假设开发中,遇到这样一个需求:“接口返回一片地区内所有的小区的电子围栏,将小区绘制到高德地图上”。很容易写出下面这样的代码:
const map = new Amap.Map();
for (const item of data) {
const polygon = new AMap.Polygon(item);
map.add(polygon);
}
效果大致可能就是这样,在实际运行中,很有可能会非常卡顿,因为绘制耗了大量时间,如果在地图上还有事件交互,也可能会非常卡顿。实际业务根本无法使用,这时候就要找办法性能优化,翻阅高德地图的文档示例,可能会发现有“集群”、“海量点”渲染优化等示例,但是实际上在项目中可能还是没法使用(比如这个需求是绘制小区)。
从接口层面来看,很有可能是后端吐出大规模地理信息数据,前端拿到数据后根据产品需求进行渲染,本质上都是在消费数据。最直接的方式是“单次消费全部数据进行全部渲染”,基本上会带来卡顿问题。让我们回到地图本身,当我们在地图上进行交互(比如移动地图、滚动缩放)时,地图看起来好像才会绘制当前视口能看到的地方,或者说就是这一片的瓦片。
所以,从地图本身的瓦片式渲染来看,我们对数据的消费也可以是这种形式,展示“当前视口内可以渲染的数据,当前缩放等级可以看到的数据”,进而大幅减少单次需要渲染的数据,性能自然就上去了。总结一下:
通过地图当前的视口、缩放登记,获取当前可以渲染的数据、被聚合的数据
站在巨人的肩膀上,通过 kd-brush
和 supercluster
对数据进行消费。
function sortKD(ids, coords, nodeSize, left, right, depth) {
if (right - left <= nodeSize) {
return;
}
const m = (left + right) >> 1;
select(ids, coords, m, left, right, depth % 2);
sortKD(ids, coords, nodeSize, left, m - 1, depth + 1);
sortKD(ids, coords, nodeSize, m + 1, right, depth + 1);
}
function select(ids, coords, k, left, right, inc) {
while (right > left) {
if (right - left > 600) {
const n = right - left + 1;
const m = k - left + 1;
const z = Math.log(n);
const s = 0.5 * Math.exp((2 * z) / 3);
const sd = 0.5 * Math.sqrt((z * s * (n - s)) / n) * (m - n / 2 < 0 ? -1 : 1);
const newLeft = Math.max(left, Math.floor(k - (m * s) / n + sd));
const newRight = Math.min(right, Math.floor(k + ((n - m) * s) / n + sd));
select(ids, coords, k, newLeft, newRight, inc);
}
const t = coords[2 * k + inc];
let i = left;
let j = right;
swapItem(ids, coords, left, k);
if (coords[2 * right + inc] > t) {
swapItem(ids, coords, left, right);
}
while (i < j) {
swapItem(ids, coords, i, j);
i++;
j--;
while (coords[2 * i + inc] < t) {
i++;
}
while (coords[2 * j + inc] > t) {
j--;
}
}
if (coords[2 * left + inc] === t) {
swapItem(ids, coords, left, j);
} else {
j++;
swapItem(ids, coords, j, right);
}
if (j <= k) {
left = j + 1;
}
if (k <= j) {
right = j - 1;
}
}
}
function swapItem(ids, coords, i, j) {
swap(ids, i, j);
swap(coords, 2 * i, 2 * j);
swap(coords, 2 * i + 1, 2 * j + 1);
}
function swap(arr, i, j) {
const tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
}
function range(ids, coords, minX, minY, maxX, maxY, nodeSize) {
const stack = [0, ids.length - 1, 0];
const result = [];
let x;
let y;
while (stack.length) {
const axis = stack.pop();
const right = stack.pop();
const left = stack.pop();
if (right - left <= nodeSize) {
for (let i = left; i <= right; i++) {
x = coords[2 * i];
y = coords[2 * i + 1];
if (x >= minX && x <= maxX && y >= minY && y <= maxY) {
result.push(ids[i]);
}
}
continue;
}
const m = Math.floor((left + right) / 2);
x = coords[2 * m];
y = coords[2 * m + 1];
if (x >= minX && x <= maxX && y >= minY && y <= maxY) {
result.push(ids[m]);
}
const nextAxis = (axis + 1) % 2;
if (axis === 0 ? minX <= x : minY <= y) {
stack.push(left);
stack.push(m - 1);
stack.push(nextAxis);
}
if (axis === 0 ? maxX >= x : maxY >= y) {
stack.push(m + 1);
stack.push(right);
stack.push(nextAxis);
}
}
return result;
}
function within(ids, coords, qx, qy, r, nodeSize) {
const stack = [0, ids.length - 1, 0];
const result = [];
const r2 = r * r;
while (stack.length) {
const axis = stack.pop();
const right = stack.pop();
const left = stack.pop();
if (right - left <= nodeSize) {
for (let i = left; i <= right; i++) {
if (sqDist(coords[2 * i], coords[2 * i + 1], qx, qy) <= r2) {
result.push(ids[i]);
}
}
continue;
}
const m = Math.floor((left + right) / 2);
const x = coords[2 * m];
