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    @dvirtz/parquets
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    0.11.4 • Public • Published

    parquets

    Fully asynchronous TypeScript implementation of the Parquet file format

    Build Status npm version npm dependency status License: MIT

    This package is derived from parquet.js, contains a fully asynchronous TypeScript implementation of the Parquet file format. The implementation conforms with the Parquet specification and is being tested for compatibility with Apache's reference implementation.

    WARNING: There are compatibility issues with the reference implementation:

    • only GZIP and SNAPPY compressions are compatible
    • Parquet Tools are command line tools that aid in the inspection of Parquet files.
    • always verify your table structure loaded with realistic data sample can be read by Parquet Tools!

    What is Parquet?: Parquet is a column-oriented file format; it allows you to write a large amount of structured data to a file, compress it and then read parts of it back out efficiently. The Parquet format is based on Google's Dremel paper.

    Installation

    To use parquets with node.js, install it using npm:

      $ npm install parquets
    

    parquets requires node.js >= 7.6.0

    Usage: Writing files

    Once you have installed the parquets library, you can import it as a single module:

    import { ParquetSchema, ParquetWriter, ParquetReader } from 'parquets';

    Parquet files have a strict schema, similar to tables in a SQL database. So, in order to produce a Parquet file we first need to declare a new schema. Here is a simple example that shows how to instantiate a ParquetSchema object:

    // declare a schema for the `fruits` table
    let schema = new ParquetSchema({
      name: { type: 'UTF8' },
      quantity: { type: 'INT64' },
      price: { type: 'DOUBLE' },
      date: { type: 'TIMESTAMP_MILLIS' },
      in_stock: { type: 'BOOLEAN' }
    });

    Note that the Parquet schema supports nesting, so you can store complex, arbitrarily nested records into a single row (more on that later) while still maintaining good compression.

    Once we have a schema, we can create a ParquetWriter object. The writer will take input rows as JSON objects, convert them to the Parquet format and store them on disk.

    // create new ParquetWriter that writes to 'fruits.parquet`
    let writer = await ParquetWriter.openFile(schema, 'fruits.parquet');
    
    // append a few rows to the file
    await writer.appendRow({name: 'apples', quantity: 10, price: 2.5, date: new Date(), in_stock: true});
    await writer.appendRow({name: 'oranges', quantity: 10, price: 2.5, date: new Date(), in_stock: true});

    Once we are finished adding rows to the file, we have to tell the writer object to flush the metadata to disk and close the file by calling the close() method:

    Usage: Reading files

    A parquet reader allows retrieving the rows from a parquet file in order. The basic usage is to create a reader and then retrieve a cursor/iterator which allows you to consume row after row until all rows have been read.

    You may open more than one cursor and use them concurrently. All cursors become invalid once close() is called on the reader object.

    // create new ParquetReader that reads from 'fruits.parquet`
    let reader = await ParquetReader.openFile('fruits.parquet');
    
    // create a new cursor
    let cursor = reader.getCursor();
    
    // read all records from the file and print them
    let record = null;
    while (record = await cursor.next()) {
      console.log(record);
    }

    When creating a cursor, you can optionally request that only a subset of the columns should be read from disk. For example:

    // create a new cursor that will only return the `name` and `price` columns
    let cursor = reader.getCursor(['name', 'price']);

    It is important that you call close() after you are finished reading the file to avoid leaking file descriptors.

    await reader.close();

    Encodings

    Internally, the Parquet format will store values from each field as consecutive arrays which can be compressed/encoded using a number of schemes.

    Plain Encoding (PLAIN)

    The most simple encoding scheme is the PLAIN encoding. It simply stores the values as they are without any compression. The PLAIN encoding is currently the default for all types except BOOLEAN:

    let schema = new ParquetSchema({
      name: { type: 'UTF8', encoding: 'PLAIN' },
    });

    Run Length Encoding (RLE)

    The Parquet hybrid run length and bitpacking encoding allows to compress runs of numbers very efficiently. Note that the RLE encoding can only be used in combination with the BOOLEAN, INT32 and INT64 types. The RLE encoding requires an additional bitWidth parameter that contains the maximum number of bits required to store the largest value of the field.

    let schema = new ParquetSchema({
      age: { type: 'UINT_32', encoding: 'RLE', bitWidth: 7 },
    });

    Optional Fields

    By default, all fields are required to be present in each row. You can also mark a field as 'optional' which will let you store rows with that field missing:

    let schema = new ParquetSchema({
      name: { type: 'UTF8' },
      quantity: { type: 'INT64', optional: true },
    });
    
    let writer = await ParquetWriter.openFile(schema, 'fruits.parquet');
    await writer.appendRow({name: 'apples', quantity: 10 });
    await writer.appendRow({name: 'banana' }); // not in stock

    Nested Rows & Arrays

    Parquet supports nested schemas that allow you to store rows that have a more complex structure than a simple tuple of scalar values. To declare a schema with a nested field, omit the type in the column definition and add a fields list instead:

    Consider this example, which allows us to store a more advanced "fruits" table where each row contains a name, a list of colours and a list of "stock" objects.

