Here’s a comprehensive solution for handling JWT token expiration during long-running warehouse management API integrations:
1. JWT Token Expiration Configuration:
Increase the JWT token lifetime specifically for your integration service account. Navigate to Security Console > JWT Configuration > Service Account Policies.
Create a custom token policy for batch operations:
- Service Account: WMS_INTEGRATION_USER
- Token Type: Service Account JWT
- Expiration Time: 7200 seconds (2 hours)
- Refresh Window: 300 seconds (5 minutes before expiration)
- Max Refresh Count: 10 (allows up to 20 hours of operation with refreshes)
This gives you more time per token while still maintaining reasonable security boundaries.
2. Token Refresh Mechanism Implementation:
Implement proactive token refresh in your integration code. Here’s a Java example:
public class JWTTokenManager {
private volatile String currentToken;
private volatile long tokenExpiresAt;
public synchronized String getValidToken() {
if (System.currentTimeMillis() >= tokenExpiresAt - 300000) {
refreshToken();
}
return currentToken;
}
}
Refresh tokens 5 minutes before expiration to ensure uninterrupted operation. The synchronized method prevents multiple threads from refreshing simultaneously.
3. Long-Running Operation Timeout Handling:
Implement proper timeout handling at multiple levels:
A) Connection Timeout:
HttpClient client = HttpClientBuilder.create()
.setConnectionTimeToLive(7200, TimeUnit.SECONDS)
.setDefaultRequestConfig(RequestConfig.custom()
.setSocketTimeout(300000) // 5 minute socket timeout
.setConnectTimeout(30000) // 30 second connect timeout
.build())
.build();
B) API Gateway Timeout Configuration:
In Oracle Fusion, navigate to Setup and Maintenance > Manage API Gateway Configuration:
- Set “Request Timeout” to 600 seconds for warehouse management endpoints
- Enable “Timeout Extension for Batch Operations” = Yes
- Configure “Batch Operation Identifier Header” to recognize your batch requests
C) Application-Level Timeout:
Implement a timeout monitor that logs progress and can resume failed operations:
private void processWithTimeout(List<Item> items, long timeoutMs) {
long startTime = System.currentTimeMillis();
for (Item item : items) {
if (System.currentTimeMillis() - startTime > timeoutMs - 60000) {
saveCheckpoint(item.getId());
throw new TimeoutException("Approaching timeout, saved checkpoint");
}
processItem(item);
}
}
4. Batch Processing for Large Datasets:
Optimize your inventory sync to handle large datasets efficiently:
A) Implement pagination and batching:
int batchSize = 500; // Process 500 SKUs per API call
int totalRecords = 50000;
for (int offset = 0; offset < totalRecords; offset += batchSize) {
List<InventoryItem> batch = inventoryItems.subList(offset,
Math.min(offset + batchSize, totalRecords));
String token = tokenManager.getValidToken();
updateInventoryBatch(batch, token);
// Brief pause to respect rate limits
Thread.sleep(100);
}
B) Implement parallel processing with token sharing:
ExecutorService executor = Executors.newFixedThreadPool(4);
List<List<InventoryItem>> batches = partitionList(inventoryItems, 500);
batches.forEach(batch -> executor.submit(() -> {
String token = tokenManager.getValidToken(); // Shared token manager
updateInventoryBatch(batch, token);
}));
executor.shutdown();
executor.awaitTermination(3, TimeUnit.HOURS);
C) Add checkpoint and resume capability:
Store progress after each successful batch:
private void saveCheckpoint(String lastProcessedId, int recordCount) {
CheckpointData checkpoint = new CheckpointData();
checkpoint.setLastProcessedId(lastProcessedId);
checkpoint.setRecordCount(recordCount);
checkpoint.setTimestamp(System.currentTimeMillis());
checkpointRepository.save(checkpoint);
}
If the job fails, resume from the last checkpoint instead of starting over.
Complete Implementation Strategy:
- Configure extended JWT token lifetime (2 hours) for service account
- Implement token manager with proactive refresh (5 min before expiration)
- Break 50,000 SKU sync into batches of 500 records
- Use 4 parallel threads to process batches concurrently
- Implement checkpoint/resume to handle failures gracefully
- Add comprehensive logging to track token refresh and batch progress
- Monitor API rate limits and adjust batch size/parallelism accordingly
With this approach, your 50,000 SKU inventory sync will complete in approximately 20-25 minutes (depending on network latency and API response times), well within the 2-hour token lifetime. The proactive token refresh ensures no authentication failures, and the checkpoint system allows recovery from any unexpected failures without reprocessing completed records.
Test the implementation with a small dataset first (1,000 SKUs) to validate the token refresh logic, then gradually scale to full volume.
This draft is based on general Oracle Fusion Cloud knowledge. It has not been verified against your specific version and environment. Practitioners: verify the steps and share your experience below.