apache-arrow/table.mjs.map

Apache Arrow columnar in-memory format

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Licensed to the Apache Software Foundation (ASF) under one\n// or more contributor license agreements.  See the NOTICE file\n// distributed with this work for additional information\n// regarding copyright ownership.  The ASF licenses this file\n// to you under the Apache License, Version 2.0 (the\n// \"License\"); you may not use this file except in compliance\n// with the License.  You may obtain a copy of the License at\n//\n//   http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing,\n// software distributed under the License is distributed on an\n// \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n// KIND, either express or implied.  See the License for the\n// specific language governing permissions and limitations\n// under the License.\n\nimport { Data } from './data';\nimport { Column } from './column';\nimport { Schema, Field } from './schema';\nimport { RecordBatch, _InternalEmptyPlaceholderRecordBatch } from './recordbatch';\nimport { DataFrame } from './compute/dataframe';\nimport { RecordBatchReader } from './ipc/reader';\nimport { DataType, RowLike, Struct } from './type';\nimport { selectColumnArgs, selectArgs } from './util/args';\nimport { Clonable, Sliceable, Applicative } from './vector';\nimport { isPromise, isIterable, isAsyncIterable } from './util/compat';\nimport { RecordBatchFileWriter, RecordBatchStreamWriter } from './ipc/writer';\nimport { distributeColumnsIntoRecordBatches, distributeVectorsIntoRecordBatches } from './util/recordbatch';\nimport { Vector, Chunked, StructVector, VectorBuilderOptions, VectorBuilderOptionsAsync } from './vector/index';\n\ntype VectorMap = { [key: string]: Vector };\ntype Fields<T extends { [key: string]: DataType }> = (keyof T)[] | Field<T[keyof T]>[];\ntype ChildData<T extends { [key: string]: DataType }> = Data<T[keyof T]>[] | Vector<T[keyof T]>[];\ntype Columns<T extends { [key: string]: DataType }> = Column<T[keyof T]>[] | Column<T[keyof T]>[][];\n\nexport interface Table<T extends { [key: string]: DataType } = any> {\n\n    get(index: number): Struct<T>['TValue'];\n    [Symbol.iterator](): IterableIterator<RowLike<T>>;\n\n    slice(begin?: number, end?: number): Table<T>;\n    concat(...others: Vector<Struct<T>>[]): Table<T>;\n    clone(chunks?: RecordBatch<T>[], offsets?: Uint32Array): Table<T>;\n\n    scan(next: import('./compute/dataframe').NextFunc, bind?: import('./compute/dataframe').BindFunc): void;\n    scanReverse(next: import('./compute/dataframe').NextFunc, bind?: import('./compute/dataframe').BindFunc): void;\n    countBy(name: import('./compute/predicate').Col | string): import('./compute/dataframe').CountByResult;\n    filter(predicate: import('./compute/predicate').Predicate): import('./compute/dataframe').FilteredDataFrame<T>;\n}\n\nexport class Table<T extends { [key: string]: DataType } = any>\n    extends Chunked<Struct<T>>\n    implements DataFrame<T>,\n               Clonable<Table<T>>,\n               Sliceable<Table<T>>,\n               Applicative<Struct<T>, Table<T>> {\n\n    /** @nocollapse */\n    public static empty<T extends { [key: string]: DataType } = {}>(schema = new Schema<T>([])) { return new Table<T>(schema, []); }\n\n    public static from(): Table<{}>;\n    public static from<T extends { [key: string]: DataType } = any>(source: RecordBatchReader<T>): Table<T>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg0): Table<T>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg2): Table<T>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg1): Promise<Table<T>>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg3): Promise<Table<T>>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg4): Promise<Table<T>>;\n    public static from<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArg5): Promise<Table<T>>;\n    public static from<T extends { [key: string]: DataType } = any>(source: PromiseLike<RecordBatchReader<T>>): Promise<Table<T>>;\n    public static from<T extends { [key: string]: DataType } = any, TNull = any>(options: VectorBuilderOptions<Struct<T>, TNull>): Table<T>;\n    public static from<T extends { [key: string]: DataType } = any, TNull = any>(options: VectorBuilderOptionsAsync<Struct<T>, TNull>): Promise<Table<T>>;\n    /** @nocollapse */\n    public static from<T extends { [key: string]: DataType } = any, TNull = any>(input?: any) {\n\n        if (!input) { return Table.empty(); }\n\n        if (typeof input === 'object') {\n            let table = isIterable(input['values']) ? tableFromIterable<T, TNull>(input)\n                 : isAsyncIterable(input['values']) ? tableFromAsyncIterable<T, TNull>(input)\n                                                    : null;\n            if (table !