GeoJSON Performance Optimization

Created on: February 4, 2025

Answered using O1 by Chat01

Question

// src/TimeSeriesMapViewer.jsx import React, { useMemo, useEffect, useState, useRef } from 'react'; import { MapContainer, TileLayer, ZoomControl, Marker, Popup, useMapEvent, } from 'react-leaflet'; import L from 'leaflet'; import 'leaflet/dist/leaflet.css'; import Supercluster from 'supercluster'; import FitBounds from './FitBounds'; import ClusterPopupContent from './ClusterPopupContent'; import MarkerPopupContent from './MarkerPopupContent'; import ReactDOM from 'react-dom/client';

import { Box, Button, Checkbox, FormControl, FormControlLabel, FormGroup, FormLabel, InputLabel, Select, MenuItem, Chip, Slider, Typography, } from '@mui/material';

import iconUrl from 'leaflet/dist/images/marker-icon.png'; import iconRetinaUrl from 'leaflet/dist/images/marker-icon-2x.png'; import shadowUrl from 'leaflet/dist/images/marker-shadow.png';

delete L.Icon.Default.prototype._getIconUrl; L.Icon.Default.mergeOptions({ iconRetinaUrl, iconUrl, shadowUrl, });

const createClusterIcon = (count) => { let size = 'small'; if (count >= 100) { size = 'large'; } else if (count >= 50) { size = 'medium'; } return L.divIcon({ html: <div class="cluster-marker ${size}"><span>${count}</span></div>, className: 'cluster-icon', iconSize: [40, 40], }); };

function flattenProps(obj, prefix = '') { const result = {}; for (const [k, v] of Object.entries(obj)) { const newKey = prefix ? prefix + '.' + k : k; if (Array.isArray(v)) { result[newKey] = v; } else if (v && typeof v === 'object') { Object.assign(result, flattenProps(v, newKey)); } else { result[newKey] = v; } } return result; }

function TimeSeriesMapViewer({ geoJSONData, selectedTags = [] }) { console.log('TimeSeriesMapViewer props:', { geoJSONData, selectedTags });

const mapRef = useRef(); const layerRegistry = useRef(new Map());

const features = useMemo(() => { if (geoJSONData?.data?.features) { return geoJSONData.data.features; } return []; }, [geoJSONData]);

const prevFeaturesCountRef = useRef(features.length);

const flattenedFeatures = useMemo(() => { return features.map((f) => ({ ...f, _flatProps: flattenProps(f.properties || {}), })); }, [features]);

const sortedTimePoints = useMemo(() => { const timeSet = new Set(); features.forEach((f) => { const ft = f.properties?.time; if (ft) { const val = typeof ft === 'string' ? new Date(ft).getTime() : ft; timeSet.add(val); } }); return Array.from(timeSet).sort((a, b) => a - b); }, [features]);

const [currentTime, setCurrentTime] = useState( sortedTimePoints.length > 0 ? sortedTimePoints[sortedTimePoints.length - 1] : 0 ); const [isPlaying, setIsPlaying] = useState(false); const intervalRef = useRef(null);

useEffect(() => { if (sortedTimePoints.length > 0) { const last = sortedTimePoints[sortedTimePoints.length - 1]; setCurrentTime(last); } }, [sortedTimePoints]);

useEffect(() => { if (isPlaying) { const id = setInterval(() => { const nextT = sortedTimePoints.find((t) => t > currentTime); if (nextT) { setCurrentTime(nextT); } else { setIsPlaying(false); } }, 1000); intervalRef.current = id; return () => clearInterval(id); } else { if (intervalRef.current) { clearInterval(intervalRef.current); intervalRef.current = null; } } }, [isPlaying, currentTime, sortedTimePoints]);

const handlePlayPause = () => { if (isPlaying) { setIsPlaying(false); } else { if (sortedTimePoints.length > 0 && currentTime === sortedTimePoints[sortedTimePoints.length - 1]) { setCurrentTime(sortedTimePoints[0]); } setIsPlaying(true); } };

const [geometryFilter, setGeometryFilter] = useState([]); const allGeomTypes = useMemo(() => { const st = new Set(); features.forEach((f) => { if (f.geometry?.type) { st.add(f.geometry.type); } }); return Array.from(st); }, [features]);

useEffect(() => { setGeometryFilter(allGeomTypes); }, [allGeomTypes]);

const handleToggleGeomType = (type) => { setGeometryFilter((prev) => { if (prev.includes(type)) { return prev.filter((t) => t !== type); } return [...prev, type]; }); };

const [propertyFilters, setPropertyFilters] = useState({}); const [activeKey, setActiveKey] = useState(''); const [activeValues, setActiveValues] = useState([]);

useEffect(() => { if (!activeKey) { setActiveValues([]); } else { setActiveValues(propertyFilters[activeKey] || []); } }, [activeKey, propertyFilters]);

const allPropertyValuesMap = useMemo(() => { const map = {}; flattenedFeatures.forEach((f) => { const flt = f._flatProps; for (const [k, v] of Object.entries(flt)) { if (!map[k]) map[k] = new Set(); if (Array.isArray(v)) { v.forEach((it) => map[k].add(it)); } else { map[k].add(v); } } }); const obj = {}; for (const k in map) { obj[k] = Array.from(map[k]); } return obj; }, [flattenedFeatures]);