const y = coords[2 * m + 1];
if (sqDist(x, y, qx, qy) <= r2) {
result.push(ids[m]);
}
const nextAxis = (axis + 1) % 2;
if (axis === 0 ? qx - r <= x : qy - r <= y) {
stack.push(left);
stack.push(m - 1);
stack.push(nextAxis);
}
if (axis === 0 ? qx + r >= x : qy + r >= y) {
stack.push(m + 1);
stack.push(right);
stack.push(nextAxis);
}
}
return result;
}
function sqDist(ax, ay, bx, by) {
const dx = ax - bx;
const dy = ay - by;
return dx * dx + dy * dy;
}
const defaultGetX = function (p) {
return p[0];
};
const defaultGetY = function (p) {
return p[1];
};
const KDBush = function KDBush(points, getX, getY, nodeSize, ArrayType) {
if (getX === void 0) getX = defaultGetX;
if (getY === void 0) getY = defaultGetY;
if (nodeSize === void 0) nodeSize = 64;
if (ArrayType === void 0) ArrayType = Float64Array;
this.nodeSize = nodeSize;
this.points = points;
const IndexArrayType = points.length < 65536 ? Uint16Array : Uint32Array;
const ids = (this.ids = new IndexArrayType(points.length));
const coords = (this.coords = new ArrayType(points.length * 2));
for (let i = 0; i < points.length; i++) {
ids[i] = i;
coords[2 * i] = getX(points[i]);
coords[2 * i + 1] = getY(points[i]);
}
sortKD(ids, coords, nodeSize, 0, ids.length - 1, 0);
};
KDBush.prototype.range = function range$1(minX, minY, maxX, maxY) {
return range(this.ids, this.coords, minX, minY, maxX, maxY, this.nodeSize);
};
KDBush.prototype.within = function within$1(x, y, r) {
return within(this.ids, this.coords, x, y, r, this.nodeSize);
};
const defaultOptions = {
minZoom: 3, // min zoom to generate clusters on
maxZoom: 18, // max zoom level to cluster the points on
minPoints: 4, // minimum points to form a cluster
radius: 80, // cluster radius in pixels
extent: 512, // tile extent (radius is calculated relative to it)
nodeSize: 64, // size of the KD-tree leaf node, affects performance
log: false, // whether to log timing info
// whether to generate numeric ids for input features (in vector tiles)
generateId: false,
// a reduce function for calculating custom cluster properties
reduce: null, // (accumulated, props) => { accumulated.sum += props.sum; }
// properties to use for individual points when running the reducer
map: (props) => props, // props => ({sum: props.my_value})
};
const fround =
Math.fround ||
((tmp) => (x) => {
tmp[0] = +x;
return tmp[0];
})(new Float32Array(1));
class Supercluster {
constructor(options) {
this.options = extend(Object.create(defaultOptions), options);
this.trees = new Array(this.options.maxZoom + 1);
}
load(points) {
const { log, minZoom, maxZoom, nodeSize } = this.options;
if (log) console.time('total time');
const timerId = `prepare ${points.length} points`;
if (log) console.time(timerId);
this.points = points;
// generate a cluster object for each point and index input points into a KD-tree
let clusters = [];
for (let i = 0; i < points.length; i++) {
// if (!points[i].geometry) continue;
clusters.push(createPointCluster(points[i], i));
}
this.trees[maxZoom + 1] = new KDBush(clusters, getX, getY, nodeSize, Float32Array);
if (log) console.timeEnd(timerId);
// cluster points on max zoom, then cluster the results on previous zoom, etc.;
// results in a cluster hierarchy across zoom levels
for (let z = maxZoom; z >= minZoom; z--) {
const now = +Date.now();
// create a new set of clusters for the zoom and index them with a KD-tree
clusters = this._cluster(clusters, z);
this.trees[z] = new KDBush(clusters, getX, getY, nodeSize, Float32Array);
