Lookup Cache is a versatile tool for storing and retrieving frequently used reference data in a centralized location. Beyond mapping static data such as country codes, it is particularly valuable for managing dynamic, environment-specific variables that may change over time, such as inventory thresholds, pricing rules, or region-specific settings.

In another article, we show you how to use a Lookup Cache for large dataset mappings.

Here, we’ll explore how Lookup Cache can be used to manage environment-specific variables through APIs.

Lookup Cache demo

Example use case

Automate buffer stock alerts with Lookup Cache and Slack

Managing buffer stock is essential for preventing stockouts and maintaining operational continuity—especially when inventory levels vary due to seasonality, shifting demand, or regional nuances. In this use case, we’ll demonstrate how to automate Slack notifications when inventory in Shopify falls below predefined buffer thresholds.

By leveraging Lookup Cache and the integrator.io API, you can dynamically manage these thresholds in a centralized repository, enabling real-time alerts and proactive inventory control.

Let’s say you need to monitor inventory levels in Shopify and notify your team when stock for specific SKUs drops below a certain threshold. Because these thresholds often differ by SKU, location, or season, managing them with static data isn’t scalable. While it’s possible to maintain these values manually using CSV files or external tools, that process is time-consuming and hard to maintain as your product catalog grows.

Instead, you can use Lookup Cache to store and manage inventory thresholds dynamically—automating updates and enabling seamless retrieval across your integration flows.

In this example, we will:

• Automatically populate and update a Lookup Cache with SKU inventory thresholds using the Integrator.io API.
• Retrieve threshold values dynamically from the Lookup Cache and trigger Slack notifications for SKUs with low stock.

Populate the Lookup Cache with thresholds

To manage thresholds dynamically, I’ve set up a flow that:

• Exports inventory data, including SKUs, stock levels, and item IDs, from Shopify.
• Uses the integrator.io API to populate a Lookup Cache with this data.

Here’s how the Lookup Cache is structured:

• Key: Inventory Item ID (unique identifier for each item).
• Value: A JSON object containing the SKU, location, and threshold.

Steps in detail:

1. Export inventory data: Create a flow to fetch inventory details from Shopify, including SKUs and stock levels.

2. Import to Lookup Cache: Add an import step to the flow that uses the Integrator.io API to update the Lookup Cache. The API supports bulk updates in a key-value array format.

3. Default thresholds: Use the Mapper to set a default threshold value during import. These thresholds can later be adjusted directly in the Lookup Cache UI or via CSV for bulk updates.

Once the flow runs, the Lookup Cache is automatically populated with SKU-specific thresholds, creating a centralized and consistent repository.

Trigger notifications for low stock

Now that the Lookup Cache is populated, let’s create a main flow to monitor stock levels and send Slack notifications when stock is low.

Steps in detail:

1. Fetch inventory levels: Set up an export step to fetch current inventory levels from Shopify.

2. Retrieve threshold values: Use the Lookup Cache API to dynamically retrieve the threshold for each SKU based on its Inventory Item ID.

• Send the Inventory Item ID as the key in the API request.
• Retrieve the associated value from the Lookup Cache, which includes the SKU, location, and threshold.

3. Validate stock levels: Add an input filter to check if the current stock is below the threshold or empty. Only records meeting these conditions proceed to the next step.

4. Send Slack notifications: For SKUs with low stock, trigger a Slack notification alerting your team.

With Lookup Cache, managing environment-specific variables like inventory thresholds becomes seamless. You can automate updates, dynamically retrieve data, and maintain consistency across flows—all without the need for manual intervention.

• Centralized management: Store thresholds in one place, making updates simple and consistent across workflows.
• Dynamic updates: Automatically populate and update thresholds using flows or APIs, eliminating manual maintenance.
• Flexibility: Retrieve and consume data in real time through APIs, enabling responsive workflows.
• Scalability: Handle complex variables like SKU-specific thresholds, pricing rules, or regional settings easily.

Make environment-specific variables easier to manage and scale

By using Lookup Cache as a dynamic, centralized data layer, teams can streamline complex logic across environments without relying on static files or manual workarounds.

Whether you’re scaling your operations or fine-tuning individual flows, Lookup Cache gives you the control, consistency, and automation you need to build smarter, more adaptive integrations.

→ Learn more about the Lookup Cache.