{"version":3,"file":"joinDataFrames.mjs","sources":["../../../../src/transformations/transformers/joinDataFrames.ts"],"sourcesContent":["import { getTimeField, sortDataFrame } from '../../dataframe/processDataFrame';\nimport { DataFrame, Field, FieldType, TIME_SERIES_VALUE_FIELD_NAME } from '../../types/dataFrame';\nimport { FieldMatcher } from '../../types/transformations';\nimport { fieldMatchers } from '../matchers';\nimport { FieldMatcherID } from '../matchers/ids';\n\nimport { JoinMode } from './joinByField';\n\nexport function pickBestJoinField(data: DataFrame[]): FieldMatcher {\n const { timeField } = getTimeField(data[0]);\n if (timeField) {\n return fieldMatchers.get(FieldMatcherID.firstTimeField).get({});\n }\n let common: string[] = [];\n for (const f of data[0].fields) {\n if (f.type === FieldType.number) {\n common.push(f.name);\n }\n }\n\n for (let i = 1; i < data.length; i++) {\n const names: string[] = [];\n for (const f of data[0].fields) {\n if (f.type === FieldType.number) {\n names.push(f.name);\n }\n }\n common = common.filter((v) => !names.includes(v));\n }\n\n return fieldMatchers.get(FieldMatcherID.byName).get(common[0]);\n}\n\n/**\n * @internal\n */\nexport interface JoinOptions {\n /**\n * The input fields\n */\n frames: DataFrame[];\n\n /**\n * The field to join -- frames that do not have this field will be dropped\n */\n joinBy?: FieldMatcher;\n\n /**\n * Optionally filter the non-join fields\n */\n keep?: FieldMatcher;\n\n /**\n * @internal -- used when we need to keep a reference to the original frame/field index\n */\n keepOriginIndices?: boolean;\n\n /**\n * @internal -- keep any pre-cached state.displayName\n */\n keepDisplayNames?: boolean;\n\n /**\n * @internal -- Optionally specify how to treat null values\n */\n nullMode?: (field: Field) => JoinNullMode;\n\n /**\n * @internal -- Optionally specify a join mode (outer or inner)\n */\n mode?: JoinMode;\n}\n\nfunction getJoinMatcher(options: JoinOptions): FieldMatcher {\n return options.joinBy ?? pickBestJoinField(options.frames);\n}\n\n/**\n * @internal\n */\nexport function maybeSortFrame(frame: DataFrame, fieldIdx: number) {\n if (fieldIdx >= 0) {\n let sortByField = frame.fields[fieldIdx];\n\n if (sortByField.type !== FieldType.string && !isLikelyAscendingVector(sortByField.values)) {\n frame = sortDataFrame(frame, fieldIdx);\n }\n }\n\n return frame;\n}\n\n/**\n * This will return a single frame joined by the first matching field. When a join field is not specified,\n * the default will use the first time field\n */\nexport function joinDataFrames(options: JoinOptions): DataFrame | undefined {\n if (!options.frames?.length) {\n return;\n }\n\n const nullMode =\n options.nullMode ??\n ((field: Field) => {\n let spanNulls = field.config.custom?.spanNulls;\n return spanNulls === true ? NULL_REMOVE : spanNulls === -1 ? NULL_RETAIN : NULL_EXPAND;\n });\n\n if (options.frames.length === 1) {\n let frame = options.frames[0];\n let frameCopy = frame;\n\n const joinFieldMatcher = getJoinMatcher(options);\n let joinIndex = frameCopy.fields.findIndex((f) => joinFieldMatcher(f, frameCopy, options.frames));\n\n if (options.keepOriginIndices) {\n frameCopy = {\n ...frame,\n fields: frame.fields.map((f, fieldIndex) => {\n const copy = { ...f };\n const origin = {\n frameIndex: 0,\n fieldIndex,\n };\n if (copy.state) {\n copy.state.origin = origin;\n } else {\n copy.state = { origin };\n }\n return copy;\n }),\n };\n\n // Make sure the join field is first\n if (joinIndex > 0) {\n const joinField = frameCopy.fields[joinIndex];\n const fields = frameCopy.fields.filter((f, idx) => idx !== joinIndex);\n fields.unshift(joinField);\n frameCopy.fields = fields;\n joinIndex = 0;\n }\n }\n\n if (joinIndex >= 0) {\n frameCopy = maybeSortFrame(frameCopy, joinIndex);\n }\n\n if (options.keep) {\n let fields = frameCopy.fields.filter(\n (f, fieldIdx) => fieldIdx === joinIndex || options.keep!(f, frameCopy, options.frames)\n );\n\n // mutate already copied frame\n if (frame !