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uDig SDK

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Performance Tuning and Optimization for the uDig SDKPerformance matters. When building GIS applications with the uDig SDK, responsiveness, fast rendering, and low memory use directly affect user satisfaction. This guide covers practical techniques, patterns, and configuration tips to improve rendering speed, data access, memory footprint, and overall responsiveness in uDig-based applications. Where helpful, I include concrete code examples and configuration pointers.

Table of contents

  • Introduction
  • Measuring performance (profiling & benchmarks)
  • Data source and I/O optimizations
  • Rendering pipeline optimizations
  • Caching strategies
  • Memory management and object lifecycle
  • Threading and concurrency
  • UI/UX responsiveness techniques
  • Deployment and JVM tuning
  • Common bottlenecks and troubleshooting checklist
  • Example: end-to-end optimization scenario
  • Further reading and references

Introduction

uDig is a flexible, Eclipse-based desktop GIS framework. Its extensibility and plugin architecture make it powerful but also introduce potential performance pitfalls: heavy SWT views, expensive map redraws, large vector datasets, or suboptimal data access layers. Optimizing uDig apps usually involves tuning data handling, rendering, caching, and JVM settings while maintaining thread-safety and UI responsiveness.

Measuring performance (profiling & benchmarks)

Before optimizing, measure. Use objective metrics to find real bottlenecks.

  • Tools:

    • Java profilers: VisualVM, YourKit, JProfiler — measure CPU hotspots, allocation hotspots, and thread contention.
    • Eclipse MAT (Memory Analyzer Tool) — analyze heap dumps for memory leaks.
    • OS tools: top/htop, iostat, vmstat for I/O and CPU usage.
    • In-app timers/logging — measure map redraw time, layer loading time, and queries.

  • Key metrics to capture:

    • Time to initial map load.
    • Time per map redraw.
    • Tile or layer load latency.
    • Frame redraw frequency during panning/zooming.
    • Heap usage growth over time and garbage collection pauses.

Record baseline measurements and re-measure after each change.

Data source and I/O optimizations

Data access is often the largest performance cost. Minimize I/O, reduce parsing, and prefer spatially-aware data access.

  • Use appropriate store types

    • Prefer spatial databases (PostGIS, GeoPackage) for large vector datasets rather than parsing large shapefiles or XML on every load.
    • For tiled raster basemaps, use optimized formats (MBTiles, GeoPackage tiles, tiled WMS/Cached services).

  • Spatial queries and indexing

    • Ensure spatial indexes are present (R-Tree for shapefiles, GiST for PostGIS).
    • Push filters and attribute queries down to the data store via native SQL or the uDig data store API so only required features are transferred.

  • Limit feature transfer

    • Use scale-dependent rendering or query extents to only request features visible in the current map extent.
    • Implement paged or on-demand loading for very large layers.

  • Use binary/compact formats

    • GeoPackage, MBTiles, and other compact binary formats load faster and use less memory than verbose formats like GeoJSON or GML.

  • Reduce network latency

    • For remote services, use caching proxies (CDN for tiled services), enable HTTP/2 where possible, and batch requests when supported.

Example: using a spatial filter to request features within the current extent (conceptual)

// Pseudocode; adapt to your uDig data access API ReferencedEnvelope env = getMapDisplay().getViewportModel().getBounds(); Filter filter = ff.bbox(ff.property(geomAttribute), env); FeatureSource source = dataStore.getFeatureSource(typeName); Query q = new Query(typeName, filter); FeatureCollection features = source.getFeatures(q);

Rendering pipeline optimizations

Rendering is core to perceived performance. Optimize paint paths, reduce overdraw, and only redraw what’s necessary.

  • Reduce redraw frequency

    • Coalesce redraw requests: when many model changes happen quickly, batch them into a single redraw.
    • Debounce UI-driven redraws (e.g., while dragging tools).

  • Use scale-dependent symbolization

    • Simplify styles at low zooms: fewer labels/symbols, simplified strokes/fills, lower-opacity effects disabled.
    • Use Level-of-Detail (LOD) approaches: switch to generalized geometries or pre-simplified datasets at small scales.

  • Simplify geometries

    • Pre-simplify (topology-preserving where required) geometries for lower zoom levels.
    • Use on-the-fly simplification with tolerances based on pixel size.

  • Avoid expensive rendering operations

    • Minimize use of antialiasing, complex strokes, gradients, pattern fills, and on-the-fly text-measurement in heavy layers.
    • Cache frequently used images or symbol caches.

  • Use image tiles for static or slowly changing layers

    • Rasterize complex vector layers into tiled images and serve them as tiled layers at appropriate zooms.

  • Clip rendering to visible area

    • Ensure renderers honor the viewport and clip geometries to the drawing area to eliminate off-screen work.

  • Leverage hardware acceleration where available

    • SWT/OS graphics acceleration or hardware-accelerated image compositing can improve throughput; test across target platforms.

  • Render in background threads where safe

    • Compute heavy rendering preparations (geometry simplification, label placement) off the UI thread and apply results when ready. Ensure drawing itself runs on SWT UI thread per SWT rules.

Caching strategies

Appropriate caching reduces repeated work and I/O.

  • Tile caching

    • Cache raster tiles locally (disk + memory). Use an LRU disk cache to limit storage.
    • For remote WMS/WMTS, set HTTP caching headers or use a local tile cache.

  • Geometry and symbol caches

    • Cache rendered symbol images for identical symbol/scale combinations.
    • Cache tessellated shapes, prepared shapes, or simplified geometries keyed by scale + object id.

  • Query result caches

    • Cache spatial queries keyed by extent and style parameters. Invalidate when underlying data changes.

