2018-10-23 23:45:42 +08:00
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'use strict';
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2018-09-21 03:12:54 +08:00
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const timeDimension = require('./time-dimension');
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2017-12-22 22:46:29 +08:00
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const DEFAULT_PLACEMENT = 'point-sample';
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2019-09-13 22:32:37 +08:00
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const WebMercatorHelper = require('cartodb-query-tables').utils.webMercatorHelper;
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const webmercator = new WebMercatorHelper();
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2017-12-22 18:31:33 +08:00
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2018-10-05 01:50:14 +08:00
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function optionsToParams (options) {
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return {
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sourceQuery: options.query,
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2019-10-22 01:07:24 +08:00
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res: 256 / options.resolution,
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2018-10-05 01:50:14 +08:00
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columns: options.columns,
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dimensions: options.dimensions,
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2019-06-28 19:50:26 +08:00
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filters: options.filters,
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2019-07-03 22:13:54 +08:00
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placement: options.placement || DEFAULT_PLACEMENT,
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isDefaultAggregation: options.isDefaultAggregation
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2018-10-05 01:50:14 +08:00
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};
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}
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2017-12-13 19:35:17 +08:00
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/**
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* Generates an aggregation query given the aggregation options:
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* - query
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2017-12-15 00:03:49 +08:00
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* - resolution - defined as in torque:
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* aggregation cell is resolution*resolution pixels, where tiles are always 256x256 pixels
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2017-12-13 19:35:17 +08:00
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* - columns
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* - placement
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2017-12-15 00:51:49 +08:00
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* - dimensions
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2017-12-22 18:31:33 +08:00
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*
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* The default aggregation (when no explicit placement, columns or dimensions are present) returns
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* a sample record (with all the original columns and _cdb_feature_count) for each aggregation group.
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* When placement, columns or dimensions are specified, columns are aggregated as requested
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* (by default only _cdb_feature_count) and with the_geom_webmercator as defined by placement.
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2017-12-13 19:35:17 +08:00
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*/
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2019-07-15 21:35:50 +08:00
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const queryForOptions = (options) => aggregationQueryTemplate(optionsToParams(options));
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2017-12-13 19:35:17 +08:00
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module.exports = queryForOptions;
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2018-10-05 01:50:14 +08:00
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module.exports.infoForOptions = (options) => {
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const params = optionsToParams(options);
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const dimensions = {};
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dimensionNamesAndExpressions(params).forEach(([dimensionName, info]) => {
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dimensions[dimensionName] = {
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sql: info.sql,
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params: info.effectiveParams,
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type: info.type
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};
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});
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return dimensions;
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};
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2017-12-12 23:17:42 +08:00
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const SUPPORTED_AGGREGATE_FUNCTIONS = {
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2019-10-22 01:07:24 +08:00
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count: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `count(${params.aggregated_column || '*'})`
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2017-12-12 23:17:42 +08:00
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},
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2019-10-22 01:07:24 +08:00
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avg: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `avg(${params.aggregated_column || columnName})`
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2017-12-12 23:17:42 +08:00
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},
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2019-10-22 01:07:24 +08:00
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sum: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `sum(${params.aggregated_column || columnName})`
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2017-12-12 23:17:42 +08:00
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},
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2019-10-22 01:07:24 +08:00
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min: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `min(${params.aggregated_column || columnName})`
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2017-12-12 23:17:42 +08:00
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},
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2019-10-22 01:07:24 +08:00
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max: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `max(${params.aggregated_column || columnName})`
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2017-12-14 23:37:40 +08:00
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},
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2019-10-22 01:07:24 +08:00
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mode: {
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2019-11-14 18:36:47 +08:00
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sql: (columnName, params) => `mode() WITHIN GROUP (ORDER BY ${params.aggregated_column || columnName})`
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2017-12-12 23:17:42 +08:00
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}
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};
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2017-12-19 03:06:16 +08:00
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module.exports.SUPPORTED_AGGREGATE_FUNCTIONS = Object.keys(SUPPORTED_AGGREGATE_FUNCTIONS);
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2017-12-15 00:51:49 +08:00
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const sep = (list) => {
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2019-10-22 01:07:24 +08:00
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const expr = list.join(', ');
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2017-12-15 00:51:49 +08:00
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return expr ? ', ' + expr : expr;
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};
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2017-12-12 23:17:42 +08:00
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const aggregateColumns = ctx => {
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2017-12-14 23:37:15 +08:00
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return Object.assign({
