a simple database that uses PNG image compression to send large numbers of rows to the browser
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readme.md

#png-db png-db uses PNG files to encode large datasets for use in the browser. This is intended to be used for tools where a large amount of data needs to be displayed in the browser all at once and server-side querying is not suitable.

##Installation

npm install --save png-db

##Creating the database (Node.js)

import { FieldTypes } from 'png-db';
import { PngDBWriter } from 'png-db';

var db = new PngDBWriter();

db.addField('Id', FieldTypes.KEY);
db.addField('Property Type', FieldTypes.TEXT);

db.addRecord({
    'Id': '007',
    'Property Type': 'House',
});

db.save('./datadir/test-db.json');

Files are saved in the target directory. Multiple databases could be saved in the same place, but this is discouraged unless unique field names can be guaranteed. We do not zip the database in order to keep things simple when serving from a static environment (such as S3).

##Reading the database (browser) The following implementation creates a button for each field in the database that loads field data when clicked.

import {PngDBReader} from 'png-db';

var db = new PngDBReader();

db.load(`test-db.json`).then(()=> {
    Object.keys(db.fields).forEach(function (fieldName) {
        $('<button>').text(fieldName).appendTo($panel).click(function () {
            db.loadField(fieldName).then(()=> {
                db.records.forEach(function (record, i) {
                    //refresh view of data 
                });                
            })
        });
    });
});

Or pre-ES6 Copy versions/client/dist/png-db.js and reference it via a script tag, then use the pngDb prefix to access the objects.

var db = new pngDb.PngDBReader();

##Field Properties The following metadata is stored for each field when the database is saved

var fieldMetaData = db.fields['numberField'];
fieldMetaData.range.min; // Minimum value stored in this field
fieldMetaData.range.max; // Maximum value stored in this field

var fieldMetaData = db.fields['textField'];
fieldMetaData.uniqueValues; //An array of all text values stored in this field

##Motivation Whereas database queries and JSON files typically get large and cumbersome at around 50K records, image files of millions of pixels are routinely loaded into web pages. Each pixel of a PNG file has access to 4 channels of 8 bits (1 byte) each. We can take advantage of this to store a large amount of records using 1 field per PNG.

Queries are simple because there is no record filtering (you get every single value). The only filtering we allow is selecting which fields to include.

Storing each field in PNGs allows for large databases to be available and fields to be queried on demand.

PNG DB can be hosted statically without a server and can be used without gzip support for JSON. Since PNG files are compressed, this can offer impressive space savings over uncompressed JSON (see Metrics below)

##Supported Field Types ###Numbers Number types related directly to the encoding of PNGs

  • INTEGER (3 bytes) up to 16,777,215 values
  • DECIMAL (basically an INTEGER with a precision value)

###Text Text values (TEXT) are also supported through an index file that references a number. Only repeating text values (not unique values) should be stored this way. The size of the integer (and number of bytes needed) depends on the number of unique values for the text field. Technically, up to 16 million unique values are supported, but bear in mind that all unique values are stored in the main JSON file.

###Keys Key values (KEY) are not stored in PNG, but as a separate JSON file. There is no efficiency gained by storing values in the key field, but sometimes you need a unique value per record. Typically this is used as a primary key for each record, but you can have multiple KEY fields if absolutely necessary. Use sparingly for large datasets.

###Arrays Support for array values is provided by tiling out the array on a grid. This is cleaner than using a separate image for each index in the array. It also compresses better and loads faster. Records can have arrays of varying length. The number of images will be equal to the longest array in the dataset. Missing values will be treated as NULL.

import { FieldTypes } from 'png-db';
import { PngDBWriter } from 'png-db';

var db = new PngDBWriter();

db.addField('Id', FieldTypes.KEY);
db.addArrayField('Lat', FieldTypes.DECIMAL);
db.addArrayField('Lon', FieldTypes.DECIMAL);

db.addRecord({
    'Id': '007',
    'Lat': [43.5,43.6,43.7],
    'Lon': [-73.5,-73.6,-73.7],
});

db.save('./datadir/test-db.json');

###Null values Null values are stored using the alpha channel of the PNG. Zero is stored as 0,0,0,255. Whereas NULL is stored as 0,0,0,0.

##Metadata Metadata is used to define what information (fields) are encoded in which PNG files and the meaning of those values. For indexed text classifications we store the text value associated with the integer.

Any additional data associated with this database can be stored using "addMetaData". This simply adds it to the JSON file.

##Why PNG? Whereas JPEG (JPG) is lossy, PNG files use lossless compression so that individual pixel values are reliable. JPEG compression is unsuitable for this type of use because we don't care about what the image looks like and do care about the exact values encoded in every pixel.

###Other PNG formats? PNG supports 1 byte through 4 byte images and also 16 bits x 4 channels. Taking advantage of other PNG file types could expand the amount of data and improve compression, however if we explore those avenues, we need to be careful to ensure compatibility for PNG types with all browsers and tools.

###Optimization thoughts In some cases, we could store multiple fields within a single PNG. However, given the way PNG compression works, there may not be much benefit to combining multiple fields into a single image vs keeping them separate as 1 field per PNG. Having multiple fields per image would also complicate loading images on-demand and keeping track of which fields have been loaded.

Small numbers encoded this way are very efficient right now - e.g. 11 classes for 156K points are 40KB, whereas point data with precision of 10000 are 500K (or 200K for precision of 100)

##Metrics Your may results may vary, but some benchmarks for a city parcel dataset:

Records: 156132
Saved as JSON: 21MB
Saved as JSON (gzip): 811KB (96.1%)
Saved as JSON (7z): 617KB (97.1%)
Saved as PNG DB: 594KB (97.2%)

Note that this test did not include any unique KEY fields that must be saved as raw JSON. For the same records, a list of IDs (KEY) of this format '0015304017000' take up 2.38MB as pure JSON, or 495KB GZipped. Not all applications will need a unique key beyond the numeric row number, so use only if needed.

##Limits Browsers can read PNG images up to 3 megapixels. Therefore the maximum #rows supported is 3,145,728 (3 * 1024 * 1024) since each row is represented by a pixel. Tiling PNGs would allow us to extend this limit, but multiple other practical limitations are likely to be hit first.

##Repository Info This database is designed to be written using Node.js and read in the browser. Code is written in ES6 and transpiled.

###Working with ES6 Rollup is used to package the files in a future-proof way. You can use the npm installed package as usual with any ES5 project or target the ES6 modules directly using the jsnext:main file (versions/*/dist/png-db.mjs)

For use with ES6 modules on the client side, we recommended bundling with jspm.

jspm install npm:png-db