Database activity monitoring: How to automatically monitor and set alerts for a database
Learn what database activity monitoring is, why it's important, and how to automatically monitor a Postgres database containing IoT data with n8n workflows.
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Slack and Postgres integration
Save yourself the work of writing custom integrations for Slack and Postgres and use n8n instead. Build adaptable and scalable Communication, Development, and Data & Storage workflows that work with your technology stack. All within a building experience you will love.
How to connect Slack and Postgres
Step 1: Set up n8n
Step 2: Create a new workflow to connect Slack and Postgres
Step 3: Add the first step
Step 4: Add the Slack node
Step 5: Authenticate Slack
Step 6: Add the Postgres node
Step 7: Authenticate Postgres
Step 8: Configure Slack and Postgres nodes
Step 9: Connect Slack and Postgres
Step 10: Customize your Slack and Postgres integration
Step 11: Save and activate workflow
Step 12: Test the workflow
🤖 Advanced Slackbot with n8n
Use case
Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do.
This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases.
In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly.
This bot can create a new thread on an alerts channel and respond there.
Or reply directly to the user.
It responds for help request to return a help page.
It automatically handles unknown commands.
It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow.
How to setup
- Add Slack command and point it up to the webhook. For example.
- Add the following to the Set config node
alerts_channelwith alerts channel to start threads oninstance_urlwith this instance url to make it easy to debugslack_tokenwith slack bot token to validate requestslack_secret_signaturewith slack secret signature to validate requesthelp_docs_urlwith help url to help users understand the commands
- Build other workflows to call and add them to
commandsin Set Config. Each command must be mapped to a workflow id with anExecute Workflow Triggernode - Activate workflow 🚀
How to adjust
- Add your own commands.
- Depending on your need, you might need to lock down who can call this.
Nodes used in this workflow
Popular Slack and Postgres workflows
+5
🤖 Advanced Slackbot with n8n
Use case
Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do.
This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases.
In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly.
This bot can create a new thread on an alerts channel and respond there.
Or reply directly to the user.
It responds for help request to return a help page.
It automatically handles unknown commands.
It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow.
How to setup
Add Slack command and point it up to the webhook. For example.
Add the following to the Set config node
alerts_channel with alerts channel to start threads on
instance_url with this instance url to make it easy to debug
slack_token with slack bot token to validate request
slack_secret_signature with slack secret signature to validate request
help_docs_url with help url to help users understand the commands
Build other workflows to call and add them to commands in Set Config. Each command must be mapped to a workflow id with an Execute Workflow Trigger node
Activate workflow 🚀
How to adjust
Add your own commands.
Depending on your need, you might need to lock down who can call this.
+5
ETL pipeline for text processing
This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel.
Cron node: Schedule the workflow to run every day
Twitter node: Collect tweets
MongoDB node: Insert the collected tweets in MongoDB
Google Cloud Natural Language node: Analyse the sentiment of the collected tweets
Set node: Extract the sentiment score and magnitude
Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database
IF node: Filter tweets with positive and negative sentiment scores
Slack node: Post tweets with a positive sentiment score in a Slack channel
NoOp node: Ignore tweets with a negative sentiment score
+5
Suspicious Login Detection
This n8n workflow is designed for security monitoring and incident response when suspicious login events are detected. It can be initiated either manually from within the n8n UI for testing or automatically triggered by a webhook when a new login event occurs.
The workflow first extracts relevant data from the incoming webhook payload, including the IP address, user agent, timestamp, URL, and user ID. It then splits into three parallel processing paths.
In the first path, it queries GreyNoise's Community API to retrieve information about the investigated IP address. Depending on the classification and trust level received from GreyNoise, the alert is given a High, Medium, or Low priority. This priority is assigned based on the best practices documentation from GreyNoise on how to apply their data to analysis. Once a priority is assigned, a message is sent to a Slack channel to notify users about the alert.
The second path involves fetching geolocation data about the IP address using IP-API's Geolocation API and merging it with data from the UserParser node. This data is then combined with the data obtained from GreyNoise.
In the third path, the UserParser node queries the Userparser IP address and user agent lookup API to obtain information about the user's IP and user agent. This data is merged with the IP-API data and GreyNoise data.
The workflow then checks if the IP address is considered an unknown threat by examining both the noise and riot fields from GreyNoise. If it is considered an unknown threat, the workflow proceeds to retrieve the last 10 login records for the same user from a Postgres database.
If there are any discrepancies in the login information, indicating a new location or device/browser, the user is informed via email.
Potential issues when setting up this workflow include ensuring that credentials are correctly entered for GreyNoise and UserParser nodes, and addressing any discrepancies in the data sources that could lead to false positives or negatives in threat detection. Additionally, the usage of hardcoded API keys should be replaced with credentials for security and flexibility. Thorough testing and validation with sample data are crucial to ensure the workflow performs as expected and aligns with security incident response procedures.
+5
Enrich up to 1500 emails per hour with Dropcontact batch requests
The template allows to make Dropcontact batch requests up to 250 requests every 10 minutes (1500/hour). Valuable if high volume email enrichment is expected.
Dropcontact will look for email & basic email qualification if first_name, last_name, company_name is provided.
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Step 1:
Node "Profiles Query"
Connect your own source (Airtable, Google Sheets, Supabase,...) the template is using Postgres by default.
Note I:
Be careful your source is only returning a maximum of 250 items.
Note II:
The next node uses the next variables, make sure you can map these from your source file:
first_name
last_name
website (company_name would work too)
full_name (see note)
Note III:
This template is using the Dropcontact Batch API, which works in a POST & GET setup. Not a GET request only to retrieve data, as Dropcontact needs to process the batch data load properly.
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Step 2:
Node "Data Transformation"
Will transform the input variables in the proper json format. This json format is expected from the Dropcontact API to make a batch request.
"full_name" is being used as a custom identifier to update the returned email to the proper contact in your source database.
To make things easy, use a unique identiefer in the full_name variable.
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Step3:
Node: "Bulk Dropcontact Requests".
Enter your Dropcontact credentials in the node: Bulk Dropcontact Requests.
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Step4:
Connect your output source by mapping the data you like to use.
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Step5:
Node: "Slack" (OPTIONAL)
Connect your slack account, if an error occur, you will be notified.
TIP: Try to run the workflow with a batch of 10 (not 250) as it might need to run initially before you will be able to map the data to your final destination. Once the data fields are properly mapped, adjust back to 250.
Build your own Slack and Postgres integration
Create custom Slack and Postgres workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.
Slack and Postgres integration details
Other integrations with Slack
Other integrations with Postgres
Slack and Postgres integration tutorials
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FAQs
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