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2017-12-14 22:02:03 +08:00
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_cdb_feature_count: {
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aggregate_function: 'count'
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}
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}, ctx.columns || {});
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2017-12-14 23:37:15 +08:00
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};
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2019-11-14 18:36:47 +08:00
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const aggregateExpression = (columnName, columnParameters) => {
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const aggregateFunction = columnParameters.aggregate_function || 'count';
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const aggregateDefinition = SUPPORTED_AGGREGATE_FUNCTIONS[aggregateFunction];
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if (!aggregateDefinition) {
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throw new Error("Invalid Aggregate function: '" + aggregateFunction + "'");
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2018-03-22 00:01:32 +08:00
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}
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2019-11-14 18:36:47 +08:00
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return aggregateDefinition.sql(columnName, columnParameters);
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2018-03-22 00:01:32 +08:00
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};
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2017-12-14 23:37:15 +08:00
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const aggregateColumnDefs = ctx => {
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2019-10-22 01:07:24 +08:00
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const columns = aggregateColumns(ctx);
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2019-11-14 18:36:47 +08:00
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return sep(Object.keys(columns).map(columnName => {
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const aggregate = aggregateExpression(columnName, columns[columnName]);
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return `${aggregate} AS ${columnName}`;
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2017-12-15 00:51:49 +08:00
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}));
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};
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const aggregateDimensions = ctx => ctx.dimensions || {};
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2018-09-21 03:12:54 +08:00
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const timeDimensionParameters = definition => {
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// definition.column should correspond to a wrapped date column
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2018-10-06 02:08:40 +08:00
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const group = definition.group || {};
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2018-09-21 03:12:54 +08:00
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return {
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time: `to_timestamp("${definition.column}")`,
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2018-10-06 02:08:40 +08:00
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timezone: group.timezone || 'utc',
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units: group.units,
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count: group.count || 1,
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starting: group.starting,
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2018-10-04 03:02:22 +08:00
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format: definition.format
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2018-09-21 03:12:54 +08:00
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};
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};
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2018-09-26 01:10:56 +08:00
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// Adapt old-style dimension definitions for backwards compatibility
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const adaptDimensionDefinition = definition => {
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2019-10-22 01:07:24 +08:00
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if (typeof (definition) === 'string') {
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2018-10-03 23:05:58 +08:00
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return { column: definition };
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2018-09-26 01:10:56 +08:00
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}
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return definition;
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};
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const dimensionExpression = definition => {
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2018-10-06 02:08:40 +08:00
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if (definition.group) {
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2018-09-26 01:10:56 +08:00
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// Currently only time dimensions are supported with parameters
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2018-10-05 01:50:14 +08:00
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return Object.assign({ type: 'timeDimension' }, timeDimension(timeDimensionParameters(definition)));
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2018-09-26 01:10:56 +08:00
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} else {
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2018-10-05 01:50:14 +08:00
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return { sql: `"${definition.column}"` };
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2018-09-21 03:12:54 +08:00
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}
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};
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2018-10-05 01:50:14 +08:00
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const dimensionNamesAndExpressions = (ctx) => {
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2019-10-22 01:07:24 +08:00
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const dimensions = aggregateDimensions(ctx);
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2018-10-05 01:50:14 +08:00
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return Object.keys(dimensions).map(dimensionName => {
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const dimension = adaptDimensionDefinition(dimensions[dimensionName]);
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const expression = dimensionExpression(dimension);
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return [dimensionName, expression];
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});
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};
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const dimensionNames = (ctx, table) => {
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2018-10-07 00:26:43 +08:00
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return sep(dimensionNamesAndExpressions(ctx).map(([dimensionName]) => {
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2018-09-26 01:10:56 +08:00
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return table ? `${table}."${dimensionName}"` : `"${dimensionName}"`;
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2018-08-16 21:34:06 +08:00
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}));
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2017-12-12 18:18:18 +08:00
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};
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2017-12-15 00:51:49 +08:00
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const dimensionDefs = ctx => {
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2018-10-07 00:26:43 +08:00
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return sep(
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dimensionNamesAndExpressions(ctx)
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2019-10-22 01:07:24 +08:00
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.map(([dimensionName, expression]) => `${expression.sql} AS "${dimensionName}"`)
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2018-10-07 00:26:43 +08:00
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);
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2017-12-15 00:51:49 +08:00
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};
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2017-12-14 23:51:55 +08:00
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2018-03-22 00:01:32 +08:00
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const aggregateFilters = ctx => ctx.filters || {};
