mirror of
https://github.com/lancedb/lancedb.git
synced 2026-01-05 03:12:57 +00:00
9645fe52c2
- Enhanced error messages for schema inference failures to suggest providing an explicit schema. - Updated embedding application logic to check for existing destination columns, allowing for filling embeddings in columns that are all null. - Added comments for clarity on handling existing columns during embedding application. Fixes https://github.com/lancedb/lancedb/issues/2183 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## Summary by CodeRabbit - **Bug Fixes** - Improved error messages for schema inference to enhance readability. - Prevented redundant embedding application by skipping columns that already contain data, avoiding unnecessary errors and computations. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
1357 lines
42 KiB
TypeScript
1357 lines
42 KiB
TypeScript
// SPDX-License-Identifier: Apache-2.0
|
|
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
|
|
|
import {
|
|
Data as ArrowData,
|
|
Table as ArrowTable,
|
|
Binary,
|
|
Bool,
|
|
BufferType,
|
|
DataType,
|
|
DateUnit,
|
|
Date_,
|
|
Decimal,
|
|
Dictionary,
|
|
Duration,
|
|
Field,
|
|
FixedSizeBinary,
|
|
FixedSizeList,
|
|
Float,
|
|
Float32,
|
|
Float64,
|
|
Int,
|
|
Int32,
|
|
Int64,
|
|
LargeBinary,
|
|
List,
|
|
Null,
|
|
Precision,
|
|
RecordBatch,
|
|
RecordBatchFileReader,
|
|
RecordBatchFileWriter,
|
|
RecordBatchStreamWriter,
|
|
Schema,
|
|
Struct,
|
|
Timestamp,
|
|
Type,
|
|
Utf8,
|
|
Vector,
|
|
makeVector as arrowMakeVector,
|
|
vectorFromArray as badVectorFromArray,
|
|
makeBuilder,
|
|
makeData,
|
|
makeTable,
|
|
} from "apache-arrow";
|
|
import { Buffers } from "apache-arrow/data";
|
|
import { type EmbeddingFunction } from "./embedding/embedding_function";
|
|
import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
|
|
import {
|
|
sanitizeField,
|
|
sanitizeSchema,
|
|
sanitizeTable,
|
|
sanitizeType,
|
|
} from "./sanitize";
|
|
export * from "apache-arrow";
|
|
export type SchemaLike =
|
|
| Schema
|
|
| {
|
|
fields: FieldLike[];
|
|
metadata: Map<string, string>;
|
|
get names(): unknown[];
|
|
};
|
|
export type FieldLike =
|
|
| Field
|
|
| {
|
|
type: string;
|
|
name: string;
|
|
nullable?: boolean;
|
|
metadata?: Map<string, string>;
|
|
};
|
|
|
|
export type DataLike =
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
| import("apache-arrow").Data<Struct<any>>
|
|
| {
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
type: any;
|
|
length: number;
|
|
offset: number;
|
|
stride: number;
|
|
nullable: boolean;
|
|
children: DataLike[];
|
|
get nullCount(): number;
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
values: Buffers<any>[BufferType.DATA];
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
typeIds: Buffers<any>[BufferType.TYPE];
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
nullBitmap: Buffers<any>[BufferType.VALIDITY];
|
|
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
|
valueOffsets: Buffers<any>[BufferType.OFFSET];
|
|
};
|
|
|
|
export type RecordBatchLike =
|
|
| RecordBatch
|
|
| {
|
|
schema: SchemaLike;
|
|
data: DataLike;
|
|
};
|
|
|
|
export type TableLike =
|
|
| ArrowTable
|
|
| { schema: SchemaLike; batches: RecordBatchLike[] };
|
|
|
|
export type IntoVector =
|
|
| Float32Array
|
|
| Float64Array
|
|
| number[]
|
|
| Promise<Float32Array | Float64Array | number[]>;
|
|
|
|
export function isArrowTable(value: object): value is TableLike {
|
|
if (value instanceof ArrowTable) return true;
|
|
return "schema" in value && "batches" in value;
|
|
}
|
|
|
|
export function isNull(value: unknown): value is Null {
|
|
return value instanceof Null || DataType.isNull(value);
|
|
}
|
|
export function isInt(value: unknown): value is Int {
|
|
return value instanceof Int || DataType.isInt(value);
|
|
}
|
|
export function isFloat(value: unknown): value is Float {
|
|
return value instanceof Float || DataType.isFloat(value);
|
|
}
|
|
export function isBinary(value: unknown): value is Binary {
|
|
return value instanceof Binary || DataType.isBinary(value);
|
|
}
|
|
export function isLargeBinary(value: unknown): value is LargeBinary {
|
|
return value instanceof LargeBinary || DataType.isLargeBinary(value);
|
|
}
|
|
export function isUtf8(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isUtf8(value);
|
|
}
|
|
export function isLargeUtf8(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isLargeUtf8(value);
|
|
}
|
|
export function isBool(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isBool(value);
|
|
}
|
|
export function isDecimal(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isDecimal(value);
|
|
}
|
|
export function isDate(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isDate(value);
|
|
}
|
|
export function isTime(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isTime(value);
|
|
}
|
|
export function isTimestamp(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isTimestamp(value);
|
|
}
|
|
export function isInterval(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isInterval(value);
|
|
}
|
|
export function isDuration(value: unknown): value is Utf8 {
|
|
return value instanceof Utf8 || DataType.isDuration(value);
|
|
}
|
|
export function isList(value: unknown): value is List {
|
|
return value instanceof List || DataType.isList(value);
|
|
}
|
|
export function isStruct(value: unknown): value is Struct {
|
|
return value instanceof Struct || DataType.isStruct(value);
|
|
}
|
|
export function isUnion(value: unknown): value is Struct {
|
|
return value instanceof Struct || DataType.isUnion(value);
|
|
}
|
|
export function isFixedSizeBinary(value: unknown): value is FixedSizeBinary {
|
|
return value instanceof FixedSizeBinary || DataType.isFixedSizeBinary(value);
|
|
}
|
|
|
|
export function isFixedSizeList(value: unknown): value is FixedSizeList {
|
|
return value instanceof FixedSizeList || DataType.isFixedSizeList(value);
|
|
}
|
|
|
|
/** Data type accepted by NodeJS SDK */
|
|
export type Data = Record<string, unknown>[] | TableLike;
|
|
|
|
/*
|
|
* Options to control how a column should be converted to a vector array
|
|
*/
|
|
export class VectorColumnOptions {
|
|
/** Vector column type. */
|
|
type: Float = new Float32();
|
|
|
|
constructor(values?: Partial<VectorColumnOptions>) {
|
|
Object.assign(this, values);
|
|
}
|
|
}
|
|
|
|
// biome-ignore lint/suspicious/noExplicitAny: skip
|
|
function vectorFromArray(data: any, type?: DataType) {
|
|
// Workaround for: https://github.com/apache/arrow/issues/45862
|
|
// If FSL type with float
|
|
if (DataType.isFixedSizeList(type) && DataType.isFloat(type.valueType)) {
|
|
const extendedData = [...data, new Array(type.listSize).fill(0.0)];
|
|
const array = badVectorFromArray(extendedData, type);
|
|
return array.slice(0, data.length);
|
|
} else if (type === undefined) {
|
|
return badVectorFromArray(data);
|
|
} else {
|
|
return badVectorFromArray(data, type);
|
|
}
|
|
}
|
|
|
|
/** Options to control the makeArrowTable call. */
|
|
export class MakeArrowTableOptions {
|
|
/*
|
|
* Schema of the data.
