configuring-lookup-caches

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Configuring Lookup Caches

A lookup cache is an in-memory key-value store managed by Celigo for fast data retrieval during integration processing. Unlike export-based lookups that query external systems per-record, lookup caches provide sub-millisecond access to pre-loaded reference data without consuming API calls.

Lookup caches handle three concerns:

• Cache metadata -- name, integration scope, and whether data is included in templates/cloning
• Data management -- loading, retrieving, updating, and purging key-value entries via dedicated data endpoints
• Size governance -- each cache has a 50 MB limit; the per-environment aggregate limit across all caches is 1 GB

Lookup caches are consumed by import and export mappings via the lookups[] array. A lookup entry references a cache by _lookupCacheId and optionally extracts a specific field from the cached object via the extract JSON path. This is configured on the import/export resource, not on the cache itself.

Used across flows, APIs, and tools.

Use Cases

Lookup caches serve several distinct patterns in production integrations:

Cross-Reference / ID Mapping

The most common use case. Map external IDs to internal IDs across systems (e.g., Shopify product IDs to NetSuite internal IDs, external customer GUIDs to Salesforce Account IDs). Avoids repeated API lookups during import processing.

Deduplication / Debouncing

Track which records have already been processed to prevent duplicate operations. Store record IDs or composite keys as cache keys; check existence before processing.

State Tracking

Maintain timestamps, batch IDs, or processing markers across flow runs. Common patterns: "last sync time" caches, "open batch ID" holders, lock tables for concurrency control.

Static Reference Data

Store relatively stable reference tables -- product catalogs, category hierarchies, shipping overrides, zip code lookups. Set includeDataInTemplatesAndCloning: true when this data is part of the integration's configuration.

Quick Reference

Minimum Required Fields

Every lookup cache needs at minimum: name.

Optional but common: description, _integrationId (scopes the cache to an integration), includeDataInTemplatesAndCloning.

Data Operations

All data operations use the cache ID. Data entries are key-value pairs where both key and value are strings. Values can be JSON strings for complex objects -- the consuming lookup's extract JSON path pulls specific fields from the parsed value.

Operation	Method	Endpoint	Notes
Upsert data	POST	/v1/lookupcaches/{id}/data	Body: { "data": [{ "key": "k", "value": "v" }] }
Get data	POST	/v1/lookupcaches/{id}/getData	No body = first page (max 1000). With { "keys": [...] } or { "startsWith": "prefix" }
Delete keys	DELETE	/v1/lookupcaches/{id}/data	Body: { "keys": ["k1", "k2"] }
Purge all	DELETE	/v1/lookupcaches/{id}/data/purge	Removes all entries

Schema Index

All schemas are in references/schemas/:

• Base fields (create/update): request.yml -- name, description, includeDataInTemplatesAndCloning, externalId
• Response shape: response.yml -- includes size, sizeInMB, timestamps, integration/connector refs

Referencing Caches in Lookups

Lookup caches are consumed through the lookups[] array on imports and exports. See the writing-mappings skill for full lookup configuration details. The key fields:

• name -- unique identifier for the lookup within the resource
• _lookupCacheId -- references the cache resource
• extract -- optional JSON path to pull a specific field from the cached value (e.g., $.details.price)
• default -- fallback value when the key is not found
• allowFailures -- when true, missing keys use the default instead of failing the record

Reference the lookup by name in Handlebars expressions: {{lookup 'lookupName' record.fieldName}}.

Related Skills

• writing-mappings > Add lookups for value translation -- configuring lookups[] with _lookupCacheId, extract, and allowFailures
• writing-handlebars > Quick Reference -- {{lookup}} helper syntax for referencing caches in templates
• configuring-imports > How to Build an Import -- imports are the primary consumer of lookup caches
• configuring-exports > Quick Reference -- lookup exports (isLookup: true) as an alternative to caches for live data
• building-flows > How to Build a Flow -- wiring lookups into flow pipelines

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How to Build a Lookup Cache

1. Determine the use case

What data needs to be cached? Cross-reference IDs, static reference tables, deduplication keys, or processing state? This determines data structure, loading strategy, and lifecycle.

2. Check for existing caches

Before creating a new cache, search for existing ones in the account:

# List all lookup caches
celigo lookup-caches list

# Search across the account
celigo account search "lookup cache"
celigo account search "<keyword related to your use case>"

3. Create the cache

Create the cache metadata first. Data is loaded separately.

# Create a new empty cache
echo '{"name":"Product SKU to NetSuite ID","description":"Maps Shopify SKUs to NetSuite internal IDs for order import"}' | celigo lookup-caches create

# With integration scope and template inclusion
echo '{"name":"State Shipping Overrides","_integrationId":"<id>","includeDataInTemplatesAndCloning":true}' | celigo lookup-caches create

4. Load data into the cache

Use the put-data command to upsert key-value entries. The CLI auto-batches by count (1000 entries) and size (5 MB) per request.