if (log) console.log('z%d: %d clusters in %dms', z, clusters.length, +Date.now() - now);
}
if (log) console.timeEnd('total time');
return this;
}
getClusters(bbox, zoom) {
let minLng = ((((bbox[0] + 180) % 360) + 360) % 360) - 180;
const minLat = Math.max(-90, Math.min(90, bbox[1]));
let maxLng = bbox[2] === 180 ? 180 : ((((bbox[2] + 180) % 360) + 360) % 360) - 180;
const maxLat = Math.max(-90, Math.min(90, bbox[3]));
if (bbox[2] - bbox[0] >= 360) {
minLng = -180;
maxLng = 180;
} else if (minLng > maxLng) {
const easternHem = this.getClusters([minLng, minLat, 180, maxLat], zoom);
const westernHem = this.getClusters([-180, minLat, maxLng, maxLat], zoom);
return easternHem.concat(westernHem);
}
const tree = this.trees[this._limitZoom(zoom)];
const ids = tree.range(lngX(minLng), latY(maxLat), lngX(maxLng), latY(minLat));
const clusters = [];
for (const id of ids) {
const c = tree.points[id];
clusters.push(c.numPoints ? getClusterJSON(c) : this.points[c.index]);
}
return clusters;
}
getChildren(clusterId) {
const originId = this._getOriginId(clusterId);
const originZoom = this._getOriginZoom(clusterId);
const errorMsg = 'No cluster with the specified id.';
const index = this.trees[originZoom];
if (!index) {
console.error(errorMsg);
return [];
}
const origin = index.points[originId];
if (!origin) {
console.error(errorMsg);
return [];
}
const r = this.options.radius / (this.options.extent * Math.pow(2, originZoom - 1));
const ids = index.within(origin.x, origin.y, r);
const children = [];
for (const id of ids) {
const c = index.points[id];
if (c.parentId === clusterId) {
children.push(c.numPoints ? getClusterJSON(c) : this.points[c.index]);
}
}
return children;
}
getLeaves(clusterId, limit, offset) {
limit = limit || 10;
offset = offset || 0;
const leaves = [];
this._appendLeaves(leaves, clusterId, limit, offset, 0);
return leaves;
}
getTile(z, x, y) {
const tree = this.trees[this._limitZoom(z)];
const z2 = Math.pow(2, z);
const { extent, radius } = this.options;
const p = radius / extent;
const top = (y - p) / z2;
const bottom = (y + 1 + p) / z2;
const tile = {
features: [],
};
this._addTileFeatures(
tree.range((x - p) / z2, top, (x + 1 + p) / z2, bottom),
tree.points,
x,
y,
z2,
tile
);
if (x === 0) {
this._addTileFeatures(tree.range(1 - p / z2, top, 1, bottom), tree.points, z2, y, z2, tile);
}
if (x === z2 - 1) {
this._addTileFeatures(tree.range(0, top, p / z2, bottom), tree.points, -1, y, z2, tile);
}
return tile.features.length ? tile : null;
}
getClusterExpansionZoom(clusterId) {
let expansionZoom = this._getOriginZoom(clusterId) - 1;
while (expansionZoom <= this.options.maxZoom) {
const children = this.getChildren(clusterId);
expansionZoom++;
if (children.length !== 1) break;
clusterId = children[0].properties.cluster_id;
}
return expansionZoom;
}
_appendLeaves(result, clusterId, limit, offset, skipped) {
const children = this.getChildren(clusterId);
for (const child of children) {
const props = child.properties;
if (props && props.cluster) {
if (skipped + props.point_count <= offset) {
// skip the whole cluster
skipped += props.point_count;
} else {
// enter the cluster
skipped = this._appendLeaves(result, props.cluster_id, limit, offset, skipped);
// exit the cluster
}
} else if (skipped < offset) {
// skip a single point
skipped++;
} else {
// add a single point
result.push(child);
}
if (result.length === limit) break;
}
return skipped;
}
_addTileFeatures(ids, points, x, y, z2, tile) {
for (const i of ids) {
const c = points[i];
