@grafana/data#FieldCache TypeScript Examples

function getIdField(fieldCache: FieldCache): FieldWithIndex | undefined {
  const idFieldNames = ['id'];
  for (const fieldName of idFieldNames) {
    const idField = fieldCache.getFieldByName(fieldName);
    if (idField) {
      return idField;
    }
  }
  return undefined;
}

prepareLogRowContextQueryTarget = (row: LogRowModel, limit: number, direction: 'BACKWARD' | 'FORWARD') => {
    const query = Object.keys(row.labels)
      .map(label => `${label}="${row.labels[label]}"`)
      .join(',');

    const contextTimeBuffer = 2 * 60 * 60 * 1000; // 2h buffer
    const commonTargetOptions = {
      limit,
      query: `{${query}}`,
      expr: `{${query}}`,
      direction,
    };

    const fieldCache = new FieldCache(row.dataFrame);
    const nsField = fieldCache.getFieldByName('tsNs')!;
    const nsTimestamp = nsField.values.get(row.rowIndex);

    if (direction === 'BACKWARD') {
      return {
        ...commonTargetOptions,
        // convert to ns, we loose some precision here but it is not that important at the far points of the context
        start: row.timeEpochMs - contextTimeBuffer + '000000',
        end: nsTimestamp,
        direction,
      };
    } else {
      return {
        ...commonTargetOptions,
        // start param in Loki API is inclusive so we'll have to filter out the row that this request is based from
        // and any other that were logged in the same ns but before the row. Right now these rows will be lost
        // because the are before but came it he response that should return only rows after.
        start: nsTimestamp,
        // convert to ns, we loose some precision here but it is not that important at the far points of the context
        end: row.timeEpochMs + contextTimeBuffer + '000000',
      };
    }
  };

describe('Graph utils', () => {
  describe('getMultiSeriesGraphHoverInfo', () => {
    describe('when series datapoints are x-axis aligned', () => {
      it('returns a datapoints that user hovers over', () => {
        const aCache = new FieldCache(aSeries);
        const aValueField = aCache.getFieldByName('value');
        const aTimeField = aCache.getFieldByName('time');
        const bCache = new FieldCache(bSeries);
        const bValueField = bCache.getFieldByName('value');
        const bTimeField = bCache.getFieldByName('time');

        const result = getMultiSeriesGraphHoverInfo([aValueField!, bValueField!], [aTimeField!, bTimeField!], 0);
        expect(result.time).toBe(100);
        expect(result.results[0]).toEqual(
          mockResult('10', 0, 0, getFixedThemedColor(aValueField!), aValueField!.name, 100)
        );
        expect(result.results[1]).toEqual(
          mockResult('30', 0, 1, getFixedThemedColor(bValueField!), bValueField!.name, 100)
        );
      });

      describe('returns the closest datapoints before the hover position', () => {
        it('when hovering right before a datapoint', () => {
          const aCache = new FieldCache(aSeries);
          const aValueField = aCache.getFieldByName('value');
          const aTimeField = aCache.getFieldByName('time');
          const bCache = new FieldCache(bSeries);
          const bValueField = bCache.getFieldByName('value');
          const bTimeField = bCache.getFieldByName('time');

          //  hovering right before middle point
          const result = getMultiSeriesGraphHoverInfo([aValueField!, bValueField!], [aTimeField!, bTimeField!], 199);
          expect(result.time).toBe(100);
          expect(result.results[0]).toEqual(
            mockResult('10', 0, 0, getFixedThemedColor(aValueField!), aValueField!.name, 100)
          );
          expect(result.results[1]).toEqual(
            mockResult('30', 0, 1, getFixedThemedColor(bValueField!), bValueField!.name, 100)
          );
        });

        it('when hovering right after a datapoint', () => {
          const aCache = new FieldCache(aSeries);
          const aValueField = aCache.getFieldByName('value');
          const aTimeField = aCache.getFieldByName('time');
          const bCache = new FieldCache(bSeries);
          const bValueField = bCache.getFieldByName('value');
          const bTimeField = bCache.getFieldByName('time');

          //  hovering right after middle point
          const result = getMultiSeriesGraphHoverInfo([aValueField!, bValueField!], [aTimeField!, bTimeField!], 201);
          expect(result.time).toBe(200);
          expect(result.results[0]).toEqual(
            mockResult('20', 1, 0, getFixedThemedColor(aValueField!), aValueField!.name, 200)
          );
          expect(result.results[1]).toEqual(
            mockResult('60', 1, 1, getFixedThemedColor(bValueField!), bValueField!.name, 200)
          );
        });
      });
    });

    describe('when series x-axes are not aligned', () => {
      // aSeries and cSeries are not aligned
      // cSeries is missing a middle point
      it('hovering over a middle point', () => {
        const aCache = new FieldCache(aSeries);
        const aValueField = aCache.getFieldByName('value');
        const aTimeField = aCache.getFieldByName('time');
        const cCache = new FieldCache(cSeries);
        const cValueField = cCache.getFieldByName('value');
        const cTimeField = cCache.getFieldByName('time');

        // hovering on a middle point
        // aSeries has point at that time, cSeries doesn't
        const result = getMultiSeriesGraphHoverInfo([aValueField!, cValueField!], [aTimeField!, cTimeField!], 200);

        // we expect a time of the hovered point
        expect(result.time).toBe(200);
        // we expect middle point from aSeries (the one we are hovering over)
        expect(result.results[0]).toEqual(
          mockResult('20', 1, 0, getFixedThemedColor(aValueField!), aValueField!.name, 200)
        );
        // we expect closest point before hovered point from cSeries (1st point)
        expect(result.results[1]).toEqual(
          mockResult('30', 0, 1, getFixedThemedColor(cValueField!), cValueField!.name, 100)
        );
      });

      it('hovering right after over the middle point', () => {
        const aCache = new FieldCache(aSeries);
        const aValueField = aCache.getFieldByName('value');
        const aTimeField = aCache.getFieldByName('time');
        const cCache = new FieldCache(cSeries);
        const cValueField = cCache.getFieldByName('value');
        const cTimeField = cCache.getFieldByName('time');

