Inventory optimization API GET inventory levels returns slow response times for large warehouse datasets

SCM Blue Yonder Luminate
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1

We’re experiencing significant performance issues with our inventory dashboard that queries the inventory-opt API. The GET request to retrieve inventory levels across our 50+ warehouse locations is taking 45-60 seconds to complete, which is causing timeout errors in our analytics dashboard.

Here’s our current API call:


GET /api/v1/inventory/levels?warehouses=all&includeDetails=true
Response time: 58.3s (timeout threshold: 30s)

The response payload is around 15MB with detailed inventory data for approximately 25,000 SKUs. We need this data for real-time dashboard updates, but the current performance makes it unusable. I suspect the issue is related to how we’re handling large datasets and possibly lack of pagination, but I’m not sure how to implement proper query optimization for this use case. Has anyone dealt with similar API response time issues when pulling inventory data at scale?

2

Have you looked at the query execution plan on the backend? Sometimes the issue isn’t just the data volume but how the query is being executed. If there are missing indexes on the warehouse or SKU lookup tables, that could explain the 45-60 second delays. Also check if your API is doing any N+1 query patterns where it’s making separate database calls for each warehouse location instead of a single optimized join. The API might need some database-level optimization beyond just pagination.

3

I’ve seen this exact issue before. Your problem is definitely the lack of pagination combined with requesting all warehouse data in a single call. The API is trying to serialize 15MB of JSON in one shot, which overwhelms both the server and client. You should implement cursor-based pagination with a reasonable page size limit, maybe 500-1000 records per request. Also, consider using query filters to only fetch what you need for the current dashboard view rather than pulling everything upfront.

4

Another angle to consider - are you using HTTP/2 or still on HTTP/1.1? We saw significant improvements by enabling HTTP/2 multiplexing for our API calls. Also, make sure you’re using compression (gzip/brotli) on the response payload. A 15MB uncompressed JSON response might compress down to 2-3MB, which significantly reduces transfer time. These infrastructure-level optimizations can give you quick wins while you work on the pagination and query optimization.

5

We had the same slow response problem with our inventory queries. One thing that helped us was adding specific field selectors to reduce payload size. Instead of includeDetails=true which pulls everything, we specified exactly which fields we needed. Our response time dropped from 40s to under 8s just by limiting the returned fields. The dashboard doesn’t need all the metadata anyway, just the key inventory counts and status flags.

6

Let me address all the key optimization areas for your inventory API performance issue systematically.

API Pagination Implementation: First, implement cursor-based pagination to handle your large dataset properly. Here’s the optimized approach:


GET /api/v1/inventory/levels?cursor=next&limit=1000&warehouses=WH001,WH002
X-Pagination-Cursor: eyJza3UiOiJTS1UtMTIzNDUifQ==
Response time: 3.2s per page

This breaks your 25,000 SKU query into manageable chunks. Use cursor tokens rather than offset pagination to maintain consistent performance as you page through results.

Large Dataset Handling: Implement field filtering to reduce payload size dramatically. Instead of includeDetails=true, use explicit field selection:


GET /api/v1/inventory/levels?fields=sku,quantity,warehouse,status&limit=1000

This typically reduces response size by 60-70%. For your dashboard, you likely only need 8-10 fields out of the 40+ available in the full inventory object.

Dashboard Performance Strategy: Implement a progressive loading pattern:

  1. Initial load: Fetch warehouse-level aggregates (fast, <2s)
  2. Background: Page through detailed SKU data in 1000-record chunks
  3. Cache: Store results client-side for 5-10 minutes
  4. Updates: Use delta queries with lastModified filters to fetch only changed records

This approach loads your dashboard in under 3 seconds with summary data while detailed data populates progressively.

Query Optimization: Work with your DBA to ensure these indexes exist:

  • Composite index on (warehouse_id, sku, last_modified)
  • Index on status for filtering active inventory
  • Consider a materialized view for warehouse-level aggregates that updates every 5 minutes

Also, verify the API isn’t doing N+1 queries. Profile a single request and ensure it’s using JOIN operations rather than iterative lookups.

Additional Performance Wins:

  • Enable response compression (reduces transfer by 75-80%)
  • Implement HTTP/2 for connection multiplexing
  • Use conditional requests (ETag/If-None-Modified) to leverage caching
  • Set up API response caching at CDN/gateway level for aggregate queries

With these optimizations, you should see response times drop from 45-60s to under 5s for paginated requests and under 2s for cached aggregate queries. The key is breaking up that monolithic 15MB request into properly paginated, filtered API calls that match your actual dashboard data requirements.