|
|
*
|
|
* If this is not provided then the data type will be inferred from the
|
|
* JS type. Integer numbers will become int64, floating point numbers
|
|
* will become float64 and arrays will become variable sized lists with
|
|
* the data type inferred from the first element in the array.
|
|
*
|
|
* The schema must be specified if there are no records (e.g. to make
|
|
* an empty table)
|
|
*/
|
|
schema?: SchemaLike;
|
|
|
|
/*
|
|
* Mapping from vector column name to expected type
|
|
*
|
|
* Lance expects vector columns to be fixed size list arrays (i.e. tensors)
|
|
* However, `makeArrowTable` will not infer this by default (it creates
|
|
* variable size list arrays). This field can be used to indicate that a column
|
|
* should be treated as a vector column and converted to a fixed size list.
|
|
*
|
|
* The keys should be the names of the vector columns. The value specifies the
|
|
* expected data type of the vector columns.
|
|
*
|
|
* If `schema` is provided then this field is ignored.
|
|
*
|
|
* By default, the column named "vector" will be assumed to be a float32
|
|
* vector column.
|
|
*/
|
|
vectorColumns: Record<string, VectorColumnOptions> = {
|
|
vector: new VectorColumnOptions(),
|
|
};
|
|
embeddings?: EmbeddingFunction<unknown>;
|
|
embeddingFunction?: EmbeddingFunctionConfig;
|
|
|
|
/**
|
|
* If true then string columns will be encoded with dictionary encoding
|
|
*
|
|
* Set this to true if your string columns tend to repeat the same values
|
|
* often. For more precise control use the `schema` property to specify the
|
|
* data type for individual columns.
|
|
*
|
|
* If `schema` is provided then this property is ignored.
|
|
*/
|
|
dictionaryEncodeStrings: boolean = false;
|
|
|
|
constructor(values?: Partial<MakeArrowTableOptions>) {
|
|
Object.assign(this, values);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* An enhanced version of the {@link makeTable} function from Apache Arrow
|
|
* that supports nested fields and embeddings columns.
|
|
*
|
|
* (typically you do not need to call this function. It will be called automatically
|
|
* when creating a table or adding data to it)
|
|
*
|
|
* This function converts an array of Record<String, any> (row-major JS objects)
|
|
* to an Arrow Table (a columnar structure)
|
|
*
|
|
* If a schema is provided then it will be used to determine the resulting array
|
|
* types. Fields will also be reordered to fit the order defined by the schema.
|
|
*
|
|
* If a schema is not provided then the types will be inferred and the field order
|
|
* will be controlled by the order of properties in the first record. If a type
|
|
* is inferred it will always be nullable.
|
|
*
|
|
* If not all fields are found in the data, then a subset of the schema will be
|
|
* returned.
|
|
*
|
|
* If the input is empty then a schema must be provided to create an empty table.
|
|
*
|
|
* When a schema is not specified then data types will be inferred. The inference
|
|
* rules are as follows:
|
|
*
|
|
* - boolean => Bool
|
|
* - number => Float64
|
|
* - bigint => Int64
|
|
* - String => Utf8
|
|
* - Buffer => Binary
|
|
* - Record<String, any> => Struct
|
|
* - Array<any> => List
|
|
* @example
|
|
* ```ts
|
|
* import { fromTableToBuffer, makeArrowTable } from "../arrow";
|
|
* import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";
|
|
*
|
|
* const schema = new Schema([
|
|
* new Field("a", new Int32()),
|
|
* new Field("b", new Float32()),
|
|
* new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
|
|
* ]);
|
|
* const table = makeArrowTable([
|
|
* { a: 1, b: 2, c: [1, 2, 3] },
|
|
* { a: 4, b: 5, c: [4, 5, 6] },
|
|
* { a: 7, b: 8, c: [7, 8, 9] },
|
|
* ], { schema });
|
|
* ```
|
|
*
|
|
* By default it assumes that the column named `vector` is a vector column
|
|
* and it will be converted into a fixed size list array of type float32.
|
|
* The `vectorColumns` option can be used to support other vector column
|
|
* names and data types.
