2017-09-10 17:25:29 +05:30
|
|
|
import _ from 'underscore';
|
|
|
|
|
2019-12-21 20:55:43 +05:30
|
|
|
export const uniqMetricsId = metric => `${metric.metric_id}_${metric.id}`;
|
|
|
|
|
|
|
|
/**
|
|
|
|
* Metrics loaded from project-defined dashboards do not have a metric_id.
|
|
|
|
* This method creates a unique ID combining metric_id and id, if either is present.
|
|
|
|
* This is hopefully a temporary solution until BE processes metrics before passing to fE
|
|
|
|
* @param {Object} metric - metric
|
|
|
|
* @returns {Object} - normalized metric with a uniqueID
|
|
|
|
*/
|
2020-01-01 13:55:28 +05:30
|
|
|
|
2019-12-21 20:55:43 +05:30
|
|
|
export const normalizeMetric = (metric = {}) =>
|
|
|
|
_.omit(
|
|
|
|
{
|
|
|
|
...metric,
|
|
|
|
metric_id: uniqMetricsId(metric),
|
2020-01-01 13:55:28 +05:30
|
|
|
metricId: uniqMetricsId(metric),
|
2019-12-21 20:55:43 +05:30
|
|
|
},
|
|
|
|
'id',
|
|
|
|
);
|
|
|
|
|
2019-09-30 21:07:59 +05:30
|
|
|
export const normalizeQueryResult = timeSeries => {
|
|
|
|
let normalizedResult = {};
|
|
|
|
|
|
|
|
if (timeSeries.values) {
|
|
|
|
normalizedResult = {
|
|
|
|
...timeSeries,
|
|
|
|
values: timeSeries.values.map(([timestamp, value]) => [
|
|
|
|
new Date(timestamp * 1000).toISOString(),
|
|
|
|
Number(value),
|
|
|
|
]),
|
|
|
|
};
|
2020-01-01 13:55:28 +05:30
|
|
|
// Check result for empty data
|
|
|
|
normalizedResult.values = normalizedResult.values.filter(series => {
|
|
|
|
const hasValue = d => !Number.isNaN(d[1]) && (d[1] !== null || d[1] !== undefined);
|
|
|
|
return series.find(hasValue);
|
|
|
|
});
|
2019-09-30 21:07:59 +05:30
|
|
|
} else if (timeSeries.value) {
|
|
|
|
normalizedResult = {
|
|
|
|
...timeSeries,
|
|
|
|
value: [new Date(timeSeries.value[0] * 1000).toISOString(), Number(timeSeries.value[1])],
|
|
|
|
};
|
|
|
|
}
|
|
|
|
|
|
|
|
return normalizedResult;
|
|
|
|
};
|