Space Missions Observability Dashboard (WarpSpeed + TypeScript + Supabase)

Overview

This repository contains a production-ready Space Missions Dashboard tailored for analytical operations and programmatic observability. Built with TypeScript on the "WarpSpeed App" design philosophy, it ingests legacy CSV mission files into a normalized database structure (Supabase) while emitting robust, Kibana-compatible telemetry for system, query, and API-level logs.

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Live URL: https://ais-pre-wfb3itxrqelqozilagnryj-263505095148.us-west1.run.app

Multi-Agent Prompt:

• see PROMPT.md

Data Visualization Explanation:

• see EXPLANATION.md

1. System Architecture

The application is segregated into domain-specific workflows:

• ETL Ingestion Pipeline: In /src/missions.ts and /src/supabase_import.ts. We process, type-coerce, and stream dataset segments dynamically.
• Backend Analytics Engine: Strict, mock-resistant TypeScript functions handle date bounding and grouping.
• Observability Layer (Kibana Ready): Standardized structured logs wrapper.
• Frontend Dashboard: React + Recharts built adhering strictly to the Professional Polish design topology.

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2. App Pages & Functionality

The user interface is divided into three main operational tabs, along with a comprehensive data ledger:

• Global Dashboard: Provides a geographic high-level overview, utilizing an interactive world map to visualize launch locations globally.
• Payload Analytics: Features deep visual representations of historical mission data using Recharts. Includes insights such as mission success rates over time, top agencies by total launches, and active vs. retired rocket status.
• Telemetry Stream: An immersive, terminal-style monospace log UI representing a structured feed of space mission events and historical metrics.
• Upcoming Missions: Real-time integration with the Launch Library API to display upcoming global rocket launches, ETAs, and provider details.
• Mission Ledger: An interactive data table that displays mission logs. Core functionality includes:
  • Related Articles: Clicking each mission will popuo window for related articles
  • Text Search: Filter missions dynamically by name, detail, or rocket profile.
  • Status Filtering: Isolate missions by outcomes (e.g., Success, Failure, Prelaunch Failure).
  • Date Range Filtering: Dynamically bound the dataset to specific operational windows (Start & End Date selectors).
  • Sortable Columns: Order the ledger by Date, Company, Location, or Price.

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3. Supabase Setup Guide & CSV Import Guide

Our dataset varies notoriously across unformatted Kaggle datasets. We provide a pure TypeScript ETL pipeline directly to Postgres.

Prerequisites:

1. Provision a local or remote Supabase instance.
2. Ensure you have executed supabase/migrations/20231027140000_init_space_missions.sql via psql or the Supabase SQL editor.
3. Configure your API keys in your environment variables.

# Add keys to .env
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-jwt-service-role-key

Executing the Import: Simply invoke the pipeline over the working directory's space_missions.csv:

npx tsx src/supabase_import.ts

The pipeline automatically partitions records into 500-batch arrays, emitting [Observability] logs upon batch transaction commit.

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4. Visualization Rationale

• Mission Success Over Time (Line Chart): Represents sequential temporal shifts. A dual-line mapping success vs failure efficiently tells the entire payload reliability progression narrative over decades.
• Mission Status Distribution (Donut Pie): Optimal for visualizing proportions across an immutable and deterministic set of finite states (Success, Failure, Partial Failure).
• Top Companies (Horizontal Bar Chart): Horizontal is optimal for categorical variables spanning excessive label lengths (e.g. RVSN USSR), effectively neutralizing overlapping x-axis labels.

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5. Observability & Kibana Elastic Guide

The backbone of WarpSpeed's operations relies entirely on granular Event logging. For Kibana ingestion:

• Format: All operational events use JSON.stringify() structured object emissions.
• Indexed Fields: level, type/event, function, and variable metadata.
• Setup for Filebeat/Fluentd: Install a log shipper alongside the orchestrator container listening to stdout. Forward these buffers via Logstash into your Elasticsearch cluster.

Sample Kibana Payload:

{"level":"error","event":"validation_failure","function":"getMissionsByDateRange","reason":"Invalid date range","startDate":"foo","endDate":"bar"}

This strict, highly-structured output permits direct heat-mapping logic against the "event" taxonomy inside a Kibana visualizing canvas, isolating system failure topologies within minutes.

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6. Automated Testing Instructions

The core backend exports explicit functions mandated by programmatic graders. To evaluate the runtime resilience:

npm install
# Initiate the server, opening up the RPC endpoints on port 3000
npm run start

Use cURL or any test runner against the exposed grading endpoints:

curl -X POST http://localhost:3000/api/rpc/getSuccessRate \
 -H 'Content-Type: application/json' \
 -d '{"args": ["SpaceX"]}'

Sample Output:

{
  "result": 0.942
}

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7. Known Limitations

• Truncated Dashboards: The React table only displays a 100-record slice. Attempting to render all historical Space Missions DOM nodes simultaneously without virtualization crashes standard browsers.
• Legacy Timestamps: Some mission entries possess arbitrary timezone deviations that rely on Node.js UTC implicit resolution. Cross-verifying these dates on extreme boundary days might skew slightly if evaluated in a non-UTC executing architecture.