We’re experiencing recurring failures in our shop-floor batch job that posts mass goods issues for production orders. The job runs nightly via SM37 to process approximately 15,000 material movements, but it consistently fails after 2-3 hours with HANA lock timeout errors.
The error log shows:
SQL Error: Lock wait timeout exceeded
Table: MATDOC
Operation: INSERT
Wait time: 900 seconds
We’ve checked HANA Studio and noticed significant lock contention on inventory tables (MATDOC, MSEG) during the batch window. The batch job scheduling seems to overlap with other material document postings from shop floor transactions. Our current HANA lock wait timeout is set to the default 900 seconds.
Has anyone dealt with similar lock timeout issues during mass goods issue posting? We need to understand the root cause - whether it’s batch job scheduling conflicts or inventory table contention patterns.
Quick diagnostic tip: Run ST04 and check the ‘Database Lock Waits’ section during your batch window. Compare that data with what you see in HANA Studio. Often the SAP application layer shows different lock patterns than what HANA reports.
Check your batch job timing first. SM37 shows the schedule, but you need to correlate that with actual shop floor activity. In my experience, lock timeouts on MATDOC usually indicate overlapping posting processes. Run transaction DBACOCKPIT and look at the ‘Blocked Transactions’ view during your batch window - you’ll see which sessions are holding locks.
We had this exact issue last year. The problem was our batch job ran at 2 AM, but shop floor users in different time zones were still posting goods movements. The solution involved two parts: First, we split the batch job into smaller chunks processing 3,000 records each with 10-minute gaps between runs. Second, we increased the HANA lock wait timeout to 1800 seconds as a temporary measure. But the real fix was analyzing lock wait patterns in HANA Studio to identify which specific material movements were causing the contention. Look at the HANA SQL trace during failure - it will show you the exact records being locked.
Have you considered the COMMIT WORK frequency in your batch program? If it’s committing too infrequently, you’re holding locks longer than necessary. Also check if parallel processing is enabled - sometimes multiple work processes trying to update the same material documents simultaneously causes this.
I’ve seen this pattern before with shop floor batch jobs. The inventory table contention usually stems from three sources: ongoing goods movements from production confirmations, automatic goods receipts from process orders, and your batch job all hitting MATDOC simultaneously. You need to implement table partitioning for MATDOC if you haven’t already - this significantly reduces lock contention. Also, analyze your batch program’s SELECT statements - if they’re locking more rows than needed during processing, that amplifies the problem. Use HANA Studio’s expensive statements trace to identify which SQL operations are taking the longest locks.
Don’t just increase timeout values blindly - that masks the real problem. Here’s the systematic approach we used:
1. HANA Lock Wait Analysis:
Run this query in HANA Studio during batch execution to identify blocking sessions:
SELECT LOCK_WAIT_NAME, WAITING_THREAD_ID,
BLOCKED_TIME, LOCK_OWNER_THREAD_ID
FROM M_BLOCKED_TRANSACTIONS
WHERE OBJECT_NAME = 'MATDOC';
This shows you exactly which processes are blocking your batch job. We discovered that online goods movements from production confirmations were the culprits.
2. Batch Job Scheduling Optimization:
Reschedule your batch job to run during true low-activity windows. Use SM37 variant scheduling to split the job by plant or material type. We created 5 separate jobs running sequentially, each processing one plant’s data. This reduced lock contention by 80%.
3. Inventory Table Contention Resolution:
Implement these changes:
- Enable MATDOC table partitioning by posting date (critical for large volumes)
- Modify your batch program to use SELECT FOR UPDATE with NOWAIT option to fail fast instead of waiting
- Add COMMIT WORK statements every 500 records instead of at the end
- Configure parallel processing with MAX 4 work processes to prevent overwhelming HANA
We also applied SAP Note 2891405 which optimizes material document posting in HANA environments. After these changes, our batch job completion time dropped from 3+ hours to 45 minutes with zero lock timeouts.
The key insight: Lock timeouts aren’t just a timeout parameter problem - they indicate architectural issues with how you’re processing mass data. Fix the processing pattern, not just the timeout value.