flechette

Flechette API Reference

Top-Level Data Types Schema Table Column

Top-Level Decoding and Encoding

# tableFromIPC(data[, options])

Decode Apache Arrow IPC data and return a new Table. The input binary data may be either an ArrayBuffer or Uint8Array. For Arrow data in the IPC ‘stream’ format, an array of Uint8Array values is also supported.

import { tableFromIPC } from '@uwdata/flechette';
const url = 'https://vega.github.io/vega-datasets/data/flights-200k.arrow';
const ipc = await fetch(url).then(r => r.arrayBuffer());
const table = tableFromIPC(ipc);

# tableToIPC(table[, options])

Encode an Arrow table into Arrow IPC binary format and return the result as a Uint8Array. Both the IPC ‘stream’ and ‘file’ formats are supported.

import { tableToIPC } from '@uwdata/flechette';
const bytes = tableFromIPC(table, { format: 'stream' });

# tableFromArrays(data[, options])

Create a new table from a set of named arrays. Data types for the resulting Arrow columns can be automatically inferred or specified using the types option. If the types option provides data types for only a subset of columns, the rest are inferred. Each input array must have the same length.

import { tableFromArrays } from '@uwdata/flechette';

// create table with inferred types
const table = tableFromArrays({
  ints: [1, 2, null, 4, 5],
  floats: [1.1, 2.2, 3.3, 4.4, 5.5],
  bools: [true, true, null, false, true],
  strings: ['a', 'b', 'c', 'b', 'a']
});

import {
  bool, dictionary, float32, int32, tableFromArrays, tableToIPC, utf8
} from '@uwdata/flechette';

// create table with specified types
const table = tableFromArrays({
  ints: [1, 2, null, 4, 5],
  floats: [1.1, 2.2, 3.3, 4.4, 5.5],
  bools: [true, true, null, false, true],
  strings: ['a', 'b', 'c', 'b', 'a']
}, {
  types: {
    ints: int32(),
    floats: float32(),
    bools: bool(),
    strings: dictionary(utf8())
  }
});

# columnFromArray(array[, type, options])

Create a new column from a provided data array. The data types for the column can be automatically inferred or specified using the type argument.

import { columnFromArray, float32, int64 } from '@uwdata/flechette';

// create column with inferred type (here, float64)
columnFromArray([1.1, 2.2, 3.3, 4.4, 5.5]);

// create column with specified type
columnFromArray([1.1, 2.2, 3.3, 4.4, 5.5], float32());

// create column with specified type and options
columnFromArray(
  [1n, 32n, 2n << 34n], int64(),
  { maxBatchRows: 1000, useBigInt: true }
);

# columnFromValues(values[, type, options])

Create a new column by iterating over provided values. The values argument can either be an iterable collection or a visitor function that applies a callback to successive data values (akin to Array.forEach). The data types for the column can be automatically inferred or specified using the type argument.

import { columnFromValues, float32, int64 } from '@uwdata/flechette';

// an iterable set of values
const set = new Set([1.1, 1.1, 2.2, 3.4]);

// create column with inferred type (here, float64)
columnFromValues(set);

// create column with specified type
columnFromValues(set, float32());

// create column using only values with odd-numbered indices
const values = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];
columnFromValues(callback => {
  values.forEach((v, i) => { if (i % 2) callback(v); })
});

# tableFromColumns(columns[, useProxy])

Create a new table from a collection of columns. This method is useful for creating new tables using one or more pre-existing column instances. Otherwise, tableFromArrays should be preferred. Input columns are assumed to have the same record batch sizes.

import { columnFromArray, tableFromColumns } from '@uwdata/flechette';

// create column with inferred type (here, float64)
const table = tableFromColumns({
  bools: columnFromArray([true, true, null, false, true]),
  floats: columnFromArray([1.1, 2.2, 3.3, 4.4, 5.5])
});
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