---
title: Send alerts using data in InfluxDB
description: Query, analyze, and send alerts using time series data stored in InfluxDB.
url: https://docs.influxdata.com/influxdb3/cloud-dedicated/process-data/send-alerts/
estimated_tokens: 1742
publisher: InfluxData
canonical: https://docs.influxdata.com/influxdb3/cloud-dedicated/process-data/send-alerts/
date: '2026-05-15T15:46:14-06:00'
lastmod: '2026-05-15T15:46:14-06:00'
---

Query, analyze, and send alerts using time series data stored in InfluxDB.

This guide uses [Python](https://www.python.org/), the[InfluxDB 3 Python client library](https://github.com/InfluxCommunity/influxdb3-python),
and the [Python Slack SDK](https://slack.dev/python-slack-sdk/) to demonstrate
how to query data from InfluxDB and send alerts to Slack, but you can use your
runtime and alerting platform of choice with any of the available[InfluxDB 3 client libraries](/influxdb3/cloud-dedicated/reference/client-libraries/v3/).
Whatever clients and platforms you choose the use, the process is the same:

#### Alerting process

1. Use an external runtime and InfluxDB client to query data from InfluxDB.
2. Use the queried data and tools available in your runtime to send alerts.

* [Create a Slack app](#create-a-slack-app)
* [Install dependencies](#install-dependencies)
* [Create an InfluxDB client](#create-an-influxdb-client)
* [Create a Slack client](#create-a-slack-client)
* [Query InfluxDB](#query-influxdb)
  * [Execute the query](#execute-the-query)

* [Send alerts](#send-alerts)
* [Full alerting script](#full-alerting-script)

## Create a Slack app

To send alerts to Slack, first create a Slack app and gather the required
connection credentials to interact with your app.
More information is provided in the[Slack basic app setup documentation](https://api.slack.com/authentication/basics).

## Install dependencies

> [!Note]
> This guide assumes you have already[setup your Python project and virtual environment](/influxdb3/cloud-dedicated/query-data/execute-queries/client-libraries/python/#create-a-python-virtual-environment).

Use `pip` to install the following dependencies:

* `influxdb_client_3`
* `pandas`
* `slack_sdk`

```sh
pip install influxdb3-python pandas slack_sdk
```

## Create an InfluxDB client

Use the `InfluxDBClient3` function in the `influxdb_client_3` module to
instantiate an InfluxDB client.
Provide the following credentials:

* **host**: InfluxDB Cloud Dedicated cluster URL *(without the protocol)*
* **org**: InfluxDB organization name
* **token**: [InfluxDB database token](/influxdb3/cloud-dedicated/admin/tokens/#database-tokens)read permissions on the database you want to query
* **database**: InfluxDB database name

```py
from influxdb_client_3 import InfluxDBClient3
import pandas

# Instantiate an InfluxDBClient3 client configured for your database
influxdb = InfluxDBClient3(
    host='cluster-id.a.influxdb.io',
    token='DATABASE_TOKEN',
    database='DATABASE_NAME'
)
```

## Create a Slack client

1. Import the `WebClient` function from the `slack.sdk` module and the `SlackApiError`function from the `slack_sdk.errors` module.

2. Use the `WebClient` function to instantiate a Slack client.
   Provide the following credentials:

   * **token**: [Slack bot token](https://api.slack.com/authentication/basics#getting-your-authentication-token)

```py
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

slack = WebClient(token='SLACK_BOT_TOKEN')
```

## Query InfluxDB

Define either a SQL or InfluxQL query to retrieve data to alert on.
Depending on what data you want to alert on, you can:

* Include logic in the query so it only returns results that should be alerted on.
* Query data necessary for further processing and then send alerts based on
  processing performed in your runtime.

The example query below only returns values above a threshold that should trigger alerts.

#### SQL ####

```sql
SELECT
  selector_last(co, time)['time'] AS time,
  selector_last(co, time)['value'] AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
```

```sql
SELECT
  LAST(co) AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
```

### Execute the query

1. Assign the query string to a variable.

2. Use the `query` method of your [instantiated client](#create-an-influxdb-client)to query raw data from InfluxDB. Provide the following arguments.

   * **query**: Query string to execute
   * **language**: `sql` or `influxql`

3. Use the `to_pandas` method to convert the returned Arrow table to a Pandas DataFrame.

#### SQL ####

```py
# ...

query = '''
SELECT
  selector_last(co, time)['time'] AS time,
  selector_last(co, time)['value'] AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
'''

table = influxdb_raw.query(query=query, language="sql")
data_frame = table.to_pandas()
```

```py
# ...

query = '''
SELECT
  LAST(co) AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
'''

table = influxdb_raw.query(query=query, language="influxql")
data_frame = table.to_pandas()
```

## Send alerts

Iterate through the DataFrame and send an alert to Slack for each row.

1. Use the `reset_index` function on the data frame to ensure indexes align
   with the number of rows in the DataFrame.

2. Iterate through each row and use the `chat_postMessage` method of your[Slack client](#create-a-slack-client) to send a message (per row) to Slack.
   Provide the following arguments:

   * **channel**: Slack channel to send the alert to.
   * **text**: Message text to send. Use string interpolation to insert column
     values from each row into the message text.

```py
# ...

data_frame = data_frame.reset_index()

for index, row in data_frame.iterrows():
    slack.chat_postMessage(
        channel="#SLACK_CHANNEL",
        text=f'Carbon monoxide (co) high in {row.room}: {row.co} ppm at {row.time}'
    )
```

## Full alerting script

#### SQL ####

```py
from influxdb_client_3 import InfluxDBClient3
import pandas
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

influxdb = InfluxDBClient3(
    host='cluster-id.a.influxdb.io',
    token='DATABASE_TOKEN',
    database='DATABASE_NAME'
)

slack = WebClient(token='SLACK_BOT_TOKEN')

query = '''
SELECT
  selector_last(co, time)['time'] AS time,
  selector_last(co, time)['value'] AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
'''

table = influxdb_raw.query(query=query, language="sql")
data_frame = table.to_pandas()
data_frame = data_frame.reset_index()

for index, row in data_frame.iterrows():
    slack.chat_postMessage(
        channel="#SLACK_CHANNEL",
        text=f'Carbon monoxide (co) high in {row.room}: {row.co} ppm at {row.time}'
    )
```

```py
from influxdb_client_3 import InfluxDBClient3
import pandas
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

influxdb = InfluxDBClient3(
    host='cluster-id.a.influxdb.io',
    token='DATABASE_TOKEN',
    database='DATABASE_NAME'
)

slack = WebClient(token='SLACK_BOT_TOKEN')

query = '''
SELECT
  LAST(co) AS co,
  room
FROM home
WHERE co > 10
GROUP BY room
'''

table = influxdb_raw.query(query=query, language="influxql")
data_frame = table.to_pandas()
data_frame = data_frame.reset_index()

for index, row in data_frame.iterrows():
    slack.chat_postMessage(
        channel="#SLACK_CHANNEL",
        text=f'Carbon monoxide (co) high in {row.room}: {row.co} ppm at {row.time}'
    )
```