const handleToggleValue = (val) => { if (!activeKey) return; setActiveValues((prev) => { let newArr; if (prev.includes(val)) { newArr = prev.filter((x) => x !== val); } else { newArr = [...prev, val]; } setPropertyFilters((old) => { const copy = { ...old }; if (newArr.length === 0) { delete copy[activeKey]; } else { copy[activeKey] = newArr; } return copy; }); return newArr; }); };

const handleSelectAllValues = (checked) => { if (!activeKey) return; const allVals = allPropertyValuesMap[activeKey] || []; const newArr = checked ? allVals : []; setActiveValues(newArr); setPropertyFilters((old) => { const copy = { ...old }; if (newArr.length === 0) { delete copy[activeKey]; } else { copy[activeKey] = newArr; } return copy; }); };

const handleSelectKey = (e) => { setActiveKey(e.target.value); };

useEffect(() => { if (features.length !== prevFeaturesCountRef.current) { setPropertyFilters({}); setActiveKey(''); setActiveValues([]); } prevFeaturesCountRef.current = features.length; }, [features]);

const finalFilteredFeatures = useMemo(() => { return flattenedFeatures.filter((f) => { if (selectedTags.length > 0) { const tags = f.properties?.tags; if (tags) { const arr = Array.isArray(tags) ? tags : [tags]; if (!arr.some((tg) => selectedTags.includes(tg))) { return false; } } } if (geometryFilter.length === 0) { return false; } if (!geometryFilter.includes(f.geometry?.type)) { return false; } for (const [k, vals] of Object.entries(propertyFilters)) { if (!vals || vals.length === 0) continue; const propVal = f._flatProps[k]; if (Array.isArray(propVal)) { if (!propVal.some((item) => vals.includes(item))) { return false; } } else { if (!vals.includes(propVal)) { return false; } } } const ft = f.properties?.time; if (ft) { const val = typeof ft === 'string' ? new Date(ft).getTime() : ft; if (val > currentTime) { return false; } } return true; }); }, [flattenedFeatures, selectedTags, geometryFilter, propertyFilters, currentTime]);

const pointFeatures = useMemo(() => { return finalFilteredFeatures.filter( (f) => f.geometry?.type === 'Point' || f.geometry?.type === 'MultiPoint' ); }, [finalFilteredFeatures]);

const otherFeatures = useMemo(() => { return finalFilteredFeatures.filter( (f) => !(f.geometry?.type === 'Point' || f.geometry?.type === 'MultiPoint') ); }, [finalFilteredFeatures]);

const [clusters, setClusters] = useState([]); const [bounds, setBounds] = useState(null); const [zoom, setZoom] = useState(5);

const superclusterInstance = useMemo(() => { return new Supercluster({ radius: 75, maxZoom: 20 }).load( pointFeatures.map((f) => ({ type: 'Feature', properties: { ...f.properties }, geometry: { type: 'Point', coordinates: f.geometry.coordinates }, })) ); }, [pointFeatures]);

const MapEvents = () => { useMapEvent('moveend', (e) => { const m = e.target; const b = m.getBounds(); setBounds([b.getWest(), b.getSouth(), b.getEast(), b.getNorth()]); setZoom(m.getZoom()); }); return null; };

useEffect(() => { if (bounds) { const c = superclusterInstance.getClusters(bounds, zoom); setClusters(c); } }, [superclusterInstance, bounds, zoom]);

useEffect(() => { if (!mapRef.current) return; layerRegistry.current.forEach((cleanup, layer) => { cleanup(); layer.remove(); }); layerRegistry.current.clear(); if (!otherFeatures.length) return; const layerGroup = L.geoJSON(otherFeatures, { style: { color: '#3388ff', weight: 2 }, onEachFeature: (feature, layer) => { const container = L.DomUtil.create('div'); const root = ReactDOM.createRoot(container); root.render( <MarkerPopupContent key={${feature.id || Date.now()}-${Math.random()}} properties={feature.properties || {}} /> ); const popup = L.popup({ autoClose: true, closeOnClick: true, className: 'isolated-popup', }).setContent(container); layer.bindPopup(popup); const cleanup = () => { root.unmount(); container.remove(); layer.unbindPopup(); }; layerRegistry.current.set(layer, cleanup); }, }).addTo(mapRef.current); return () => { if (mapRef.current && mapRef.current.hasLayer(layerGroup)) { mapRef.current.removeLayer(layerGroup); } }; }, [otherFeatures]);

const renderMarkers = () => { return clusters.map((c) => { const [lng, lat] = c.geometry.coordinates; const { cluster: isCluster, point_count } = c.properties; if (isCluster) { let leaves = []; try { leaves = superclusterInstance.getLeaves(c.properties.cluster_id, 1000, 0); } catch (err) { console.error('Error calling getLeaves:', err); } return ( <Marker key={cluster-${c.properties.cluster_id}} position={[lat, lng]} icon={createClusterIcon(point_count)} > <Popup> <ClusterPopupContent leaves={leaves} count={point_count} maxMarkers={10} maxHeight={300} /> </Popup> </Marker> ); } else { return ( <Marker key={marker-${lng}-${lat}-${c.properties.name || 'unknown'}} position={[lat, lng]} > <Popup> <MarkerPopupContent properties={c.properties} coords={[lat, lng]} hideCoordinate={false} /> </Popup> </Marker> ); } }); };

