets delivered with Cloud are used throughout this documentation site to provide context, best practices, and clear examples for how InfluxDB Cloud features and functions work.

  1. Click Data > Bucket and click Create Bucket to create a bucket and name it noaa.
  2. Do one of the following to download sample data:

Add sample data with community templates

  1. In the navigation menu on the left, click Settings > Templates.

  2. Paste the Sample Data community template URL in the resource manifest file field and click the Lookup Template button.

Sample Data community template URL

The sample data template installs a task that collects sample data and dashboards to visualize the sample data. After installing the template, sample data is downloaded and stored in the sample data bucket at regular intervals.

Write sample data with an InfluxDB task

Use the Flux InfluxDB sample package to download and write sample data to InfluxDB.

Add the following as an InfluxDB task.

import "influxdata/influxdb/sample"

option task = {name: "Collect NOAA NDBC data", every: 15m} "noaa")
    |> to(bucket: "noaa")

For more information about this and other InfluxDB sample datasets, see InfluxDB sample data.

Create a notebook

Now that we’ve added the sample data bucket, we’re ready to create our notebook. Notebooks can build and annotate processes and data flows for your time series data, including writing to, querying from, and visualizing your data. For more information, see Notebooks.

Select metrics

  1. In the navigation menu on the left, click Notebooks.

  2. Click +New Notebook. By default, a Metric Selector and Visualization cell appear. For an overview of cell types, see Overview of notebooks.

  3. Enter a name for your notebook in the Name this notebook field.

  4. In the Metric Selector cell, click the Select a bucket dropdown and chooose the bucket you want to explore data from. A list of measurements, fields, and tags from the bucket appear.

  5. Select a column value to analyze.

  6. Click Preview to preview the raw data in a table.

Visualize data

Next, we’ll make it easier to visualize the raw data by viewing it in a graph. For more information on how to visualize data, see here.

By default, a visualization cell appears below your initial metric selector cell showing the mean values on a graph.

  1. Use the function dropdown menu to view different functions to apply to your data. By default, the visualization is set to the mean function.
  2. Use the visualization type dropdown to view different visualization types available. For a complete list of options, see Visualization types.
  3. Click the gear icon () to edit specific settings for the visualization type, such as colors, orientation, and labels.

Process data

A common processing task is downsampling data to view it at different levels of granularity and reduce overall data disk usage over time. The following steps use a Flux script to downsample your data to every 3 hours 30 seconds.

For more information on how to process data, see here.

  1. Click + to add a cell, then select Transform > Downsample.
  2. In the Apply aggregate field, select mean.
  3. In the Every Window Period field, enter a value. For this example, we’ll use 3h30s, but any duration is supported by InfluxDB Cloud. For more information, see duration literals.
  4. Click Preview to view the downsampled data.

Monitor data

Get notifications every time your data crosses a threshold. Specify your alerts by time and decide how the information will be sent to you.

  1. In your notebook, click to add a cell, then select Output > Alert.
  2. Customize the conditions to send an alert.
  3. Enter a time range to automatically check the data and enter your query offset.
  4. Select an endpoint to receive an alert:
    • Slack and a Slack Channel
    • HTTP post
    • Pager Duty
  5. (Optional) Personalize your message. By default, the message is “${strings.title(v: r._type)} for ${r._source_measurement} triggered at ${time(v: r._source_timestamp)}!”
  6. Click Export as Alert Task to save your alarm.

Output to a new bucket and export as a task

After processing, send the downsampled data to a new bucket to store and view it. You can write it to the bucket a single time, or export it as a task, which is a scheduled Flux script, for it write to the bucket continuously.

  1. Click + to add a cell, then select an Output to Bucket cell.
  2. In the Choose a bucket dropdown, choose +Create a Bucket.
  3. Enter a name for your bucket and click Create.
  4. Click Run from the Preview dropdown list to run the notebook and write to the output bucket a single time.
  5. To write continuously, click Export as Task in the upper right corner of the Output to Bucket cell. For details about working with tasks, see Manage tasks.

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