EVOLUTION-MANAGER

Edit File: result_transformer.ts

import _ from 'lodash';
import md5 from 'md5';

import {
  parseLabels,
  FieldType,
  TimeSeries,
  Labels,
  DataFrame,
  ArrayVector,
  MutableDataFrame,
  findUniqueLabels,
  FieldConfig,
  DataFrameView,
  dateTime,
} from '@grafana/data';
import templateSrv from 'app/features/templating/template_srv';
import TableModel from 'app/core/table_model';
import {
  LokiLegacyStreamResult,
  LokiRangeQueryRequest,
  LokiResponse,
  LokiMatrixResult,
  LokiVectorResult,
  TransformerOptions,
  LokiLegacyStreamResponse,
  LokiResultType,
  LokiStreamResult,
  LokiTailResponse,
  LokiQuery,
  LokiOptions,
} from './types';

import { formatQuery, getHighlighterExpressionsFromQuery } from './query_utils';
import { of } from 'rxjs';

/**
 * Transforms LokiLogStream structure into a dataFrame. Used when doing standard queries and older version of Loki.
 */
export function legacyLogStreamToDataFrame(
  stream: LokiLegacyStreamResult,
  reverse?: boolean,
  refId?: string
): DataFrame {
  let labels: Labels = stream.parsedLabels;
  if (!labels && stream.labels) {
    labels = parseLabels(stream.labels);
  }

const times = new ArrayVector<string>([]);
  const timesNs = new ArrayVector<string>([]);
  const lines = new ArrayVector<string>([]);
  const uids = new ArrayVector<string>([]);

for (const entry of stream.entries) {
    const ts = entry.ts || entry.timestamp;
    // iso string with nano precision, will be truncated but is parse-able
    times.add(ts);
    // So this matches new format, we are loosing precision here, which sucks but no easy way to keep it and this
    // is for old pre 1.0.0 version Loki so probably does not affect that much.
    timesNs.add(dateTime(ts).valueOf() + '000000');
    lines.add(entry.line);
    uids.add(createUid(ts, stream.labels, entry.line));
  }

return constructDataFrame(times, timesNs, lines, uids, labels, reverse, refId);
}

/**
 * Transforms LokiStreamResult structure into a dataFrame. Used when doing standard queries and newer version of Loki.
 */
export function lokiStreamResultToDataFrame(stream: LokiStreamResult, reverse?: boolean, refId?: string): DataFrame {
  const labels: Labels = stream.stream;
  const labelsString = Object.entries(labels)
    .map(([key, val]) => `${key}="${val}"`)
    .sort()
    .join('');

const times = new ArrayVector<string>([]);
  const timesNs = new ArrayVector<string>([]);
  const lines = new ArrayVector<string>([]);
  const uids = new ArrayVector<string>([]);

for (const [ts, line] of stream.values) {
    // num ns epoch in string, we convert it to iso string here so it matches old format
    times.add(new Date(parseInt(ts.substr(0, ts.length - 6), 10)).toISOString());
    timesNs.add(ts);
    lines.add(line);
    uids.add(createUid(ts, labelsString, line));
  }

return constructDataFrame(times, timesNs, lines, uids, labels, reverse, refId);
}

/**
 * Constructs dataFrame with supplied fields and other data. Also makes sure it is properly reversed if needed.
 */
function constructDataFrame(
  times: ArrayVector<string>,
  timesNs: ArrayVector<string>,
  lines: ArrayVector<string>,
  uids: ArrayVector<string>,
  labels: Labels,
  reverse?: boolean,
  refId?: string
) {
  const dataFrame = {
    refId,
    fields: [
      { name: 'ts', type: FieldType.time, config: { title: 'Time' }, values: times }, // Time
      { name: 'line', type: FieldType.string, config: {}, values: lines, labels }, // Line
      { name: 'id', type: FieldType.string, config: {}, values: uids },
      { name: 'tsNs', type: FieldType.time, config: { title: 'Time ns' }, values: timesNs }, // Time
    ],
    length: times.length,
  };

if (reverse) {
    const mutableDataFrame = new MutableDataFrame(dataFrame);
    mutableDataFrame.reverse();
    return mutableDataFrame;
  }

return dataFrame;
}

/**
 * Transform LokiResponse data and appends it to MutableDataFrame. Used for streaming where the dataFrame can be
 * a CircularDataFrame creating a fixed size rolling buffer.
 * TODO: Probably could be unified with the logStreamToDataFrame function.
 * @param response
 * @param data Needs to have ts, line, labels, id as fields
 */
export function appendLegacyResponseToBufferedData(response: LokiLegacyStreamResponse, data: MutableDataFrame) {
  // Should we do anything with: response.dropped_entries?