const isCluster = c.numPoints;
const f = {
type: 1,
geometry: [
[
Math.round(this.options.extent * (c.x * z2 - x)),
Math.round(this.options.extent * (c.y * z2 - y)),
],
],
tags: isCluster ? getClusterProperties(c) : this.points[c.index].properties,
};
// assign id
let id;
if (isCluster) {
id = c.id;
} else if (this.options.generateId) {
// optionally generate id
id = c.index;
} else if (this.points[c.index].id) {
// keep id if already assigned
id = this.points[c.index].id;
}
if (id !== undefined) f.id = id;
tile.features.push(f);
}
}
_limitZoom(z) {
return Math.max(this.options.minZoom, Math.floor(Math.min(+z, this.options.maxZoom + 1)));
}
_cluster(points, zoom) {
const clusters = [];
const { radius, extent, reduce, minPoints } = this.options;
const r = radius / (extent * Math.pow(2, zoom));
// loop through each point
for (let i = 0; i < points.length; i++) {
const p = points[i];
// if we've already visited the point at this zoom level, skip it
if (p.zoom <= zoom) continue;
p.zoom = zoom;
// find all nearby points
const tree = this.trees[zoom + 1];
const neighborIds = tree.within(p.x, p.y, r);
const numPointsOrigin = p.numPoints || 1;
let numPoints = numPointsOrigin;
// count the number of points in a potential cluster
for (const neighborId of neighborIds) {
const b = tree.points[neighborId];
// filter out neighbors that are already processed
if (b.zoom > zoom) numPoints += b.numPoints || 1;
}
if (numPoints >= minPoints) {
// enough points to form a cluster
let wx = p.x * numPointsOrigin;
let wy = p.y * numPointsOrigin;
let clusterProperties = reduce && numPointsOrigin > 1 ? this._map(p, true) : null;
// encode both zoom and point index on which the cluster originated -- offset by total length of features
const id = (i << 5) + (zoom + 1) + this.points.length;
for (const neighborId of neighborIds) {
const b = tree.points[neighborId];
if (b.zoom <= zoom) continue;
b.zoom = zoom; // save the zoom (so it doesn't get processed twice)
const numPoints2 = b.numPoints || 1;
wx += b.x * numPoints2; // accumulate coordinates for calculating weighted center
wy += b.y * numPoints2;
b.parentId = id;
if (reduce) {
if (!clusterProperties) clusterProperties = this._map(p, true);
reduce(clusterProperties, this._map(b));
}
}
p.parentId = id;
clusters.push(
createCluster(wx / numPoints, wy / numPoints, id, numPoints, clusterProperties)
);
} else {
// left points as unclustered
clusters.push(p);
if (numPoints > 1) {
for (const neighborId of neighborIds) {
const b = tree.points[neighborId];
if (b.zoom <= zoom) continue;
b.zoom = zoom;
clusters.push(b);
}
}
}
}
return clusters;
}
// get index of the point from which the cluster originated
_getOriginId(clusterId) {
return (clusterId - this.points.length) >> 5;
}
// get zoom of the point from which the cluster originated
_getOriginZoom(clusterId) {
return (clusterId - this.points.length) % 32;
}
_map(point, clone) {
if (point.numPoints) {
return clone ? extend({}, point.properties) : point.properties;
}
const original = this.points[point.index].properties;
const result = this.options.map(original);
return clone && result === original ? extend({}, result) : result;
}
}
function createCluster(x, y, id, numPoints, properties) {
return {
x: fround(x), // weighted cluster center; round for consistency with Float32Array index
y: fround(y),
zoom: Infinity, // the last zoom the cluster was processed at
id, // encodes index of the first child of the cluster and its zoom level
parentId: -1, // parent cluster id
