All files / src/utils druid-type.ts

66.67% Statements 34/51
33.33% Branches 10/30
63.64% Functions 7/11
73.33% Lines 33/45

Press n or j to go to the next uncovered block, b, p or k for the previous block.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116                                    2x   2x     2x 6x 6x                   6x       2x       5x         2x       2x 2x 2x 2x                 2x 2x   2x 2x 2x           2x           2x           1x   1x     1x   1x 1x 1x       1x 1x   1x 1x 1x             1x 1x    
/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
import { filterMap } from './general';
import { DimensionMode, DimensionSpec, IngestionSpec, MetricSpec } from './ingestion-spec';
import { deepDelete, deepSet } from './object-change';
import { HeaderAndRows } from './sampler';
 
export function guessTypeFromSample(sample: any[]): string {
  const definedValues = sample.filter(v => v != null);
  Iif (
    definedValues.length &&
    definedValues.every(v => !isNaN(v) && (typeof v === 'number' || typeof v === 'string'))
  ) {
    if (definedValues.every(v => v % 1 === 0)) {
      return 'long';
    } else {
      return 'float';
    }
  } else {
    return 'string';
  }
}
 
export function getColumnTypeFromHeaderAndRows(
  headerAndRows: HeaderAndRows,
  column: string,
): string {
  return guessTypeFromSample(
    filterMap(headerAndRows.rows, (r: any) => (r.parsed ? r.parsed[column] : undefined)),
  );
}
 
export function getDimensionSpecs(
  headerAndRows: HeaderAndRows,
  hasRollup: boolean,
): (string | DimensionSpec)[] {
  return filterMap(headerAndRows.header, h => {
    Iif (h === '__time') return;
    const guessedType = getColumnTypeFromHeaderAndRows(headerAndRows, h);
    Eif (guessedType === 'string') return h;
    if (hasRollup) return;
    return {
      type: guessedType,
      name: h,
    };
  });
}
 
export function getMetricSecs(headerAndRows: HeaderAndRows): MetricSpec[] {
  return [{ name: 'count', type: 'count' }].concat(
    filterMap(headerAndRows.header, h => {
      Iif (h === '__time') return;
      const guessedType = getColumnTypeFromHeaderAndRows(headerAndRows, h);
      switch (guessedType) {
        case 'double':
          return { name: `sum_${h}`, type: 'doubleSum', fieldName: h };
        case 'long':
          return { name: `sum_${h}`, type: 'longSum', fieldName: h };
        default:
          return;
      }
    }),
  );
}
 
export function updateSchemaWithSample(
  spec: IngestionSpec,
  headerAndRows: HeaderAndRows,
  dimensionMode: DimensionMode,
  rollup: boolean,
): IngestionSpec {
  let newSpec = spec;
 
  Iif (dimensionMode === 'auto-detect') {
    newSpec = deepSet(newSpec, 'dataSchema.dimensionsSpec.dimensions', []);
  } else {
    newSpec = deepDelete(newSpec, 'dataSchema.dimensionsSpec.dimensionExclusions');
 
    const dimensions = getDimensionSpecs(headerAndRows, rollup);
    Eif (dimensions) {
      newSpec = deepSet(newSpec, 'dataSchema.dimensionsSpec.dimensions', dimensions);
    }
  }
 
  Eif (rollup) {
    newSpec = deepSet(newSpec, 'dataSchema.granularitySpec.queryGranularity', 'HOUR');
 
    const metrics = getMetricSecs(headerAndRows);
    Eif (metrics) {
      newSpec = deepSet(newSpec, 'dataSchema.metricsSpec', metrics);
    }
  } else {
    newSpec = deepSet(newSpec, 'dataSchema.granularitySpec.queryGranularity', 'NONE');
    newSpec = deepDelete(newSpec, 'dataSchema.metricsSpec');
  }
 
  newSpec = deepSet(newSpec, 'dataSchema.granularitySpec.rollup', rollup);
  return newSpec;
}