const filteredGeoJSON = useMemo( () => ({ type: 'FeatureCollection', features: finalFilteredFeatures, }), [finalFilteredFeatures] );

const noData = features.length === 0;

useEffect(() => { return () => { layerRegistry.current.forEach((cleanup) => cleanup()); layerRegistry.current.clear(); }; }, []);

const applyGeometryAndPropertyFilters = (baseArr, geomFilter, propFilter) => { if (!geomFilter || geomFilter.length === 0) return []; return baseArr.filter((f) => { if (selectedTags.length > 0) { const tags = f.properties?.tags; if (tags) { const arr = Array.isArray(tags) ? tags : [tags]; if (!arr.some((tg) => selectedTags.includes(tg))) { return false; } } } if (!geomFilter.includes(f.geometry?.type)) { return false; } for (const [k, vals] of Object.entries(propFilter)) { if (!vals || vals.length === 0) continue; const propVal = f._flatProps[k]; if (Array.isArray(propVal)) { if (!propVal.some((x) => vals.includes(x))) { return false; } } else { if (!vals.includes(propVal)) { return false; } } } return true; }); };

const isGeomTypeDisabled = (type) => { const newGeomFilter = geometryFilter.includes(type) ? geometryFilter : [...geometryFilter, type]; const testArr = applyGeometryAndPropertyFilters(flattenedFeatures, newGeomFilter, propertyFilters); return testArr.length === 0; };

const isPropValueDisabled = (key, val) => { const newPF = { ...propertyFilters }; const oldVals = newPF[key] || []; if (!oldVals.includes(val)) { newPF[key] = [...oldVals, val]; } const testArr = applyGeometryAndPropertyFilters(flattenedFeatures, geometryFilter, newPF); return testArr.length === 0; };

const renderFilterChips = () => { return Object.entries(propertyFilters).map(([k, vals]) => { const joined = vals.join(','); const display = joined.length > 20 ? joined.slice(0, 20) + '...' : joined; return ( <Chip key={k} label={${k}: ${display}} variant="outlined" size="small" onDelete={() => { setPropertyFilters((old) => { const copy = { ...old }; delete copy[k]; return copy; }); if (activeKey === k) { setActiveKey(''); setActiveValues([]); } }} sx={{ mr: 1, mb: 1 }} /> ); }); };

return ( <div style={{ width: '100%', height: '100%', position: 'relative' }}> {noData ? ( <div style={{ width: '100%', height: '100%', background: '#fafafa', display: 'flex', alignItems: 'center', justifyContent: 'center', }} > <Typography style={{ textAlign: 'center', fontFamily: 'Arial' }}> No valid GeoJSON data </Typography> </div> ) : ( <MapContainer center={[39.9, 116.4]} zoom={5} style={{ width: '100%', height: '100%' }} zoomControl={false} ref={(m) => { mapRef.current = m; }} > <ZoomControl position="bottomright" /> <TileLayer attribution="&copy; OpenStreetMap contributors" crossOrigin="anonymous" url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png" /> <MapEvents /> {clusters.length > 0 && renderMarkers()} <FitBounds geoJSONData={filteredGeoJSON} maxZoom={15} /> </MapContainer> )}

  {!noData && (
    <Box
      sx={{
        position: 'absolute',
        top: 10,
        left: 10,
        width: 230,
        maxHeight: 480,
        overflow: 'auto',
        backgroundColor: 'rgba(255,255,255,0.9)',
        p: 2,
        borderRadius: 2,
        zIndex: 999,
      }}
    >
      <Box sx={{ mb: 2, display: 'flex', flexWrap: 'wrap' }}>
        {renderFilterChips()}
      </Box>

      <FormControl component="fieldset" variant="standard" sx={{ mb: 2 }}>
        <FormLabel component="legend" sx={{ fontSize: '0.9rem' }}>
          Geometry Types
        </FormLabel>
        <FormGroup>
          {allGeomTypes.map((type) => {
            const disabled = isGeomTypeDisabled(type);
            return (
              <FormControlLabel
                key={type}
                control={
                  <Checkbox
                    checked={geometryFilter.includes(type)}
                    onChange={() => handleToggleGeomType(type)}
                    disabled={disabled}
                  />
                }
                label={type}
              />
            );
          })}
        </FormGroup>
      </FormControl>

      <FormControl fullWidth sx={{ mb: 2 }}>
        <InputLabel id="select-property-key-label">Property Key</InputLabel>
        <Select
          labelId="select-property-key-label"
          label="Property Key"
          value={activeKey}
          onChange={handleSelectKey}
          MenuProps={{
            PaperProps: { style: { zIndex: 10000 } },
            disablePortal: false,
          }}
        >
          <MenuItem value="">(none)</MenuItem>
          {Object.keys(allPropertyValuesMap).map((k) => (
            <MenuItem key={k} value={k}>
              {k}
            </MenuItem>
          ))}
        </Select>
      </FormControl>