== frameCopy) {\n frameCopy.fields = fields;\n } else {\n frameCopy = {\n ...frame,\n fields,\n };\n }\n }\n\n return frameCopy;\n }\n\n const nullModes: JoinNullMode[][] = [];\n const allData: AlignedData[] = [];\n const originalFields: Field[] = [];\n const joinFieldMatcher = getJoinMatcher(options);\n\n for (let frameIndex = 0; frameIndex < options.frames.length; frameIndex++) {\n const frame = options.frames[frameIndex];\n\n if (!frame || !frame.fields?.length) {\n continue; // skip the frame\n }\n\n const nullModesFrame: JoinNullMode[] = [NULL_REMOVE];\n let join: Field | undefined = undefined;\n let fields: Field[] = [];\n\n for (let fieldIndex = 0; fieldIndex < frame.fields.length; fieldIndex++) {\n const field = frame.fields[fieldIndex];\n field.state = field.state || {};\n\n if (!join && joinFieldMatcher(field, frame, options.frames)) {\n join = field;\n } else {\n if (options.keep && !options.keep(field, frame, options.frames)) {\n continue; // skip field\n }\n\n // Support the standard graph span nulls field config\n nullModesFrame.push(nullMode(field));\n\n let labels = field.labels ?? {};\n let name = field.name;\n if (frame.name) {\n if (field.name === TIME_SERIES_VALUE_FIELD_NAME) {\n name = frame.name;\n } else if (labels.name == null) {\n // add the name label from frame\n labels = { ...labels, name: frame.name };\n }\n }\n\n fields.push({\n ...field,\n name,\n labels,\n });\n }\n\n if (options.keepOriginIndices) {\n field.state.origin = {\n frameIndex,\n fieldIndex,\n };\n }\n }\n\n if (!join) {\n continue; // skip the frame\n }\n\n if (originalFields.length === 0) {\n originalFields.push(join); // first join field\n }\n\n nullModes.push(nullModesFrame);\n const a: AlignedData = [join.values]; //\n\n for (const field of fields) {\n a.push(field.values);\n originalFields.push(field);\n if (!options.keepDisplayNames) {\n // clear field displayName state\n delete field.state?.displayName;\n }\n }\n allData.push(a);\n }\n\n let joined: Array> = [];\n\n if (options.mode === JoinMode.outerTabular) {\n joined = joinTabular(allData, true);\n } else if (options.mode === JoinMode.inner) {\n joined = joinTabular(allData);\n } else {\n joined = join(allData, nullModes, options.mode);\n }\n\n return {\n // ...options.data[0], // keep name, meta?\n length: joined[0] ? joined[0].length : 0,\n fields: originalFields.map((f, index) => ({\n ...f,\n values: joined[index],\n })),\n };\n}\n\n/**\n * SQL-style join of tables, using the first column in each\n */\nfunction joinTabular(tables: AlignedData[], outer = false) {\n // console.time('joinTabular');\n\n let ltable = tables[0];\n let lfield = ltable[0];\n\n // iterate tables, merging right table with left, with the result becoming the new left\n // rinse and repeat for each tables in the array\n for (let ti = 1; ti < tables.length; ti++) {\n let rtable = tables[ti];\n let rfield = rtable[0];\n\n /**\n * Build an inverted index of the right table's join column like { \"foo\": [1,2,3], \"bar\": [7,12], ... }\n * where the keys are unique values and the arrays are indices where these values were found\n */\n // console.time('index right');\n let index: Record = {};\n\n for (let i = 0; i < rfield.length; i++) {\n let val = rfield[i];\n\n let idxs = index[val];\n\n if (idxs == null) {\n idxs = index[val] = [];\n }\n\n idxs.push(i);\n }\n // console.timeEnd('index right');\n\n /**\n * Loop over the left table's join column and match each non-null value to the right index,\n * copying the matched ridxs array into new matched list, like [33, [45,79,233]], where first\n * value is left idx and second value is right idxs\n *\n * Also keep track of unmatched or null left values for outer join, since we'll need to include these\n */\n let matchedKeys = new Set();\n let unmatchedLeft = [];\n let unmatchedRight = [];\n\n // console.time('match left');\n let matched: Array<[lidx: number, ridxs: number[]]> = [];\n\n // count of total number of output rows, so we can\n // pre-allocate the final array size during materialization\n