        // aSeries has point at that time, cSeries doesn't
        const result = getMultiSeriesGraphHoverInfo([aValueField!, cValueField!], [aTimeField!, cTimeField!], 201);

        // we expect the time of the closest point before hover
        expect(result.time).toBe(200);
        // we expect the closest datapoint before hover from aSeries
        expect(result.results[0]).toEqual(
          mockResult('20', 1, 0, getFixedThemedColor(aValueField!), aValueField!.name, 200)
        );
        // we expect the closest datapoint before  hover from cSeries (1st point)
        expect(result.results[1]).toEqual(
          mockResult('30', 0, 1, getFixedThemedColor(cValueField!), cValueField!.name, 100)
        );
      });
    });
  });

  describe('findHoverIndexFromData', () => {
    it('returns index of the closest datapoint before hover position', () => {
      const cache = new FieldCache(aSeries);
      const timeField = cache.getFieldByName('time');
      // hovering over 1st datapoint
      expect(findHoverIndexFromData(timeField!, 0)).toBe(0);
      // hovering over right before 2nd datapoint
      expect(findHoverIndexFromData(timeField!, 199)).toBe(0);
      // hovering over 2nd datapoint
      expect(findHoverIndexFromData(timeField!, 200)).toBe(1);
      // hovering over right before 3rd datapoint
      expect(findHoverIndexFromData(timeField!, 299)).toBe(1);
      // hovering over 3rd datapoint
      expect(findHoverIndexFromData(timeField!, 300)).toBe(2);
    });
  });
});

getRowContexts = async (
  getRowContext: (row: LogRowModel, options?: any) => Promise<DataQueryResponse>,
  row: LogRowModel,
  limit: number
) => {
  const promises = [
    getRowContext(row, {
      limit,
    }),
    getRowContext(row, {
      // The start time is inclusive so we will get the one row we are using as context entry
      limit: limit + 1,
      direction: 'FORWARD',
    }),
  ];

  const results: Array<DataQueryResponse | DataQueryError> = await Promise.all(promises.map(p => p.catch(e => e)));

  return {
    data: results.map(result => {
      const dataResult: DataQueryResponse = result as DataQueryResponse;
      if (!dataResult.data) {
        return [];
      }

      const data: any[] = [];
      for (let index = 0; index < dataResult.data.length; index++) {
        const dataFrame = toDataFrame(dataResult.data[index]);
        const fieldCache = new FieldCache(dataFrame);
        const timestampField: Field<string> = fieldCache.getFieldByName('ts')!;
        const idField: Field<string> | undefined = fieldCache.getFieldByName('id');

        for (let fieldIndex = 0; fieldIndex < timestampField.values.length; fieldIndex++) {
          // TODO: this filtering is datasource dependant so it will make sense to move it there so the API is
          //  to return correct list of lines handling inclusive ranges or how to filter the correct line on the
          //  datasource.

          // Filter out the row that is the one used as a focal point for the context as we will get it in one of the
          // requests.
          if (idField) {
            // For Loki this means we filter only the one row. Issue is we could have other rows logged at the same
            // ns which came before but they come in the response that search for logs after. This means right now
            // we will show those as if they came after. This is not strictly correct but seems better than loosing them
            // and making this correct would mean quite a bit of complexity to shuffle things around and messing up
            //counts.
            if (idField.values.get(fieldIndex) === row.uid) {
              continue;
            }
          } else {
            // Fallback to timestamp. This should not happen right now as this feature is implemented only for loki
            // and that has ID. Later this branch could be used in other DS but mind that this could also filter out
            // logs which were logged in the same timestamp and that can be a problem depending on the precision.
            if (parseInt(timestampField.values.get(fieldIndex), 10) === row.timeEpochMs) {
              continue;
            }
          }

          const lineField: Field<string> = dataFrame.fields.filter(field => field.name === 'line')[0];
          const line = lineField.values.get(fieldIndex); // assuming that both fields have same length

          if (data.length === 0) {
            data[0] = [line];
          } else {
            data[0].push(line);
          }
        }
      }

      return data;
    }),
    errors: results.map(result => {
      const errorResult: DataQueryError = result as DataQueryError;
      if (!errorResult.message) {
        return '';
      }

      return errorResult.message;
    }),
  };
}

/**
 * Converts dataFrames into LogsModel. This involves merging them into one list, sorting them and computing metadata
 * like common labels.
 */
export function logSeriesToLogsModel(logSeries: DataFrame[]): LogsModel | undefined {
  if (logSeries.length === 0) {
    return undefined;
  }
  const allLabels: Labels[] = [];

  // Find the fields we care about and collect all labels
  const allSeries: LogFields[] = logSeries.map(series => {
    const fieldCache = new FieldCache(series);

