Editorial photograph of a server room and database monitoring screens during an Oracle LMS review
Oracle / Audit Defense

The Oracle LMS script. What it really reads.

Oracle's data collection script reads installed options, feature usage history, and user counts. Knowing what it reports, before you submit, decides the claim.

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The Oracle data collection script reads your databases in detail. It captures installed options, feature usage history, and user counts. Knowing exactly what it reports, before you submit it, is the difference between a clean result and a six figure surprise.

Key takeaways

  • The LMS measurement script reads option installs and feature usage, not just license counts.
  • Feature usage history can flag a priced option you touched once and never used in earnest.
  • Partitioning, Diagnostics Pack, and Tuning Pack are the most common surprise findings.
  • Named user and processor data feed directly into the compliance calculation.
  • You can and should review the full output before anything is sent to Oracle.
  • A reviewed, annotated submission settles lower than raw output every time.

Oracle License Management Services ships a set of measurement scripts during an audit. For databases this is a SQL collection that queries internal views and writes a result file. The auditor then interprets that file against your entitlements.

The script is accurate at what it does. The risk is interpretation. A flag is not proof of licensable use, but Oracle treats it as a starting claim. Your job is to read the same data first.

What is the Oracle LMS audit script and why does Oracle run it?

The script standardizes measurement. Instead of trusting your inventory, Oracle reads the databases directly.

Its purpose

It produces a consistent, machine generated picture of what is installed and what has been used. Oracle prefers script output to self declaration because it removes ambiguity in their favor.

What form it takes

For Oracle Database it is a SQL script run with elevated privileges that queries data dictionary and feature usage views. The output is a structured file you return to the auditor.

What it does not decide

The script measures. It does not license. Whether a flagged feature requires a paid option depends on your contract and the Oracle Database option documentation, not on the flag alone.

What data does the LMS script actually collect?

The collection is broad. It spans installed components, usage history, and user metrics.

  • Installed options: which Enterprise Edition options and management packs are present in the binary.
  • Feature usage history: whether and when specific features were used, drawn from the feature usage statistics view.
  • Named users: account counts that feed Named User Plus calculations.
  • Configuration: processor, core, and edition details used with the core factor table.

Feature usage history is the trap

The dictionary records that a feature ran, with a first and last used date, but not the business context. A feature triggered by a default maintenance job reads identically to deliberate production use.

User and processor metrics

Counts feed straight into the metric. Processor based licensing uses the Oracle processor core factor table to convert cores to licenses, so configuration accuracy matters as much as option flags.

Common script flags and what they mean

Flag What Oracle claims Common true cause
Partitioning usedPartitioning option owedDefault partitioned dictionary objects
Diagnostics Pack usedPack license owedAWR running by default
Tuning Pack usedPack license owedSQL tuning advisor invoked once
Advanced Compression usedOption owedDefault secure file compression
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Which option and pack flags create the biggest exposure?

A few options account for most surprise findings. Knowing them lets you check the right places first.

Management packs

Diagnostics Pack and Tuning Pack are enabled by default on Enterprise Edition. Unless control parameters are set to none, normal operation records usage that Oracle reads as licensable.

Partitioning

Partitioning shows usage even when only Oracle managed dictionary objects are partitioned. The Oracle partitioning policy document matters here, but the script does not distinguish your intent from a default.

Advanced features

Advanced Compression and Advanced Security can flag from default behaviors. Each requires a separate option and each is a frequent false positive.

Where the common advice on the LMS script is wrong

The common advice is that the script is neutral and objective, so you should run it and return the file unchanged. We disagree. In the outputs we reviewed, a large share of flagged usage was a default behavior, not a deliberate deployment, yet the raw file presents both the same way. Returning it unannotated concedes claims you could defend. The buyer side move is to read every flag, separate default and incidental usage from real use, and submit the output with a written interpretation. The script is a measurement tool. The compliance conclusion is an argument, and you are entitled to make yours.

Editorial photograph of a database administrator analyzing Oracle feature usage output on a monitor
Feature usage history records that a feature ran, never why. The why is the buyer's argument to make, and it is where most disputed options are won.
120+
LMS script outputs we reviewed
38%
Flags that were default or incidental
3
Weeks median pre submission review

Source: Redress Compliance advisory engagement file, 2024 to 2025.

The script does not lie. It just does not know the difference between a feature you chose and one Oracle turned on for you. That difference is the whole negotiation.

How do you review the script output before you submit it?

A disciplined review turns raw output into a defensible submission. It runs in a few clear steps.

  1. Run the script in a controlled copy and keep a private master of the output.
  2. List every flagged option and map it to a contract entitlement.
  3. For each flag, trace the cause. Default job, sample schema, one off test, or real use.
  4. Mark false positives with evidence and a short written rationale.
  5. Reconcile processor and user counts against the core factor table.
  6. Submit the output with your annotations inside the agreed scope.

Keep your own evidence

Screenshots, parameter settings, and job histories support your interpretation. Evidence you gather now is far stronger than an argument made after the claim lands.

When to bring in a specialist

If the output flags multiple priced options or a large user base, an independent reviewer pays for itself. The reading of feature usage is specialized and Oracle does it for a living.

Suggested reading

What should a buyer do next?

  1. Run the LMS script in a non production environment first.
  2. Keep a private copy of the full output before anything is shared.
  3. Map every flagged option to a contract entitlement.
  4. Trace each flag to its true cause and mark false positives with evidence.
  5. Reconcile processor and user counts against the core factor table.
  6. Submit the output with written annotations inside the agreed scope.
  7. Engage independent Oracle advisory before returning the file.

Frequently asked questions

What does the Oracle LMS audit script measure?

It measures installed options, feature usage history, named user counts, and database configuration. It reads internal data dictionary and feature usage views and writes a structured output file. It records what ran, not why it ran.

Does a feature usage flag mean I owe Oracle money?

Not on its own. A flag shows a feature was used, but whether that use requires a paid option depends on your contract and on whether the use was deliberate. Many flags trace to default jobs or one off tests and are defensible.

Which options most often cause surprise findings?

Diagnostics Pack and Tuning Pack lead, because they are enabled by default on Enterprise Edition. Partitioning, Advanced Compression, and Advanced Security follow. Each requires a separate license and each commonly appears as a false positive.

Can I review the script output before sending it to Oracle?

Yes, and you should. Run it internally, keep a private copy, and review every flag before submission. You are entitled to understand and annotate your own data rather than hand over raw output unexamined.

Why do Diagnostics and Tuning Packs flag so often?

Their management features, including the automatic workload repository, run by default unless control parameters are explicitly set to none. Normal operation then records usage that Oracle reads as licensable, even when no one consciously used the pack.

How long should a script review take?

In our experience two to three weeks is typical for a mid size estate. The time is spent tracing flags to their cause and gathering supporting evidence. That review consistently lowers the initial claim by a meaningful margin.

Does the core factor table affect the script result?

Yes. Processor based licensing uses the core factor table to convert physical cores to license counts. If the configuration data is wrong, the calculated requirement is wrong, so verify processor and core details alongside option flags.

Should I disable the packs after an audit?

If you do not license Diagnostics or Tuning Pack, set the control parameters to prevent their use going forward and document the change. This stops future flags. Do it deliberately and record the date, since it affects later measurements.

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Never let Oracle read your databases before you have read them yourself. The script output is the first draft of the claim, and you should write the second.

Fredrik Filipsson
Co Founder and Group CEO, Redress Compliance