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const filterConditionSQL = (expr, filter) => {
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// TODO: validate filter parameters (e.g. cannot have both greater_than and greater_than or equal to)
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if (filter) {
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if (!Array.isArray(filter)) {
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filter = [filter];
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}
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if (filter.length > 0) {
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return filter.map(f => filterSingleConditionSQL(expr, f)).join(' OR ');
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}
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}
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};
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const filterSingleConditionSQL = (expr, filter) => {
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let cond;
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Object.keys(FILTERS).some(f => {
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cond = FILTERS[f](expr, filter);
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return cond;
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});
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return cond;
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};
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const sqlQ = (value) => {
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if (isFinite(value)) {
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return String(value);
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}
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return `'${value}'`; // TODO: escape single quotes! (by doubling them)
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};
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/* jshint eqeqeq: false */
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/* x != null is used to check for both null and undefined; triple !== wouldn't do the trick */
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const FILTERS = {
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between: (expr, filter) => {
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2019-10-22 01:07:24 +08:00
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const lo = filter.greater_than_or_equal_to; const hi = filter.less_than_or_equal_to;
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2018-03-22 00:01:32 +08:00
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if (lo != null && hi != null) {
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return `(${expr} BETWEEN ${sqlQ(lo)} AND ${sqlQ(hi)})`;
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}
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},
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in: (expr, filter) => {
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if (filter.in != null) {
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return `(${expr} IN (${filter.in.map(v => sqlQ(v)).join(',')}))`;
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}
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},
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notin: (expr, filter) => {
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if (filter.not_in != null) {
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return `(${expr} NOT IN (${filter.not_in.map(v => sqlQ(v)).join(',')}))`;
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}
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},
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equal: (expr, filter) => {
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if (filter.equal != null) {
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return `(${expr} = ${sqlQ(filter.equal)})`;
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}
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},
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not_equal: (expr, filter) => {
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if (filter.not_equal != null) {
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return `(${expr} <> ${sqlQ(filter.not_equal)})`;
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}
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},
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range: (expr, filter) => {
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2019-10-22 01:07:24 +08:00
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const conds = [];
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2018-03-22 00:01:32 +08:00
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if (filter.greater_than_or_equal_to != null) {
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conds.push(`(${expr} >= ${sqlQ(filter.greater_than_or_equal_to)})`);
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}
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if (filter.greater_than != null) {
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conds.push(`(${expr} > ${sqlQ(filter.greater_than)})`);
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}
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if (filter.less_than_or_equal_to != null) {
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conds.push(`(${expr} <= ${sqlQ(filter.less_than_or_equal_to)})`);
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}
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if (filter.less_than != null) {
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conds.push(`(${expr} < ${sqlQ(filter.less_than)})`);
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}
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if (conds.length > 0) {
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return conds.join(' AND ');
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}
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}
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};
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const filterConditions = ctx => {
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2019-10-22 01:07:24 +08:00
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const columns = aggregateColumns(ctx);
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const dimensions = aggregateDimensions(ctx);
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const filters = aggregateFilters(ctx);
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2019-11-14 18:36:47 +08:00
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return Object.keys(filters).map(filteredColumn => {
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let filteredExpr;
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if (columns[filteredColumn]) {
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filteredExpr = aggregateExpression(filteredColumn, columns[filteredColumn]);
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} else if (dimensions[filteredColumn]) {
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filteredExpr = dimensions[filteredColumn];
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2018-03-22 00:01:32 +08:00
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}
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2019-11-14 18:36:47 +08:00
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if (!filteredExpr) {
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throw new Error("Invalid filtered column: '" + filteredColumn + "'");
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2018-03-22 00:01:32 +08:00
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}
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2019-11-14 18:36:47 +08:00
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return filterConditionSQL(filteredExpr, filters[filteredColumn]);
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2018-03-22 00:01:32 +08:00
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}).join(' AND ');
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};
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const havingClause = ctx => {
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2019-10-22 01:07:24 +08:00
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const cond = filterConditions(ctx);
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2018-03-22 00:01:32 +08:00
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return cond ? `HAVING ${cond}` : '';
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};
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2017-12-14 23:51:55 +08:00
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// SQL expression to compute the aggregation resolution (grid cell size).
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// This is defined by the ctx.res parameter, which is the number of grid cells per tile linear dimension
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// (i.e. each tile is divided into ctx.res*ctx.res cells).