# Upsert entries
echo '{"data":[{"key":"SKU-001","value":"{\"nsId\":\"12345\",\"name\":\"Widget A\"}"},{"key":"SKU-002","value":"{\"nsId\":\"12346\",\"name\":\"Widget B\"}"}]}' | celigo lookup-caches put-data <cacheId>

# Simple string values for deduplication
echo '{"data":[{"key":"order-10001","value":"processed"},{"key":"order-10002","value":"processed"}]}' | celigo lookup-caches put-data <cacheId>

For large datasets, pipe from a script or file that generates the { "data": [...] } JSON. The CLI handles batching automatically.

5. Verify the data

# Get first page of all data (max 1000 keys)
celigo lookup-caches get-data <cacheId>

# Get specific keys
echo '{"keys":["SKU-001","SKU-002"]}' | celigo lookup-caches get-data <cacheId>

# Get keys by prefix
echo '{"startsWith":"SKU-"}' | celigo lookup-caches get-data <cacheId>

# Check cache size
celigo lookup-caches get <cacheId>

6. Reference the cache in a lookup

On the import or export resource, add an entry to lookups[] referencing the cache ID, then use the lookup name in field mappings or Handlebars expressions. See writing-mappings > Add lookups for value translation for the full configuration pattern.

7. Plan the data refresh strategy

Lookup caches are not automatically refreshed. Choose a strategy:

• Manual -- update data via CLI or API as needed. Good for static reference tables.
• Flow-driven -- use a scheduled flow with a postSubmit or preSavePage hook that calls the lookup cache data API to refresh entries. Good for cross-reference caches that need periodic sync.
• Purge and reload -- purge all data and reload from scratch on a schedule. Good when the full dataset is small enough to reload quickly.

CLI Commands

# CRUD
celigo lookup-caches list
celigo lookup-caches get <id>
celigo lookup-caches create < cache.json
celigo lookup-caches update <id> < cache.json
celigo lookup-caches set <id> key=value [key2=value2 ...]
celigo lookup-caches delete <id> [-y]

# Data operations
echo '{"data":[{"key":"k","value":"v"}]}' | celigo lookup-caches put-data <id>
celigo lookup-caches get-data <id>                                      # first page, all keys
echo '{"keys":["k1","k2"]}' | celigo lookup-caches get-data <id>       # specific keys
echo '{"startsWith":"prefix"}' | celigo lookup-caches get-data <id>     # prefix search
echo '{"keys":["k1","k2"]}' | celigo lookup-caches delete-data <id> [-y]
celigo lookup-caches purge-data <id> [-y]

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Gotchas

1. PUT erases omitted fields. Always GET first, modify, then PUT. The set command handles this.
2. Data upsert uses POST, not PUT. POST /v1/lookupcaches/{id}/data upserts entries. This is different from the resource-level PUT that updates cache metadata.
3. Values must be strings. To store complex objects, JSON-stringify the value. The consuming lookup's extract path operates on the parsed JSON.
4. 50 MB per cache, 1 GB per environment. Caches near the 50 MB limit (like the ~49 MB NetSuite Item caches seen in production) risk hitting the ceiling on the next upsert. Monitor size and sizeInMB on the response.
5. get-data returns max 1000 keys per call. For caches with more than 1000 entries, use startsWith prefix queries or specific key lookups to retrieve data.
6. No automatic TTL or expiry. Lookup cache data persists until explicitly deleted or purged. Stale data is a common source of bugs -- plan a refresh strategy.
7. includeDataInTemplatesAndCloning defaults to false. If the cache data is part of the integration's configuration (static reference tables, shipping overrides), set this to true or the data will be lost when cloning or installing from a template.
8. Sandbox and production caches are separate. A sandbox: true cache is only accessible to sandbox flows. Production flows cannot read sandbox caches and vice versa.
9. Deleting a cache is a soft delete. The cache is retained for 30 days before permanent removal. During this window, a cache with the same name cannot be re-created with the same _id.

Common Errors

Error	Cause	Fix
422 on data upsert	Entries exceed 5 MB batch limit or individual entry too large	Reduce batch size; the CLI auto-batches at 1000 entries / 5 MB
404 on data operations	Cache ID does not exist or was deleted	Verify the cache ID with celigo lookup-caches get <id>
Lookup returns null in flow	Key does not exist in the cache	Check that the cache is populated and the key format matches exactly (case-sensitive)
Lookup returns full object instead of field	Missing extract path on the lookup definition	Add extract: "$.fieldName" to the lookup entry on the import/export
403 on cache operations	Account does not have the Lookup Cache license	Contact Celigo to enable the lookup cache feature
Cache size approaching 50 MB	Too much data for a single cache	Purge stale entries, split into multiple caches by category, or archive old data