      {activeKey && allPropertyValuesMap[activeKey] && (() => {
        const allVals = allPropertyValuesMap[activeKey];
        const allDisabled = allVals.every((val) => isPropValueDisabled(activeKey, val));
        return (
          <FormControl component="fieldset" variant="standard">
            <FormLabel component="legend" sx={{ fontSize: '0.9rem' }}>
              Values for [{activeKey}]
            </FormLabel>
            <FormGroup>
              <FormControlLabel
                control={
                  <Checkbox
                    checked={
                      activeValues.length > 0 &&
                      activeValues.length === allVals.length
                    }
                    indeterminate={
                      activeValues.length > 0 &&
                      activeValues.length < allVals.length
                    }
                    onChange={(e) => handleSelectAllValues(e.target.checked)}
                    disabled={allDisabled}
                  />
                }
                label="(Select All)"
              />
              {allVals.map((val) => {
                const disabled = isPropValueDisabled(activeKey, val);
                return (
                  <FormControlLabel
                    key={String(val)}
                    control={
                      <Checkbox
                        checked={activeValues.includes(val)}
                        onChange={() => handleToggleValue(val)}
                        disabled={disabled}
                      />
                    }
                    label={String(val)}
                  />
                );
              })}
            </FormGroup>
          </FormControl>
        );
      })()}
    </Box>
  )}

  {sortedTimePoints.length > 0 && (
    <Box
      sx={{
        position: 'absolute',
        top: 10,
        right: 10,
        width: 220,
        backgroundColor: 'rgba(255,255,255,0.9)',
        p: 2,
        borderRadius: 2,
        zIndex: 999,
      }}
    >
      <Typography variant="h6" gutterBottom>
        Time-Series
      </Typography>
      <Button variant="contained" onClick={handlePlayPause} sx={{ mb: 1 }}>
        {isPlaying ? 'Pause' : 'Play'}
      </Button>
      <Typography variant="body2" sx={{ mb: 1 }}>
        Current: {new Date(currentTime).toLocaleString()}
      </Typography>
      <Slider
        value={currentTime}
        onChange={(e, val) => {
          setCurrentTime(val);
          setIsPlaying(false);
        }}
        min={sortedTimePoints[0]}
        max={sortedTimePoints[sortedTimePoints.length - 1]}
        step={1000}
        valueLabelDisplay="auto"
        marks={sortedTimePoints.map((t) => ({
          value: t,
          label: '',
        }))}
        sx={{
          '& .MuiSlider-mark': {
            backgroundColor: 'white',
            height: 6,
            width: 6,
            borderRadius: '50%',
          },
          '& .MuiSlider-markLabel': {
            display: 'none',
          },
        }}
      />
    </Box>
  )}
</div>

); }

export default TimeSeriesMapViewer; 怎么提升一下大文件render的performance

Thought

Enhancing performance

I’m working through improving React Leaflet performance by reducing re-renders and using react memo. I'm also considering smarter ways to handle large datasets and refine supercluster creation.

Optimizing rendering

I'm exploring various strategies, like defining a data bounding box and using web workers, to tackle large file and polygon rendering in a React Leaflet map.

Assessing performance

I'm gathering info on optimizing rendering by simplifying polygons, using tile-based methods, minimizing re-renders, and exploring sorting features by time for more efficient handling.

Reasoned about optimizing map rendering performance for 17 seconds

Answer

Improving performance for large GeoJSON files usually involves reducing unnecessary renders and costly computations. Below are several strategies you can combine:


1. Memoization & Avoiding Unnecessary Re-renders

Use React.memo or useMemo more aggressively

  • Why: You already use useMemo for computing derived data (e.g. flattenedFeatures, sortedTimePoints, superclusterInstance, etc.), which is good. However, if your component (or child components) re-renders frequently, you should ensure that your memoization dependencies are as narrow as possible.
  • What to do:
    • Wrap sub-components (e.g. your popup content or filter panels) with React.memo.
    • In parent components, carefully structure the props so that any prop that doesn’t actually change reference does not force a remount.

Use stable callback references

  • Why: If you pass inline callbacks to child components, each render creates a new function reference, potentially causing re-renders down the tree.
  • What to do:
    • Use useCallback for callbacks that do not need to change often.

2. Defer or Throttle Expensive Operations (Filtering + Clustering)

Throttle filtering or clustering triggers

  • Why: If filtering or clustering recomputes on every small change (like each slider movement), it can cause performance issues.
  • What to do:
    • Instead of computing immediately on every map move or filter change, wait until the user “stops” interacting. For example, you can use a small debounce or throttle around your filter/cluster logic.

Pre-split / Pre-index data if possible

  • Why: If you have frequent filter changes (property-based filters, time-based filters), repeatedly scanning a large feature array is expensive.
  • What to do:
    • Pre-group or index your data by properties. For instance, if you know you have a few keys that get filtered often, keep a dictionary mapping property values to IDs or subsets of features. This way, you don’t do a full pass over all features every time.
    • Do time-based grouping (e.g. keep your features sorted by time and do a binary search cut-off rather than filter them one by one).