    // Assume the first string field in the dataFrame is the message. This was right so far but probably needs some
    // more explicit checks.
    const stringField = fieldCache.getFirstFieldOfType(FieldType.string);
    if (stringField.labels) {
      allLabels.push(stringField.labels);
    }
    return {
      series,
      timeField: fieldCache.getFirstFieldOfType(FieldType.time),
      stringField,
      logLevelField: fieldCache.getFieldByName('level'),
      idField: getIdField(fieldCache),
    };
  });

  const commonLabels = allLabels.length > 0 ? findCommonLabels(allLabels) : {};

  const rows: LogRowModel[] = [];
  let hasUniqueLabels = false;

  for (const info of allSeries) {
    const { timeField, stringField, logLevelField, idField, series } = info;
    const labels = stringField.labels;
    const uniqueLabels = findUniqueLabels(labels, commonLabels);
    if (Object.keys(uniqueLabels).length > 0) {
      hasUniqueLabels = true;
    }

    let seriesLogLevel: LogLevel | undefined = undefined;
    if (labels && Object.keys(labels).indexOf('level') !== -1) {
      seriesLogLevel = getLogLevelFromKey(labels['level']);
    }

    for (let j = 0; j < series.length; j++) {
      const ts = timeField.values.get(j);
      const time = dateTime(ts);

      const messageValue: unknown = stringField.values.get(j);
      // This should be string but sometimes isn't (eg elastic) because the dataFrame is not strongly typed.
      const message: string = typeof messageValue === 'string' ? messageValue : JSON.stringify(messageValue);

      const hasAnsi = hasAnsiCodes(message);
      const searchWords = series.meta && series.meta.searchWords ? series.meta.searchWords : [];

      let logLevel = LogLevel.unknown;
      if (logLevelField && logLevelField.values.get(j)) {
        logLevel = getLogLevelFromKey(logLevelField.values.get(j));
      } else if (seriesLogLevel) {
        logLevel = seriesLogLevel;
      } else {
        logLevel = getLogLevel(message);
      }

      rows.push({
        entryFieldIndex: stringField.index,
        rowIndex: j,
        dataFrame: series,
        logLevel,
        timeFromNow: time.fromNow(),
        timeEpochMs: time.valueOf(),
        timeLocal: time.format(logTimeFormat),
        timeUtc: toUtc(time.valueOf()).format(logTimeFormat),
        uniqueLabels,
        hasAnsi,
        searchWords,
        entry: hasAnsi ? ansicolor.strip(message) : message,
        raw: message,
        labels: stringField.labels,
        uid: idField ? idField.values.get(j) : j.toString(),
      });
    }
  }

  const deduplicatedLogRows = deduplicateLogRowsById(rows);

  // Meta data to display in status
  const meta: LogsMetaItem[] = [];
  if (_.size(commonLabels) > 0) {
    meta.push({
      label: 'Common labels',
      value: commonLabels,
      kind: LogsMetaKind.LabelsMap,
    });
  }

  const limits = logSeries.filter(series => series.meta && series.meta.limit);
  const limitValue = Object.values(
    limits.reduce((acc: any, elem: any) => {
      acc[elem.refId] = elem.meta.limit;
      return acc;
    }, {})
  ).reduce((acc: number, elem: any) => (acc += elem), 0);

  if (limits.length > 0) {
    meta.push({
      label: 'Limit',
      value: `${limitValue} (${deduplicatedLogRows.length} returned)`,
      kind: LogsMetaKind.String,
    });
  }

  return {
    hasUniqueLabels,
    meta,
    rows: deduplicatedLogRows,
  };
}

describe('ElasticResponse', () => {
  let targets;
  let response: any;
  let result: any;

  describe('simple query and count', () => {
    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'count', id: '1' }],
          bucketAggs: [{ type: 'date_histogram', field: '@timestamp', id: '2' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    doc_count: 10,
                    key: 1000,
                  },
                  {
                    doc_count: 15,
                    key: 2000,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 1 series', () => {
      expect(result.data.length).toBe(1);
      expect(result.data[0].target).toBe('Count');
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].datapoints[0][0]).toBe(10);
      expect(result.data[0].datapoints[0][1]).toBe(1000);
    });
  });

  describe('simple query count & avg aggregation', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [
            { type: 'count', id: '1' },
            { type: 'avg', field: 'value', id: '2' },
          ],
          bucketAggs: [{ type: 'date_histogram', field: '@timestamp', id: '3' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '3': {
                buckets: [
                  {
                    '2': { value: 88 },
                    doc_count: 10,
                    key: 1000,
                  },
                  {
                    '2': { value: 99 },
                    doc_count: 15,
                    key: 2000,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(2);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].datapoints[0][0]).toBe(10);
      expect(result.data[0].datapoints[0][1]).toBe(1000);

      expect(result.data[1].target).toBe('Average value');
      expect(result.data[1].datapoints[0][0]).toBe(88);
      expect(result.data[1].datapoints[1][0]).toBe(99);
    });
  });

  describe('single group by query one metric', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'count', id: '1' }],
          bucketAggs: [
            { type: 'terms', field: 'host', id: '2' },
            { type: 'date_histogram', field: '@timestamp', id: '3' },
          ],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '3': {
                      buckets: [
                        { doc_count: 1, key: 1000 },
                        { doc_count: 3, key: 2000 },
                      ],
                    },
                    doc_count: 4,
                    key: 'server1',
                  },
                  {
                    '3': {
                      buckets: [
                        { doc_count: 2, key: 1000 },
                        { doc_count: 8, key: 2000 },
                      ],
                    },
                    doc_count: 10,
                    key: 'server2',
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(2);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('server1');
      expect(result.data[1].target).toBe('server2');
    });
  });

  describe('single group by query two metrics', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [
            { type: 'count', id: '1' },
            { type: 'avg', field: '@value', id: '4' },
          ],
          bucketAggs: [
            { type: 'terms', field: 'host', id: '2' },
            { type: 'date_histogram', field: '@timestamp', id: '3' },
          ],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '3': {
                      buckets: [
                        { '4': { value: 10 }, doc_count: 1, key: 1000 },
                        { '4': { value: 12 }, doc_count: 3, key: 2000 },
                      ],
                    },
                    doc_count: 4,
                    key: 'server1',
                  },
                  {
                    '3': {
                      buckets: [
                        { '4': { value: 20 }, doc_count: 1, key: 1000 },
                        { '4': { value: 32 }, doc_count: 3, key: 2000 },
                      ],
                    },
                    doc_count: 10,
                    key: 'server2',
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(4);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('server1 Count');
      expect(result.data[1].target).toBe('server1 Average @value');
      expect(result.data[2].target).toBe('server2 Count');
      expect(result.data[3].target).toBe('server2 Average @value');
    });
  });

