JD Edwards UBE Runtime Log Worksheet
Batch performance complaints in JD Edwards are usually vague: the job is slow. A short runtime log turns that into numbers you can compare, so you can tell if a version slowed down, a queue is overloaded, or the data volume simply grew.
| UBE and version | Server / queue | Run date | Records processed | Runtime (min) | Records per second | Result | Observation | |
|---|---|---|---|---|---|---|---|---|
13.9394 | ||||||||
39.0244 | ||||||||
7.3446 | ||||||||
13.5185 | ||||||||
68.8889 |
Take records and runtime from your own Work With Submitted Jobs and report logs. Example rows are illustrative only. Compare the same version across days rather than different reports against each other.
Trend your UBE runtimes automatically
Optional: ask how batch history could be queried in plain language instead of copied into a log.
Optional. The tool and every export work without this. No spam.
How to use the worksheet
Log every notable run of a UBE and version: the server and queue, the date, the records processed and the runtime in minutes. The worksheet calculates records per second for each row and the overall throughput at the bottom. Add a short observation such as month end, lock wait or large batch. Export to Excel to sort by throughput or filter by queue.
- Take runtimes from Work With Submitted Jobs or the job log.
- Use a consistent record count source, such as the report summary.
- Log failures too, with a note on the cause.
Reading the numbers
Throughput is records divided by seconds. It is a rough comparison tool, not a precise benchmark. If the same version drops sharply between two dates with similar volumes, look for locking, a changed index, a database statistics issue or competing jobs on the same queue. If throughput is steady but runtime grew, volume grew. Compare like with like: a posting UBE and a print UBE do very different work per record.
Where the time usually goes
In batch runs the time is commonly spent in database reads and writes, waiting on locks, or in business functions called per record. Logic or data selection changes in a processing option or data selection can change the number of records read without changing the records printed. Note the processing option template and data selection used for every logged run so results stay comparable.
Common mistakes
Teams compare a run that was alone on the queue with one that shared it with a dozen jobs. They log wall-clock time that includes waiting in queue, not the execution time. They forget to record the version. Keep the log short and consistent rather than detailed and abandoned.
Frequently Asked Questions
Where do I find runtime for a UBE?
Work With Submitted Jobs shows submitted, start and end times for each job, and the job log and report footers give more detail. Use the execution start and end, not the time the job spent waiting in the queue.
Is records per second a reliable benchmark?
It is useful for comparing the same version over time, not for comparing different reports. Record size, joins, business functions and I/O differ greatly between UBEs, so use it as a trend signal rather than a score.
How many runs should I log?
Start with the jobs people complain about and your month end critical path. Five or six runs of each give a baseline. Add more only if they help you make a decision.
Can I export the log?
Yes. Export to Excel, CSV or PDF from the tool. Excel is handy for pivoting by queue, server or version once you have a few weeks of data.
If you want runtime trends pulled automatically from your job history and asked in plain language, Netray can show how that works on your own data.
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