# All files / utils fromFlux.ts

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     import {csvParse, csvParseRows} from 'd3-dsv'
    import {Table, ColumnType, FluxDataType} from '../types'
    import {assert} from './assert'
    import {newTable} from './newTable'

    export interface FromFluxResult {
      error?: Error
      // The single parsed `Table`
      table: Table

      // The union of unique group keys from all input Flux tables
      fluxGroupKeyUnion: string[]
    }

    type Column =
      | {
          name: string
          type: 'number'
          fluxDataType: FluxDataType
          data: Array<number | null>
        } //  parses empty numeric values as null
      | {name: string; type: 'time'; fluxDataType: FluxDataType; data: number[]}
      | {name: string; type: 'boolean'; fluxDataType: FluxDataType; data: boolean[]}
      | {name: string; type: 'string'; fluxDataType: FluxDataType; data: string[]}

    interface Columns {
      [columnKey: string]: Column
    }

    /*
      Convert a [Flux CSV response][0] to a `Table`.
      For example, given a series of Flux tables that look like this:
          column_a | column_b | column_c  <-- name
          long     | string   | long      <-- type
          ------------------------------
                 1 |      "g" |       34
                 2 |      "f" |       58
                 3 |      "c" |       21
          column_b | column_d   <-- name
          double   | boolean    <-- type
          -------------------
               1.0 |     true
               2.0 |     true
               3.0 |     true
      This function will spread them out to a single wide table that looks like
      this instead:
          column_a | column_b (string) | column_c | column_b (number) | column_d  <-- key
          column_a | column_b          | column_c | column_b          | column_d  <-- name
          number   | string            | number   | number            | bool      <-- type
          ---------------------------------------------------------------------
                 1 |               "g" |       34 |                   |
                 2 |               "f" |       58 |                   |
                 3 |                   |       21 |                   |
                   |                   |          |               1.0 |     true
                   |                   |          |               2.0 |     true
                   |                   |          |               3.0 |     true
      The `#group`, `#datatype`, and `#default` [annotations][1] are expected to be
      in the input Flux CSV.
      Values are coerced into appropriate JavaScript types based on the Flux
      `#datatype` annotation for the table
      The `Table` stores a `key` for each column which is seperate from the column
      `name`. If multiple Flux tables have the same column but with different
      types, they will be distinguished by different keys in the resulting `Table`;
      otherwise the `key` and `name` for each column in the result table will be
      identical.
      [0]: https://github.com/influxdata/flux/blob/master/docs/SPEC.md#csv
      [1]: https://github.com/influxdata/flux/blob/master/docs/SPEC.md#annotations
    */
    export const fromFlux = (fluxCSV: string): FromFluxResult => {
      const columns: Columns = {}
      const fluxGroupKeyUnion = new Set<string>()
      let tableLength = 0

      try {
        const chunks = splitChunks(fluxCSV)

        // declaring all nested variables here to reduce memory drain
        let tableText = ''
        let tableData: any = []
        let annotationText = ''
        let columnType: any = ''
        let columnKey = ''
        let columnDefault: any = ''

        for (const chunk of chunks) {
          tableText = chunk
            .split('\n')
            .filter(line => !line.startsWith('#'))
            .join('\n')
            .trim()

          assert(
            !!tableText,
            'could not find annotation lines in Flux response; are `annotations` enabled in the Flux query `dialect` option?'
          )

          tableData = csvParse(tableText)

          annotationText = chunk
            .split('\n')
            .filter(line => line.startsWith('#'))
            .join('\n')
            .trim()

          assert(
            !!annotationText,
            'could not find annotation lines in Flux response; are `annotations` enabled in the Flux query `dialect` option?'
          )
          const annotationData = parseAnnotations(annotationText, tableData.columns)

          for (const columnName of tableData.columns.slice(1)) {
            columnType =
              TO_COLUMN_TYPE[annotationData.datatypeByColumnName[columnName]]

            assert(
              !!columnType,
              `encountered unknown Flux column type ${annotationData.datatypeByColumnName[columnName]}`
            )

            columnKey = `${columnName} (${columnType})`

            Eif (!columns[columnKey]) {
              columns[columnKey] = {
                name: columnName,
                type: columnType,
                fluxDataType: annotationData.datatypeByColumnName[columnName],
                data: [],
              } as Column
            }

            columnDefault = annotationData.defaultByColumnName[columnName]

            for (let i = 0; i < tableData.length; i++) {
              columns[columnKey].data[tableLength + i] = parseValue(
                tableData[i][columnName] || columnDefault,
                columnType
              )
            }

            if (annotationData.groupKey.includes(columnName)) {
              fluxGroupKeyUnion.add(columnKey)
            }
          }

          tableLength += tableData.length
        }

        resolveNames(columns, fluxGroupKeyUnion)

        const table = Object.entries(columns).reduce(
          (table, [key, {name, fluxDataType, type, data}]) => {
            data.length = tableLength
            return table.addColumn(key, fluxDataType, type, data, name)
          },
          newTable(tableLength)
        )

        const result = {
          table,
          fluxGroupKeyUnion: Array.from(fluxGroupKeyUnion),
        }

        return result
      } catch (error) {
        return {
          table: newTable(0),
          fluxGroupKeyUnion: [],
          error,
        }
      }
    }