  describe('with percentiles ', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'percentiles', settings: { percents: [75, 90] }, id: '1' }],
          bucketAggs: [{ type: 'date_histogram', field: '@timestamp', id: '3' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '3': {
                buckets: [
                  {
                    '1': { values: { '75': 3.3, '90': 5.5 } },
                    doc_count: 10,
                    key: 1000,
                  },
                  {
                    '1': { values: { '75': 2.3, '90': 4.5 } },
                    doc_count: 15,
                    key: 2000,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(2);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('p75');
      expect(result.data[1].target).toBe('p90');
      expect(result.data[0].datapoints[0][0]).toBe(3.3);
      expect(result.data[0].datapoints[0][1]).toBe(1000);
      expect(result.data[1].datapoints[1][0]).toBe(4.5);
    });
  });

  describe('with extended_stats', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [
            {
              type: 'extended_stats',
              meta: { max: true, std_deviation_bounds_upper: true },
              id: '1',
            },
          ],
          bucketAggs: [
            { type: 'terms', field: 'host', id: '3' },
            { type: 'date_histogram', id: '4' },
          ],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '3': {
                buckets: [
                  {
                    key: 'server1',
                    '4': {
                      buckets: [
                        {
                          '1': {
                            max: 10.2,
                            min: 5.5,
                            std_deviation_bounds: { upper: 3, lower: -2 },
                          },
                          doc_count: 10,
                          key: 1000,
                        },
                      ],
                    },
                  },
                  {
                    key: 'server2',
                    '4': {
                      buckets: [
                        {
                          '1': {
                            max: 10.2,
                            min: 5.5,
                            std_deviation_bounds: { upper: 3, lower: -2 },
                          },
                          doc_count: 10,
                          key: 1000,
                        },
                      ],
                    },
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 4 series', () => {
      expect(result.data.length).toBe(4);
      expect(result.data[0].datapoints.length).toBe(1);
      expect(result.data[0].target).toBe('server1 Max');
      expect(result.data[1].target).toBe('server1 Std Dev Upper');

      expect(result.data[0].datapoints[0][0]).toBe(10.2);
      expect(result.data[1].datapoints[0][0]).toBe(3);
    });
  });

  describe('single group by with alias pattern', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'count', id: '1' }],
          alias: '{{term @host}} {{metric}} and {{not_exist}} {{@host}}',
          bucketAggs: [
            { type: 'terms', field: '@host', id: '2' },
            { type: 'date_histogram', field: '@timestamp', id: '3' },
          ],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '3': {
                      buckets: [
                        { doc_count: 1, key: 1000 },
                        { doc_count: 3, key: 2000 },
                      ],
                    },
                    doc_count: 4,
                    key: 'server1',
                  },
                  {
                    '3': {
                      buckets: [
                        { doc_count: 2, key: 1000 },
                        { doc_count: 8, key: 2000 },
                      ],
                    },
                    doc_count: 10,
                    key: 'server2',
                  },
                  {
                    '3': {
                      buckets: [
                        { doc_count: 2, key: 1000 },
                        { doc_count: 8, key: 2000 },
                      ],
                    },
                    doc_count: 10,
                    key: 0,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(3);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('server1 Count and {{not_exist}} server1');
      expect(result.data[1].target).toBe('server2 Count and {{not_exist}} server2');
      expect(result.data[2].target).toBe('0 Count and {{not_exist}} 0');
    });
  });

  describe('histogram response', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'count', id: '1' }],
          bucketAggs: [{ type: 'histogram', field: 'bytes', id: '3' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '3': {
                buckets: [
                  { doc_count: 1, key: 1000 },
                  { doc_count: 3, key: 2000 },
                  { doc_count: 2, key: 1000 },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return table with byte and count', () => {
      expect(result.data[0].rows.length).toBe(3);
      expect(result.data[0].columns).toEqual([{ text: 'bytes', filterable: true }, { text: 'Count' }]);
    });
  });

  describe('with two filters agg', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'count', id: '1' }],
          bucketAggs: [
            {
              id: '2',
              type: 'filters',
              settings: {
                filters: [{ query: '@metric:cpu' }, { query: '@metric:logins.count' }],
              },
            },
            { type: 'date_histogram', field: '@timestamp', id: '3' },
          ],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: {
                  '@metric:cpu': {
                    '3': {
                      buckets: [
                        { doc_count: 1, key: 1000 },
                        { doc_count: 3, key: 2000 },
                      ],
                    },
                  },
                  '@metric:logins.count': {
                    '3': {
                      buckets: [
                        { doc_count: 2, key: 1000 },
                        { doc_count: 8, key: 2000 },
                      ],
                    },
                  },
                },
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 2 series', () => {
      expect(result.data.length).toBe(2);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('@metric:cpu');
      expect(result.data[1].target).toBe('@metric:logins.count');
    });
  });

  describe('with dropfirst and last aggregation', () => {
    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'avg', id: '1' }, { type: 'count' }],
          bucketAggs: [
            {
              id: '2',
              type: 'date_histogram',
              field: 'host',
              settings: { trimEdges: 1 },
            },
          ],
        },
      ];

      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '1': { value: 1000 },
                    key: 1,
                    doc_count: 369,
                  },
                  {
                    '1': { value: 2000 },
                    key: 2,
                    doc_count: 200,
                  },
                  {
                    '1': { value: 2000 },
                    key: 3,
                    doc_count: 200,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should remove first and last value', () => {
      expect(result.data.length).toBe(2);
      expect(result.data[0].datapoints.length).toBe(1);
    });
  });

  describe('No group by time', () => {
    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'avg', id: '1' }, { type: 'count' }],
          bucketAggs: [{ id: '2', type: 'terms', field: 'host' }],
        },
      ];

