



Have any questions? We’re here to help You
The initial sync occurs when you first connect a system and retrieves historical data to establish a baseline. This includes records from a configurable time period (typically 30-90 days) and can take several minutes to hours depending on data volume. Initial syncs are complete snapshots of the requested data. Incremental syncs occur on subsequent runs and retrieve only records created or modified since the last successful sync. Makini tracks sync timestamps and uses them to query for changes efficiently. Incremental syncs are much faster, usually completing in seconds to minutes. This approach minimizes API load on source systems while keeping your data current.
All Makini webhooks include a signature header for verification. The signature is an HMAC hash of the webhook payload using your webhook secret as the key. To verify a webhook, compute the HMAC using your secret and compare it to the signature header using constant-time comparison to avoid timing attacks. Never process webhook data without verification, as this could expose your system to forged requests. Your webhook secret is provided when you configure webhooks and should be stored securely. Webhook verification ensures that only legitimate requests from Makini are processed by your application.
Makini uses cursor-based pagination for retrieving large datasets. API responses include a `next_cursor` field when additional results are available. To retrieve the next page, include the cursor value in your next request: `GET /api/v1/purchase-orders?cursor=CURSOR_VALUE`. Cursor-based pagination is more reliable than offset-based pagination because it handles data changes between requests—if records are added or deleted while you're paginating, you won't miss records or see duplicates. Page size is configurable up to a maximum limit (typically 100-500 records per page depending on entity type). For optimal performance, use the largest page size your application can handle efficiently. The API response also includes total count when available from the source system.
Makini provides several performance monitoring capabilities. API responses include timing information showing request processing time. The dashboard includes performance metrics showing average response times, throughput, and error rates over time. You can set up alerts for performance degradation or error rate increases. Each request generates a unique request ID that enables detailed performance analysis. For workflow-based integrations, execution logs show per-step timing, helping identify bottlenecks. We recommend implementing client-side monitoring to track end-to-end latency including network time. Monitor trends over time rather than individual requests—occasional slow requests are normal, but sustained increases may indicate issues requiring investigation.