    // advanced fruits table
    let schema = new ParquetSchema({
      name: { type: 'UTF8' },
      colours: { type: 'UTF8', repeated: true },
      stock: {
        repeated: true,
        fields: {
          price: { type: 'DOUBLE' },
          quantity: { type: 'INT64' },
        }
      }
    });
    
    // the above schema allows us to store the following rows:
    let writer = await ParquetWriter.openFile(schema, 'fruits.parquet');
    
    await writer.appendRow({
      name: 'banana',
      colours: ['yellow'],
      stock: [
        { price: 2.45, quantity: 16 },
        { price: 2.60, quantity: 420 }
      ]
    });
    
    await writer.appendRow({
      name: 'apple',
      colours: ['red', 'green'],
      stock: [
        { price: 1.20, quantity: 42 },
        { price: 1.30, quantity: 230 }
      ]
    });
    
    await writer.close();
    
    // reading nested rows with a list of explicit columns
    let reader = await ParquetReader.openFile('fruits.parquet');
    
    let cursor = reader.getCursor([['name'], ['stock', 'price']]);
    let record = null;
    while (record = await cursor.next()) {
      console.log(record);
    }
    
    await reader.close();

    It might not be obvious why one would want to implement or use such a feature when the same can - in principle - be achieved by serializing the record using JSON (or a similar scheme) and then storing it into a UTF8 field:

    Putting aside the philosophical discussion on the merits of strict typing, knowing about the structure and subtypes of all records (globally) means we do not have to duplicate this metadata (i.e. the field names) for every record. On top of that, knowing about the type of a field allows us to compress the remaining data more efficiently.

    List of Supported Types & Encodings

    We aim to be feature-complete and add new features as they are added to the Parquet specification; this is the list of currently implemented data types and encodings:

    Logical Type Primitive Type Encodings
    UTF8 BYTE_ARRAY PLAIN
    JSON BYTE_ARRAY PLAIN
    BSON BYTE_ARRAY PLAIN
    BYTE_ARRAY BYTE_ARRAY PLAIN
    TIME_MILLIS INT32 PLAIN, RLE
    TIME_MICROS INT64 PLAIN, RLE
    TIMESTAMP_MILLIS INT64 PLAIN, RLE
    TIMESTAMP_MICROS INT64 PLAIN, RLE
    BOOLEAN BOOLEAN PLAIN, RLE
    FLOAT FLOAT PLAIN
    DOUBLE DOUBLE PLAIN
    INT32 INT32 PLAIN, RLE
    INT64 INT64 PLAIN, RLE
    INT96 INT96 PLAIN
    INT_8 INT32 PLAIN, RLE
    INT_16 INT32 PLAIN, RLE
    INT_32 INT32 PLAIN, RLE
    INT_64 INT64 PLAIN, RLE
    UINT_8 INT32 PLAIN, RLE
    UINT_16 INT32 PLAIN, RLE
    UINT_32 INT32 PLAIN, RLE
    UINT_64 INT64 PLAIN, RLE

    Buffering & Row Group Size

    When writing a Parquet file, the ParquetWriter will buffer rows in memory until a row group is complete (or close() is called) and then write out the row group to disk.

    The size of a row group is configurable by the user and controls the maximum number of rows that are buffered in memory at any given time as well as the number of rows that are co-located on disk:

    let writer = await ParquetWriter.openFile(schema, 'fruits.parquet');
    writer.setRowGroupSize(8192);

    Dependencies

    Parquet uses thrift to encode the schema and other metadata, but the actual data does not use thrift.

    Contributions

    Please make sure you sign the contributor license agreement in order for us to be able to accept your contribution. We thank you very much!

    License

    parquet.js Copyright (c) 2017 ironSource Ltd.

    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

    Install

    npm i @dvirtz/parquets

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    Version

    0.11.4

    License

    MIT

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    • dvirtz