      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '1': { value: 1000 },
                    key: 'server-1',
                    doc_count: 369,
                  },
                  {
                    '1': { value: 2000 },
                    key: 'server-2',
                    doc_count: 200,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return table', () => {
      expect(result.data.length).toBe(1);
      expect(result.data[0].type).toBe('table');
      expect(result.data[0].rows.length).toBe(2);
      expect(result.data[0].rows[0][0]).toBe('server-1');
      expect(result.data[0].rows[0][1]).toBe(1000);
      expect(result.data[0].rows[0][2]).toBe(369);

      expect(result.data[0].rows[1][0]).toBe('server-2');
      expect(result.data[0].rows[1][1]).toBe(2000);
    });
  });

  describe('No group by time with percentiles ', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'percentiles', field: 'value', settings: { percents: [75, 90] }, id: '1' }],
          bucketAggs: [{ type: 'term', field: 'id', id: '3' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '3': {
                buckets: [
                  {
                    '1': { values: { '75': 3.3, '90': 5.5 } },
                    doc_count: 10,
                    key: 'id1',
                  },
                  {
                    '1': { values: { '75': 2.3, '90': 4.5 } },
                    doc_count: 15,
                    key: 'id2',
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return table', () => {
      expect(result.data.length).toBe(1);
      expect(result.data[0].type).toBe('table');
      expect(result.data[0].columns[0].text).toBe('id');
      expect(result.data[0].columns[1].text).toBe('p75 value');
      expect(result.data[0].columns[2].text).toBe('p90 value');
      expect(result.data[0].rows.length).toBe(2);
      expect(result.data[0].rows[0][0]).toBe('id1');
      expect(result.data[0].rows[0][1]).toBe(3.3);
      expect(result.data[0].rows[0][2]).toBe(5.5);
      expect(result.data[0].rows[1][0]).toBe('id2');
      expect(result.data[0].rows[1][1]).toBe(2.3);
      expect(result.data[0].rows[1][2]).toBe(4.5);
    });
  });

  describe('Multiple metrics of same type', () => {
    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [
            { type: 'avg', id: '1', field: 'test' },
            { type: 'avg', id: '2', field: 'test2' },
          ],
          bucketAggs: [{ id: '2', type: 'terms', field: 'host' }],
        },
      ];

      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    '1': { value: 1000 },
                    '2': { value: 3000 },
                    key: 'server-1',
                    doc_count: 369,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should include field in metric name', () => {
      expect(result.data[0].type).toBe('table');
      expect(result.data[0].rows[0][1]).toBe(1000);
      expect(result.data[0].rows[0][2]).toBe(3000);
    });
  });

  describe('Raw documents query', () => {
    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [{ type: 'raw_document', id: '1' }],
          bucketAggs: [],
        },
      ];
      response = {
        responses: [
          {
            hits: {
              total: 100,
              hits: [
                {
                  _id: '1',
                  _type: 'type',
                  _index: 'index',
                  _source: { sourceProp: 'asd' },
                  fields: { fieldProp: 'field' },
                },
                {
                  _source: { sourceProp: 'asd2' },
                  fields: { fieldProp: 'field2' },
                },
              ],
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return docs', () => {
      expect(result.data.length).toBe(1);
      expect(result.data[0].type).toBe('docs');
      expect(result.data[0].total).toBe(100);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].datapoints[0].sourceProp).toBe('asd');
      expect(result.data[0].datapoints[0].fieldProp).toBe('field');
    });
  });

  describe('with bucket_script ', () => {
    let result: any;

    beforeEach(() => {
      targets = [
        {
          refId: 'A',
          metrics: [
            { id: '1', type: 'sum', field: '@value' },
            { id: '3', type: 'max', field: '@value' },
            {
              id: '4',
              field: 'select field',
              pipelineVariables: [
                { name: 'var1', pipelineAgg: '1' },
                { name: 'var2', pipelineAgg: '3' },
              ],
              settings: { script: 'params.var1 * params.var2' },
              type: 'bucket_script',
            },
          ],
          bucketAggs: [{ type: 'date_histogram', field: '@timestamp', id: '2' }],
        },
      ];
      response = {
        responses: [
          {
            aggregations: {
              '2': {
                buckets: [
                  {
                    1: { value: 2 },
                    3: { value: 3 },
                    4: { value: 6 },
                    doc_count: 60,
                    key: 1000,
                  },
                  {
                    1: { value: 3 },
                    3: { value: 4 },
                    4: { value: 12 },
                    doc_count: 60,
                    key: 2000,
                  },
                ],
              },
            },
          },
        ],
      };

      result = new ElasticResponse(targets, response).getTimeSeries();
    });

    it('should return 3 series', () => {
      expect(result.data.length).toBe(3);
      expect(result.data[0].datapoints.length).toBe(2);
      expect(result.data[0].target).toBe('Sum @value');
      expect(result.data[1].target).toBe('Max @value');
      expect(result.data[2].target).toBe('Sum @value * Max @value');
      expect(result.data[0].datapoints[0][0]).toBe(2);
      expect(result.data[1].datapoints[0][0]).toBe(3);
      expect(result.data[2].datapoints[0][0]).toBe(6);
      expect(result.data[0].datapoints[1][0]).toBe(3);
      expect(result.data[1].datapoints[1][0]).toBe(4);
      expect(result.data[2].datapoints[1][0]).toBe(12);
    });
  });