const streams: LokiLegacyStreamResult[] = response.streams;
  if (!streams || !streams.length) {
    return;
  }

let baseLabels: Labels = {};
  for (const f of data.fields) {
    if (f.type === FieldType.string) {
      if (f.labels) {
        baseLabels = f.labels;
      }
      break;
    }
  }

for (const stream of streams) {
    // Find unique labels
    const labels = parseLabels(stream.labels);
    const unique = findUniqueLabels(labels, baseLabels);

// Add each line
    for (const entry of stream.entries) {
      const ts = entry.ts || entry.timestamp;
      data.values.ts.add(ts);
      data.values.line.add(entry.line);
      data.values.labels.add(unique);
      data.values.id.add(createUid(ts, stream.labels, entry.line));
    }
  }
}

export function appendResponseToBufferedData(response: LokiTailResponse, data: MutableDataFrame) {
  // Should we do anything with: response.dropped_entries?

const streams: LokiStreamResult[] = response.streams;
  if (!streams || !streams.length) {
    return;
  }

let baseLabels: Labels = {};
  for (const f of data.fields) {
    if (f.type === FieldType.string) {
      if (f.labels) {
        baseLabels = f.labels;
      }
      break;
    }
  }

for (const stream of streams) {
    // Find unique labels
    const unique = findUniqueLabels(stream.stream, baseLabels);
    const allLabelsString = Object.entries(stream.stream)
      .map(([key, val]) => `${key}="${val}"`)
      .sort()
      .join('');

// Add each line
    for (const [ts, line] of stream.values) {
      data.values.ts.add(new Date(parseInt(ts.substr(0, ts.length - 6), 10)).toISOString());
      data.values.tsNs.add(ts);
      data.values.line.add(line);
      data.values.labels.add(unique);
      data.values.id.add(createUid(ts, allLabelsString, line));
    }
  }
}

function createUid(ts: string, labelsString: string, line: string): string {
  return md5(`${ts}_${labelsString}_${line}`);
}

function lokiMatrixToTimeSeries(matrixResult: LokiMatrixResult, options: TransformerOptions): TimeSeries {
  return {
    target: createMetricLabel(matrixResult.metric, options),
    datapoints: lokiPointsToTimeseriesPoints(matrixResult.values, options),
    tags: matrixResult.metric,
  };
}

function lokiPointsToTimeseriesPoints(
  data: Array<[number, string]>,
  options: TransformerOptions
): Array<[number, number]> {
  const stepMs = options.step * 1000;
  const datapoints: Array<[number, number]> = [];

let baseTimestampMs = options.start / 1e6;
  for (const [time, value] of data) {
    let datapointValue = parseFloat(value);
    if (isNaN(datapointValue)) {
      datapointValue = null;
    }

const timestamp = time * 1000;
    for (let t = baseTimestampMs; t < timestamp; t += stepMs) {
      datapoints.push([0, t]);
    }

baseTimestampMs = timestamp + stepMs;
    datapoints.push([datapointValue, timestamp]);
  }

const endTimestamp = options.end / 1e6;
  for (let t = baseTimestampMs; t <= endTimestamp; t += stepMs) {
    datapoints.push([0, t]);
  }

return datapoints;
}

export function lokiResultsToTableModel(
  lokiResults: Array<LokiMatrixResult | LokiVectorResult>,
  resultCount: number,
  refId: string,
  valueWithRefId?: boolean
): TableModel {
  if (!lokiResults || lokiResults.length === 0) {
    return new TableModel();
  }

// Collect all labels across all metrics
  const metricLabels: Set<string> = new Set<string>(
    lokiResults.reduce((acc, cur) => acc.concat(Object.keys(cur.metric)), [])
  );

// Sort metric labels, create columns for them and record their index
  const sortedLabels = [...metricLabels.values()].sort();
  const table = new TableModel();
  table.columns = [
    { text: 'Time', type: FieldType.time },
    ...sortedLabels.map(label => ({ text: label, filterable: true })),
    { text: resultCount > 1 || valueWithRefId ? `Value #${refId}` : 'Value', type: FieldType.time },
  ];