|
|
*
|
|
* ```ts
|
|
* const schema = new Schema([
|
|
* new Field("a", new Float64()),
|
|
* new Field("b", new Float64()),
|
|
* new Field(
|
|
* "vector",
|
|
* new FixedSizeList(3, new Field("item", new Float32()))
|
|
* ),
|
|
* ]);
|
|
* const table = makeArrowTable([
|
|
* { a: 1, b: 2, vector: [1, 2, 3] },
|
|
* { a: 4, b: 5, vector: [4, 5, 6] },
|
|
* { a: 7, b: 8, vector: [7, 8, 9] },
|
|
* ]);
|
|
* assert.deepEqual(table.schema, schema);
|
|
* ```
|
|
*
|
|
* You can specify the vector column types and names using the options as well
|
|
*
|
|
* ```ts
|
|
* const schema = new Schema([
|
|
* new Field('a', new Float64()),
|
|
* new Field('b', new Float64()),
|
|
* new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
|
|
* new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
|
|
* ]);
|
|
* const table = makeArrowTable([
|
|
* { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
|
|
* { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
|
|
* { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
|
|
* ], {
|
|
* vectorColumns: {
|
|
* vec1: { type: new Float16() },
|
|
* vec2: { type: new Float16() }
|
|
* }
|
|
* }
|
|
* assert.deepEqual(table.schema, schema)
|
|
* ```
|
|
*/
|
|
export function makeArrowTable(
|
|
data: Array<Record<string, unknown>>,
|
|
options?: Partial<MakeArrowTableOptions>,
|
|
metadata?: Map<string, string>,
|
|
): ArrowTable {
|
|
const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
|
|
let schema: Schema | undefined = undefined;
|
|
if (opt.schema !== undefined && opt.schema !== null) {
|
|
schema = sanitizeSchema(opt.schema);
|
|
schema = validateSchemaEmbeddings(
|
|
schema as Schema,
|
|
data,
|
|
options?.embeddingFunction,
|
|
);
|
|
}
|
|
|
|
let schemaMetadata = schema?.metadata || new Map<string, string>();
|
|
if (metadata !== undefined) {
|
|
schemaMetadata = new Map([...schemaMetadata, ...metadata]);
|
|
}
|
|
|
|
if (
|
|
data.length === 0 &&
|
|
(options?.schema === undefined || options?.schema === null)
|
|
) {
|
|
throw new Error("At least one record or a schema needs to be provided");
|
|
} else if (data.length === 0) {
|
|
if (schema === undefined) {
|
|
throw new Error("A schema must be provided if data is empty");
|
|
} else {
|
|
schema = new Schema(schema.fields, schemaMetadata);
|
|
return new ArrowTable(schema);
|
|
}
|
|
}
|
|
|
|
let inferredSchema = inferSchema(data, schema, opt);
|
|
inferredSchema = new Schema(inferredSchema.fields, schemaMetadata);
|
|
|
|
const finalColumns: Record<string, Vector> = {};
|
|
for (const field of inferredSchema.fields) {
|
|
finalColumns[field.name] = transposeData(data, field);
|
|
}
|
|
|
|
return new ArrowTable(inferredSchema, finalColumns);
|
|
}
|
|
|
|
function inferSchema(
|
|
data: Array<Record<string, unknown>>,
|
|
schema: Schema | undefined,
|
|
opts: MakeArrowTableOptions,
|
|
): Schema {
|
|
// We will collect all fields we see in the data.
|
|
const pathTree = new PathTree<DataType>();
|
|
|
|
for (const [rowI, row] of data.entries()) {
|
|
for (const [path, value] of rowPathsAndValues(row)) {
|
|
if (!pathTree.has(path)) {
|
|
// First time seeing this field.
|
|
if (schema !== undefined) {
|
|
const field = getFieldForPath(schema, path);
|
|
if (field === undefined) {
|
|
throw new Error(
|
|
`Found field not in schema: ${path.join(".")} at row ${rowI}`,
|
|
);
|
|
} else {
|
|
pathTree.set(path, field.type);
|
|
}
|
|
} else {
|
|
const inferredType = inferType(value, path, opts);
|
|
if (inferredType === undefined) {
|
|
throw new Error(`Failed to infer data type for field ${path.join(
|
|
".",
|
|
)} at row ${rowI}. \
|
|
Consider providing an explicit schema.`);
|
|
}
|
|
pathTree.set(path, inferredType);
|
|
}
|
|
} else if (schema === undefined) {
|
|
const currentType = pathTree.get(path);
|
|
const newType = inferType(value, path, opts);
|
|
if (currentType !== newType) {
|
|
new Error(`Failed to infer schema for data. Previously inferred type \
|
|
${currentType} but found ${newType} at row ${rowI}. Consider \
|
|
providing an explicit schema.`);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (schema === undefined) {
|
|
function fieldsFromPathTree(pathTree: PathTree<DataType>): Field[] {
|
|
const fields = [];
|
|
for (const [name, value] of pathTree.map.entries()) {
|
|
if (value instanceof PathTree) {
|
|
const children = fieldsFromPathTree(value);
|
|
fields.push(new Field(name, new Struct(children), true));
|
|
} else {
|
|
fields.push(new Field(name, value, true));
|
|
}
|
|
}
|
|
return fields;
|
|
}
|
|
const fields = fieldsFromPathTree(pathTree);
|
|
return new Schema(fields);
|
|
} else {
|
|
function takeMatchingFields(
|
|
fields: Field[],
|
|
pathTree: PathTree<DataType>,
|
|
): Field[] {
|
|
const outFields = [];
|
|
for (const field of fields) {
|
|
if (pathTree.map.has(field.name)) {
|
|
const value = pathTree.get([field.name]);
|
|
if (value instanceof PathTree) {
|
|
const struct = field.type as Struct;
|
|
const children = takeMatchingFields(struct.children, value);
|
|
outFields.push(
|
|
new Field(field.name, new Struct(children), field.nullable),
|
|
);
|
|
} else {
|
|
outFields.push(
|
|
new Field(field.name, value as DataType, field.nullable),
|
|
);
|
|
}
|
|
}
|
|
}
|
|
return outFields;
|
|
}
|
|
const fields = takeMatchingFields(schema.fields, pathTree);
|
|
return new Schema(fields);
|
|
}
|
|
}
|
|
|
|
function* rowPathsAndValues(
|
|
row: Record<string, unknown>,
|
|
basePath: string[] = [],
|
|
): Generator<[string[], unknown]> {
|
|
for (const [key, value] of Object.entries(row)) {
|
|
if (isObject(value)) {
|
|
yield* rowPathsAndValues(value, [...basePath, key]);
|
|
} else {
|
|
yield [[...basePath, key], value];
|
|
}
|
|
}
|
|
}
|
|
|
|
function isObject(value: unknown): value is Record<string, unknown> {
|
|
return (
|
|
typeof value === "object" &&
|
|
value !== null &&
|
|
!Array.isArray(value) &&
|
|
!(value instanceof RegExp) &&
|
|
!(value instanceof Date) &&
|
|
!(value instanceof Set) &&
|
|
!(value instanceof Map) &&
|
|
!(value instanceof Buffer)
|
|
);
|
|
}
|
|
|
|
function getFieldForPath(schema: Schema, path: string[]): Field | undefined {
|
|
let current: Field | Schema = schema;
|
|
for (const key of path) {
|
|
if (current instanceof Schema) {
|
|
const field: Field | undefined = current.fields.find(
|
|
(f) => f.name === key,
|
|
);
|
|
if (field === undefined) {
|
|
return undefined;
|
|
}
|
|
current = field;
|
|
} else if (current instanceof Field && DataType.isStruct(current.type)) {
|
|
const struct: Struct = current.type;
|
|
const field = struct.children.find((f) => f.name === key);
|
|
if (field === undefined) {
|
|
return undefined;
|
|
}
|
|
current = field;
|
|
} else {
|
|
return undefined;
|
|
}
|
|
}
|
|
if (current instanceof Field) {
|
|
return current;
|
|
} else {
|
|
return undefined;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Try to infer which Arrow type to use for a given value.