3. Offload Heavy Computation to Web Workers

  • Why: If your dataset is really large (tens or hundreds of thousands of features), even a single pass filter or a Supercluster step can block the main thread and cause UI jank.
  • What to do:
    • Move your filtering / clustering logic to a web worker.
    • supercluster can be run in a web worker, and you can pass in bounding box and zoom to get clusters back, without freezing the main UI.
    • On the main thread, you only handle the final results (clusters) which you then pass to React-Leaflet for rendering.

4. Optimize Rendering of GeoJSON Polygons / Lines

Points are relatively cheap to cluster, but polygons and lines can be very large. If you have non-point geometries:

  1. Vector Tile Approaches

    • Instead of storing a single massive GeoJSON, consider serving your polygons/lines as vector tiles. Tools like geojson-vt can create vector tiles on the fly, or you can preprocess them.
    • Then use a tile layer that draws these vector tiles. This reduces the overhead of loading and rendering the full dataset at once.
  2. Simplification

    • If your polygons are highly detailed, apply a geometry simplification library (e.g. Turf.js simplify) before rendering.
    • This can drastically reduce the coordinate count while preserving the general shape.
  3. Canvas Rendering

    • The Leaflet default approach for polygons is SVG, which can be slower with large data.
    • Switch to a canvas-based renderer (e.g. react-leaflet-canvas-layer or the standard Leaflet L.canvas()) to handle large geometry layers more efficiently.

5. Lazy / Progressive Loading of Data

  • Why: If your data is worldwide (or just extremely large), rendering everything at once isn’t always necessary. Most times, users only look at a smaller region of the map.
  • What to do:
    • Subset your data based on the current map bounds and fetch/cluster only those features in the visible (plus a buffer) area.
    • Tools: The combination of bounding box queries (e.g., a server or local bounding box filter) plus on-the-fly clustering can be more efficient than working with the entire dataset.

6. Further Code-level Optimizations

  1. Minimize how often you call setState. Each call can trigger re-renders:
    • For example, you call setClusters every moveend. Possibly wrap that logic in a useCallback with a small throttle.
  2. Check if you really need a new Supercluster instance.
    • Currently, you do useMemo(() => new Supercluster(...). If pointFeatures changes a lot due to time filtering, that’s unavoidable. But if only part of the dataset changes, you might re-initialize unnecessarily.
    • If time-based filtering is your main concern, you might keep a single supercluster of all points, but pass a subset to Leaflet (not perfect, but can reduce overhead if you aren’t removing/adding features each time).
  3. Reduce the size of your feature properties.
    • If you can prune or remove large or nested property objects that you don’t need, it can help both memory usage and time needed for flattening / filtering.

7. Summarizing a Potential Approach

  1. Web Worker: Move Supercluster creation and property/time filtering off the main thread if your data is truly large.
  2. Debounce/Throttle: When the user moves the map or updates the filters/time slider, send the new bounding box, zoom, and filter selection to the worker. Wait ~200ms (or until user stops dragging) to reduce spamming.
  3. Canvas Layer for Polygons: If you have large polygon layers, use a canvas-based approach to avoid heavy DOM overhead of many SVG paths.
  4. Pre-sorting/Indexing:
    • Keep your features sorted by time so you can quickly pick the subset up to currentTime with a binary search.
    • For property-based filtering, store property => featureID maps so that toggling a filter doesn’t require scanning everything.

These strategies, in combination, typically yield substantial performance improvements for large maps.


In short, focus on reducing repeated computations (via memoization, indexing, or throttling), avoid rendering everything at once (lazy load or tile-based approach), and keep heavy-lifting (clustering, big geometry) off the main thread or in specialized data structures.

Question

说中文

Thought

Reasoned about提升大文件渲染性能 for a second

Answer

如果你的 GeoJSON 数据量很大,需要提高地图渲染性能,通常可以从以下几个方面入手:


1. 避免不必要的重新渲染

1.1 更广泛地使用 useMemo / React.memo

  • 原因:每次渲染都可能会重复执行一些昂贵的运算(例如数据过滤、数据转化等)。
  • 做法
    • 尽量把昂贵或复杂的处理逻辑放到 useMemo 中,并正确设置依赖项,确保只有在必要时才会重新计算。
    • 对一些相对独立的子组件(如 Popup 内容或 Filter 面板)使用 React.memo 包裹,减少父组件更新时子组件的无谓更新。

1.2 让回调函数保持稳定引用(useCallback

  • 原因:如果在子组件里依赖父组件传进来的回调,当父组件每次渲染都创建一个新函数引用,会导致子组件不必要的更新。
  • 做法
    • useCallback 包裹事件处理函数,确保在依赖不变时,回调不会变成新引用。

2. 推迟或限制昂贵操作(过滤、聚合/聚类等)

2.1 对过滤和聚类进行“节流”或“防抖”