== null) { return table; }\n        }\n\n        let reader = RecordBatchReader.from<T>(input) as RecordBatchReader<T> | Promise<RecordBatchReader<T>>;\n\n        if (isPromise<RecordBatchReader<T>>(reader)) {\n            return (async () => await Table.from(await reader))();\n        }\n        if (reader.isSync() && (reader = reader.open())) {\n            return !reader.schema ? Table.empty() : new Table<T>(reader.schema, [...reader]);\n        }\n        return (async (opening) => {\n            const reader = await opening;\n            const schema = reader.schema;\n            const batches: RecordBatch[] = [];\n            if (schema) {\n                for await (let batch of reader) {\n                    batches.push(batch);\n                }\n                return new Table<T>(schema, batches);\n            }\n            return Table.empty();\n        })(reader.open());\n    }\n\n    /** @nocollapse */\n    public static async fromAsync<T extends { [key: string]: DataType } = any>(source: import('./ipc/reader').FromArgs): Promise<Table<T>> {\n        return await Table.from<T>(source as any);\n    }\n\n    /** @nocollapse */\n    public static fromStruct<T extends { [key: string]: DataType } = any>(vector: Vector<Struct<T>>) {\n        return Table.new<T>(vector.data.childData as Data<T[keyof T]>[], vector.type.children);\n    }\n\n    /**\n     * @summary Create a new Table from a collection of Columns or Vectors,\n     * with an optional list of names or Fields.\n     *\n     *\n     * `Table.new` accepts an Object of\n     * Columns or Vectors, where the keys will be used as the field names\n     * for the Schema:\n     * ```ts\n     * const i32s = Int32Vector.from([1, 2, 3]);\n     * const f32s = Float32Vector.from([.1, .2, .3]);\n     * const table = Table.new({ i32: i32s, f32: f32s });\n     * assert(table.schema.fields[0].name === 'i32');\n     * ```\n     *\n     * It also accepts a a list of Vectors with an optional list of names or\n     * Fields for the resulting Schema. If the list is omitted or a name is\n     * missing, the numeric index of each Vector will be used as the name:\n     * ```ts\n     * const i32s = Int32Vector.from([1, 2, 3]);\n     * const f32s = Float32Vector.from([.1, .2, .3]);\n     * const table = Table.new([i32s, f32s], ['i32']);\n     * assert(table.schema.fields[0].name === 'i32');\n     * assert(table.schema.fields[1].name === '1');\n     * ```\n     *\n     * If the supplied arguments are Columns, `Table.new` will infer the Schema\n     * from the Columns:\n     * ```ts\n     * const i32s = Column.new('i32', Int32Vector.from([1, 2, 3]));\n     * const f32s = Column.new('f32', Float32Vector.from([.1, .2, .3]));\n     * const table = Table.new(i32s, f32s);\n     * assert(table.schema.fields[0].name === 'i32');\n     * assert(table.schema.fields[1].name === 'f32');\n     * ```\n     *\n     * If the supplied Vector or Column lengths are unequal, `Table.new` will\n     * extend the lengths of the shorter Columns, allocating additional bytes\n     * to represent the additional null slots. The memory required to allocate\n     * these additional bitmaps can be computed as:\n     * ```ts\n     * let additionalBytes = 0;\n     * for (let vec in shorter_vectors) {\n     *     additionalBytes += (((longestLength - vec.length) + 63) & ~63) >> 3;\n     * }\n     * ```\n     *\n     * For example, an additional null bitmap for one million null values would require\n     * 125,000 bytes (`((1e6 + 63) & ~63) >> 3`), or approx. `0.11MiB`\n     */\n    public static new<T extends { [key: string]: DataType } = any>(...columns: Columns<T>): Table<T>;\n    public static new<T extends VectorMap = any>(children: T): Table<{ [P in keyof T]: T[P]['type'] }>;\n    public static new<T extends { [key: string]: DataType } = any>(children: ChildData<T>, fields?: Fields<T>): Table<T>;\n    /** @nocollapse */\n    public static new(...cols: any[]) {\n        return new Table(...distributeColumnsIntoRecordBatches(selectColumnArgs(cols)));\n    }\n\n    constructor(batches: RecordBatch<T>[]);\n    constructor(...batches: RecordBatch<T>[]);\n    constructor(schema: Schema<T>, batches: RecordBatch<T>[]);\n    constructor(schema: Schema<T>, ...batches: RecordBatch<T>[]);\n    constructor(...args: any[]) {\n\n        let schema: Schema<T> = null!;\n\n        if (args[0] instanceof Schema) { schema = args.shift(); }\n\n        let chunks = selectArgs<RecordBatch<T>>(RecordBatch, args);\n\n        if (!schema && !