grafana/public/app/plugins/datasource/loki/backendResultTransformer.ts

126 lines
4.0 KiB
TypeScript

import { DataQueryResponse, DataFrame, isDataFrame, FieldType, QueryResultMeta } from '@grafana/data';
import { getDerivedFields } from './getDerivedFields';
import { makeTableFrames } from './makeTableFrames';
import { formatQuery, getHighlighterExpressionsFromQuery } from './query_utils';
import { DerivedFieldConfig, LokiQuery, LokiQueryType } from './types';
function isMetricFrame(frame: DataFrame): boolean {
return frame.fields.every((field) => field.type === FieldType.time || field.type === FieldType.number);
}
// returns a new frame, with meta merged with it's original meta
function setFrameMeta(frame: DataFrame, meta: QueryResultMeta): DataFrame {
const { meta: oldMeta, ...rest } = frame;
// meta maybe be undefined, we need to handle that
const newMeta = { ...oldMeta, ...meta };
return {
...rest,
meta: newMeta,
};
}
function processStreamFrame(
frame: DataFrame,
query: LokiQuery | undefined,
derivedFieldConfigs: DerivedFieldConfig[]
): DataFrame {
const meta: QueryResultMeta = {
preferredVisualisationType: 'logs',
limit: query?.maxLines,
searchWords: query !== undefined ? getHighlighterExpressionsFromQuery(formatQuery(query.expr)) : undefined,
custom: {
// used by logs_model
lokiQueryStatKey: 'Summary: total bytes processed',
},
};
const newFrame = setFrameMeta(frame, meta);
const derivedFields = getDerivedFields(newFrame, derivedFieldConfigs);
return {
...newFrame,
fields: [...newFrame.fields, ...derivedFields],
};
}
function processStreamsFrames(
frames: DataFrame[],
queryMap: Map<string, LokiQuery>,
derivedFieldConfigs: DerivedFieldConfig[]
): DataFrame[] {
return frames.map((frame) => {
const query = frame.refId !== undefined ? queryMap.get(frame.refId) : undefined;
return processStreamFrame(frame, query, derivedFieldConfigs);
});
}
function processMetricInstantFrames(frames: DataFrame[]): DataFrame[] {
return frames.length > 0 ? makeTableFrames(frames) : [];
}
function processMetricRangeFrames(frames: DataFrame[]): DataFrame[] {
const meta: QueryResultMeta = { preferredVisualisationType: 'graph' };
return frames.map((frame) => setFrameMeta(frame, meta));
}
// we split the frames into 3 groups, because we will handle
// each group slightly differently
function groupFrames(
frames: DataFrame[],
queryMap: Map<string, LokiQuery>
): {
streamsFrames: DataFrame[];
metricInstantFrames: DataFrame[];
metricRangeFrames: DataFrame[];
} {
const streamsFrames: DataFrame[] = [];
const metricInstantFrames: DataFrame[] = [];
const metricRangeFrames: DataFrame[] = [];
frames.forEach((frame) => {
if (!isMetricFrame(frame)) {
streamsFrames.push(frame);
} else {
const isInstantFrame = frame.refId != null && queryMap.get(frame.refId)?.queryType === LokiQueryType.Instant;
if (isInstantFrame) {
metricInstantFrames.push(frame);
} else {
metricRangeFrames.push(frame);
}
}
});
return { streamsFrames, metricInstantFrames, metricRangeFrames };
}
export function transformBackendResult(
response: DataQueryResponse,
queries: LokiQuery[],
derivedFieldConfigs: DerivedFieldConfig[]
): DataQueryResponse {
const { data, ...rest } = response;
// in the typescript type, data is an array of basically anything.
// we do know that they have to be dataframes, so we make a quick check,
// this way we can be sure, and also typescript is happy.
const dataFrames = data.map((d) => {
if (!isDataFrame(d)) {
throw new Error('transformation only supports dataframe responses');
}
return d;
});
const queryMap = new Map(queries.map((query) => [query.refId, query]));
const { streamsFrames, metricInstantFrames, metricRangeFrames } = groupFrames(dataFrames, queryMap);
return {
...rest,
data: [
...processMetricRangeFrames(metricRangeFrames),
...processMetricInstantFrames(metricInstantFrames),
...processStreamsFrames(streamsFrames, queryMap, derivedFieldConfigs),
],
};
}