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asynchronous, pure JavaScript implementation of the Parquet file format","homepage":"https://github.com/eduardocalazansjr/parquetjs","keywords":["dremel","parquet"],"repository":{"type":"git","url":"git://github.com/eduardocalazansjr/parquetjs.git"},"bugs":{"url":"https://github.com/eduardocalazansjr/parquetjs/issues"},"license":"MIT","readme":"# parquet.js\n\nfully asynchronous, pure node.js implementation of the Parquet file format\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n[![npm version](https://badge.fury.io/js/%40unfinishedlabs%2Fparquet-js.svg)](https://badge.fury.io/js/%40dsnp%2Fparquetjs)\n\nThis package contains a fully asynchronous, pure JavaScript implementation of\nthe [Parquet](https://parquet.apache.org/) file format. The implementation conforms with the\n[Parquet specification](https://github.com/apache/parquet-format) and is tested\nfor compatibility with Apache's Java [reference implementation](https://github.com/apache/parquet-mr).\n\n**What is Parquet?**: Parquet is a column-oriented file format; it allows you to\nwrite a large amount of structured data to a file, compress it and then read parts\nof it back out efficiently. The Parquet format is based on [Google's Dremel paper](https://www.google.co.nz/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj_tJelpv3UAhUCm5QKHfJODhUQFggsMAE&url=http%3A%2F%2Fwww.vldb.org%2Fpvldb%2Fvldb2010%2Fpapers%2FR29.pdf&usg=AFQjCNGyMk3_JltVZjMahP6LPmqMzYdCkw).\n\n## Forked Notice\n\nThis is a forked repository with code from various sources:\n- Primary source [ironSource](https://github.com/ironSource/parquetjs) [npm: parquetjs](https://www.npmjs.com/package/parquetjs)\n- Secondary source [ZJONSSON](https://github.com/ZJONSSON/parquetjs) [npm: parquetjs-lite](https://www.npmjs.com/package/parquetjs-lite)\n\n## Installation\n_parquet.js requires node.js >= 14.16.0_\n\n```\n  $ npm install @dsnp/parquetjs\n```\n\n### NodeJS\nTo use with nodejs:\n```javascript\nimport parquetjs from \"@dsnp/parquetjs\"\n```\n\n### Browser with Bundler\nTo use in a browser with a bundler, depending on your needs, write the appropriate plugin or resolver to point to either the Common JS or ES Module version:\n```javascript\n// Common JS\n\"node_modules/@dsnp/parquetjs/dist/browser/parquetjs.cjs\"\n// ES Modules\n\"node_modules/@dsnp/parquetjs/dist/browser/parquetjs.esm\"\n```\nor:\n```javascript\n// Common JS\nimport parquetjs from \"@dsnp/parquetjs/dist/browser/parquetjs.cjs\"\n// ES Modules\nimport parquetjs from \"@dsnp/parquetjs/dist/browser/parquetjs.esm\"\n```\n\n### Browser Direct: ES Modules\nTo use directly in the browser without a bundler using ES Modules:\n\n1. Build the package: `npm install && npm run build:browser`\n2. Copy to `dist/browser/parquetjs.esm.js` the server\n3. Use it in your html or other ES Modules:\n    ```html\n    <script type=\"module\">\n      import parquetjs from '../parquet.esm.js';\n      // Use parquetjs\n    </script>\n    ```\n\n### Browser Direct: Plain Ol' JavaScript\nTo use directly in the browser without a bundler or ES Modules:\n\n1. Build the package: `npm install && npm run build:browser`\n2. Copy to `dist/browser/parquetjs.js` the server\n2. Use the global `parquetjs` variable to access parquetjs functions\n   ```html\n   <script>\n    // console.log(parquetjs)\n    </script>\n    ```\n\n## Usage: Writing files\n\nOnce you have installed the parquet.js library, you can import it as a single\nmodule:\n\n``` js\nvar parquet = require('@dsnp/parquetjs');\n```\n\nParquet files have a strict schema, similar to tables in a SQL database. So,\nin order to produce a Parquet file we first need to declare a new schema. Here\nis a simple example that shows how to instantiate a `ParquetSchema` object:\n\n### Native Schema Definition\n\n``` js\n// declare a schema for the `fruits` table\nvar schema = new parquet.ParquetSchema({\n  name: { type: 'UTF8' },\n  quantity: { type: 'INT64' },\n  price: { type: 'DOUBLE' },\n  date: { type: 'TIMESTAMP_MILLIS' },\n  in_stock: { type: 'BOOLEAN' }\n});\n```\n\n### Helper Functions\n\n```js\nvar schema = new parquet.ParquetSchema({\n  name: parquet.ParquetFieldBuilder.createStringField(),\n  quantity: parquet.ParquetFieldBuilder.createIntField(64),\n  price: parquet.ParquetFieldBuilder.createDoubleField(),\n  date: parquet.ParquetFieldBuilder.createTimestampField(),\n  in_stock: parquet.ParquetFieldBuilder.createBooleanField()\n});\n```\n\n### JSON Schema\n\n``` js\n// declare a schema for the `fruits` JSON Schema\nvar schema = new parquet.ParquetSchema.fromJsonSchema({\n  \"type\": \"object\",\n  \"properties\": {\n    \"name\": {\n      \"type\": \"string\"\n    },\n    \"quantity\": {\n      \"type\": \"integer\"\n    },\n    \"price\": {\n      \"type\": \"number\"\n    },\n    \"date\": {\n      \"type\": \"string\"\n    },\n    \"in_stock\": {\n      \"type\": \"boolean\"\n    }\n  },\n  \"required\": [\"name\", \"quantity\", \"price\", \"date\", \"in_stock\"]\n});\n```\n\n\nNote that the Parquet schema supports nesting, so you can store complex, arbitrarily\nnested records into a single row (more on that later) while still maintaining good\ncompression.\n\nOnce we have a schema, we can create a `ParquetWriter` object. The writer will\ntake input rows as JSON objects, convert them to the Parquet format and store\nthem on disk.\n\n``` js\n// create new ParquetWriter that writes to 'fruits.parquet`\nvar writer = await parquet.ParquetWriter.openFile(schema, 'fruits.parquet');\n\n// append a few rows to the file\nawait writer.appendRow({name: 'apples', quantity: 10, price: 2.5, date: new Date(), in_stock: true});\nawait writer.appendRow({name: 'oranges', quantity: 10, price: 2.5, date: new Date(), in_stock: true});\n```\n\nOnce we are finished adding rows to the file, we have to tell the writer object\nto flush the metadata to disk and close the file by calling the `close()` method:\n\n### Adding bloom filters\n\nBloom filters can be added to multiple columns as demonstrated below:\n\n``` js\n  const options = {\n    bloomFilters: [\n      {\n        column: \"name\",\n        numFilterBytes: 1024,\n      },\n      {\n        column: \"quantity\",\n        numFilterBytes: 1024,\n      },\n    ]\n  };\n\nvar writer = await parquet.ParquetWriter.openFile(schema, 'fruits.parquet', options);\n```\n\nBy default, not passing any additional options calculates the optimal number of blocks according to the default number of distinct values (128*1024) and default false positive probability (0.001), which gives a filter byte size of 29,920.\n\nThe following options are provided to have the ability to adjust the split-block bloom filter settings.\n\n`numFilterBytes` - sets the desire size of bloom filter in bytes. Defaults to 128 * 1024 * 1024 bits.\n\n`falsePositiveRate` - set the desired false positive percentage for bloom filter. Defaults to 0.001.\n\n`numDistinct` - sets the number of distinct values. Defaults to 128 * 1024 bits.\n\nNote that if numFilterBytes is provided then falsePositiveRate and numDistinct options are ignored.\n\n## Usage: Reading files\n\nA parquet reader allows retrieving the rows from a parquet file in order.\nThe basic usage is to create a reader and then retrieve a cursor/iterator\nwhich allows you to consume row after row until all rows have been read.\n\nYou may open more than one cursor and use them concurrently. All cursors become\ninvalid once close() is called on\nthe reader object.\n\n``` js\n// create new ParquetReader that reads from 'fruits.parquet`\nlet reader = await parquet.ParquetReader.openFile('fruits.parquet');\n\n// create a new cursor\nlet cursor = reader.getCursor();\n\n// read all records from the file and print them\nlet record = null;\nwhile (record = await cursor.next()) {\n  console.log(record);\n}\n```\n\nWhen creating a cursor, you can optionally request that only a subset of the\ncolumns should be read from disk. For example:\n\n``` js\n// create a new cursor that will only return the `name` and `price` columns\nlet cursor = reader.getCursor(['name', 'price']);\n```\n\nIt is important that you call close() after you are finished reading the file to\navoid leaking file descriptors.\n\n``` js\nawait reader.close();\n```\n\n### Reading a bloom filter\n\nBloom filters can be fetched from a parquet file by creating a reader\nand calling `getBloomFiltersFor`.