|
|
*
|
|
* May return undefined if the type cannot be inferred.
|
|
*/
|
|
function inferType(
|
|
value: unknown,
|
|
path: string[],
|
|
opts: MakeArrowTableOptions,
|
|
): DataType | undefined {
|
|
if (typeof value === "bigint") {
|
|
return new Int64();
|
|
} else if (typeof value === "number") {
|
|
// Even if it's an integer, it's safer to assume Float64. Users can
|
|
// always provide an explicit schema or use BigInt if they mean integer.
|
|
return new Float64();
|
|
} else if (typeof value === "string") {
|
|
if (opts.dictionaryEncodeStrings) {
|
|
return new Dictionary(new Utf8(), new Int32());
|
|
} else {
|
|
return new Utf8();
|
|
}
|
|
} else if (typeof value === "boolean") {
|
|
return new Bool();
|
|
} else if (value instanceof Buffer) {
|
|
return new Binary();
|
|
} else if (Array.isArray(value)) {
|
|
if (value.length === 0) {
|
|
return undefined; // Without any values we can't infer the type
|
|
}
|
|
if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
|
|
const floatType = sanitizeType(opts.vectorColumns[path[0]].type);
|
|
return new FixedSizeList(
|
|
value.length,
|
|
new Field("item", floatType, true),
|
|
);
|
|
}
|
|
const valueType = inferType(value[0], path, opts);
|
|
if (valueType === undefined) {
|
|
return undefined;
|
|
}
|
|
// Try to automatically detect embedding columns.
|
|
if (valueType instanceof Float && path[path.length - 1] === "vector") {
|
|
// We default to Float32 for vectors.
|
|
const child = new Field("item", new Float32(), true);
|
|
return new FixedSizeList(value.length, child);
|
|
} else {
|
|
const child = new Field("item", valueType, true);
|
|
return new List(child);
|
|
}
|
|
} else {
|
|
// TODO: timestamp
|
|
return undefined;
|
|
}
|
|
}
|
|
|
|
class PathTree<V> {
|
|
map: Map<string, V | PathTree<V>>;
|
|
|
|
constructor(entries?: [string[], V][]) {
|
|
this.map = new Map();
|
|
if (entries !== undefined) {
|
|
for (const [path, value] of entries) {
|
|
this.set(path, value);
|
|
}
|
|
}
|
|
}
|
|
has(path: string[]): boolean {
|
|
let ref: PathTree<V> = this;
|
|
for (const part of path) {
|
|
if (!(ref instanceof PathTree) || !ref.map.has(part)) {
|
|
return false;
|
|
}
|
|
ref = ref.map.get(part) as PathTree<V>;
|
|
}
|
|
return true;
|
|
}
|
|
get(path: string[]): V | undefined {
|
|
let ref: PathTree<V> = this;
|
|
for (const part of path) {
|
|
if (!(ref instanceof PathTree) || !ref.map.has(part)) {
|
|
return undefined;
|
|
}
|
|
ref = ref.map.get(part) as PathTree<V>;
|
|
}
|
|
return ref as V;
|
|
}
|
|
set(path: string[], value: V): void {
|
|
let ref: PathTree<V> = this;
|
|
for (const part of path.slice(0, path.length - 1)) {
|
|
if (!ref.map.has(part)) {
|
|
ref.map.set(part, new PathTree<V>());
|
|
}
|
|
ref = ref.map.get(part) as PathTree<V>;
|
|
}
|
|
ref.map.set(path[path.length - 1], value);
|
|
}
|
|
}
|
|
|
|
function transposeData(
|
|
data: Record<string, unknown>[],
|
|
field: Field,
|
|
path: string[] = [],
|
|
): Vector {
|
|
if (field.type instanceof Struct) {
|
|
const childFields = field.type.children;
|
|
const fullPath = [...path, field.name];
|
|
const childVectors = childFields.map((child) => {
|
|
return transposeData(data, child, fullPath);
|
|
});
|
|
const structData = makeData({
|
|
type: field.type,
|
|
children: childVectors as unknown as ArrowData<DataType>[],
|
|
});
|
|
return arrowMakeVector(structData);
|
|
} else {
|
|
const valuesPath = [...path, field.name];
|
|
const values = data.map((datum) => {
|
|
let current: unknown = datum;
|
|
for (const key of valuesPath) {
|
|
if (current == null) {
|
|
return null;
|
|
}
|
|
|
|
if (
|
|
isObject(current) &&
|
|
(Object.hasOwn(current, key) || key in current)
|
|
) {
|
|
current = current[key];
|
|
} else {
|
|
return null;
|
|
}
|
|
}
|
|
return current;
|
|
});
|
|
return makeVector(values, field.type);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Create an empty Arrow table with the provided schema
|
|
*/
|
|
export function makeEmptyTable(
|
|
schema: SchemaLike,
|
|
metadata?: Map<string, string>,
|
|
): ArrowTable {
|
|
return makeArrowTable([], { schema }, metadata);
|
|
}
|
|
|
|
/**
|
|
* Helper function to convert Array<Array<any>> to a variable sized list array
|
|
*/
|
|
// @ts-expect-error (Vector<unknown> is not assignable to Vector<any>)
|
|
function makeListVector(lists: unknown[][]): Vector<unknown> {
|
|
if (lists.length === 0 || lists[0].length === 0) {
|
|
throw Error("Cannot infer list vector from empty array or empty list");
|
|
}
|
|
const sampleList = lists[0];
|
|
// biome-ignore lint/suspicious/noExplicitAny: skip
|
|
let inferredType: any;
|
|
try {
|
|
const sampleVector = makeVector(sampleList);
|
|
inferredType = sampleVector.type;
|
|
} catch (error: unknown) {
|
|
// eslint-disable-next-line @typescript-eslint/restrict-template-expressions
|
|
throw Error(`Cannot infer list vector. Cannot infer inner type: ${error}`);
|
|
}
|
|
|
|