  • Label placement caches

    • Computing label positions can be expensive. Cache placements when layers or view transforms haven’t changed.

  • Memory vs disk trade-offs

    • Use in-memory caches for high-speed reuse; fall back to disk caches for larger datasets to avoid OOM.

Example cache key concept:

  • key = layerId + styleHash + mapScale + tileRow + tileCol

Memory management and object lifecycle

Memory issues cause GC pauses and slowdowns. Reduce allocations and control object lifetimes.

  • Avoid creating many short-lived objects during rendering

    • Reuse objects like AffineTransform, Paint, Stroke, and temporary geometry buffers where safe.
    • Use object pools for frequently used helper objects.

  • Manage large collections

    • Stream features when possible instead of loading entire collections into memory.
    • Use iterators that fetch on demand.

  • Prevent leaks

    • Unregister listeners when views/layers are removed.
    • Dispose of SWT resources (Images, Fonts, Colors) promptly using dispose() on the UI thread.

  • Monitor and tune JVM heap

    • Increase heap for large datasets, but avoid unnecessarily large heaps which can increase GC pause times if not tuned.
    • Use G1 or Shenandoah collectors depending on JVM version and latency needs.

  • Use weak/soft references wisely

    • SoftReferences for large caches allow GC to free memory under pressure; weak references for listeners where appropriate.

Threading and concurrency

Concurrency must be used carefully with SWT and uDig’s architecture.

  • UI thread rules

    • All SWT UI updates must occur on the SWT/UI thread. Background threads must route UI changes via Display.asyncExec/syncExec.

  • Offload heavy work

    • Move I/O, spatial queries, and heavy geometry processing to background threads to keep the UI responsive.
    • Use Executors or thread pools sized to the hardware (commonly #cores or #cores * 2 for I/O).

  • Avoid contention

    • Minimize synchronization in hot paths. Use concurrent collections (ConcurrentHashMap, ConcurrentLinkedQueue) for shared caches.

  • Task prioritization

    • Prioritize map interactions (pan/zoom) tasks over low-priority background indexing or prefetch tasks.

  • Safe caching

    • Ensure caches accessed by multiple threads are thread-safe or appropriately synchronized.

UI/UX responsiveness techniques

Perceived performance matters almost as much as raw speed.

  • Progressive rendering

    • Render low-fidelity or simplified layers first, then progressively refine detail.
    • Example: render simplified vector shapes or a coarse raster as a placeholder, then replace with detailed render when ready.

  • Visual feedback

    • Show progress indicators for long operations and avoid blocking dialogs.

  • Instant feedback for interactions

    • Snap panning to an immediate response using previously rendered tiles or simplified layers, then refine.

  • Smart event handling

    • Debounce user drag/zoom events; skip intermediate redraws and render at the final viewport after interaction ends.

Deployment and JVM tuning

Application-level and JVM settings impact performance.

  • JVM flags

    • Use modern JVM (Java 11+ or the project target). Example flags to consider (tune for your workload):
      • -Xmx (set an appropriate max heap)
      • -XX:+UseG1GC (or Shenandoah if low-latency)
      • -XX:MaxGCPauseMillis=200 (for G1)
      • -XX:+UseStringDeduplication (on large heaps with many strings)
    • Measure — don’t guess. Different collectors and settings behave differently with your workload.

  • Packaging and native libraries

    • Ship optimized native SWT libraries for target OSes when possible.
    • Use a JRE/JDK matching your users’ environment to avoid compatibility overhead.

  • OS-level considerations

    • Ensure disk I/O isn’t throttled; use SSDs for faster tile/cache performance.
    • For multi-user deployments, ensure each instance has sufficient CPU and memory.

Common bottlenecks and troubleshooting checklist

  • Slow initial load:

    • Check heavy plugin initialization, large default datasets, or synchronous blocking I/O during startup.

  • Slow redraws / low FPS:

    • Look for expensive per-feature painting, label recalculation, complex symbolizers, or missing clipping.

  • High memory use / OOM:

    • Inspect caches without bounds, retained listeners, or loading entire datasets into memory.

  • Long GC pauses:

    • Consider a different garbage collector or reduce object allocation rate.

  • UI freezes:

    • Identify work performed on SWT thread — move to background threads and sync results.

Example: end-to-end optimization scenario

Problem: Map with large vector layer (1M features) is slow to pan/zoom and consumes lots of memory.

Steps:

  1. Measure: profile to confirm rendering and feature iteration are hotspots.
  2. Data: move data into PostGIS, ensure spatial indexing.
  3. Queries: change layer to request only features inside current extent; page results.
  4. LOD: generate simplified geometry tables for low zoom levels (e.g., via ST_SimplifyPreserveTopology) and switch based on scale.
  5. Rendering: disable expensive strokes/antialiasing at low zooms, and cache symbol images.
  6. Caching: implement tile cache for pre-rendered image tiles of the dense layer.
  7. Threading: run feature fetch and simplification in background thread, update UI when ready.
  8. JVM: increase heap moderately and enable G1 GC with MaxGCPauseMillis tuned for interactivity.
  9. Verify: re-run profiler and benchmark; iterate.

Expected result: faster panning (placeholder tiles + background fetch), fewer redraws, and reduced memory pressure.

Further reading and references

  • Java profiling guides (VisualVM, YourKit)
  • PostGIS performance tuning and indexing guides
  • JVM GC tuning documentation for G1/Shenandoah
  • SWT best practices for desktop UI responsiveness
  • Vector tiling and pre-rendered tile strategies (MBTiles, GeoPackage)

If you want, I can:

  • audit a specific uDig project or config (share code snippets or logs), or
  • produce code examples for integrating PostGIS, tile caching, or background rendering with uDig.

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