    /*
      A Flux CSV response can contain multiple CSV files each joined by a newline.
      This function splits up a CSV response into these individual CSV files.
      See https://github.com/influxdata/flux/blob/master/docs/SPEC.md#multiple-tables.
    */
    const splitChunks = (fluxCSV: string): string[] => {
      const trimmedResponse = fluxCSV.trim()

      Iif (trimmedResponse === '') {
        return []
      }

      // Split the response into separate chunks whenever we encounter:
      //
      // 1. A newline
      // 2. Followed by any amount of whitespace
      // 3. Followed by a newline
      // 4. Followed by a `#` character
      //
      // The last condition is [necessary][0] for handling CSV responses with
      // values containing newlines.
      //
      // [0]: https://github.com/influxdata/influxdb/issues/15017
      const chunks = trimmedResponse
        .split(/\n\s*\n#/)
        .map((s, i) => (i === 0 ? s : `#${s}`)) // Add back the `#` characters that were removed by splitting

      return chunks
    }

    const parseAnnotations = (
      annotationData: string,
      headerRow: string[]
    ): {
      groupKey: string[]
      datatypeByColumnName: {[columnName: string]: any}
      defaultByColumnName: {[columnName: string]: any}
    } => {
      const rows = csvParseRows(annotationData)

      const groupRow = rows.find(row => row[0] === '#group')
      const datatypeRow = rows.find(row => row[0] === '#datatype')
      const defaultRow = rows.find(row => row[0] === '#default')

      assert(!!groupRow, 'could not find group annotation in Flux response')
      assert(!!datatypeRow, 'could not find datatype annotation in Flux response')
      assert(!!defaultRow, 'could not find default annotation in Flux response')

      const groupKey = groupRow.reduce(
        (acc, val, i) => (val === 'true' ? [...acc, headerRow[i]] : acc),
        []
      )

      const datatypeByColumnName = datatypeRow
        .slice(1)
        .reduce((acc, val, i) => ({...acc, [headerRow[i + 1]]: val}), {})

      const defaultByColumnName = defaultRow
        .slice(1)
        .reduce((acc, val, i) => ({...acc, [headerRow[i + 1]]: val}), {})

      return {groupKey, datatypeByColumnName, defaultByColumnName}
    }

    const TO_COLUMN_TYPE: {[fluxDatatype: string]: ColumnType} = {
      boolean: 'boolean',
      unsignedLong: 'number',
      long: 'number',
      double: 'number',
      string: 'string',
      'dateTime:RFC3339': 'time',
    }

    const parseValue = (value: string | undefined, columnType: ColumnType): any => {
      Iif (value === undefined) {
        return undefined
      }

      Iif (value === 'null') {
        return null
      }

      Iif (value === 'NaN') {
        return NaN
      }

      Iif (columnType === 'boolean' && value === 'true') {
        return true
      }

      Iif (columnType === 'boolean' && value === 'false') {
        return false
      }

      if (columnType === 'string') {
        return value
      }

      if (columnType === 'time') {
        return Date.parse(value.trim())
      }

      Iif (columnType === 'number' && value === '') {
        return null
      }

      Eif (columnType === 'number') {
        return Number(value)
      }

      return null
    }

    /*
      Each column in a parsed `Table` can only have a single type, but because we
      combine columns from multiple Flux tables into a single table, we may
      encounter conflicting types for a given column during parsing.
      To avoid this issue, we seperate the concept of the column _key_ and column
      _name_ in the `Table` object, where each key is unique but each name is not
      necessarily unique. We name the keys something like "foo (int)", where "foo"
      is the name and "int" is the type.
      But since type conflicts are rare and the public API requires referencing
      columns by key, we want to avoid unwieldy keys whenever possible. So the last
      stage of parsing is to rename all column keys from the `$NAME ($TYPE)` format
      to just `$NAME` if we can do so safely. That is what this function does.
    */
    const resolveNames = (
      columns: Columns,
      fluxGroupKeyUnion: Set<string>
    ): void => {
      const colNameCounts = Object.values(columns)
        .map(col => col.name)
        .reduce((acc, name) => ({...acc, [name]: (acc[name] || 0) + 1}), {})

      const uniqueColNames = Object.entries(colNameCounts)
        .filter(([_, count]) => count === 1)
        .map(([name]) => name)

      for (const uniqueName of uniqueColNames) {
        const [columnKey, column] = Object.entries(columns).find(
          ([_, col]) => col.name === uniqueName
        )

        columns[uniqueName] = column

        delete columns[columnKey]

        if (fluxGroupKeyUnion.has(columnKey)) {
          fluxGroupKeyUnion.delete(columnKey)
          fluxGroupKeyUnion.add(uniqueName)
        }
      }
    }