const listBuilder = makeBuilder({
|
|
type: new List(new Field("item", inferredType, true)),
|
|
});
|
|
for (const list of lists) {
|
|
listBuilder.append(list);
|
|
}
|
|
return listBuilder.finish().toVector();
|
|
}
|
|
|
|
/** Helper function to convert an Array of JS values to an Arrow Vector */
|
|
function makeVector(
|
|
values: unknown[],
|
|
type?: DataType,
|
|
stringAsDictionary?: boolean,
|
|
// biome-ignore lint/suspicious/noExplicitAny: skip
|
|
): Vector<any> {
|
|
if (type !== undefined) {
|
|
// No need for inference, let Arrow create it
|
|
if (type instanceof Int) {
|
|
if (DataType.isInt(type) && type.bitWidth === 64) {
|
|
// wrap in BigInt to avoid bug: https://github.com/apache/arrow/issues/40051
|
|
values = values.map((v) => {
|
|
if (v === null) {
|
|
return v;
|
|
} else if (typeof v === "bigint") {
|
|
return v;
|
|
} else if (typeof v === "number") {
|
|
return BigInt(v);
|
|
} else {
|
|
return v;
|
|
}
|
|
});
|
|
} else {
|
|
// Similarly, bigint isn't supported for 16 or 32-bit ints.
|
|
values = values.map((v) => {
|
|
if (typeof v == "bigint") {
|
|
return Number(v);
|
|
} else {
|
|
return v;
|
|
}
|
|
});
|
|
}
|
|
}
|
|
return vectorFromArray(values, type);
|
|
}
|
|
if (values.length === 0) {
|
|
throw Error(
|
|
"makeVector requires at least one value or the type must be specfied",
|
|
);
|
|
}
|
|
const sampleValue = values.find((val) => val !== null && val !== undefined);
|
|
if (sampleValue === undefined) {
|
|
throw Error(
|
|
"makeVector cannot infer the type if all values are null or undefined",
|
|
);
|
|
}
|
|
if (Array.isArray(sampleValue)) {
|
|
// Default Arrow inference doesn't handle list types
|
|
return makeListVector(values as unknown[][]);
|
|
} else if (Buffer.isBuffer(sampleValue)) {
|
|
// Default Arrow inference doesn't handle Buffer
|
|
return vectorFromArray(values, new Binary());
|
|
} else if (
|
|
!(stringAsDictionary ?? false) &&
|
|
(typeof sampleValue === "string" || sampleValue instanceof String)
|
|
) {
|
|
// If the type is string then don't use Arrow's default inference unless dictionaries are requested
|
|
// because it will always use dictionary encoding for strings
|
|
return vectorFromArray(values, new Utf8());
|
|
} else {
|
|
// Convert a JS array of values to an arrow vector
|
|
return vectorFromArray(values);
|
|
}
|
|
}
|
|
|
|
/** Helper function to apply embeddings from metadata to an input table */
|
|
async function applyEmbeddingsFromMetadata(
|
|
table: ArrowTable,
|
|
schema: Schema,
|
|
): Promise<ArrowTable> {
|
|
const registry = getRegistry();
|
|
const functions = await registry.parseFunctions(schema.metadata);
|
|
|
|
const columns = Object.fromEntries(
|
|
table.schema.fields.map((field) => [
|
|
field.name,
|
|
table.getChild(field.name)!,
|
|
]),
|
|
);
|
|
|
|
for (const functionEntry of functions.values()) {
|
|
const sourceColumn = columns[functionEntry.sourceColumn];
|
|
const destColumn = functionEntry.vectorColumn ?? "vector";
|
|
if (sourceColumn === undefined) {
|
|
throw new Error(
|
|
`Cannot apply embedding function because the source column '${functionEntry.sourceColumn}' was not present in the data`,
|
|
);
|
|
}
|
|
|
|
// Check if destination column exists and handle accordingly
|
|
if (columns[destColumn] !== undefined) {
|
|
const existingColumn = columns[destColumn];
|
|
// If the column exists but is all null, we can fill it with embeddings
|
|
if (existingColumn.nullCount !== existingColumn.length) {
|
|
// Column has non-null values, skip embedding application
|
|
continue;
|
|
}
|
|
}
|
|
|
|
if (table.batches.length > 1) {
|
|
throw new Error(
|
|
"Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
|
|
);
|
|
}
|
|
const values = sourceColumn.toArray();
|
|
|
|
const vectors =
|
|
await functionEntry.function.computeSourceEmbeddings(values);
|
|
if (vectors.length !== values.length) {
|
|
throw new Error(
|
|
"Embedding function did not return an embedding for each input element",
|
|
);
|
|
}
|
|
let destType: DataType;
|
|
const dtype = schema.fields.find((f) => f.name === destColumn)!.type;
|
|
if (isFixedSizeList(dtype)) {
|
|
destType = sanitizeType(dtype);
|
|
} else {
|
|
throw new Error(
|
|
"Expected FixedSizeList as datatype for vector field, instead got: " +
|
|
dtype,
|
|
);
|
|
}
|
|
const vector = makeVector(vectors, destType);
|
|
columns[destColumn] = vector;
|
|
}
|
|
const newTable = new ArrowTable(columns);
|
|
return alignTable(newTable, schema);
|
|
}
|
|
|
|
/** Helper function to apply embeddings to an input table */
|
|
async function applyEmbeddings<T>(
|
|
table: ArrowTable,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: SchemaLike,
|
|
): Promise<ArrowTable> {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = sanitizeSchema(schema);
|
|
}
|
|
if (schema?.metadata.has("embedding_functions")) {
|
|
return applyEmbeddingsFromMetadata(table, schema! as Schema);
|
|