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2018-02-01 17:26:52 +08:00
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// We limit the the minimum resolution to avoid division by zero problems. The limit used is
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// the pixel size of zoom level 30 (i.e. 1/2*(30+8) of the full earth web-mercator extent), which is about 0.15 mm.
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2019-07-03 22:13:54 +08:00
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//
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2019-07-15 21:54:31 +08:00
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// NOTE: We'd rather use !pixel_width!, but in Mapnik this value is extent / 256 for raster
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2019-07-03 22:13:54 +08:00
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// and extent / tile_extent {4096 default} for MVT, so since aggregations are always based
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// on 256 we can't have the same query in both cases
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// As this scale change doesn't happen in !scale_denominator! we use that instead
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2019-07-15 21:54:31 +08:00
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// NOTE 2: The 0.00028 is used in Mapnik (and replicated in pg-mvt) and comes from
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// OGC's Styled Layer Descriptor Implementation Specification
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2018-02-01 00:46:13 +08:00
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const gridResolution = ctx => {
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2019-10-22 01:07:24 +08:00
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const minimumResolution = webmercator.getResolution({ z: 38 });
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return `${256 / ctx.res} * GREATEST(!scale_denominator! * 0.00028, ${minimumResolution})::double precision`;
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2018-02-01 00:46:13 +08:00
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};
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2017-12-14 23:51:55 +08:00
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2019-07-15 21:54:31 +08:00
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// SQL query to extract the boundaries of the area to be aggregated and the grid resolution
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2019-07-08 21:51:55 +08:00
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// cdb_{x-y}{min_max} return the limits of the tile. Aggregations do [min, max) in both axis
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// cdb_res: Aggregation resolution (as specified by gridResolution)
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// cdb_point_bbox: Tile bounding box [min, max]
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2019-07-04 23:15:53 +08:00
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const gridInfoQuery = ctx => {
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return `
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SELECT
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2019-07-08 21:51:55 +08:00
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cdb_xmin,
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cdb_ymin,
|
|
|
|
cdb_xmax,
|
|
|
|
cdb_ymax,
|