  describe('simple logs query and count', () => {
    const targets: any = [
      {
        refId: 'A',
        metrics: [{ type: 'count', id: '1' }],
        bucketAggs: [{ type: 'date_histogram', settings: { interval: 'auto' }, id: '2' }],
        context: 'explore',
        interval: '10s',
        isLogsQuery: true,
        key: 'Q-1561369883389-0.7611823271062786-0',
        liveStreaming: false,
        maxDataPoints: 1620,
        query: '',
        timeField: '@timestamp',
      },
    ];
    const response = {
      responses: [
        {
          aggregations: {
            '2': {
              buckets: [
                {
                  doc_count: 10,
                  key: 1000,
                },
                {
                  doc_count: 15,
                  key: 2000,
                },
              ],
            },
          },
          hits: {
            hits: [
              {
                _id: 'fdsfs',
                _type: '_doc',
                _index: 'mock-index',
                _source: {
                  '@timestamp': '2019-06-24T09:51:19.765Z',
                  host: 'djisaodjsoad',
                  message: 'hello, i am a message',
                  level: 'debug',
                  fields: {
                    lvl: 'debug',
                  },
                },
              },
              {
                _id: 'kdospaidopa',
                _type: '_doc',
                _index: 'mock-index',
                _source: {
                  '@timestamp': '2019-06-24T09:52:19.765Z',
                  host: 'dsalkdakdop',
                  message: 'hello, i am also message',
                  level: 'error',
                  fields: {
                    lvl: 'info',
                  },
                },
              },
            ],
          },
        },
      ],
    };

    it('should return histogram aggregation and documents', () => {
      const result = new ElasticResponse(targets, response).getLogs();
      expect(result.data.length).toBe(2);
      const logResults = result.data[0] as MutableDataFrame;
      const fields = logResults.fields.map(f => {
        return {
          name: f.name,
          type: f.type,
        };
      });

      expect(fields).toContainEqual({ name: '@timestamp', type: 'time' });
      expect(fields).toContainEqual({ name: 'host', type: 'string' });
      expect(fields).toContainEqual({ name: 'message', type: 'string' });

      let rows = new DataFrameView(logResults);
      for (let i = 0; i < rows.length; i++) {
        const r = rows.get(i);
        expect(r._id).toEqual(response.responses[0].hits.hits[i]._id);
        expect(r._type).toEqual(response.responses[0].hits.hits[i]._type);
        expect(r._index).toEqual(response.responses[0].hits.hits[i]._index);
        expect(r._source).toEqual(flatten(response.responses[0].hits.hits[i]._source, null));
      }

      // Make a map from the histogram results
      const hist: KeyValue<number> = {};
      const histogramResults = new MutableDataFrame(result.data[1]);
      rows = new DataFrameView(histogramResults);
      for (let i = 0; i < rows.length; i++) {
        const row = rows.get(i);
        hist[row.Time] = row.Count;
      }

      response.responses[0].aggregations['2'].buckets.forEach((bucket: any) => {
        expect(hist[bucket.key]).toEqual(bucket.doc_count);
      });
    });

    it('should map levels field', () => {
      const result = new ElasticResponse(targets, response).getLogs(undefined, 'level');
      const fieldCache = new FieldCache(result.data[0]);
      const field = fieldCache.getFieldByName('level');
      expect(field.values.toArray()).toEqual(['debug', 'error']);
    });

    it('should re map levels field to new field', () => {
      const result = new ElasticResponse(targets, response).getLogs(undefined, 'fields.lvl');
      const fieldCache = new FieldCache(result.data[0]);
      const field = fieldCache.getFieldByName('level');
      expect(field.values.toArray()).toEqual(['debug', 'info']);
    });
  });
});

describe('LokiDatasource', () => {
  const instanceSettings: any = {
    url: 'myloggingurl',
  };

  const legacyTestResp: { data: LokiLegacyStreamResponse; status: number } = {
    data: {
      streams: [
        {
          entries: [{ ts: '2019-02-01T10:27:37.498180581Z', line: 'hello' }],
          labels: '{}',
        },
      ],
    },
    status: 404, // for simulating legacy endpoint
  };

  const testResp: { data: LokiResponse } = {
    data: {
      data: {
        resultType: LokiResultType.Stream,
        result: [
          {
            stream: {},
            values: [['1573646419522934000', 'hello']],
          },
        ],
      },
      status: 'success',
    },
  };

  beforeEach(() => {
    jest.clearAllMocks();
    datasourceRequestMock.mockImplementation(() => Promise.resolve());
  });

  const templateSrvMock = ({
    getAdhocFilters: (): any[] => [],
    replace: (a: string) => a,
  } as unknown) as TemplateSrv;

  describe('when creating range query', () => {
    let ds: LokiDatasource;
    let adjustIntervalSpy: jest.SpyInstance;
    beforeEach(() => {
      const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
      const customSettings = { ...instanceSettings, jsonData: customData };
      ds = new LokiDatasource(customSettings, templateSrvMock);
      adjustIntervalSpy = jest.spyOn(ds, 'adjustInterval');
    });

    it('should use default intervalMs if one is not provided', () => {
      const target = { expr: '{job="grafana"}', refId: 'B' };
      const raw = { from: 'now', to: 'now-1h' };
      const range = { from: dateTime(), to: dateTime(), raw: raw };
      const options = {
        range,
      };

      const req = ds.createRangeQuery(target, options);
      expect(req.start).toBeDefined();
      expect(req.end).toBeDefined();
      expect(adjustIntervalSpy).toHaveBeenCalledWith(1000, expect.anything());
    });

    it('should use provided intervalMs', () => {
      const target = { expr: '{job="grafana"}', refId: 'B' };
      const raw = { from: 'now', to: 'now-1h' };
      const range = { from: dateTime(), to: dateTime(), raw: raw };
      const options = {
        range,
        intervalMs: 2000,
      };

      const req = ds.createRangeQuery(target, options);
      expect(req.start).toBeDefined();
      expect(req.end).toBeDefined();
      expect(adjustIntervalSpy).toHaveBeenCalledWith(2000, expect.anything());
    });
  });

  describe('when running range query with fallback', () => {
    let ds: LokiDatasource;
    beforeEach(() => {
      const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
      const customSettings = { ...instanceSettings, jsonData: customData };
      ds = new LokiDatasource(customSettings, templateSrvMock);
      datasourceRequestMock.mockImplementation(() => Promise.resolve(legacyTestResp));
    });