\n``` js\n// create new ParquetReader that reads from 'fruits.parquet`\nlet reader = await parquet.ParquetReader.openFile('fruits.parquet');\n\n// fetches bloom filter for the columns provided.\nconst bloomFilters = reader.getBloomFiltersFor(['name']);\n\n=> {\n  name: [\n    {\n      rowGroupIndex: 0\n      columnName: 'name',\n      sbbf: SplitBlockBloomFilter<instance>\n    }\n  ]\n}\n\n```\nCalling `getBloomFiltersFor` on the reader returns an object with the keys being a column name and value being an array of length equal to the number of row groups that the column spans.\n\nGiven the SplitBlockBloomFilter<instance> inclusion of a value in the filter can be checked as follows:\n\n``` js\nconst sbbf = bloomFilters.name[0].ssbf;\n\nsbbf.check('apples') ===> true\n```\n\n\n### Reading data from a url\n\nParquet files can be read from a url without having to download the whole file.\nYou will have to supply the request library as a first argument and the request parameters\nas a second argument to the function `parquetReader.openUrl`.\n\n``` js\nconst request = require('request');\nlet reader = await parquet.ParquetReader.openUrl(request,'https://domain/fruits.parquet');\n```\n\n### Reading data from S3\n\nParquet files can be read from an S3 object without having to download the whole file.\nYou will have to supply the aws-sdk client as first argument and the bucket/key information\nas second argument to the function `parquetReader.openS3`.\n\n``` js\nconst AWS = require('aws-sdk');\nconst client = new AWS.S3({\n  accessKeyId: 'xxxxxxxxxxx',\n  secretAccessKey: 'xxxxxxxxxxx'\n});\n\nconst params = {\n  Bucket: 'xxxxxxxxxxx',\n  Key: 'xxxxxxxxxxx'\n};\n\nlet reader = await parquet.ParquetReader.openS3(client,params);\n```\n\n### Reading data from a buffer\n\nIf the complete parquet file is in buffer it can be read directly from memory without incurring any additional I/O.\n\n``` js\nconst file = fs.readFileSync('fruits.parquet');\nlet reader = await parquet.ParquetReader.openBuffer(file);\n```\n\n## Encodings\n\nInternally, the Parquet format will store values from each field as consecutive\narrays which can be compressed/encoded using a number of schemes.\n\n#### Plain Encoding (PLAIN)\n\nThe most simple encoding scheme is the PLAIN encoding. It simply stores the\nvalues as they are without any compression. The PLAIN encoding is currently\nthe default for all types except `BOOLEAN`:\n\n``` js\nvar schema = new parquet.ParquetSchema({\n  name: { type: 'UTF8', encoding: 'PLAIN' },\n});\n```\n\n#### Run Length Encoding (RLE)\n\nThe Parquet hybrid run length and bitpacking encoding allows to compress runs\nof numbers very efficiently. Note that the RLE encoding can only be used in\ncombination with the `BOOLEAN`, `INT32` and `INT64` types. The RLE encoding\nrequires an additional `bitWidth` parameter that contains the maximum number of\nbits required to store the largest value of the field.\n\n``` js\nvar schema = new parquet.ParquetSchema({\n  age: { type: 'UINT_32', encoding: 'RLE', bitWidth: 7 },\n});\n```\n\n\n### Optional Fields\n\nBy default, all fields are required to be present in each row. You can also mark\na field as 'optional' which will let you store rows with that field missing:\n\n``` js\nvar schema = new parquet.ParquetSchema({\n  name: { type: 'UTF8' },\n  quantity: { type: 'INT64', optional: true },\n});\n\nvar writer = await parquet.ParquetWriter.openFile(schema, 'fruits.parquet');\nawait writer.appendRow({name: 'apples', quantity: 10 });\nawait writer.appendRow({name: 'banana' }); // not in stock\n```\n\n\n### Nested Rows & Arrays\n\nParquet supports nested schemas that allow you to store rows that have a more\ncomplex structure than a simple tuple of scalar values. To declare a schema\nwith a nested field, omit the `type` in the column definition and add a `fields`\nlist instead:\n\nConsider this example, which allows us to store a more advanced \"fruits\" table\nwhere each row contains a name, a list of colours and a list of \"stock\" objects.