This example shows how to change the performance profile when creating a 51Degrees device detection engine. It also includes a simple method of benchmarking the engine that can illustrate the performance differences. Note that benchmarking is a complex area and this is not a sophisticated solution. It is simply intended to demonstrate the approximate, relative performance of the pre-configured profiles.
This example is available in full on GitHub.
This example requires a local data file. The free 'Lite' data file can be acquired by pulling the git submodules under this repository (run `git submodule update --recursive`) or from the device-detection-data GitHub repository.
The Lite data file is only used for illustration, and has limited accuracy and capabilities. Find out about the more capable data files that are available on our pricing page
This example require module 'n-readlines' to operate. Please install the module before running the example, by using the following command:
npm install n-readlines
Expected output:
Processing [...] User-Agents from [...]/20000 User Agents.csv
Calibrating
Processing
Average [...] detections per second.
Average [...] ms per User-Agent.
ismobile = true : [...]
ismobile = false : [...]
ismobile = unknown : [...]
/* *********************************************************************
* This Original Work is copyright of 51 Degrees Mobile Experts Limited.
* Copyright 2022 51 Degrees Mobile Experts Limited, Davidson House,
* Forbury Square, Reading, Berkshire, United Kingdom RG1 3EU.
*
* This Original Work is licensed under the European Union Public Licence
* (EUPL) v.1.2 and is subject to its terms as set out below.
*
* If a copy of the EUPL was not distributed with this file, You can obtain
* one at https://opensource.org/licenses/EUPL-1.2.
*
* The 'Compatible Licences' set out in the Appendix to the EUPL (as may be
* amended by the European Commission) shall be deemed incompatible for
* the purposes of the Work and the provisions of the compatibility
* clause in Article 5 of the EUPL shall not apply.
*
* If using the Work as, or as part of, a network application, by
* including the attribution notice(s) required under Article 5 of the EUPL
* in the end user terms of the application under an appropriate heading,
* such notice(s) shall fulfill the requirements of that article.
* ********************************************************************* */
const events = require('events');
const path = require('path');
const LineReader = require('n-readlines');
require((process.env.directory || __dirname) +
'/../../../deviceDetectionOnPremisePipelineBuilder');
// In this example, by default, the 51degrees "Lite" file needs to be in the
// fiftyone.devicedetection.onpremise/device-detection-cxx/device-detection-data,
// or you may specify another file as a command line parameter.
//
// Note that the Lite data file is only used for illustration, and has
// limited accuracy and capabilities.
// Find out about the Enterprise data file on our pricing page:
// https://51degrees.com/pricing
const LITE_V_4_1_HASH = '51Degrees-LiteV4.1.hash';
const UA_CSV = '20000 User Agents.csv';
// Load in a datafile
const sliceLen = process.env.JEST_WORKER_ID === undefined ? 2 : process.argv.length;
const args = process.argv.slice(sliceLen);
// Use the supplied path for the data file or find the lite
// file that is included in the repository.
// Load in a user-agents file
const uafile = args.length > 1 ? args[1] : ExampleUtils.findFile(UA_CSV);
// Check if files exists
const fs = require('fs');
if (!fs.existsSync(datafile)) {
console.error('Failed to find a device detection ' +
'data file. Make sure the device-detection-data ' +
'submodule has been updated by running ' +
'`git submodule update --recursive`.');
throw ("No data file at '" + datafile + "'");
}
if (!fs.existsSync(uafile)) {
console.error('Failed to find a User-Agents ' +
'file. Make sure the device-detection-data ' +
'submodule has been updated by running ' +
'`git submodule update --recursive`.');
throw ("No User-Agents file at '" + datafile + "'");
}
const secToNanoSec = 1e9;
const msecToNanoSec = 1e6;
let userAgentsProcessed = 0;
let isMobileTrue = 0;
let isMobileFalse = 0;
let isMobileUnknown = 0;
let userAgentsCount = 0;
let startTime, diffTime, calibrationTime, actualTime;
// Create an event listener for when User-Agents
// have been processed.
// This event listener is used in two stages.
// The first stage is when the calibration has finished
// and the actual processing will be kicked off.
// The second stage is when the actual processing has
// finished and the final report is displayed.
const eventEmitter = new events.EventEmitter();
eventEmitter.on('FinishProcessing', (calibration) => {
diffTime = process.hrtime(startTime);
if (calibration) {
// Record the calibration time
calibrationTime = diffTime[0] * secToNanoSec + diffTime[1];
// Run without calibration
console.log('Processing');
run(function (userAgent) {
processUA(userAgent, false);
});
} else {
// Record the actual time
actualTime = diffTime[0] * secToNanoSec + diffTime[1];
// Display benchmarks
console.log(
'Average ' +
`${(userAgentsCount / (actualTime - calibrationTime)) * secToNanoSec} ` +
'detections per second per thread.');
console.log(
'Average ' +
`${((actualTime - calibrationTime) / msecToNanoSec) / userAgentsCount} ` +
'ms per User-Agent.');
console.log(`ismobile = true : ${isMobileTrue}`);
console.log(`ismobile = false : ${isMobileFalse}`);
console.log(`ismobile = unknown : ${isMobileUnknown}`);
}
});
// Create the device detection pipeline with the desired settings.
performanceProfile: 'MaxPerformance',
dataFile: datafile,
restrictedProperties: ['ismobile'],
autoUpdate: false,
shareUsage: false,
usePredictiveGraph: false,
usePerformanceGraph: true,
addJavaScriptBuilder: false
}).build();
// To monitor the pipeline we can put in listeners for various log events.
// Valid types are info, debug, warn, error
pipeline.on('error', console.error);
// Print out the progress report
const progressBar = '========================================';
const reportProgress = function (uaProcessed) {
const bars = Math.round((uaProcessed / userAgentsCount) * progressBar.length);
process.stdout.write(progressBar.substring(0, bars) +
(uaProcessed === userAgentsCount ? '\n' : '\r'));
};
// Here we make a function that gets a userAgent as evidence and
// uses the Device Detection Engine to obtain the properties of the
// device and output it to file.
const processUA = async function (userAgent, calibration) {
if (calibration) {
isMobileFalse++;
} else {
// Create a FlowData element
// This is used to add evidence and process it through the
// FlowElements in the Pipeline.
const flowData = pipeline.createFlowData();
// Add the User-Agent as evidence
flowData.evidence.add('header.user-agent', userAgent);
// Run process on the flowData (this returns a promise)
await flowData.process();
// Construct the output line
const ismobile = flowData.device.ismobile;
if (ismobile.hasValue) {
if (ismobile.value) {
isMobileTrue++;
} else {
isMobileFalse++;
}
} else {
isMobileUnknown++;
}
}
// Increment the number of User-Agent processed and
// signal if the required number has been reached
reportProgress(++userAgentsProcessed);
if (userAgentsProcessed === userAgentsCount) {
eventEmitter.emit('FinishProcessing', calibration);
}
};
// Loop through the User-Agents in the file
// and execute callback on each User-Agent
const run = function (callback) {
const liner = new LineReader(uafile);
let line;
// Reset User-Agents processed count and start time
userAgentsProcessed = 0;
startTime = process.hrtime();
line = liner.next();
while (line) {
callback(line.toString('utf8').replace(/\r?\n|\r/g, ''));
line = liner.next();
}
};
// Get the number of User-Agents
run(function () {
userAgentsCount++;
});
console.log('Processing ' + userAgentsCount + ' User-Agents from ' + uafile);
// Run with calibration
console.log('Calibrating');
run(function (userAgent) {
processUA(userAgent, true);
});