How to use a Lookup Cache for large dataset mappings

Lookup Caches act as a central repository for frequently used data, stored in a key-value format. This allows you to store, find, and reuse data across flows without relying on repeated API calls or external database queries.

→ Learn more about the Lookup Cache.

Lookup Cache demo

Lookup Cache overview

Why use Lookup Caches in your flows?

• Faster data lookups: Reduces dependencies on external systems, improving flow efficiency.
• Scalable mappings: Handles large datasets better than static or dynamic lookups.
• Reusable and flexible: Acts as a lookup table, environment-specific variable store, or centralized reference data repository.
• Easier maintenance: Data can be loaded via CSV files or updated dynamically using Integrator.io APIs.

Many integrations require mapping and transforming data between different formats.

For example:

• One system may store country and state data as two-letter codes, while another requires full names.
• Maintaining these mappings manually is time-consuming, error-prone, and difficult to scale, especially with large datasets.

A Lookup Cache solves these challenges by storing transformation rules centrally, ensuring faster, more reliable, and scalable mappings.

Use case: Mapping country codes to full names

Imagine syncing customer data from Microsoft Dynamics 365 Business Central to Shopify:

• Business Central stores country and state information as two-letter codes.
• Shopify requires full country and state names.

Instead of relying on:

• Static lookups, which are difficult to maintain
• External API calls, which slow down processing

You can use Lookup Cache to transform values within your integration instantly.

How to use Lookup Cache for mappings

Step 1: Configure the mapper

• Open Mapper 2.0 in your flow.
• Select the destination field (e.g., “Country” in Shopify).
• Select the source field (e.g., “Country Letter Code” from Business Central).

Step 2: Create a Lookup Cache

• Change the field mapping type to Lookup and select Lookup Cache.
• If an existing Lookup Cache is available, select it. Otherwise, create a new one.
• Upload a CSV file containing mappings for two-letter country codes and full names.
• Choose the key column (e.g., “Alpha-2” for two-letter country codes).
• Choose the value column (e.g., “Full Country Name”).
• Configure whether the data should persist when cloning or moving the flow.

Step 3: Apply the Lookup Cache in the mapper

• Select Lookup Cache as the mapping source.
• Set the value field to return the full country name.
• Save and apply the changes.

Step 4: Run the flow and verify the data

• Execute the flow to sync customer records.
• In Shopify, confirm that country names now appear in full instead of two-letter codes.

Benefits of using the Lookup Cache

Lookup Cache provides a scalable, efficient solution for transforming large datasets in integrations. Storing and applying mappings improves accuracy, reduces reliance on external systems, and accelerates data processing.

• Faster processing: Avoids repeated API calls for lookups.
• Scalability: Handles large datasets without performance issues.
• Easy maintenance: Update lookup values centrally without modifying each flow.
• Greater accuracy: Eliminates errors from manual mappings.

→ Learn more about the Lookup Cache.