  • 原因:如果每次地图移动/缩放、或者每次筛选器更改都立即触发过滤和聚类,会造成频繁且重复的开销。
  • 做法
    • 在地图的 moveend 事件中,可以先用一个短的延迟(比如 200ms)再触发 setBounds 和聚类计算。如果用户还在不断拖动,就合并成一次最终更新。
    • 在属性过滤或时间切换的地方也同样可以做防抖(debounce)或节流(throttle),等用户停下操作后再一次性地计算。

2.2 预先分组 / 建立索引

  • 原因:每次做属性过滤时,都要从头到尾扫一遍大量 Feature 效率低。
  • 做法
    • 针对一些常用的过滤字段,可以先建立 {属性值 => 对应要素列表} 的映射关系,这样当你只过滤某个属性值时,能快速获取子集,而不是全量再扫一遍。
    • 对时间序列的要素也可以事先排序,然后通过二分查找的方式快速拿到当前时间范围内的要素,而不是用 filter 逐个判断。

3. 使用 Web Worker 分担主线程负载

  • 原因:如果数据非常大(几万、几十万甚至更多要素),在主线程里完成所有过滤/聚合,会导致页面卡顿。
  • 做法
    • 把如 Supercluster 这样的聚类操作放到 Web Worker 中,主线程只负责把 bounds/zoom/filters 之类的信息发给 Worker,Worker 计算完成后再把结果(clusters)回传给主线程。
    • 这样主线程依旧能保持流畅的界面交互,避免长时间阻塞。

4. 优化多边形 / 线要素渲染

如果数据不仅包含点,还有大量的多边形或线段,SVG 渲染在 Leaflet 中可能比较吃力。

4.1 考虑使用矢量瓦片(Vector Tiles)

  • 原因:一整份大体量的 GeoJSON(尤其是世界范围)渲染非常耗性能,而矢量瓦片可以分块加载、分层渲染。
  • 做法
    • 可以用 geojson-vt 之类的工具把 GeoJSON 转成矢量瓦片格式,前端再用 Leaflet 的相应瓦片图层来渲染。
    • 这样就不需要一次性在前端加载和渲染所有要素。

4.2 对几何形状进行简化(Simplification)

  • 原因:如果多边形或线段的坐标点很多,可以先做几何简化,减少顶点数量,从而降低渲染负担。
  • 做法
    • Turf.js 的 simplify 或其它几何简化库,对数据先做适度简化后再加载到地图。

4.3 考虑使用 Canvas 渲染

  • 原因:Leaflet 默认是用 SVG,数据量较大时,DOM 节点或 Path 对象会很多,影响性能。
  • 做法
    • 可以使用 Leaflet 的 Canvas 模式(如 L.canvas()) 或者第三方的 react-leaflet-canvas-layer 来渲染几何。
    • Canvas 不会像 SVG 那样创建大量的 DOM 对象,性能会更好。

5. 分批 / 惰性加载数据

  • 原因:如果你的数据覆盖范围特别大(例如全球),用户一次也只能看一个区域,没必要一次加载全部。
  • 做法
    • 可以在后端根据地图的当前可视范围做查询,前端只加载当前范围或加个边界缓冲区内的要素。
    • 结合动态聚类,这样就不会在前端持有和处理所有数据。

6. 一些其他代码层面的优化

  1. 减少多次 setState

    • 每次 setState 都会导致潜在的重渲染。可以合并多次更新,或者在一个大的 reducer 里一次性更新。
  2. 尽可能不要重复创建 Supercluster 实例

    • 你在 useMemo 中实例化 new Supercluster(...) 时,要确保依赖数组不要轻易变动,否则依赖一变就会重新构建聚类索引。
    • 如果只是增量或小范围数据变动,考虑是否能复用同一个聚类对象,或者在后台做部分更新。
  3. 缩减属性数据

    • 如果你的要素 properties 里面有很多不需要的字段或者大段文本,可以在数据源就先做精简,节省内存并加快处理/传输。

7. 综合建议

  • Web Worker + 防抖/节流:大数据量时最好把聚类或过滤放进 Worker,中途用户持续拖动或滑动时间轴,就用防抖把请求合并,避免频繁计算。
  • 分层渲染 / Canvas:针对非点要素(线、面),改用 Canvas 或矢量瓦片减少负载。
  • 预处理 / 索引:针对时间和常用属性过滤提前做索引或分组,减少实时扫描。
  • 适度懒加载:只获取、渲染用户关注的地图区域数据。

通过以上手段,可以大幅减轻前端一次性处理、渲染超大文件的压力,让地图在用户交互时更加流畅。

Question

使用矢量瓦片试一下

Thought

Creating vector tiles

I’m setting up instructions for using vector tiles in Leaflet. This includes leveraging geojson-vt for server-side tile creation and a snippet with react-leaflet for client-side use.