|
|
|
cdb_res,
|
|
|
|
ST_MakeEnvelope(cdb_xmin, cdb_ymin, cdb_xmax, cdb_ymax, 3857) AS cdb_point_bbox
|
2019-07-04 23:15:53 +08:00
|
|
|
FROM
|
|
|
|
(
|
|
|
|
SELECT
|
2019-07-08 21:51:55 +08:00
|
|
|
cdb_res,
|
|
|
|
CEIL (ST_XMIN(cdb_full_bbox) / cdb_res) * cdb_res AS cdb_xmin,
|
|
|
|
FLOOR(ST_XMAX(cdb_full_bbox) / cdb_res) * cdb_res AS cdb_xmax,
|
|
|
|
CEIL (ST_YMIN(cdb_full_bbox) / cdb_res) * cdb_res AS cdb_ymin,
|
|
|
|
FLOOR(ST_YMAX(cdb_full_bbox) / cdb_res) * cdb_res AS cdb_ymax
|
|
|
|
FROM
|
|
|
|
(
|
|
|
|
SELECT
|
|
|
|
${gridResolution(ctx)} AS cdb_res,
|
|
|
|
!bbox! cdb_full_bbox
|
|
|
|
) _cdb_input_resources
|
|
|
|
) _cdb_grid_bbox_margins
|
2019-07-04 23:15:53 +08:00
|
|
|
`;
|
|
|
|
};
|
|
|
|
|
2019-07-08 21:51:55 +08:00
|
|
|
// Function to generate the resulting point for a cell from the aggregated data
|
2019-07-09 00:52:35 +08:00
|
|
|
const aggregatedPointWebMercator = (ctx) => {
|
|
|
|
switch (ctx.placement) {
|
2019-10-22 01:07:24 +08:00
|
|
|
// For centroid, we return the average of the cell
|
|
|
|
case 'centroid':
|
|
|
|
return ', ST_SetSRID(ST_MakePoint(AVG(cdb_x), AVG(cdb_y)), 3857) AS the_geom_webmercator';
|
2019-07-03 22:13:54 +08:00
|
|
|
|
|
|
|
// Middle point of the cell
|
2019-10-22 01:07:24 +08:00
|
|
|
case 'point-grid':
|
|
|
|
return ', ST_SetSRID(ST_MakePoint(cdb_pos_grid_x, cdb_pos_grid_y), 3857) AS the_geom_webmercator';
|
2019-07-09 00:52:35 +08:00
|
|
|
|
|
|
|
// For point-sample we'll get a single point directly from the source
|
|
|
|
// If it's default aggregation we'll add the extra columns to keep backwards compatibility
|
2019-10-22 01:07:24 +08:00
|
|
|
case 'point-sample':
|
|
|
|
return '';
|
2019-07-09 00:52:35 +08:00
|
|
|
|
2019-10-22 01:07:24 +08:00
|
|
|
default:
|
|
|
|
throw new Error(`Invalid aggregation placement "${ctx.placement}"`);
|
2019-07-09 00:52:35 +08:00
|
|
|
}
|
|
|
|
};
|
|
|
|
|
|
|
|
// Function to generate the resulting point for a cell from the a join with the source
|
|
|
|
const aggregatedPointJoin = (ctx) => {
|
|
|
|
switch (ctx.placement) {
|
2019-10-22 01:07:24 +08:00
|
|
|
case 'centroid':
|
|
|
|
return '';
|
|
|
|
case 'point-grid':
|
|
|
|
return '';
|
2019-11-06 20:29:03 +08:00
|
|
|
// For point-sample we'll get a single point directly from the source
|
|
|
|
// If it's default aggregation we'll add the extra columns to keep backwards compatibility
|
2019-10-22 01:07:24 +08:00
|
|
|
case 'point-sample':
|
|
|
|
return `
|
2019-11-06 20:29:03 +08:00
|
|
|
NATURAL JOIN
|
|
|
|
(
|
|
|
|
SELECT ${ctx.isDefaultAggregation ? '*' : 'cartodb_id, the_geom_webmercator'}
|
|
|
|
FROM
|
|
|
|
(
|
|
|
|
${ctx.sourceQuery}
|
|
|
|
) __cdb_src_query
|
|
|
|
) __cdb_query_columns
|
|
|
|
`;
|
2019-10-22 01:07:24 +08:00
|
|
|
default:
|
2019-11-06 20:29:03 +08:00
|
|
|
throw new Error(`Invalid aggregation placement "${ctx.placement}"`);
|
2019-06-28 19:50:26 +08:00
|
|
|
}
|
|
|
|
};
|
2018-07-16 04:26:37 +08:00
|
|
|
|
2019-07-08 21:51:55 +08:00
|
|
|
// Function to generate the values common to all points in a cell
|
|
|
|
// By default we use the cell number (which is fast), but for point-grid we
|
|
|
|
// get the coordinates of the mid point so we don't need to calculate them later
|
|
|
|
// which requires extra data in the group by clause
|
|
|
|
const aggregatedPosCoordinate = (ctx, coordinate) => {
|
2019-07-09 00:52:35 +08:00
|
|
|
switch (ctx.placement) {
|
2019-10-22 01:07:24 +08:00
|
|
|
// For point-grid we return the coordinate of the middle point of the grid
