Laravel's geospatial: Optimization of interactive maps and large amounts of data

Efficiently process 7 million records and create interactive maps using geospatial technology

This article explores how to efficiently process over 7 million records using Laravel and MySQL and convert them into interactive map visualizations.

Initial Challenge

Project requirements: Use 7 million records in MySQL database to extract valuable insights. Many people first consider programming languages, but ignore the database itself: Can it meet the needs? Is data migration or structural adjustment required? Can MySQL withstand such a large data load?

Preliminary analysis: Key filters and properties need to be identified. After analysis, it was found that only a few attributes were related to the solution. We verified the feasibility of the filter and set some restrictions to optimize the search. Map search is based on a city or community, and users can select communities by selecting states and cities, thereby achieving accurate search. As community selection is determined, other filters (name, category, evaluation, etc.) will be displayed dynamically, thereby improving search accuracy and avoiding affecting system performance. In this way, we create dynamic and well-defined filters and ensure the accuracy of the search by adding appropriate indexes. At this point, the filter problem has been resolved.

Next is the challenge of polygon processing. Before this, let’s discuss the architecture that supports the entire application.

Application Architecture

Considering the huge amount of data, the map can only render part of the data at the same time. Therefore, applications focus on efficiency. I chose Laravel and React, a powerful and flexible technology stack:

Laravel (Backend)

The backend built by Laravel 11 uses Breeze to quickly build the project foundation and focus on core functions. In addition to the standard MVC architecture, I also added service and warehouse models to organize responsibilities to facilitate code maintenance.

React (front end)

Front-end applications are fully modular. Clearly defined components and modules ensure smooth code reuse and communication between components. This architecture allows the front-end to efficiently interact with the back-end API, ensuring simplicity and efficiency.

Scalability

Although the project was initially an internal project and has low demand, its architecture is designed to support future expansions, such as using standalone services on AWS (e.g., Fargate for APIs, CloudFront for front-end). This is because all interactions are conducted through the API and the server does not maintain the state, thus achieving separation of responsibilities.

test

The system stability is ensured through PestPHP's comprehensive test suite, covering 22 endpoints and about 500 test cases. Test-driven development improves deployment and maintenance efficiency, demonstrating its importance in building scalable and reliable software.

Application Core

The core of the application is the map. I used Leaflet, a lightweight JavaScript map library, and combined some plugins to improve efficiency and resource utilization.

Tag aggregation

To optimize rendering of large numbers of markers, react-leaflet-markercluster plugin is used. The plugin aggregates adjacent markers together, reducing rendering burden, improving user experience, and providing a clearer map display that maintains stable performance even with millions of records.

Polygon drawing

react-leaflet-draw plugin allows users to draw polygons directly on the map. This feature allows:

• Gets the coordinates of polygon vertices for database query filtering.
• Integrate other filters (state, city, community choice) into the map interaction process to provide an intuitive experience.
• Use custom layers to distinguish records, categories, and other properties.
• Map optimization adopts a lazy loading strategy, loading only data in visible areas, reducing the load on clients and servers.

Database and index

The tables used are similar to user tables, but focus on addresses and coordinates. The coordinates are stored in the POINT column, which represents a point in the geographic coordinate system. Added geospatial indexes to optimize queries.

How geospatial indexes work

Geospatial indexing is a special data structure that accelerates query of spatial data (points, lines, polygons). MySQL uses R-tree to implement spatial indexing for POINT , LINESTRING , or POLYGON columns. It organizes spatial data through a hierarchy, dividing the space into smaller areas, thereby quickly positioning areas related to specific queries.

Geospatial functions

MySQL's geospatial functions (such as ST_Contains , ST_Within , ST_Intersects ) use indexes to identify records within a specific region. For example:

 <code class="language-sql">SELECT id, name, address FROM users WHERE ST_Contains( ST_GeomFromText('POLYGON((...))'), coordinates );</code>

ST_GeomFromText creates polygons based on the coordinates sent by the application, ST_Contains uses geospatial index to check points within the polygon.

Final summary

After the project is completed, some lessons worth sharing:

1. Coordinate migration: The previous coordinates are stored in separate latitude and longitude columns and geospatial indexes cannot be used. The solution is to create a new coordinate column and migrate existing data to that column.
2. JavaScript efficiency: Performance needs to be considered when choosing an iterative method. For example, Array.map has a clean syntax, but may not perform as well as loops. Performance testing is required according to the specific situation.
3. Optimization solution: Use technologies such as lazy loading and aggregation to improve efficiency and user experience.
4. Data processing and verification: Avoid unnecessary duplicate data searches. Optimize data update strategies, such as local updates, batch updates, etc.

This project shows that details determine success or failure. Targeted optimization, avoiding waste of resources and good development practices can not only improve performance, but also improve the overall quality of the project. Finally, it is crucial to keep a constant focus on project delivery.

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