} else if (embeddings == null || embeddings === undefined) {
|
|
return table;
|
|
}
|
|
|
|
let schemaMetadata = schema?.metadata || new Map<string, string>();
|
|
|
|
if (!(embeddings == null || embeddings === undefined)) {
|
|
const registry = getRegistry();
|
|
const embeddingMetadata = registry.getTableMetadata([embeddings]);
|
|
schemaMetadata = new Map([...schemaMetadata, ...embeddingMetadata]);
|
|
}
|
|
|
|
// Convert from ArrowTable to Record<String, Vector>
|
|
const colEntries = [...Array(table.numCols).keys()].map((_, idx) => {
|
|
const name = table.schema.fields[idx].name;
|
|
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
|
|
const vec = table.getChildAt(idx)!;
|
|
return [name, vec];
|
|
});
|
|
const newColumns = Object.fromEntries(colEntries);
|
|
|
|
const sourceColumn = newColumns[embeddings.sourceColumn];
|
|
const destColumn = embeddings.vectorColumn ?? "vector";
|
|
const innerDestType =
|
|
embeddings.function.embeddingDataType() ?? new Float32();
|
|
if (sourceColumn === undefined) {
|
|
throw new Error(
|
|
`Cannot apply embedding function because the source column '${embeddings.sourceColumn}' was not present in the data`,
|
|
);
|
|
}
|
|
|
|
if (table.numRows === 0) {
|
|
if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
|
|
// We have an empty table and it already has the embedding column so no work needs to be done
|
|
// Note: we don't return an error like we did below because this is a common occurrence. For example,
|
|
// if we call convertToTable with 0 records and a schema that includes the embedding
|
|
return table;
|
|
}
|
|
const dimensions = embeddings.function.ndims();
|
|
if (dimensions !== undefined) {
|
|
const destType = newVectorType(dimensions, innerDestType);
|
|
newColumns[destColumn] = makeVector([], destType);
|
|
} else if (schema != null) {
|
|
const destField = schema.fields.find((f) => f.name === destColumn);
|
|
if (destField != null) {
|
|
newColumns[destColumn] = makeVector([], destField.type);
|
|
} else {
|
|
throw new Error(
|
|
`Attempt to apply embeddings to an empty table failed because schema was missing embedding column '${destColumn}'`,
|
|
);
|
|
}
|
|
} else {
|
|
throw new Error(
|
|
"Attempt to apply embeddings to an empty table when the embeddings function does not specify `embeddingDimension`",
|
|
);
|
|
}
|
|
} else {
|
|
// Check if destination column exists and handle accordingly
|
|
if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
|
|
const existingColumn = newColumns[destColumn];
|
|
// If the column exists but is all null, we can fill it with embeddings
|
|
if (existingColumn.nullCount !== existingColumn.length) {
|
|
// Column has non-null values, skip embedding application and return table as-is
|
|
let newTable = new ArrowTable(newColumns);
|
|
if (schema != null) {
|
|
newTable = alignTable(newTable, schema as Schema);
|
|
}
|
|
return new ArrowTable(
|
|
new Schema(newTable.schema.fields, schemaMetadata),
|
|
newTable.batches,
|
|
);
|
|
}
|
|
}
|
|
|
|
if (table.batches.length > 1) {
|
|
throw new Error(
|
|
"Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
|
|
);
|
|
}
|
|
const values = sourceColumn.toArray();
|
|
const vectors = await embeddings.function.computeSourceEmbeddings(
|
|
values as T[],
|
|
);
|
|
if (vectors.length !== values.length) {
|
|
throw new Error(
|
|
"Embedding function did not return an embedding for each input element",
|
|
);
|
|
}
|
|
const destType = newVectorType(vectors[0].length, innerDestType);
|
|
newColumns[destColumn] = makeVector(vectors, destType);
|
|
}
|
|
|
|
let newTable = new ArrowTable(newColumns);
|
|
if (schema != null) {
|
|
if (schema.fields.find((f) => f.name === destColumn) === undefined) {
|
|
throw new Error(
|
|
`When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`,
|
|
);
|
|
}
|
|
newTable = alignTable(newTable, schema as Schema);
|
|
}
|
|
|
|
newTable = new ArrowTable(
|
|
new Schema(newTable.schema.fields, schemaMetadata),
|
|
newTable.batches,
|
|
);
|
|
|
|
return newTable;
|
|
}
|
|
|
|
/**
|
|
* Convert an Array of records into an Arrow Table, optionally applying an
|
|
* embeddings function to it.
|
|
*
|
|
* This function calls `makeArrowTable` first to create the Arrow Table.
|
|
* Any provided `makeTableOptions` (e.g. a schema) will be passed on to
|
|
* that call.
|
|
*
|
|
* The embedding function will be passed a column of values (based on the
|
|
* `sourceColumn` of the embedding function) and expects to receive back
|
|
* number[][] which will be converted into a fixed size list column. By
|
|
* default this will be a fixed size list of Float32 but that can be
|
|
* customized by the `embeddingDataType` property of the embedding function.
|
|
*
|
|
* If a schema is provided in `makeTableOptions` then it should include the
|
|
* embedding columns. If no schema is provded then embedding columns will
|
|
* be placed at the end of the table, after all of the input columns.