|
|
|
|
case 'point-grid':
|
|
|
|
return `(FLOOR(cdb_${coordinate} / __cdb_grid_params.cdb_res) + 0.5) * __cdb_grid_params.cdb_res`;
|
2019-07-08 21:51:55 +08:00
|
|
|
|
|
|
|
// For other, we return the cell position (relative to the world)
|
2019-10-22 01:07:24 +08:00
|
|
|
default:
|
|
|
|
return `FLOOR(cdb_${coordinate} / __cdb_grid_params.cdb_res)`;
|
2019-07-08 21:51:55 +08:00
|
|
|
}
|
|
|
|
};
|
2017-12-12 01:33:06 +08:00
|
|
|
|
2019-07-15 21:35:50 +08:00
|
|
|
const aggregationQueryTemplate = ctx => `
|
2019-07-08 21:51:55 +08:00
|
|
|
WITH __cdb_grid_params AS
|
|
|
|
(
|
|
|
|
${gridInfoQuery(ctx)}
|
|
|
|
)
|
2019-07-03 22:13:54 +08:00
|
|
|
SELECT * FROM
|
2019-06-28 19:50:26 +08:00
|
|
|
(
|
|
|
|
SELECT
|
2019-07-03 22:13:54 +08:00
|
|
|
min(cartodb_id) as cartodb_id
|
2019-07-09 00:52:35 +08:00
|
|
|
${aggregatedPointWebMercator(ctx)}
|
2019-07-03 22:13:54 +08:00
|
|
|
${dimensionDefs(ctx)}
|
|
|
|
${aggregateColumnDefs(ctx)}
|
2017-12-22 18:31:33 +08:00
|
|
|
FROM
|
2019-06-28 19:50:26 +08:00
|
|
|
(
|
|
|
|
SELECT
|
2019-07-08 21:51:55 +08:00
|
|
|
*,
|
|
|
|
${aggregatedPosCoordinate(ctx, 'x')} as cdb_pos_grid_x,
|
|
|
|
${aggregatedPosCoordinate(ctx, 'y')} as cdb_pos_grid_y
|
2019-06-28 19:50:26 +08:00
|
|
|
FROM
|
|
|
|
(
|
|
|
|
SELECT
|
2019-07-08 21:51:55 +08:00
|
|
|
__cdb_src_query.*,
|
|
|
|
ST_X(the_geom_webmercator) cdb_x,
|
|
|
|
ST_Y(the_geom_webmercator) cdb_y
|
2019-06-28 19:50:26 +08:00
|
|
|
FROM
|
|
|
|
(
|
2019-07-08 21:51:55 +08:00
|
|
|
${ctx.sourceQuery}
|
|
|
|
) __cdb_src_query, __cdb_grid_params
|
|
|
|
WHERE the_geom_webmercator && cdb_point_bbox
|
|
|
|
OFFSET 0
|
|
|
|
) __cdb_src_get_x_y, __cdb_grid_params
|
|
|
|
WHERE cdb_x < __cdb_grid_params.cdb_xmax AND cdb_y < __cdb_grid_params.cdb_ymax
|
2019-07-09 00:32:04 +08:00
|
|
|
) __cdb_src_gridded
|
2019-07-08 21:51:55 +08:00
|
|
|
GROUP BY cdb_pos_grid_x, cdb_pos_grid_y ${dimensionNames(ctx)}
|
2019-07-03 22:13:54 +08:00
|
|
|
${havingClause(ctx)}
|
|
|
|
) __cdb_aggregation_src
|
2019-07-09 00:52:35 +08:00
|
|
|
${aggregatedPointJoin(ctx)}
|
2017-12-22 18:31:33 +08:00
|
|
|
`;
|
|
|
|
|
2019-07-15 21:35:50 +08:00
|
|
|
module.exports.SUPPORTED_PLACEMENTS = ['centroid', 'point-grid', 'point-sample'];
|
2018-01-29 22:48:35 +08:00
|
|
|
module.exports.GEOMETRY_COLUMN = 'the_geom_webmercator';
|
2019-02-26 02:40:18 +08:00
|
|
|
|
|
|
|
const clusterFeaturesQuery = ctx => `
|
|
|
|
WITH
|
|
|
|
_cdb_params AS (
|
|
|
|
SELECT
|
|
|
|
${gridResolution(ctx)} AS res
|
|
|
|
),
|
|
|
|
_cell AS (
|
|
|
|
SELECT
|
|
|
|
ST_MakeEnvelope(
|
|
|
|
Floor(ST_X(_cdb_query.the_geom_webmercator)/_cdb_params.res)*_cdb_params.res,
|
|
|
|
Floor(ST_Y(_cdb_query.the_geom_webmercator)/_cdb_params.res)*_cdb_params.res,
|
|
|
|
Floor(ST_X(_cdb_query.the_geom_webmercator)/_cdb_params.res + 1)*_cdb_params.res,
|
|
|
|
Floor(ST_Y(_cdb_query.the_geom_webmercator)/_cdb_params.res + 1)*_cdb_params.res,
|
|
|
|
3857
|
|
|
|
) AS bbox
|
|
|
|
FROM (${ctx.sourceQuery}) _cdb_query, _cdb_params
|
|
|
|
WHERE _cdb_query.cartodb_id = ${ctx.id}
|
|
|
|
)
|
|
|
|
SELECT _cdb_query.* FROM _cell, (${ctx.sourceQuery}) _cdb_query
|
|
|
|
WHERE ST_Intersects(_cdb_query.the_geom_webmercator, _cell.bbox)
|
|
|
|
`;
|
|
|
|
|
|
|
|
module.exports.featuresQuery = (id, options) => clusterFeaturesQuery({
|
|
|
|
id,
|
|
|
|
sourceQuery: options.query,
|
2019-10-22 01:07:24 +08:00
|
|
|
res: 256 / options.resolution
|
2019-02-26 02:40:18 +08:00
|
|
|
});
|