tilly:Tilly是一个简单的列式数据库，可以在浏览器或node.js的服务器端使用

# 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. [](https://codeclimate.com/github/steelbreeze/tilly/maintainability) ## 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: ```TypeScript import { readFileSync } from 'fs'; import { brotliDecompressSync } from 'zlib'; import { Table, and, not } from '../core'; // read and uncompress source data const json = JSON.parse(brotliDecompressSync(readFileSync(process.argv[2])).toString('utf-8')); // 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.columns.find(column => column.name === 'Country Code')!; const countryName = estimates.columns.find(column => column.name === 'Country Name')!; const indicatorName = estimates.columns.find(column => column.name === 'Indicator Name')!; const value = estimates.columns.find(column => column.name === '2020')!.as('population').to(Number); // a query to filter our country codes that are not countries const countries = estimates.where(not(countryCode.in(['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 to home in just on population data const query = countries.where(and(indicatorName.equals('Population, total'), not(value.equals(null)))); // iterate the query results and display for (const row of query.select(countryCode.as('code'), countryName.as('name'), value)) { console.log(row); } // re-run the same query without the console overhead for timings let count = 0; const start = process.hrtime(); for (const row of query.select(countryCode.as('code'), countryName.as('name'), value)) { count++; } const elapsed = process.hrtime(start); console.log(`Returned ${count} rows from ${estimates.count()} in ${elapsed[0]}s ${elapsed[1] / 1000000}ms`); ``` 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 } Returned 212 rows from 45325 in 0s 2.425424ms ``` This example uses population forecast data from the World Bank available [here](https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics). Performance based on a 2019 MacBook Pro 2.3GHz 8-core Intel Core i9. ## Licence Licensed under the [MIT License](LICENSE). ## Depencencies @steelbreeze/tilly depends on the following packages: |Package|Use|Licence| |:-|:-|:-| |[csv-parser](https://github.com/mafintosh/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](https://github.com/sindresorhus/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|