Migrating Existing Tools to LibOpenOffice: Tips and Common Pitfalls

Performance Tuning LibOpenOffice: Optimization Techniques and Benchmarks

Overview

Focus on minimizing IPC/UNO calls, reducing document model churn, and using batch operations and native code where appropriate. Measure with real-world workloads and microbenchmarks.

Key Techniques

• Minimize UNO round-trips: Cache interfaces (XText, XModel, XComponent) and avoid repeatedly resolving services. Bundle changes into single calls.
• Batch edits: Use dispatch commands or group changes via com.sun.star.text.XTextCursor or com.sun.star.sheet.XCellRange to apply many edits at once.
• Use direct model access: Work on the document’s internal model (XText, XTextRange, XTable) rather than higher-level UI operations to avoid extra layers.
• Move heavy logic to native code: Implement CPU-heavy processing in C/C++ and call via JNI/SO to avoid interpreter overhead in scripting languages.
• Asynchronous processing: Perform background tasks off the UI thread; use com.sun.star.task.XJob or remote workers to keep UI responsive.
• Optimize I/O: Stream-load large documents; disable autosave during bulk operations; use binary formats (ODF zipped) efficiently.
• Memory management: Release UNO references promptly; call com.sun.star.uno.UnoRuntime.queryInterface and dispose components (XComponent.dispose) to free resources.
• Avoid XPath/expensive queries repeatedly: Cache query results or compute indices once.
• Reduce formatting operations: Apply styles in bulk rather than per-character; use cell styles and paragraph styles.
• Profile and instrument: Insert timing around UNO calls and key algorithms to find hotspots.

Implementation Patterns

• Cache service factories and use typed interfaces:

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  comp = desktop.getCurrentComponent() text = UnoRuntime.queryInterface(XText, comp) 

• Use range-based edits:

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  cursor = text.createTextCursor() cursor.gotoStart(False) text.insertString(cursor, “large content”, False) 

• Group UI updates:
  • Disable/enable UI refresh if embedding (platform-specific).

Benchmarks to Run

• Microbenchmark: Measure time per UNO call (simple property get/set) to estimate overhead.
• Bulk-edit benchmark: Time inserting N kilobytes of text in one operation vs N repeated small inserts.
• Formatting benchmark: Apply a style to 10k paragraphs in bulk vs per-paragraph.
• Open/save benchmark: Measure load/save times for documents of 1MB, 10MB, 100MB in ODT and flat XML.
• Memory/stability test: Repeatedly open/modify/save documents to detect leaks.
• Concurrency test: Run background tasks while performing UI operations to check responsiveness.

Suggested metrics: wall-clock time, CPU usage, memory footprint, peak working set, and GC pauses (for JVM/Python hosts).

Example Results (expected patterns)

• Single large insert is often 5–50× faster than many small inserts.
• Caching interfaces can reduce total runtime by ~30–70% depending on call volume.
• Native-processing for heavy computation often yields 2–10× CPU time reduction vs scripted implementations.

Practical Checklist

1. Cache frequently used UNO interfaces.
2. Batch edits via ranges or dispatch commands.
3. Move hotspots to native modules.
4. Disable autosave/UI refresh during bulk ops.
5. Profile with timed segments and iterate.
6. Test with representative large documents and repeated runs.

If you want, I can produce a short benchmark script (Python/uno) and a measurement plan for your specific workload.