let count = 0;\n\n for (let i = 0; i < lfield.length; i++) {\n let v = lfield[i];\n\n if (v != null) {\n let idxs = index[v];\n\n if (idxs != null) {\n matched.push([i, idxs]);\n count += idxs.length;\n outer && matchedKeys.add(v);\n } else if (outer) {\n unmatchedLeft.push(i);\n }\n } else if (outer) {\n unmatchedLeft.push(i);\n }\n }\n count += unmatchedLeft.length;\n // console.timeEnd('match left');\n\n /**\n * For outer joins, also loop over the right index to record unmatched values\n */\n // console.time('unmatched right');\n if (outer) {\n for (let k in index) {\n if (!matchedKeys.has(k)) {\n unmatchedRight.push(...index[k]);\n }\n }\n count += unmatchedRight.length;\n }\n // console.timeEnd('unmatched right');\n\n /**\n * Now we can use matched, unmatchedLeft, unmatchedRight, ltable, and rtable to assemble the final table\n * Instead of using 3-deep nested loops, we eliminate the loops over the known column structure\n * For this we compile a new function using the schemas from both tables, and filling that struct by looping\n * over the matched lookup array, then appending the unmatched left rows (and null-filling the right values),\n * then appending the unmatched right rows (and null-filling the left values).\n *\n * The assembled function looks something like this when joining 2-col left + 2-col right:\n *\n * function anonymous(matched, unmatchedLeft, unmatchedRight, ltable, rtable) {\n * const joined = [Array(99991),Array(99991),Array(99991)];\n *\n * let rowIdx = 0;\n *\n * for (let i = 0; i < matched.length; i++) {\n * let [lidx, ridxs] = matched[i];\n *\n * for (let j = 0; j < ridxs.length; j++, rowIdx++) {\n * let ridx = ridxs[j];\n * joined[0][rowIdx] = ltable[0][lidx];\n * joined[1][rowIdx] = ltable[1][lidx];\n * joined[2][rowIdx] = rtable[1][ridx];\n * }\n * }\n *\n * for (let i = 0; i < unmatchedLeft.length; i++, rowIdx++) {\n * let lidx = unmatchedLeft[i];\n * joined[0][rowIdx] = ltable[0][lidx];\n * joined[1][rowIdx] = ltable[1][lidx];\n * joined[2][rowIdx] = null;\n * }\n *\n * for (let i = 0; i < unmatchedRight.length; i++, rowIdx++) {\n * let ridx = unmatchedRight[i];\n * joined[0][rowIdx] = rtable[0][ridx];\n * joined[1][rowIdx] = null;\n * joined[2][rowIdx] = rtable[1][ridx];\n * }\n *\n * return joined;\n * }\n */\n // console.time('materialize');\n let outFieldsTpl = Array.from({ length: ltable.length + rtable.length - 1 }, () => `Array(${count})`).join(',');\n let copyLeftRowTpl = ltable.map((c, i) => `joined[${i}][rowIdx] = ltable[${i}][lidx]`).join(';');\n // (skips join field in right table)\n let copyRightRowTpl = rtable\n .slice(1)\n .map((c, i) => `joined[${ltable.length + i}][rowIdx] = rtable[${i + 1}][ridx]`)\n .join(';');\n\n // for outer joins, when we null-fill the left row values, we still populate the first (join) column\n // with the right row's join column value, rather than omitting it as we do for matched left/right where\n // that value is already filled by the left row\n let nullLeftRowTpl = ltable\n .map((c, i) => `joined[${i}][rowIdx] = ${i === 0 ? `rtable[${i}][ridx]` : `null`}`)\n .join(';');\n // (skips join field in right table)\n let nullRightRowTpl = rtable.slice(1).map((c, i) => `joined[${ltable.length + i}][rowIdx] = null`);\n\n let materialize = new Function(\n 'matched',\n 'unmatchedLeft',\n 'unmatchedRight',\n 'ltable',\n 'rtable',\n `\n const joined = [${outFieldsTpl}];\n\n let rowIdx = 0;\n\n for (let i = 0; i < matched.length; i++) {\n let [lidx, ridxs] = matched[i];\n\n for (let j = 0; j < ridxs.length; j++, rowIdx++) {\n let ridx = ridxs[j];\n ${copyLeftRowTpl};\n ${copyRightRowTpl};\n }\n }\n\n for (let i = 0; i < unmatchedLeft.length; i++, rowIdx++) {\n let lidx = unmatchedLeft[i];\n ${copyLeftRowTpl};\n ${nullRightRowTpl};\n }\n\n for (let i = 0; i < unmatchedRight.length; i++, rowIdx++) {\n let ridx = unmatchedRight[i];\n ${nullLeftRowTpl};\n ${copyRightRowTpl};\n }\n\n return joined;\n `\n );\n\n let joined = materialize(matched, unmatchedLeft, unmatchedRight, ltable, rtable);\n // console.timeEnd('materialize');\n\n ltable = joined;\n lfield = ltable[0];\n }\n\n // console.timeEnd('joinTabular');\n\n // eslint-disable-next-line @typescript-eslint/consistent-type-assertions\n return ltable as Array>;\n}\n\n//--------------------------------------------------------------------------------\n// Below here is copied from uplot (MIT License)\n// https://github.com/leeoniya/uPlot/blob/master/src/utils.js#L325\n// This avoids needing to import uplot into the data package\n//--------------------------------------------------------------------------------\n\n// Copied from uplot\nexport type TypedArray =\n | Int8Array\n | Uint8Array\n | Int16Array\n | Uint16Array\n | Int32Array\n | Uint32Array\n | Uint8ClampedArray\n | Float32Array\n | Float64Array;\n\nexport type AlignedData =\n | TypedArray[]\n | [xValues: number[] | TypedArray, ...yValues: Array | TypedArray>];\n\n// nullModes\nexport const NULL_REMOVE = 0; // nulls are converted to undefined (e.g. for spanGaps: true)\nexport const NULL_RETAIN = 1; // nulls are retained, with alignment artifacts set to undefined (default)\nexport const NULL_EXPAND = 2; // nulls are expanded to include any adjacent alignment artifacts\n\ntype JoinNullMode = number; // NULL_IGNORE | NULL_RETAIN | NULL_EXPAND;\n\n// sets undefined values to nulls when adjacent to existing nulls (minesweeper)\nfunction nullExpand(yVals: Array, nullIdxs: number[], alignedLen: number) {\n for (let i = 0, xi, lastNullIdx = -1; i < nullIdxs.length; i++) {\n let nullIdx = nullIdxs[i];\n\n if (nullIdx > lastNullIdx) {\n xi = nullIdx - 1;\n while (xi >= 0 && yVals[xi] == null) {\n yVals[xi--] = null;\n }\n\n xi = nullIdx + 1;\n while (xi < alignedLen && yVals[xi] == null) {\n yVals[(lastNullIdx = xi++)] = null;\n }\n }\n }\n}\n\n// nullModes is a tables-matched array indicating how to treat nulls in each series\nexport function join(tables: AlignedData[], nullModes?: number[][], mode: JoinMode = JoinMode.outer) {\n let xVals: Set = new Set();\n\n for (let ti = 0; ti < tables.length; ti++) {\n let t = tables[ti];\n let xs = t[0];\n let len = xs.length;\n\n for (let i = 0; i < len; i++) {\n xVals.add(xs[i]);\n }\n }\n\n let data = [Array.from(xVals).sort((a, b) => a - b)];\n\n let alignedLen = data[0].length;\n\n let xIdxs = new Map();\n\n for (let i = 0; i < alignedLen; i++) {\n xIdxs.set(data[0][i], i);\n }\n\n for (let ti = 0; ti < tables.length; ti++) {\n let t = tables[ti];\n let xs = t[0];\n\n for (let si = 1; si < t.length; si++) {\n let ys = t[si];\n\n let yVals = Array(alignedLen).fill(undefined);\n\n let nullMode = nullModes ? nullModes[ti][si] : NULL_RETAIN;\n\n let nullIdxs = [];\n\n for (let i = 0; i < ys.length; i++) {\n let yVal = ys[i];\n let alignedIdx = xIdxs.get(xs[i]);\n\n if (yVal === null) {\n if (nullMode !== NULL_REMOVE) {\n yVals[alignedIdx] = yVal;\n\n if (nullMode === NULL_EXPAND) {\n nullIdxs.push(alignedIdx);\n }\n }\n } else {\n yVals[alignedIdx] = yVal;\n }\n }\n\n nullExpand(yVals, nullIdxs, alignedLen);\n\n data.push(yVals);\n }\n }\n\n return data;\n}\n\n// Test a few samples to see if the values are ascending\n// Only exported for tests\nexport function isLikelyAscendingVector(data: unknown[], samples = 50) {\n const len = data.length;\n\n // empty or single value\n if (len <= 1) {\n return true;\n }\n\n // skip leading & trailing nullish\n let firstIdx = 0;\n let lastIdx = len - 1;\n\n while (firstIdx <= lastIdx && data[firstIdx] == null) {\n firstIdx++;\n }\n\n while (lastIdx >= firstIdx && data[lastIdx] == null) {\n lastIdx--;\n }\n\n // all nullish or one value surrounded by nullish\n if (lastIdx <= firstIdx) {\n return true;\n }\n\n const stride = Math.max(1, Math.floor((lastIdx - firstIdx + 1) / samples));\n\n for (let prevVal = data[firstIdx], i = firstIdx + stride; i <= lastIdx; i += stride) {\n const v = data[i];\n\n if (v != null && prevVal != null) {\n if (v <= prevVal) {\n return false;\n }\n\n prevVal = v;\n }\n }\n\n return 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