|
|
*/
|
|
export async function convertToTable(
|
|
data: Array<Record<string, unknown>>,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
makeTableOptions?: Partial<MakeArrowTableOptions>,
|
|
): Promise<ArrowTable> {
|
|
const table = makeArrowTable(data, makeTableOptions);
|
|
return await applyEmbeddings(table, embeddings, makeTableOptions?.schema);
|
|
}
|
|
|
|
/** Creates the Arrow Type for a Vector column with dimension `dim` */
|
|
export function newVectorType<T extends Float>(
|
|
dim: number,
|
|
innerType: unknown,
|
|
): FixedSizeList<T> {
|
|
// in Lance we always default to have the elements nullable, so we need to set it to true
|
|
// otherwise we often get schema mismatches because the stored data always has schema with nullable elements
|
|
const children = new Field("item", <T>sanitizeType(innerType), true);
|
|
return new FixedSizeList(dim, children);
|
|
}
|
|
|
|
/**
|
|
* Serialize an Array of records into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will call `convertToTable` and pass on `embeddings` and `schema`
|
|
*
|
|
* `schema` is required if data is empty
|
|
*/
|
|
export async function fromRecordsToBuffer(
|
|
data: Array<Record<string, unknown>>,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: Schema,
|
|
): Promise<Buffer> {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = sanitizeSchema(schema);
|
|
}
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
const writer = RecordBatchFileWriter.writeAll(table);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
|
|
/**
|
|
* Serialize an Array of records into a buffer using the Arrow IPC Stream serialization
|
|
*
|
|
* This function will call `convertToTable` and pass on `embeddings` and `schema`
|
|
*
|
|
* `schema` is required if data is empty
|
|
*/
|
|
export async function fromRecordsToStreamBuffer(
|
|
data: Array<Record<string, unknown>>,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: Schema,
|
|
): Promise<Buffer> {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = sanitizeSchema(schema);
|
|
}
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
const writer = RecordBatchStreamWriter.writeAll(table);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
export async function fromTableToBuffer(
|
|
table: ArrowTable,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: SchemaLike,
|
|
): Promise<Buffer> {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = sanitizeSchema(schema);
|
|
}
|
|
const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
|
|
const writer = RecordBatchFileWriter.writeAll(tableWithEmbeddings);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
export async function fromDataToBuffer(
|
|
data: Data,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: Schema,
|
|
): Promise<Buffer> {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = sanitizeSchema(schema);
|
|
}
|
|
if (isArrowTable(data)) {
|
|
return fromTableToBuffer(sanitizeTable(data), embeddings, schema);
|
|
} else {
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
return fromTableToBuffer(table);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Read a single record batch from a buffer.
|
|
*
|
|
* Returns null if the buffer does not contain a record batch
|
|
*/
|
|
export async function fromBufferToRecordBatch(
|
|
data: Buffer,
|
|
): Promise<RecordBatch | null> {
|
|
const iter = await RecordBatchFileReader.readAll(Buffer.from(data)).next()
|
|
.value;
|
|
const recordBatch = iter?.next().value;
|
|
return recordBatch || null;
|
|
}
|
|
|
|
/**
|
|
* Create a buffer containing a single record batch
|
|
*/
|
|
export async function fromRecordBatchToBuffer(
|
|
batch: RecordBatch,
|
|
): Promise<Buffer> {
|
|
const writer = new RecordBatchFileWriter().writeAll([batch]);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC Stream serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
export async function fromTableToStreamBuffer(
|
|
table: ArrowTable,
|
|
embeddings?: EmbeddingFunctionConfig,
|
|
schema?: SchemaLike,
|
|
): Promise<Buffer> {
|
|
const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
|
|
const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
|
|
/**
|
|
* Reorder the columns in `batch` so that they agree with the field order in `schema`
|
|
*/
|
|
function alignBatch(batch: RecordBatch, schema: Schema): RecordBatch {
|
|
const alignedChildren = [];
|
|
for (const field of schema.fields) {
|
|
const indexInBatch = batch.schema.fields?.findIndex(
|
|
(f) => f.name === field.name,
|
|
);
|
|
if (indexInBatch < 0) {
|
|
throw new Error(
|
|
`The column ${field.name} was not found in the Arrow Table`,
|
|
);
|
|
}
|
|
alignedChildren.push(batch.data.children[indexInBatch]);
|
|
}
|
|
const newData = makeData({
|
|
type: new Struct(schema.fields),
|
|
length: batch.numRows,
|
|
nullCount: batch.nullCount,
|
|
children: alignedChildren,
|
|
});
|
|
return new RecordBatch(schema, newData);
|
|
}
|
|
|
|
/**
|
|
* Reorder the columns in `table` so that they agree with the field order in `schema`
|
|
*/
|
|
function alignTable(table: ArrowTable, schema: Schema): ArrowTable {
|
|
const alignedBatches = table.batches.map((batch) =>
|
|
alignBatch(batch, schema),
|
|
);
|
|
return new ArrowTable(schema, alignedBatches);
|
|
}
|
|
|
|
/**
|
|
* Create an empty table with the given schema
|
|
*/
|
|
export function createEmptyTable(schema: Schema): ArrowTable {