    test('should try latest endpoint but fall back to legacy endpoint if it cannot be reached', async () => {
      const options = getQueryOptions<LokiQuery>({
        targets: [{ expr: '{job="grafana"}', refId: 'B' }],
        exploreMode: ExploreMode.Logs,
      });

      ds.runLegacyQuery = jest.fn();
      await ds.runRangeQueryWithFallback(options.targets[0], options).toPromise();
      expect(ds.runLegacyQuery).toBeCalled();
    });
  });

  describe('when querying', () => {
    let ds: LokiDatasource;
    let testLimit: any;

    beforeAll(() => {
      testLimit = makeLimitTest(instanceSettings, datasourceRequestMock, templateSrvMock, legacyTestResp);
    });

    beforeEach(() => {
      const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
      const customSettings = { ...instanceSettings, jsonData: customData };
      ds = new LokiDatasource(customSettings, templateSrvMock);
      datasourceRequestMock.mockImplementation(() => Promise.resolve(testResp));
    });

    test('should run instant query and range query when in metrics mode', async () => {
      const options = getQueryOptions<LokiQuery>({
        targets: [{ expr: 'rate({job="grafana"}[5m])', refId: 'A' }],
        exploreMode: ExploreMode.Metrics,
      });

      ds.runInstantQuery = jest.fn(() => of({ data: [] }));
      ds.runLegacyQuery = jest.fn();
      ds.runRangeQueryWithFallback = jest.fn(() => of({ data: [] }));
      await ds.query(options).toPromise();

      expect(ds.runInstantQuery).toBeCalled();
      expect(ds.runLegacyQuery).not.toBeCalled();
      expect(ds.runRangeQueryWithFallback).toBeCalled();
    });

    test('should just run range query when in logs mode', async () => {
      const options = getQueryOptions<LokiQuery>({
        targets: [{ expr: '{job="grafana"}', refId: 'B' }],
        exploreMode: ExploreMode.Logs,
      });

      ds.runInstantQuery = jest.fn(() => of({ data: [] }));
      ds.runRangeQueryWithFallback = jest.fn(() => of({ data: [] }));
      await ds.query(options).toPromise();

      expect(ds.runInstantQuery).not.toBeCalled();
      expect(ds.runRangeQueryWithFallback).toBeCalled();
    });

    test('should use default max lines when no limit given', () => {
      testLimit({
        expectedLimit: 1000,
      });
    });

    test('should use custom max lines if limit is set', () => {
      testLimit({
        maxLines: 20,
        expectedLimit: 20,
      });
    });

    test('should use custom maxDataPoints if set in request', () => {
      testLimit({
        maxDataPoints: 500,
        expectedLimit: 500,
      });
    });

    test('should use datasource maxLimit if maxDataPoints is higher', () => {
      testLimit({
        maxLines: 20,
        maxDataPoints: 500,
        expectedLimit: 20,
      });
    });

    test('should return series data', async () => {
      const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
      const customSettings = { ...instanceSettings, jsonData: customData };
      const ds = new LokiDatasource(customSettings, templateSrvMock);
      datasourceRequestMock.mockImplementation(
        jest
          .fn()
          .mockReturnValueOnce(Promise.resolve(legacyTestResp))
          .mockReturnValueOnce(Promise.resolve(omit(legacyTestResp, 'status')))
      );

      const options = getQueryOptions<LokiQuery>({
        targets: [{ expr: '{job="grafana"} |= "foo"', refId: 'B' }],
      });

      const res = await ds.query(options).toPromise();

      const dataFrame = res.data[0] as DataFrame;
      const fieldCache = new FieldCache(dataFrame);
      expect(fieldCache.getFieldByName('line').values.get(0)).toBe('hello');
      expect(dataFrame.meta.limit).toBe(20);
      expect(dataFrame.meta.searchWords).toEqual(['foo']);
    });
  });

  describe('When interpolating variables', () => {
    let ds: LokiDatasource;
    let variable: CustomVariable;

    beforeEach(() => {
      const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
      const customSettings = { ...instanceSettings, jsonData: customData };
      ds = new LokiDatasource(customSettings, templateSrvMock);
      variable = new CustomVariable({}, {} as any);
    });

    it('should only escape single quotes', () => {
      expect(ds.interpolateQueryExpr("abc'$^*{}[]+?.()|", variable)).toEqual("abc\\\\'$^*{}[]+?.()|");
    });

    it('should return a number', () => {
      expect(ds.interpolateQueryExpr(1000, variable)).toEqual(1000);
    });

    describe('and variable allows multi-value', () => {
      beforeEach(() => {
        variable.multi = true;
      });

      it('should regex escape values if the value is a string', () => {
        expect(ds.interpolateQueryExpr('looking*glass', variable)).toEqual('looking\\\\*glass');
      });

      it('should return pipe separated values if the value is an array of strings', () => {
        expect(ds.interpolateQueryExpr(['a|bc', 'de|f'], variable)).toEqual('a\\\\|bc|de\\\\|f');
      });
    });

    describe('and variable allows all', () => {
      beforeEach(() => {
        variable.includeAll = true;
      });

      it('should regex escape values if the array is a string', () => {
        expect(ds.interpolateQueryExpr('looking*glass', variable)).toEqual('looking\\\\*glass');
      });

      it('should return pipe separated values if the value is an array of strings', () => {
        expect(ds.interpolateQueryExpr(['a|bc', 'de|f'], variable)).toEqual('a\\\\|bc|de\\\\|f');
      });
    });
  });

  describe('when performing testDataSource', () => {
    let ds: DataSourceApi<any, any>;
    let result: any;

