mirror of
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240 lines
5.7 KiB
TypeScript
240 lines
5.7 KiB
TypeScript
import _ from 'lodash';
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import flatten from '../../../core/utils/flatten';
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import TimeSeries from '../../../core/time_series2';
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import TableModel from '../../../core/table_model';
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var transformers = {};
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transformers['timeseries_to_rows'] = {
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description: 'Time series to rows',
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getColumns: function() {
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return [];
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},
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transform: function(data, panel, model) {
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model.columns = [
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{text: 'Time', type: 'date'},
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{text: 'Metric'},
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{text: 'Value'},
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];
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for (var i = 0; i < data.length; i++) {
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var series = data[i];
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for (var y = 0; y < series.datapoints.length; y++) {
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var dp = series.datapoints[y];
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model.rows.push([dp[1], series.target, dp[0]]);
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}
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}
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},
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};
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transformers['timeseries_to_columns'] = {
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description: 'Time series to columns',
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getColumns: function() {
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return [];
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},
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transform: function(data, panel, model) {
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model.columns.push({text: 'Time', type: 'date'});
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// group by time
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var points = {};
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for (let i = 0; i < data.length; i++) {
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var series = data[i];
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model.columns.push({text: series.target});
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for (var y = 0; y < series.datapoints.length; y++) {
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var dp = series.datapoints[y];
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var timeKey = dp[1].toString();
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if (!points[timeKey]) {
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points[timeKey] = {time: dp[1]};
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points[timeKey][i] = dp[0];
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} else {
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points[timeKey][i] = dp[0];
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}
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}
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}
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for (var time in points) {
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var point = points[time];
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var values = [point.time];
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for (let i = 0; i < data.length; i++) {
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var value = point[i];
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values.push(value);
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}
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model.rows.push(values);
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}
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}
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};
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transformers['timeseries_aggregations'] = {
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description: 'Time series aggregations',
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getColumns: function() {
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return [
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{text: 'Avg', value: 'avg'},
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{text: 'Min', value: 'min'},
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{text: 'Max', value: 'max'},
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{text: 'Total', value: 'total'},
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{text: 'Current', value: 'current'},
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{text: 'Count', value: 'count'},
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];
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},
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transform: function(data, panel, model) {
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var i, y;
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model.columns.push({text: 'Metric'});
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for (i = 0; i < panel.columns.length; i++) {
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model.columns.push({text: panel.columns[i].text});
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}
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for (i = 0; i < data.length; i++) {
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var series = new TimeSeries({
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datapoints: data[i].datapoints,
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alias: data[i].target,
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});
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series.getFlotPairs('connected');
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var cells = [series.alias];
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for (y = 0; y < panel.columns.length; y++) {
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cells.push(series.stats[panel.columns[y].value]);
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}
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model.rows.push(cells);
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}
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}
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};
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transformers['annotations'] = {
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description: 'Annotations',
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getColumns: function() {
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return [];
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},
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transform: function(data, panel, model) {
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model.columns.push({text: 'Time', type: 'date'});
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model.columns.push({text: 'Title'});
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model.columns.push({text: 'Text'});
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model.columns.push({text: 'Tags'});
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if (!data || !data.annotations || data.annotations.length === 0) {
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return;
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}
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for (var i = 0; i < data.annotations.length; i++) {
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var evt = data.annotations[i];
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model.rows.push([evt.time, evt.title, evt.text, evt.tags]);
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}
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}
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};
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transformers['table'] = {
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description: 'Table',
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getColumns: function(data) {
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if (!data || data.length === 0) {
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return [];
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}
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return data[0].columns;
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},
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transform: function(data, panel, model) {
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if (!data || data.length === 0) {
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return;
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}
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if (data[0].type !== 'table') {
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throw {message: 'Query result is not in table format, try using another transform.'};
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}
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model.columns = data[0].columns;
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model.rows = data[0].rows;
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}
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};
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transformers['json'] = {
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description: 'JSON Data',
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getColumns: function(data) {
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if (!data || data.length === 0) {
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return [];
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}
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var names: any = {};
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for (var i = 0; i < data.length; i++) {
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var series = data[i];
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if (series.type !== 'docs') {
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continue;
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}
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// only look at 100 docs
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var maxDocs = Math.min(series.datapoints.length, 100);
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for (var y = 0; y < maxDocs; y++) {
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var doc = series.datapoints[y];
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var flattened = flatten(doc, null);
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for (var propName in flattened) {
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names[propName] = true;
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}
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}
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}
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return _.map(names, function(value, key) {
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return {text: key, value: key};
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});
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},
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transform: function(data, panel, model) {
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var i, y, z;
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for (let column of panel.columns) {
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var tableCol: any = {text: column.text};
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// if filterable data then set columns to filterable
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if (data.length > 0 && data[0].filterable) {
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tableCol.filterable = true;
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}
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model.columns.push(tableCol);
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}
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if (model.columns.length === 0) {
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model.columns.push({text: 'JSON'});
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}
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for (i = 0; i < data.length; i++) {
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var series = data[i];
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for (y = 0; y < series.datapoints.length; y++) {
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var dp = series.datapoints[y];
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var values = [];
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if (_.isObject(dp) && panel.columns.length > 0) {
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var flattened = flatten(dp, null);
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for (z = 0; z < panel.columns.length; z++) {
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values.push(flattened[panel.columns[z].value]);
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}
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} else {
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values.push(JSON.stringify(dp));
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}
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model.rows.push(values);
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}
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}
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}
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};
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function transformDataToTable(data, panel) {
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var model = new TableModel();
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if (!data || data.length === 0) {
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return model;
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}
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var transformer = transformers[panel.transform];
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if (!transformer) {
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throw {message: 'Transformer ' + panel.transform + ' not found'};
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}
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transformer.transform(data, panel, model);
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return model;
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}
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export {transformers, transformDataToTable};
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