import {
  AbsoluteTimeRange,
  DataFrame,
  FieldType,
  getDisplayProcessor,
  PanelData,
  sortLogsResult,
  standardTransformers,
  DataQuery,
} from '@grafana/data';
import { config } from '@grafana/runtime';
import { groupBy } from 'lodash';
import { Observable, of } from 'rxjs';
import { map } from 'rxjs/operators';
import { dataFrameToLogsModel } from '../../../core/logs_model';
import { refreshIntervalToSortOrder } from '../../../core/utils/explore';
import { ExplorePanelData } from '../../../types';

/**
 * When processing response first we try to determine what kind of dataframes we got as one query can return multiple
 * dataFrames with different type of data. This is later used for type specific processing. As we use this in
 * Observable pipeline, it decorates the existing panelData to pass the results to later processing stages.
 */
export const decorateWithFrameTypeMetadata = (data: PanelData): ExplorePanelData => {
  const graphFrames: DataFrame[] = [];
  const tableFrames: DataFrame[] = [];
  const logsFrames: DataFrame[] = [];
  const traceFrames: DataFrame[] = [];
  const nodeGraphFrames: DataFrame[] = [];

  for (const frame of data.series) {
    switch (frame.meta?.preferredVisualisationType) {
      case 'logs':
        logsFrames.push(frame);
        break;
      case 'graph':
        graphFrames.push(frame);
        break;
      case 'trace':
        traceFrames.push(frame);
        break;
      case 'table':
        tableFrames.push(frame);
        break;
      case 'nodeGraph':
        nodeGraphFrames.push(frame);
        break;
      default:
        if (isTimeSeries(frame)) {
          graphFrames.push(frame);
          tableFrames.push(frame);
        } else {
          // We fallback to table if we do not have any better meta info about the dataframe.
          tableFrames.push(frame);
        }
    }
  }

  return {
    ...data,
    graphFrames,
    tableFrames,
    logsFrames,
    traceFrames,
    nodeGraphFrames,
    graphResult: null,
    tableResult: null,
    logsResult: null,
  };
};

export const decorateWithGraphResult = (data: ExplorePanelData): ExplorePanelData => {
  if (!data.graphFrames.length) {
    return { ...data, graphResult: null };
  }

  return { ...data, graphResult: data.graphFrames };
};

/**
 * This processing returns Observable because it uses Transformer internally which result type is also Observable.
 * In this case the transformer should return single result but it is possible that in the future it could return
 * multiple results and so this should be used with mergeMap or similar to unbox the internal observable.
 */
export const decorateWithTableResult = (data: ExplorePanelData): Observable<ExplorePanelData> => {
  if (data.tableFrames.length === 0) {
    return of({ ...data, tableResult: null });
  }

  data.tableFrames.sort((frameA: DataFrame, frameB: DataFrame) => {
    const frameARefId = frameA.refId!;
    const frameBRefId = frameB.refId!;

    if (frameARefId > frameBRefId) {
      return 1;
    }
    if (frameARefId < frameBRefId) {
      return -1;
    }
    return 0;
  });

  const hasOnlyTimeseries = data.tableFrames.every((df) => isTimeSeries(df));

  // If we have only timeseries we do join on default time column which makes more sense. If we are showing
  // non timeseries or some mix of data we are not trying to join on anything and just try to merge them in
  // single table, which may not make sense in most cases, but it's up to the user to query something sensible.
  const transformer = hasOnlyTimeseries
    ? of(data.tableFrames).pipe(standardTransformers.seriesToColumnsTransformer.operator({}))
    : of(data.tableFrames).pipe(standardTransformers.mergeTransformer.operator({}));

  return transformer.pipe(
    map((frames) => {
      const frame = frames[0];

      // set display processor
      for (const field of frame.fields) {
        field.display =
          field.display ??
          getDisplayProcessor({
            field,
            theme: config.theme2,
            timeZone: data.request?.timezone ?? 'browser',
          });
      }

      return { ...data, tableResult: frame };
    })
  );
};

export const decorateWithLogsResult = (
  options: { absoluteRange?: AbsoluteTimeRange; refreshInterval?: string; queries?: DataQuery[] } = {}
) => (data: ExplorePanelData): ExplorePanelData => {
  if (data.logsFrames.length === 0) {
    return { ...data, logsResult: null };
  }

  const timeZone = data.request?.timezone ?? 'browser';
  const intervalMs = data.request?.intervalMs;
  const newResults = dataFrameToLogsModel(
    data.logsFrames,
    intervalMs,
    timeZone,
    options.absoluteRange,
    options.queries
  );
  const sortOrder = refreshIntervalToSortOrder(options.refreshInterval);
  const sortedNewResults = sortLogsResult(newResults, sortOrder);
  const rows = sortedNewResults.rows;
  const series = sortedNewResults.series;
  const logsResult = { ...sortedNewResults, rows, series };

  return { ...data, logsResult };
};

/**
 * Check if frame contains time series, which for our purpose means 1 time column and 1 or more numeric columns.
 */
function isTimeSeries(frame: DataFrame): boolean {
  const grouped = groupBy(frame.fields, (field) => field.type);
  return Boolean(
    Object.keys(grouped).length === 2 && grouped[FieldType.time]?.length === 1 && grouped[FieldType.number]
  );
}