    describe('and call succeeds', () => {
      beforeEach(async () => {
        datasourceRequestMock.mockImplementation(async () => {
          return Promise.resolve({
            status: 200,
            data: {
              values: ['avalue'],
            },
          });
        });
        ds = new LokiDatasource(instanceSettings, {} as TemplateSrv);
        result = await ds.testDatasource();
      });

      it('should return successfully', () => {
        expect(result.status).toBe('success');
      });
    });

    describe('and call fails with 401 error', () => {
      let ds: LokiDatasource;
      beforeEach(() => {
        datasourceRequestMock.mockImplementation(() =>
          Promise.reject({
            statusText: 'Unauthorized',
            status: 401,
            data: {
              message: 'Unauthorized',
            },
          })
        );

        const customData = { ...(instanceSettings.jsonData || {}), maxLines: 20 };
        const customSettings = { ...instanceSettings, jsonData: customData };
        ds = new LokiDatasource(customSettings, templateSrvMock);
      });

      it('should return error status and a detailed error message', async () => {
        const result = await ds.testDatasource();
        expect(result.status).toEqual('error');
        expect(result.message).toBe('Loki: Unauthorized. 401. Unauthorized');
      });
    });

    describe('and call fails with 404 error', () => {
      beforeEach(async () => {
        datasourceRequestMock.mockImplementation(() =>
          Promise.reject({
            statusText: 'Not found',
            status: 404,
            data: '404 page not found',
          })
        );
        ds = new LokiDatasource(instanceSettings, {} as TemplateSrv);
        result = await ds.testDatasource();
      });

      it('should return error status and a detailed error message', () => {
        expect(result.status).toEqual('error');
        expect(result.message).toBe('Loki: Not found. 404. 404 page not found');
      });
    });

    describe('and call fails with 502 error', () => {
      beforeEach(async () => {
        datasourceRequestMock.mockImplementation(() =>
          Promise.reject({
            statusText: 'Bad Gateway',
            status: 502,
            data: '',
          })
        );
        ds = new LokiDatasource(instanceSettings, {} as TemplateSrv);
        result = await ds.testDatasource();
      });

      it('should return error status and a detailed error message', () => {
        expect(result.status).toEqual('error');
        expect(result.message).toBe('Loki: Bad Gateway. 502');
      });
    });
  });

  describe('when creating a range query', () => {
    const ds = new LokiDatasource(instanceSettings, templateSrvMock);
    const query: LokiQuery = { expr: 'foo', refId: 'bar' };

    // Loki v1 API has an issue with float step parameters, can be removed when API is fixed
    it('should produce an integer step parameter', () => {
      const range: TimeRange = {
        from: dateTime(0),
        to: dateTime(1e9 + 1),
        raw: { from: '0', to: '1000000001' },
      };
      // Odd timerange/interval combination that would lead to a float step
      const options: RangeQueryOptions = { range, intervalMs: 2000 };
      expect(Number.isInteger(ds.createRangeQuery(query, options).step)).toBeTruthy();
    });
  });

  describe('annotationQuery', () => {
    it('should transform the loki data to annotation response', async () => {
      const ds = new LokiDatasource(instanceSettings, templateSrvMock);
      datasourceRequestMock.mockImplementation(
        jest
          .fn()
          .mockReturnValueOnce(
            Promise.resolve({
              data: [],
              status: 404,
            })
          )
          .mockReturnValueOnce(
            Promise.resolve({
              data: {
                streams: [
                  {
                    entries: [{ ts: '2019-02-01T10:27:37.498180581Z', line: 'hello' }],
                    labels: '{label="value"}',
                  },
                  {
                    entries: [{ ts: '2019-02-01T12:27:37.498180581Z', line: 'hello 2' }],
                    labels: '{label2="value2"}',
                  },
                ],
              },
            })
          )
      );
      const query = makeAnnotationQueryRequest();

      const res = await ds.annotationQuery(query);
      expect(res.length).toBe(2);
      expect(res[0].text).toBe('hello');
      expect(res[0].tags).toEqual(['value']);

      expect(res[1].text).toBe('hello 2');
      expect(res[1].tags).toEqual(['value2']);
    });
  });

  describe('metricFindQuery', () => {
    const ds = new LokiDatasource(instanceSettings, templateSrvMock);
    const mocks = makeMetadataAndVersionsMocks();

    mocks.forEach((mock, index) => {
      it(`should return label names for Loki v${index}`, async () => {
        ds.getVersion = mock.getVersion;
        ds.metadataRequest = mock.metadataRequest;
        const query = 'label_names()';
        const res = await ds.metricFindQuery(query);
        expect(res[0].text).toEqual('label1');
        expect(res[1].text).toEqual('label2');
        expect(res.length).toBe(2);
      });
    });

    mocks.forEach((mock, index) => {
      it(`should return label names for Loki v${index}`, async () => {
        ds.getVersion = mock.getVersion;
        ds.metadataRequest = mock.metadataRequest;
        const query = 'label_names()';
        const res = await ds.metricFindQuery(query);
        expect(res[0].text).toEqual('label1');
        expect(res[1].text).toEqual('label2');
        expect(res.length).toBe(2);
      });
    });

    mocks.forEach((mock, index) => {
      it(`should return label values for Loki v${index}`, async () => {
        ds.getVersion = mock.getVersion;
        ds.metadataRequest = mock.metadataRequest;
        const query = 'label_values(label1)';
        const res = await ds.metricFindQuery(query);
        expect(res[0].text).toEqual('value1');
        expect(res[1].text).toEqual('value2');
        expect(res.length).toBe(2);
      });
    });

    mocks.forEach((mock, index) => {
      it(`should return empty array when incorrect query for Loki v${index}`, async () => {
        ds.getVersion = mock.getVersion;
        ds.metadataRequest = mock.metadataRequest;
        const query = 'incorrect_query';
        const res = await ds.metricFindQuery(query);
        expect(res.length).toBe(0);
      });
    });
  });
});