\n\n``` js\n// advanced fruits table\nvar schema = new parquet.ParquetSchema({\n  name: { type: 'UTF8' },\n  colours: { type: 'UTF8', repeated: true },\n  stock: {\n    repeated: true,\n    fields: {\n      price: { type: 'DOUBLE' },\n      quantity: { type: 'INT64' },\n    }\n  }\n});\n\n// the above schema allows us to store the following rows:\nvar writer = await parquet.ParquetWriter.openFile(schema, 'fruits.parquet');\n\nawait writer.appendRow({\n  name: 'banana',\n  colours: ['yellow'],\n  stock: [\n    { price: 2.45, quantity: 16 },\n    { price: 2.60, quantity: 420 }\n  ]\n});\n\nawait writer.appendRow({\n  name: 'apple',\n  colours: ['red', 'green'],\n  stock: [\n    { price: 1.20, quantity: 42 },\n    { price: 1.30, quantity: 230 }\n  ]\n});\n\nawait writer.close();\n\n// reading nested rows with a list of explicit columns\nlet reader = await parquet.ParquetReader.openFile('fruits.parquet');\n\nlet cursor = reader.getCursor([['name'], ['stock', 'price']]);\nlet record = null;\nwhile (record = await cursor.next()) {\n  console.log(record);\n}\n\nawait reader.close();\n```\n\nIt might not be obvious why one would want to implement or use such a feature when\nthe same can - in  principle - be achieved by serializing the record using JSON\n(or a similar scheme) and then storing it into a UTF8 field:\n\nPutting aside the philosophical discussion on the merits of strict typing,\nknowing about the structure and subtypes of all records (globally) means we do not\nhave to duplicate this metadata (i.e. the field names) for every record. On top\nof that, knowing about the type of a field allows us to compress the remaining\ndata more efficiently.\n\n\n### Nested Lists for Hive / Athena\n\nLists have to be annotated to be queriable with AWS Athena.   See [parquet-format](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#lists) for more detail and a full working example with comments in the test directory ([`test/list.js`](test/list.js))\n\n\n### List of Supported Types & Encodings\n\nWe aim to be feature-complete and add new features as they are added to the\nParquet specification; this is the list of currently implemented data types and\nencodings:\n\n<table>\n  <tr><th>Logical Type</th><th>Primitive Type</th><th>Encodings</th></tr>\n  <tr><td>UTF8</td><td>BYTE_ARRAY</td><td>PLAIN</td></tr>\n  <tr><td>JSON</td><td>BYTE_ARRAY</td><td>PLAIN</td></tr>\n  <tr><td>BSON</td><td>BYTE_ARRAY</td><td>PLAIN</td></tr>\n  <tr><td>BYTE_ARRAY</td><td>BYTE_ARRAY</td><td>PLAIN</td></tr>\n  <tr><td>TIME_MILLIS</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>TIME_MICROS</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n  <tr><td>TIMESTAMP_MILLIS</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n  <tr><td>TIMESTAMP_MICROS</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n  <tr><td>BOOLEAN</td><td>BOOLEAN</td><td>PLAIN, RLE</td></tr>\n  <tr><td>FLOAT</td><td>FLOAT</td><td>PLAIN</td></tr>\n  <tr><td>DOUBLE</td><td>DOUBLE</td><td>PLAIN</td></tr>\n  <tr><td>INT32</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>INT64</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n  <tr><td>INT96</td><td>INT96</td><td>PLAIN</td></tr>\n  <tr><td>INT_8</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>INT_16</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>INT_32</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>INT_64</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n  <tr><td>UINT_8</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>UINT_16</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>UINT_32</td><td>INT32</td><td>PLAIN, RLE</td></tr>\n  <tr><td>UINT_64</td><td>INT64</td><td>PLAIN, RLE</td></tr>\n</table>\n\n\n## Buffering & Row Group Size\n\nWhen writing a Parquet file, the `ParquetWriter` will buffer rows in memory\nuntil a row group is complete (or `close()` is called) and then write out the row\ngroup to disk.\n\nThe size of a row group is configurable by the user and controls the maximum\nnumber of rows that are buffered in memory at any given time as well as the number\nof rows that are co-located on disk:\n\n``` js\nvar writer = await parquet.ParquetWriter.openFile(schema, 'fruits.parquet');\nwriter.setRowGroupSize(8192);\n```\n\n\n## Dependencies\n\nParquet uses [thrift](https://thrift.apache.org/) to encode the schema and other\nmetadata, but the actual data does not use thrift.\n\n\n## Notes\n\nCurrently parquet-cpp doesn't fully support DATA_PAGE_V2. You can work around this\nby setting the useDataPageV2 option to false.\n","readmeFilename":"README.md"}