Reasoned about vector tile approach for 13 seconds

Answer

在前端处理大体量的多边形/线要素时,直接加载整个大文件的 GeoJSON 并用 L.geoJSON 或 SVG 渲染往往会比较吃力。**矢量瓦片(Vector Tiles)**可以将数据分块切片,在用户只浏览一小块地图时就只请求对应瓦片,极大提升性能。下面给出一个常见的使用思路和示例,帮助你在 React + Leaflet 项目中接入矢量瓦片。


整体流程

  1. GeoJSON → 矢量瓦片

    • 可以在服务器端或离线脚本中,利用 geojson-vt(或其他工具,如 tippecanoe)把大体量的 GeoJSON 切割成一系列 .pbf 瓦片文件。
    • 然后在服务端配置好瓦片访问路径,比如 https://yourserver/tiles/{z}/{x}/{y}.pbf
  2. 前端(React + Leaflet)加载矢量瓦片

  3. 根据需要叠加底图

    • 你可以同时叠加传统栅格底图(例如 OSM)来进行对照,然后加载矢量瓦片图层以显示你的数据。
  4. 选择合适的样式和交互

    • 矢量瓦片可以定义渲染样式(线颜色、填充颜色、线宽等)。
    • 也可以在鼠标移过/点击时动态地获取要素信息,显示 Popup 等。

示例:使用 Leaflet.VectorGrid + React-Leaflet

下面给出一个最简单的示例,假设:

  • 你已经通过脚本在后端生成了 https://myserver.com/tiles/{z}/{x}/{y}.pbf 的矢量瓦片服务。
  • 你想在 React-Leaflet 中加载这些瓦片,并给它们加一个简单的样式。

1. 安装依赖

npm install leaflet.vectorgrid react-leaflet-vectorgrid

注意:leaflet.vectorgrid 实际上会在 react-leaflet-vectorgrid 中作为依赖,你只需要确保它能被正确引入即可。

2. 创建一个 VectorTileLayer 组件

// VectorTileLayer.jsx import React from 'react'; import { VectorGrid } from 'react-leaflet-vectorgrid'; function VectorTileLayer() { // 这里定义你想要的渲染样式 const vectorTileStyling = { // 这里的"layerName"要对应你在生成瓦片时的图层名称 layerName: { fill: true, weight: 2, fillColor: '#3887be', color: '#3887be', fillOpacity: 0.5, opacity: 1, }, }; return ( <VectorGrid.Protobuf url="https://myserver.com/tiles/{z}/{x}/{y}.pbf" vectorTileLayerStyles={vectorTileStyling} maxNativeZoom={14} maxZoom={20} subdomains={['a','b','c']} // 你可以根据需求开启/关闭 interactive interactive={true} // 监听点击事件 onClick={(e) => { console.log('Clicked vector tile feature:', e.layer.properties); }} /> ); } export default VectorTileLayer;
  • url:指向你服务器上矢量瓦片的路径,使用 {z}/{x}/{y} 这种占位符。
  • vectorTileLayerStyles:定义不同图层名对应的样式,也可以写成一个函数根据属性值动态决定样式。
  • maxNativeZoom:表示你实际切片的最大层级,比如你只切到了14级,就写14。maxZoom 可以允许继续放大但只是地图放大,并不会请求更细的瓦片。
  • interactive:如果需要点击高亮、Tooltip 等交互,可以开启。

3. 在主 Map 组件中使用

假设你在 TimeSeriesMapViewer.jsx 或你自己的地图组件中,把这个矢量瓦片图层加进去即可:

import React from 'react'; import { MapContainer, TileLayer } from 'react-leaflet'; import VectorTileLayer from './VectorTileLayer'; function MyMap() { return ( <MapContainer center={[39.9, 116.4]} zoom={5} style={{ width: '100%', height: '100%' }} > {/* 通常先放一个普通的栅格底图 */} <TileLayer attribution="&copy; OpenStreetMap contributors" url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png" /> {/* 然后叠加矢量瓦片图层 */} <VectorTileLayer /> </MapContainer> ); } export default MyMap;

这样就可以从你的服务器加载矢量瓦片并进行渲染了。


在服务器上使用 geojson-vt 生成瓦片(简要示例)

如果你想自己在 Node.js 环境快速弄一下 .pbf 瓦片,可以做如下示例(伪代码):

// server.js const express = require('express'); const geojsonvt = require('geojson-vt'); const vtpbf = require('vt-pbf'); const fs = require('fs'); const path = require('path'); const app = express(); // 假设 largeData.json 是一个超大的 GeoJSON const geojsonData = JSON.parse(fs.readFileSync(path.join(__dirname, 'largeData.json'), 'utf8')); // 用 geojson-vt 预处理: const tileIndex = geojsonvt(geojsonData, { maxZoom: 14, // 生成的瓦片最大层级 tolerance: 3, extent: 4096, debug: 0, }); // 提供一个路由来返回 {z}/{x}/{y} 瓦片 app.get('/tiles/:z/:x/:y.pbf', (req, res) => { const { z, x, y } = req.params; const tile = tileIndex.getTile(+z, +x, +y); if (!tile) { // 没有数据则返回空瓦片 return res.status(204).send(); } // geojson-vt 返回的是一个瓦片结构,用 vt-pbf 转成 pbf buffer const pbfData = vtpbf.fromGeojsonVt({ 'layerName': tile }); res.setHeader('Content-Type', 'application/x-protobuf'); res.send(Buffer.from(pbfData)); }); app.listen(8080, () => { console.log('Server running on http://localhost:8080'); });
  • 上面例子中,一旦用户在前端访问 http://localhost:8080/tiles/6/53/26.pbf 之类的 URL,服务器就会动态生成对应瓦片并返回。
  • 真实生产环境你会考虑缓存生成的瓦片,否则每次都计算会消耗性能,也可以使用离线脚本事先把 .pbf 文件全部生成,放到静态服务器上。