// Populate rows, set value to empty string when label not present.
  lokiResults.forEach(series => {
    const newSeries: LokiMatrixResult = {
      metric: series.metric,
      values: (series as LokiVectorResult).value
        ? [(series as LokiVectorResult).value]
        : (series as LokiMatrixResult).values,
    };

if (!newSeries.values) {
      return;
    }

if (!newSeries.metric) {
      table.rows.concat(newSeries.values.map(([a, b]) => [a * 1000, parseFloat(b)]));
    } else {
      table.rows.push(
        ...newSeries.values.map(([a, b]) => [
          a * 1000,
          ...sortedLabels.map(label => newSeries.metric[label] || ''),
          parseFloat(b),
        ])
      );
    }
  });

return table;
}

function createMetricLabel(labelData: { [key: string]: string }, options?: TransformerOptions) {
  let label =
    options === undefined || _.isEmpty(options.legendFormat)
      ? getOriginalMetricName(labelData)
      : renderTemplate(templateSrv.replace(options.legendFormat), labelData);

if (!label) {
    label = options.query;
  }
  return label;
}

function renderTemplate(aliasPattern: string, aliasData: { [key: string]: string }) {
  const aliasRegex = /\{\{\s*(.+?)\s*\}\}/g;
  return aliasPattern.replace(aliasRegex, (_, g1) => (aliasData[g1] ? aliasData[g1] : g1));
}

export function lokiStreamsToDataframes(
  data: LokiStreamResult[],
  target: { refId: string; expr?: string; regexp?: string },
  limit: number,
  config: LokiOptions,
  reverse = false
): DataFrame[] {
  const series: DataFrame[] = data.map(stream => {
    const dataFrame = lokiStreamResultToDataFrame(stream, reverse);
    enhanceDataFrame(dataFrame, config);
    return {
      ...dataFrame,
      refId: target.refId,
      meta: {
        searchWords: getHighlighterExpressionsFromQuery(formatQuery(target.expr, target.regexp)),
        limit,
      },
    };
  });

return series;
}

export function lokiLegacyStreamsToDataframes(
  data: LokiLegacyStreamResult | LokiLegacyStreamResponse,
  target: { refId: string; query?: string; regexp?: string },
  limit: number,
  config: LokiOptions,
  reverse = false
): DataFrame[] {
  if (Object.keys(data).length === 0) {
    return [];
  }

if (isLokiLogsStream(data)) {
    return [legacyLogStreamToDataFrame(data, false, target.refId)];
  }

const series: DataFrame[] = data.streams.map(stream => {
    const dataFrame = legacyLogStreamToDataFrame(stream, reverse);
    enhanceDataFrame(dataFrame, config);

return {
      ...dataFrame,
      refId: target.refId,
      meta: {
        searchWords: getHighlighterExpressionsFromQuery(formatQuery(target.query, target.regexp)),
        limit,
      },
    };
  });

return series;
}

/**
 * Adds new fields and DataLinks to DataFrame based on DataSource instance config.
 * @param dataFrame
 */
export const enhanceDataFrame = (dataFrame: DataFrame, config: LokiOptions | null): void => {
  if (!config) {
    return;
  }

const derivedFields = config.derivedFields ?? [];
  if (!derivedFields.length) {
    return;
  }

acc[field.name] = dataFrameField;
    return acc;
  }, {} as Record<string, any>);

dataFrame.fields = [...dataFrame.fields, ...Object.values(fields)];
};

export function rangeQueryResponseToTimeSeries(
  response: LokiResponse,
  query: LokiRangeQueryRequest,
  target: LokiQuery,
  responseListLength: number
): TimeSeries[] {
  const transformerOptions: TransformerOptions = {
    format: target.format,
    legendFormat: target.legendFormat,
    start: query.start,
    end: query.end,
    step: query.step,
    query: query.query,
    responseListLength,
    refId: target.refId,
    valueWithRefId: target.valueWithRefId,
  };

switch (response.data.resultType) {
    case LokiResultType.Vector:
      return response.data.result.map(vecResult =>
        lokiMatrixToTimeSeries({ metric: vecResult.metric, values: [vecResult.value] }, transformerOptions)
      );
    case LokiResultType.Matrix:
      return response.data.result.map(matrixResult => lokiMatrixToTimeSeries(matrixResult, transformerOptions));
    default:
      return [];
  }
}

export function processRangeQueryResponse(
  response: LokiResponse,
  target: LokiQuery,
  query: LokiRangeQueryRequest,
  responseListLength: number,
  limit: number,
  config: LokiOptions,
  reverse = false
) {
  switch (response.data.resultType) {
    case LokiResultType.Stream:
      return of({
        data: lokiStreamsToDataframes(response.data.result, target, limit, config, reverse),
        key: `${target.refId}_log`,
      });

case LokiResultType.Vector:
    case LokiResultType.Matrix:
      return of({
        data: rangeQueryResponseToTimeSeries(
          response,
          query,
          {
            ...target,
            format: 'time_series',
          },
          responseListLength
        ),
        key: target.refId,
      });
    default:
      throw new Error(`Unknown result type "${(response.data as any).resultType}".`);
  }
}

export function isLokiLogsStream(
  data: LokiLegacyStreamResult | LokiLegacyStreamResponse
): data is LokiLegacyStreamResult {
  return !data.hasOwnProperty('streams');
}