numPoints,
properties,
};
}
function createPointCluster(p, id) {
const x = 'x' in p ? p.x : p.position && p.position[0];
const y = 'y' in p ? p.y : p.position && p.position[1];
return {
x: fround(lngX(x)), // projected point coordinates
y: fround(latY(y)),
zoom: Infinity, // the last zoom the point was processed at
index: id, // index of the source feature in the original input array,
parentId: -1, // parent cluster id
};
}
function getClusterJSON(cluster) {
return {
isClutser: true,
id: cluster.id,
...getClusterProperties(cluster),
x: xLng(cluster.x),
y: yLat(cluster.y),
position: [xLng(cluster.x), yLat(cluster.y)],
};
}
function getClusterProperties(cluster) {
const count = cluster.numPoints;
const abbrev =
count >= 10000
? `${Math.round(count / 1000)}k`
: count >= 1000
? `${Math.round(count / 100) / 10}k`
: count;
return extend(extend({}, cluster.properties), {
cluster: true,
cluster_id: cluster.id,
count,
abbreviatedCount: abbrev,
});
}
// longitude/latitude to spherical mercator in [0..1] range
function lngX(lng) {
return lng / 360 + 0.5;
}
function latY(lat) {
const sin = Math.sin((lat * Math.PI) / 180);
const y = 0.5 - (0.25 * Math.log((1 + sin) / (1 - sin))) / Math.PI;
return y < 0 ? 0 : y > 1 ? 1 : y;
}
// spherical mercator to longitude/latitude
function xLng(x) {
return (x - 0.5) * 360;
}
function yLat(y) {
const y2 = ((180 - y * 360) * Math.PI) / 180;
return (360 * Math.atan(Math.exp(y2))) / Math.PI - 90;
}
function extend(dest, src) {
for (const id in src) dest[id] = src[id];
return dest;
}
function getX(p) {
return p.x;
}
function getY(p) {
return p.y;
}
function debounce(func, wait, immediate) {
let timeout;
const debounced = function () {
const context = this;
const args = arguments;
const later = function () {
timeout = null;
if (!immediate) {
func.apply(context, args);
}
};
const callNow = immediate && !timeout;
clearTimeout(timeout);
timeout = setTimeout(later, wait);
if (callNow) {
func.apply(context, args);
}
};
debounced.cancel = () => {
clearTimeout(timeout);
};
return debounced;
}
const data = areas.map((area, index) => {
return {
...area,
path: area.lnglat,
name: `模拟社区${index}`,
index,
x: area.lnglat[0][0],
y: area.lnglat[0][1],
};
});
const map = new AMap.Map(document.querySelector('#app'), {
mapStyle: 'amap://styles/grey',
zoom: 14,
center: [116.467987, 39.992613],
});
const index = new Supercluster();
index.load(data);
let markers = [];
let polygons = [];
const render = () => {
let bounds = map.getBounds();
if (bounds.toBounds) {
bounds = bounds.toBounds();
}
const bbox = [
bounds.southWest.lng,
bounds.southWest.lat,
bounds.northEast.lng,
bounds.northEast.lat,
];
const views = index.getClusters(bbox, map.getZoom());
const clusters = views.filter((view) => view.isClutser);
const data = views.filter((view) => !view.isClutser);
map.remove(markers);
markers = clusters.map((cluster) => {
const marker = new AMap.Marker({
...cluster,
content: `<div style="width: 32px; height: 32px; line-height: 32px; border-radius: 50%; background-color: green; color: #fff; text-align: center;">${cluster.count}</div>`,
});
return marker;
});
map.add(markers);
map.remove(polygons);
polygons = data.map((item) => {
const marker = new AMap.Polygon({
...item,
fillColor: 'rgba(256, 0, 0, 0.2)', // 多边形填充颜色
borderWeight: 2, // 线条宽度,默认为 1
strokeColor: 'rgba(256, 0, 0, 1)', // 线条颜色
});
return marker;
});
map.add(polygons);
};
render();
const listener = debounce(render, 200);
map.on('zoom', listener);
map.on('moveend', listener);