https://github.com/hamidfarmani/nasa-full-text-search
A full-text search application for NASA's Astronomy Picture of the Day (APOD) using Next.js, Elasticsearch, and Docker. Search through NASA's vast collection of astronomical images and descriptions with advanced full-text search capabilities.
https://github.com/hamidfarmani/nasa-full-text-search
datagrid docker elasticsearch full-text-search mui nextjs typescript
Last synced: 3 months ago
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A full-text search application for NASA's Astronomy Picture of the Day (APOD) using Next.js, Elasticsearch, and Docker. Search through NASA's vast collection of astronomical images and descriptions with advanced full-text search capabilities.
- Host: GitHub
- URL: https://github.com/hamidfarmani/nasa-full-text-search
- Owner: hamidfarmani
- Created: 2025-04-13T15:23:42.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-13T15:27:01.000Z (6 months ago)
- Last Synced: 2025-04-13T19:51:34.890Z (6 months ago)
- Topics: datagrid, docker, elasticsearch, full-text-search, mui, nextjs, typescript
- Language: TypeScript
- Homepage:
- Size: 1.22 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NASA APOD Search
A full-text search application for NASA's Astronomy Picture of the Day (APOD) archive. This application provides a modern interface to search through thousands of astronomy pictures and their descriptions.
## Features
- **Full-Text Search**: Search through titles and explanations of NASA's astronomy pictures
- **Advanced Search Capabilities**:
- Exact phrase matching for titles
- Fuzzy matching for typo tolerance
- Relevance-based sorting
- **Year-based Filtering**: Filter results by year with aggregated counts
- **Responsive Grid Display**: View results in a clean, organized table format
- **Real-time Search**: Search results update as you type
- **Server-side Pagination**: Efficient loading of large datasets
## Tech Stack
- **Frontend**: Next.js with React
- **UI Components**: Material-UI (MUI)
- **Search Engine**: Elasticsearch
- **Language**: TypeScript
- **Package Manager**: pnpm
## Prerequisites
- Node.js (v18 or higher)
- pnpm
- Elasticsearch (v8.x)
- Docker (optional, for running Elasticsearch)
## Setup
1. **Clone the repository**
```bash
git clone https://github.com/hamidfarmani/nasa-full-text-search.git
cd nasa-full-text-search
```
2. **Install dependencies**
```bash
pnpm install
```
3. **Start Elasticsearch**
Make sure Elasticsearch is running on `http://localhost:9200`
If using Docker:
```bash
docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" elasticsearch:8.x
```
4. **Load the data**
```bash
pnpm run load-data
```
5. **Start the development server**
```bash
pnpm dev
```
## Search Features
### Text Search
- Searches across title and explanation fields
- Title matches are boosted for higher relevance
- Exact phrase matching for titles
- Fuzzy matching for typo tolerance
- Minimum match threshold for better quality results
### Filtering
- Year-based filtering using Elasticsearch aggregations
- Real-time counts of items per year
- Combines with text search
### Sorting
- Results are sorted by relevance when searching
- Chronological sorting (newest first) when not searching
## Data Loading
The application includes a data loading script that:
- Creates the Elasticsearch index with proper mappings
- Loads APOD data in chunks to prevent overwhelming the server
- Includes error handling and retry logic
- Shows progress during loading
## Elasticsearch Mapping
The application uses a custom Elasticsearch mapping that includes:
- Text fields with standard analyzer for better search
- Date fields with proper formatting
- Runtime fields for year extraction
- Keyword fields for exact matching
- Custom analyzers for improved search quality
## Development
### Available Scripts
- `pnpm dev`: Start development server
- `pnpm build`: Build for production
- `pnpm start`: Start production server
- `pnpm load-data`: Load data into Elasticsearch
### Environment Variables
Create a `.env.local` file with:
```
ELASTICSEARCH_URL=http://localhost:9200
```
## Contributing
1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request