mirror of https://github.com/grafana/grafana
ArrayDataFrame: Convert to a simple utility function rather than dynamically loaded values (#67427)
Ryan McKinley
2 years ago
committed by
GitHub
parent
63383ef545
commit
8352e716dd
@ -1,118 +1,69 @@ |
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import { makeArrayIndexableVector, QueryResultMeta } from '../types'; |
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import { Field, FieldType, DataFrame } from '../types/dataFrame'; |
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import { FunctionalVector } from '../vector/FunctionalVector'; |
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import { vectorToArray } from '../vector/vectorToArray'; |
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import { QueryResultMeta } from '../types'; |
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import { Field, FieldType, DataFrame, TIME_SERIES_VALUE_FIELD_NAME } from '../types/dataFrame'; |
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import { guessFieldTypeFromNameAndValue, toDataFrameDTO } from './processDataFrame'; |
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/** @public */ |
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export type ValueConverter<T = any> = (val: unknown) => T; |
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const NOOP: ValueConverter = (v) => v; |
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class ArrayPropertyVector<T = any> extends FunctionalVector<T> { |
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converter = NOOP; |
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constructor(private source: any[], private prop: string) { |
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super(); |
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return makeArrayIndexableVector(this); |
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} |
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get length(): number { |
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return this.source.length; |
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} |
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get(index: number): T { |
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return this.converter(this.source[index][this.prop]); |
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} |
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toArray(): T[] { |
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return vectorToArray(this); |
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} |
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toJSON(): T[] { |
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return vectorToArray(this); |
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} |
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} |
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import { guessFieldTypeForField } from './processDataFrame'; |
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/** |
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* The ArrayDataFrame takes an array of objects and presents it as a DataFrame |
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* |
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* @alpha |
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* @deprecated use arrayToDataFrame |
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*/ |
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export class ArrayDataFrame<T = any> extends FunctionalVector<T> implements DataFrame { |
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export class ArrayDataFrame<T = any> implements DataFrame { |
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fields: Field[] = []; |
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length = 0; |
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name?: string; |
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refId?: string; |
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meta?: QueryResultMeta; |
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fields: Field[] = []; |
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length = 0; |
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constructor(source: T[], names?: string[]) { |
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return arrayToDataFrame(source, names) as ArrayDataFrame<T>; // returns a standard DataFrame
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} |
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} |
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constructor(private source: T[], names?: string[]) { |
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super(); |
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/** |
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* arrayToDataFrame will convert any array into a DataFrame |
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* |
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* @public |
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*/ |
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export function arrayToDataFrame(source: any[], names?: string[]): DataFrame { |
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const df: DataFrame = { |
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fields: [], |
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length: source.length, |
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}; |
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if (!source?.length) { |
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return df; |
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} |
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this.length = source.length; |
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const first: any = source.length ? source[0] : {}; |
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if (names) { |
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this.fields = names.map((name) => { |
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return { |
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if (names) { |
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for (const name of names) { |
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df.fields.push( |
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makeFieldFromValues( |
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name, |
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type: guessFieldTypeFromNameAndValue(name, first[name]), |
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config: {}, |
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values: new ArrayPropertyVector(source, name), |
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}; |
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}); |
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} else { |
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this.setFieldsFromObject(first); |
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source.map((v) => v[name]) |
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) |
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); |
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} |
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return makeArrayIndexableVector(this); |
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return df; |
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} |
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/** |
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* Add a field for each property in the object. This will guess the type |
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*/ |
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setFieldsFromObject(obj: Record<string, unknown>) { |
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this.fields = Object.keys(obj).map((name) => { |
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return { |
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name, |
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type: guessFieldTypeFromNameAndValue(name, obj[name]), |
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config: {}, |
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values: new ArrayPropertyVector(this.source, name), |
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}; |
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}); |
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} |
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/** |
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* Configure how the object property is passed to the data frame |
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*/ |
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setFieldType(name: string, type: FieldType, converter?: ValueConverter): Field { |
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let field = this.fields.find((f) => f.name === name); |
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if (field) { |
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field.type = type; |
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const first = source.find((v) => v != null); // first not null|undefined
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if (first != null) { |
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if (typeof first === 'object') { |
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df.fields = Object.keys(first).map((name) => { |
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return makeFieldFromValues( |
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name, |
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source.map((v) => v[name]) |
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); |
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}); |
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} else { |
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field = { |
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name, |
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type, |
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config: {}, |
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values: new ArrayPropertyVector(this.source, name), |
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}; |
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this.fields.push(field); |
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df.fields.push(makeFieldFromValues(TIME_SERIES_VALUE_FIELD_NAME, source)); |
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} |
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(field.values as any).converter = converter ?? NOOP; |
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return field; |
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} |
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/** |
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* Get an object with a property for each field in the DataFrame |
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*/ |
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get(idx: number): T { |
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return this.source[idx]; |
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} |
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return df; |
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} |
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/** |
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* The simplified JSON values used in JSON.stringify() |
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*/ |
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toJSON() { |
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return toDataFrameDTO(this); |
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} |
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function makeFieldFromValues(name: string, values: unknown[]): Field { |
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const f = { name, config: {}, values, type: FieldType.other }; |
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f.type = guessFieldTypeForField(f) ?? FieldType.other; |
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return f; |
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} |
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