(schema = chunks[0] && chunks[0].schema)) {\n            throw new TypeError('Table must be initialized with a Schema or at least one RecordBatch');\n        }\n\n        chunks[0] || (chunks[0] = new _InternalEmptyPlaceholderRecordBatch(schema));\n\n        super(new Struct(schema.fields), chunks);\n\n        this._schema = schema;\n        this._chunks = chunks;\n    }\n\n    protected _schema: Schema<T>;\n    // List of inner RecordBatches\n    protected _chunks: RecordBatch<T>[];\n    protected _children?: Column<T[keyof T]>[];\n\n    public get schema() { return this._schema; }\n    public get length() { return this._length; }\n    public get chunks() { return this._chunks; }\n    public get numCols() { return this._numChildren; }\n\n    public clone(chunks = this._chunks) {\n        return new Table<T>(this._schema, chunks);\n    }\n\n    public getColumn<R extends keyof T>(name: R): Column<T[R]> {\n        return this.getColumnAt(this.getColumnIndex(name)) as Column<T[R]>;\n    }\n    public getColumnAt<R extends DataType = any>(index: number): Column<R> | null {\n        return this.getChildAt(index);\n    }\n    public getColumnIndex<R extends keyof T>(name: R) {\n        return this._schema.fields.findIndex((f) => f.name === name);\n    }\n    public getChildAt<R extends DataType = any>(index: number): Column<R> | null {\n        if (index < 0 || index >= this.numChildren) { return null; }\n        let field: Field<R>, child: Column<R>;\n        const fields = (this._schema as Schema<any>).fields;\n        const columns = this._children || (this._children = []) as Column[];\n        if (child = columns[index]) { return child as Column<R>; }\n        if (field = fields[index]) {\n            const chunks = this._chunks\n                .map((chunk) => chunk.getChildAt<R>(index))\n                .filter((vec): vec is Vector<R> => vec != null);\n            if (chunks.length > 0) {\n                return (columns[index] = new Column<R>(field, chunks));\n            }\n        }\n        return null;\n    }\n\n    // @ts-ignore\n    public serialize(encoding = 'binary', stream = true) {\n        const Writer = !stream\n            ? RecordBatchFileWriter\n            : RecordBatchStreamWriter;\n        return Writer.writeAll(this).toUint8Array(true);\n    }\n    public count(): number {\n        return this._length;\n    }\n    public select<K extends keyof T = any>(...columnNames: K[]) {\n        const nameToIndex = this._schema.fields.reduce((m, f, i) => m.set(f.name as K, i), new Map<K, number>());\n        return this.selectAt(...columnNames.map((columnName) => nameToIndex.get(columnName)!).filter((x) => x > -1));\n    }\n    public selectAt<K extends T[keyof T] = any>(...columnIndices: number[]) {\n        const schema = this._schema.selectAt<K>(...columnIndices);\n        return new Table(schema, this._chunks.map(({ length, data: { childData } }) => {\n            return new RecordBatch(schema, length, columnIndices.map((i) => childData[i]).filter(Boolean));\n        }));\n    }\n    public assign<R extends { [key: string]: DataType } = any>(other: Table<R>) {\n\n        const fields = this._schema.fields;\n        const [indices, oldToNew] = other.schema.fields.reduce((memo, f2, newIdx) => {\n            const [indices, oldToNew] = memo;\n            const i = fields.findIndex((f) => f.name === f2.name);\n            ~i ? (oldToNew[i] = newIdx) : indices.push(newIdx);\n            return memo;\n        }, [[], []] as number[][]);\n\n        const schema = this._schema.assign(other.schema);\n        const columns = [\n            ...fields.map((_f, i, _fs, j = oldToNew[i]) =>\n                (j === undefined ? this.getColumnAt(i) : other.getColumnAt(j))!),\n            ...indices.map((i) => other.getColumnAt(i)!)\n        ].filter(Boolean) as Column<(T & R)[keyof T | keyof R]>[];\n\n        return new Table<T & R>(...distributeVectorsIntoRecordBatches<any>(schema, columns));\n    }\n}\n\nfunction tableFromIterable<T extends { [key: string]: DataType } = any, TNull = any>(input: VectorBuilderOptions<Struct<T>, TNull>) {\n    const { type } = input;\n    if (type instanceof Struct) {\n        return Table.fromStruct(StructVector.from(input as VectorBuilderOptions<Struct<T>, TNull>));\n    }\n    return null;\n}\n\nfunction tableFromAsyncIterable<T extends { [key: string]: DataType } = any, TNull = any>(input: VectorBuilderOptionsAsync<Struct<T>, TNull>) {\n    const { type } = input;\n    if (type instanceof Struct) {\n        return StructVector.from(input as VectorBuilderOptionsAsync<Struct<T>, TNull>).then((vector) => Table.fromStruct(vector));\n    }\n    return null;\n}\n"]}