|
|
return new ArrowTable(sanitizeSchema(schema));
|
|
}
|
|
|
|
function validateSchemaEmbeddings(
|
|
schema: Schema,
|
|
data: Array<Record<string, unknown>>,
|
|
embeddings: EmbeddingFunctionConfig | undefined,
|
|
): Schema {
|
|
const fields = [];
|
|
const missingEmbeddingFields = [];
|
|
|
|
// First we check if the field is a `FixedSizeList`
|
|
// Then we check if the data contains the field
|
|
// if it does not, we add it to the list of missing embedding fields
|
|
// Finally, we check if those missing embedding fields are `this._embeddings`
|
|
// if they are not, we throw an error
|
|
for (let field of schema.fields) {
|
|
if (isFixedSizeList(field.type)) {
|
|
field = sanitizeField(field);
|
|
if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
|
|
if (schema.metadata.has("embedding_functions")) {
|
|
const embeddings = JSON.parse(
|
|
schema.metadata.get("embedding_functions")!,
|
|
);
|
|
if (
|
|
// biome-ignore lint/suspicious/noExplicitAny: we don't know the type of `f`
|
|
embeddings.find((f: any) => f["vectorColumn"] === field.name) ===
|
|
undefined
|
|
) {
|
|
missingEmbeddingFields.push(field);
|
|
}
|
|
} else {
|
|
missingEmbeddingFields.push(field);
|
|
}
|
|
} else {
|
|
fields.push(field);
|
|
}
|
|
} else {
|
|
fields.push(field);
|
|
}
|
|
}
|
|
|
|
if (missingEmbeddingFields.length > 0 && embeddings === undefined) {
|
|
throw new Error(
|
|
`Table has embeddings: "${missingEmbeddingFields
|
|
.map((f) => f.name)
|
|
.join(",")}", but no embedding function was provided`,
|
|
);
|
|
}
|
|
|
|
return new Schema(fields, schema.metadata);
|
|
}
|
|
|
|
interface JsonDataType {
|
|
type: string;
|
|
fields?: JsonField[];
|
|
length?: number;
|
|
}
|
|
|
|
interface JsonField {
|
|
name: string;
|
|
type: JsonDataType;
|
|
nullable: boolean;
|
|
metadata: Map<string, string>;
|
|
}
|
|
|
|
// Matches format of https://github.com/lancedb/lance/blob/main/rust/lance/src/arrow/json.rs
|
|
export function dataTypeToJson(dataType: DataType): JsonDataType {
|
|
switch (dataType.typeId) {
|
|
// For primitives, matches https://github.com/lancedb/lance/blob/e12bb9eff2a52f753668d4b62c52e4d72b10d294/rust/lance-core/src/datatypes.rs#L185
|
|
case Type.Null:
|
|
return { type: "null" };
|
|
case Type.Bool:
|
|
return { type: "bool" };
|
|
case Type.Int8:
|
|
return { type: "int8" };
|
|
case Type.Int16:
|
|
return { type: "int16" };
|
|
case Type.Int32:
|
|
return { type: "int32" };
|
|
case Type.Int64:
|
|
return { type: "int64" };
|
|
case Type.Uint8:
|
|
return { type: "uint8" };
|
|
case Type.Uint16:
|
|
return { type: "uint16" };
|
|
case Type.Uint32:
|
|
return { type: "uint32" };
|
|
case Type.Uint64:
|
|
return { type: "uint64" };
|
|
case Type.Int: {
|
|
const bitWidth = (dataType as Int).bitWidth;
|
|
const signed = (dataType as Int).isSigned;
|
|
const prefix = signed ? "" : "u";
|
|
return { type: `${prefix}int${bitWidth}` };
|
|
}
|
|
case Type.Float: {
|
|
switch ((dataType as Float).precision) {
|
|
case Precision.HALF:
|
|
return { type: "halffloat" };
|
|
case Precision.SINGLE:
|
|
return { type: "float" };
|
|
case Precision.DOUBLE:
|
|
return { type: "double" };
|
|
}
|
|
throw Error("Unsupported float precision");
|
|
}
|
|
case Type.Float16:
|
|
return { type: "halffloat" };
|
|
case Type.Float32:
|
|
return { type: "float" };
|
|
case Type.Float64:
|
|
return { type: "double" };
|
|
case Type.Utf8:
|
|
return { type: "string" };
|
|
case Type.Binary:
|
|
return { type: "binary" };
|
|
case Type.LargeUtf8:
|
|
return { type: "large_string" };
|
|
case Type.LargeBinary:
|
|
return { type: "large_binary" };
|
|
case Type.List:
|
|
return {
|
|
type: "list",
|
|
fields: [fieldToJson((dataType as List).children[0])],
|
|
};
|
|
case Type.FixedSizeList: {
|
|
const fixedSizeList = dataType as FixedSizeList;
|
|
return {
|
|
type: "fixed_size_list",
|
|
fields: [fieldToJson(fixedSizeList.children[0])],
|
|
length: fixedSizeList.listSize,
|
|
};
|
|
}
|
|
case Type.Struct:
|
|
return {
|
|
type: "struct",
|
|
fields: (dataType as Struct).children.map(fieldToJson),
|
|
};
|
|
case Type.Date: {
|
|
const unit = (dataType as Date_).unit;
|
|
return {
|
|
type: unit === DateUnit.DAY ? "date32:day" : "date64:ms",
|
|
};
|
|
}
|
|
case Type.Timestamp: {
|
|
const timestamp = dataType as Timestamp;
|
|
const timezone = timestamp.timezone || "-";
|
|
return {
|
|
type: `timestamp:${timestamp.unit}:${timezone}`,
|
|
};
|
|
}
|
|
case Type.Decimal: {
|
|
const decimal = dataType as Decimal;
|
|
return {
|
|
type: `decimal:${decimal.bitWidth}:${decimal.precision}:${decimal.scale}`,
|
|
};
|
|
}
|
|
case Type.Duration: {
|
|
const duration = dataType as Duration;
|
|
return { type: `duration:${duration.unit}` };
|
|
}
|
|
case Type.FixedSizeBinary: {
|
|
const byteWidth = (dataType as FixedSizeBinary).byteWidth;
|
|
return { type: `fixed_size_binary:${byteWidth}` };
|
|
}
|
|
case Type.Dictionary: {
|
|
const dict = dataType as Dictionary;
|
|
const indexType = dataTypeToJson(dict.indices);
|
|
const valueType = dataTypeToJson(dict.valueType);
|
|
return {
|
|
type: `dict:${valueType.type}:${indexType.type}:false`,
|
|
};
|
|
}
|
|
}
|
|
throw new Error("Unsupported data type");
|
|
}
|
|
|
|
function fieldToJson(field: Field): JsonField {
|
|
return {
|
|
name: field.name,
|
|
type: dataTypeToJson(field.type),
|
|
nullable: field.nullable,
|
|
metadata: field.metadata,
|
|
};
|
|
}
|