总结

  1. 矢量瓦片是应对大规模 GeoJSON 的一种常见方式,尤其适合非点要素(多边形、线等)。
  2. 配合 Leaflet.VectorGrid(或其 React 封装 react-leaflet-vectorgrid)可以轻松在浏览器里渲染 .pbf 瓦片。
  3. 数据端可以用 geojson-vttippecanoe 等工具生成。根据规模与访问频率,可以选择离线预切或者实时切片并缓存

这样就能大幅减少一次性加载并渲染整个大文件的压力,让你的大体量数据在浏览器中也能相对流畅地查看和交互。

Question

我现在所有js文件都平铺在src里,是不是不太好

Thought

思考文件结构

我正在探讨一个大项目的 React 文件结构。即使是小项目也会建议将文件分不同文件夹中,避免所有 .js 文件混在一起。

Reasoned about file organization in React projects for 8 seconds

Answer

在 React 项目里把所有 .js 文件都扔在 src 根目录下虽然能跑,但随着项目越来越大、文件越来越多,会导致可维护性和可读性变差。一般来说,更推荐按照“功能”或“职责”对代码进行分层或分组。以下是一些常见的结构组织思路,供参考:


1. 基础思路:按功能/类型分类

最常见的一种做法是按照文件用途进行分类,比如:

src/
 ┣ components/       // 放各种可复用的UI组件
 ┣ pages/            // 放页面级别的组件(路由对应的页面)
 ┣ hooks/            // 自定义Hook
 ┣ context/          // React Context 相关
 ┣ services/         // 数据请求、API封装等
 ┣ utils/            // 工具函数、通用方法
 ┣ styles/           // 全局样式或主题
 ┗ index.js
  • components:这里放与业务相对独立的通用组件,比如各种表单控件、地图组件、图表组件等。
  • pages:如果你使用 React Router,每个路由对应一个页面组件,这些页面往往会自己组合多个子组件。
  • hooks:如果有很多自定义的逻辑封装成 Hook(如 useFetchuseMapuseDebounce 等),可以统一放在一个目录。
  • services:通常放和后端交互的逻辑,比如 api.jsauthService.js 之类。
  • utils:存放纯函数工具、常量定义、格式化函数等等。

这样分类的好处是高内聚,文件职责清晰,查看和维护都比较方便。


2. 大型项目:按业务模块拆分

如果你的项目规模更大、业务逻辑更丰富,也可以把各个业务域拆分到各自文件夹。比如:

src/
 ┣ features/
 │   ┣ Map/
 │   │   ┣ components/
 │   │   ┣ hooks/
 │   │   ┣ services/
 │   │   ┗ index.js
 │   ┣ Auth/
 │   │   ┣ components/
 │   │   ┣ services/
 │   │   ┗ ...
 │   ┗ Dashboard/
 │       ┣ components/
 │       ┣ services/
 │       ┗ ...
 ┣ shared/            // 存放跨业务模块的通用组件、hooks、utils
 ┗ index.js
  • features/Map:跟地图功能相关的所有内容(组件、hooks、API服务等)都放这里,让“地图”这个功能模块内部高度内聚。
  • features/Auth:登录、注册、权限验证等放在这里。
  • shared:多个功能模块都会用到的通用资源,比如公用组件库、hooks、工具等。

这种做法更倾向“按业务模块”划分,适合大型或复杂应用,找文件时也能第一眼就能定位到具体功能的文件夹。


3. 不同层次的示例

3.1 Components 层级

有时候会在 components/ 目录下再进行细分,比如按照Atomic Design组件类型进行分层:

components/
 ┣ atoms/       // 最小UI单元,如 Button、Input 等
 ┣ molecules/   // 由 atoms 组合而成的稍大型组件
 ┣ organisms/   // 由多个 molecules/atoms 组合,形成比较完整的UI模块
 ┣ templates/   // 页面模板结构(可选)
 ┗ ...

不过这种是更偏 UI/设计驱动的思路,并不一定必须。

3.2 其他约定

  • test 文件:可以放在跟被测文件同目录下(命名为 xxx.test.js),或单独放在 __tests__ 目录下。
  • 路由文件:常见一个 src/routes.js 或者在 pages/ 里写 AppRouter.jsx
  • 常量、配置:有时会建 config/constants/ 放全局常量或环境配置。

4. 最后建议

  • 不必追求一步到位:文件结构是服务于可维护性和可扩展性的,项目小的时候也可以暂时简单点,等业务复杂了再逐步拆分。
  • 保持一致:一旦选择了某种结构(按功能 / 按类型 / 按业务模块等),团队要统一风格,避免多个结构混在一起导致更混乱。
  • 命名清晰:目录和文件名要能直接表意,方便后来者或自己回头看都能快速找到内容。

所以,“所有 .js 文件都平铺在 src/”确实不太好管理。你可以根据以上的思路,把它们按照功能或业务拆分一下,这样后续维护和扩展都会更轻松。

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