Lightning Alert is a project that processes lightning events and generates alerts for affected assets. It provides functionalities for reading lightning events, converting coordinates to quadKeys, checking if a strike has occurred for a particular asset, and generating alerts based on lightning events and assets.
- Task
- Concept
- Installation
- Usage
- Folder Structure
- Dependencies
- Getting Started
- Lightning Alerts Result
- Scaling and Performance Improvements
- Monitor
- Conclusion
Write a program that reads lightning events data as a stream from standard input (one lightning strike per line as a JSON object, and matches that data against a source of assets (also in JSON format) to produce an alert.
An example 'strike' coming off of the exchange looks like this:
{
"flashType": 1,
"strikeTime": 1386285909025,
"latitude": 33.5524951,
"longitude": -94.5822016,
"peakAmps": 15815,
"reserved": "000",
"icHeight": 8940,
"receivedTime": 1386285919187,
"numberOfSensors": 17,
"multiplicity": 1
}
Where:
- flashType=(0='cloud to ground', 1='cloud to cloud', 9='heartbeat')
- strikeTime=the number of milliseconds since January 1, 1970, 00:00:00 GMT
Note
-
A 'heartbeat' flashType is not a lightning strike. It is used internally by the software to maintain connection.
An example of an 'asset' is as follows:
{
"assetName":"Dante Street",
"quadKey":"023112133033",
"assetOwner":"6720"
}
For this purpose, you can assume that all asset locations are at a zoom level of '12'.
For each strike received, you should simply print to the console the following message:
lightning alert for <assetOwner>:<assetName>
But substituting the proper assetOwner and assetName.
i.e.:
lightning alert for 6720:Dante Street
The term "QuadKey" is derived from "quadtree key." It represents a unique code that encodes a square region in latitude and longitude space, organized by different levels of detail. At the initial level, the entire mapable surface of the Earth is divided into four QuadKeys. Think of it as a zoom level that provides a view of the entire world. Each QuadKey is represented by a single-digit code, ranging from zero to three.
As we zoom in to the next level, each of the four original QuadKeys is further divided into four, and an additional digit is added to the code. This process continues recursively, allowing for more detailed subdivision of the Earth's surface. The diagram below, sourced from the official Microsoft documentation, illustrates this progression.
For more detailed information about QuadKeys, you can refer to the official Microsoft documentation on Quadkeys and Tile Grids.
To use Lightning Alert, follow these steps:
- Clone the repository:
https://github.com/CarlosGatti/lightning-alert.git - Navigate to the project directory:
cd lightning-alert - Install the dependencies:
npm install
The project contains the following modules:
lightning.js: Provides functions for reading lightning events and converting coordinates to quadKeys.alerts.js: Implements functionalities for checking if a strike has occurred for an asset and generating alerts.
You can import and use these modules in your own code as needed.
The convertToQuadKey(latitude, longitude, precision) function in the lightning.js module is used to convert latitude and longitude coordinates to quadKeys. The latitude and longitude parameters represent the coordinates of the location, and the precision parameter determines the level of detail in the quadKey.
Example usage:
const { convertToQuadKey } = require('./src/app/lightning');
const latitude = 36.3492004;
const longitude = -94.9762348;
const precision = 12;
const quadKey = convertToQuadKey(latitude, longitude, precision);
console.log(`QuadKey for the coordinates (${latitude}, ${longitude}) is: ${quadKey}`);The project follows the following folder structure:
|- src
| |- app
| | |- lightning.js
| | |- assets.js
| | |- alerts.js
| | |- monitor.js
| |- utils
| | |- jsonUtils.js
| | |- quadKeyUtils.js
|- test
| |- lightning.test.js
| |- assets.test.js
| |- alerts.test.js
|- data
| |- lightning.json
| |- assets.json
|- docs
| |- grid.jpeg
|- index.js
|- README.md
|- .eslintrc.json
|- package.json
The project directory has the following structure:
- The project directory has the following structure:
The project has the following dependencies:
readline: Version 1.3.0
jest: Version 29.5.0 (dev dependency)
nodemon: Version ^2.0.22"
1 - Clone the repository: git clone <repository-url>
2 - Navigate to the project directory: cd lightning-alert
3 - Install the dependencies: npm install or yarn install
4 - Run the tests: npm test or yarn test
5 - Start the application: npm start or yarn start
Total Lightning Alerts: 2
- Asset: 0002 - Owner: Test 2
Location: 4.9678, -72.688
---------------------------------
- Asset: 0001 - Owner: Test 1
Location: 11.03, 96.5556
---------------------------------
Done
If the code needs to scale to handle many more users or more frequent strikes, you can consider implementing the following performance improvements:
-
Optimizing the data structures used: Analyze if there are more efficient data structures that can be used to store and query the lightning events and assets. One such system is H3, a hierarchical hexagonal geospatial indexing system developed by Uber. H3 uses hexagons instead of squares to represent spatial regions, providing more flexibility and efficiency in spatial operations.
-
Caching and memoization: Implement caching mechanisms to store computed results for faster access, especially for repetitive operations or expensive calculations. Caching can help avoid redundant computations and improve response times, especially for scenarios where lightning events or assets don't frequently change.
-
Parallel processing: Investigate if the code can benefit from parallelization. Identify independent operations or calculations that can be executed concurrently, utilizing multiple CPU cores effectively. Parallel processing can lead to significant performance gains, especially when dealing with large datasets or computationally intensive tasks.
-
Database optimizations: If the size of the data becomes large or the application requires complex querying, consider using a database system for efficient data storage, indexing, and querying. Geospatial databases or indexing techniques, such as quadtree or R-tree, can provide optimized spatial querying capabilities.
-
Algorithmic optimizations: Evaluate if there are any algorithmic improvements that can reduce the computational complexity of critical operations. This could involve optimizing search algorithms, spatial algorithms, or aggregations. Analyze time and space complexity, and consider alternative algorithms or data structures that offer better performance characteristics.
-
Profiling and monitoring: Measure the performance of the code using profiling tools and identify bottlenecks. Continuously monitor and analyze the system's performance to detect and address any performance issues. Profiling can help pinpoint specific areas of the code that require optimization and guide your efforts in improving performance.
The monitoring functionality implemented in the code allows you to keep track of changes in the data/lightning.json file. By setting up a file watch using the fs.watchFile function, the code detects modifications to the file. When changes occur, a callback function is triggered, checking if the current modified time of the file is greater than the previous modified time. If a change is detected, the lightning events and assets are read from their respective sources using the readLightningEvents and readAssets functions. Finally, the generateAlerts function is called with the updated lightning events and assets to process the data and generate any necessary alerts. This monitoring mechanism ensures that the system stays up to date with the latest data in the data/lightning.json file.
Lightning Alert is a project that provides functionalities to process lightning events and generate alerts for affected assets. It utilizes the concept of QuadKeys to represent geographic regions and enables the conversion of coordinates to QuadKeys. Additionally, it offers features to check if a strike has occurred for a specific asset and generate alerts based on lightning events and assets.
By following the installation and usage steps outlined in this README, you will be able to use Lightning Alert to process lightning events and generate alerts with ease. Remember to consider the scalability and performance improvements mentioned to handle a larger number of users or more frequent lightning events.
We hope that Lightning Alert proves to be useful for your needs. Feel free to explore the source code, adapt it to your specific requirements, and contribute improvements.
If you have any questions or encounter any issues, please don't hesitate to reach out to the development team.