Tilly

Tilly is a simple columnar database that can be used within the browser, or server-side in node.js. Stores the distinct values for each column and rows become integer indexes into the set of distinct values.

Installation

Until the first release, use the contents of the lib directory, the entery point is index.js. Run npm update after downloading to install dependencies.

Use

There are two main ways to add data to a table:

1. Dynamically create Tables and Columns, then add Rows.
2. Load a JSON file of previously saved data.

Both options then allow you to query tables.

For option 2 above, a utility is provided to convert CSV formatted data into the Tilly JSON format. To run this utility, simply invoke the command:

node lib/tools/csv [source CSV path] [target JSON path]

Then compress the file with the following:

node lib/tools/compress [source JSON path] brotli

Example

An example of option two would be:

// load zipped JSON from a file
import { readFileSync } from 'fs';
import { brotliDecompressSync } from 'zlib';
const json = JSON.parse(brotliDecompressSync(readFileSync(process.argv[2])).toString('utf-8')); // improve read/unzip time with streams? 

// example really starts here
import { Table, Query, and, not } from '../core';

// load the database and table from file
const estimates = new Table(json);

// for conveniance, find the columns we are interested in; some aliased
const countryCode = estimates.column('Country Code')!;
const countryName = estimates.column('Country Name')!;
const indicatorName = estimates.column('Indicator Name')!;
const value = estimates.column('2020')!.as('population').to(Number); // NOTE: "as" and "to" can be used here or in query; they are not fluent and create new virtual columns

// a list of country codes in the data that are not countries, but aggregates
const notCountry = ['ARB', 'CSS', 'CEB', 'EAR', 'EAS', 'EAP', 'TEA', 'ECS', 'ECA', 'TEC', 'EUU', 'FCS', 'HPC', 'HIC', 'INX', 'LTE', 'EMU', 'LCN', 'LAC', 'TLA', 'LDC', 'LIC', 'LMY', 'LMC', 'MEA', 'MNA', 'TMN', 'MIC', 'NAC', 'OED', 'OSS', 'PSS', 'PST', 'PRE', 'SST', 'SAS', 'TSA', 'SSF', 'SSA', 'TSS', 'UMC', 'WLD'];

// create a query with just three returned columns and a complex filter criteria
const query = new Query(estimates)
	.select(countryCode.as('code'), countryName.as('name'), value)
	.where(and(indicatorName.equals('Population, total'), not(value.equals(null)), not(countryCode.in(notCountry))));

// iterate the query results
for (const row of query) {
	console.log(row);
}

The results of which is :

{ code: 'AFG', name: 'Afghanistan', population: 38928000 }
{ code: 'ALB', name: 'Albania', population: 2850000 }
...
{ code: 'ZMB', name: 'Zambia', population: 18384000 }
{ code: 'ZWE', name: 'Zimbabwe', population: 14863000 }

This example uses population forecast data from the World Bank available here.

Licence

Licensed under the MIT License.

Depencencies

@steelbreeze/tilly depends on the following packages: |Package|Use|Licence| |:-|:-|:-| |csv-parser|Stream parser for CSV files. Not a requirement for the core code, but used within the CSV to JSON conversion tool.|MIT License| |strip-bom-stream|Removes byte order marks from files. Not a requirement for the core code, but used within the CSV to JSON conversion tool.|MIT License|