Storing Data in JSON: A Lightweight Alternative to Databases for Aerospace Projects

In the world of aerospace engineering, where systems are often synonymous with immense complexity and massive datasets, the idea of using a simple .json file as a database might seem heretical. We picture sprawling SQL databases tracking thousands of flight parameters or NoSQL clusters storing terabytes of satellite imagery.

However, for a specific class of problems—prototyping, simulations, small-scale data logging, and static sites—a JSON file can be an elegant, powerful, and surprisingly capable data store. It’s the aerospace engineering equivalent of using a precise, hand-drawn schematic for a component design before committing to a full CAD model and finite element analysis.

Let’s explore the when, why, and how of using JSON files as a database, with our sights set firmly on the aerospace domain.

The Allure: Why Consider JSON for Aerospace Data?

Simplicity and Speed of Development

Setting up a traditional database (SQL or NoSQL) requires installing server software, defining schemas, configuring connections, and managing credentials. For a rapid prototype or a proof-of-concept simulation, this is often overkill.

A JSON file requires zero setup. You create a file, write your data in a structured format, and your application can read it immediately. This is perfect for an agile development environment where requirements are evolving quickly.

Example: Rapid Satellite Component Catalog
Imagine you’re designing a CubeSat and need a quick way to track potential components for your bill of materials. Instead of setting up a SQL database, you can start with a components.json file:

{
  "components": [
    {
      "id": "CS-001",
      "name": "UHF Band Transceiver",
      "type": "Communication",
      "vendor": "AeroComm Inc.",
      "mass_grams": 145.5,
      "power_consumption_watts": 2.1,
      "voltage_range": "3.3V - 5V",
      "qualified_for_low_earth_orbit": true,
      "radiation_tolerance_level": "Commercial"
    },
    {
      "id": "CS-002",
      "name": "Reaction Wheel Assembly",
      "type": "Attitude Determination and Control System (ADCS)",
      "vendor": "SpaceDynamics Ltd.",
      "mass_grams": 420.0,
      "power_consumption_watts": 1.8,
      "voltage_range": "5V",
      "qualified_for_low_earth_orbit": true,
      "radiation_tolerance_level": "MIL-STD-883"
    }
  ]
}

This file is human-readable, easily version-controlled with Git, and can be parsed by any programming language in a single line of code.

Portability and Version Control

A JSON file is self-contained. You can email it, store it in a GitHub repository, or embed it directly within an application. This makes it ideal for configuration data.

Example: Mission Configuration for a UAV Swarm
Consider a research project involving a swarm of 10 small UAVs (Unmanned Aerial Vehicles). Each drone’s flight controller needs specific parameters for a coordinated search pattern. Instead of individually configuring each one, you can distribute a mission_config.json file:

{
  "mission_id": "ALPHA-SEARCH-12",
  "creation_timestamp": "2024-05-15T14:30:00Z",
  "swarm_parameters": {
    "formation": "VIC",
    "inter_vehicle_distance_meters": 50,
    "communication_protocol": "TDMA"
  },
  "vehicles": [
    {
      "uav_id": "LEAD-1",
      "role": "Leader",
      "initial_coordinates": {
        "lat": 34.0522,
        "lon": -118.2437,
        "alt_meters": 100
      },
      "search_pattern": "SPIRAL"
    },
    {
      "uav_id": "WING-2",
      "role": "Follower",
      "leader_id": "LEAD-1",
      "offset": {
        "x_meters": -50,
        "y_meters": 50
      }
    }
  ]
}

If the mission parameters change, you can track every change in Git, see who made it, and roll back if necessary—a process that is more cumbersome with a traditional database.

Perfect for Static Data and Lookup Tables

Aerospace relies heavily on standardized, static data: atmospheric models, material properties, standard part numbers, and celestial body data.

Example: International Standard Atmosphere (ISA) Model
A flight performance calculator needs access to the ISA model, which defines temperature, pressure, and density against altitude. This data is standardized and rarely changes. Storing it in a isa_model.json file is far more efficient than querying a database.

{
  "model_name": "International Standard Atmosphere",
  "reference": "ISO 2533:1975",
  "layers": [
    {
      "layer_name": "Troposphere",
      "base_altitude_geopotential_meters": 0,
      "top_altitude_geopotential_meters": 11000,
      "base_temperature_kelvin": 288.15,
      "lapse_rate_kelvin_per_meter": -0.0065
    },
    {
      "layer_name": "Tropopause",
      "base_altitude_geopotential_meters": 11000,
      "top_altitude_geopotential_meters": 20000,
      "base_temperature_kelvin": 216.65,
      "lapse_rate_kelvin_per_meter": 0.0
    }
  ]
}

The Perils: When JSON Files Fall Short

Lack of Concurrency and Data Integrity

This is the single biggest drawback. A JSON file is typically read and written as a whole. If two processes try to write to the same file simultaneously, data will be corrupted. There are no transactions, no atomicity, and no built-in locking mechanisms.

Aerospace Scenario: You cannot use a JSON file to log real-time sensor data from multiple subsystems of an aircraft (e.g., avionics, engines, flight controls) if they all need to write to the same file concurrently. The result would be a garbled, invalid JSON structure.

Performance Bottlenecks with Large Datasets

While reading a 100 KB JSON file is instantaneous, reading a 1 GB file is not. The entire file must be loaded into memory (RAM) and parsed, which becomes slow and resource-intensive.

Aerospace Scenario: Storing high-frequency telemetry data from a single rocket launch—where thousands of parameters are sampled hundreds of times per second—would quickly result in a JSON file that is impractical to query or manipulate as a whole. A time-series database is the correct tool for this job.

No Query Language

You can’t say “SELECT * FROM telemetry WHERE altitude > 10000 AND velocity < 200”. To find data in a JSON file, you must write custom code to filter and search through the entire dataset in memory. This is inefficient for complex queries.

Aerospace Scenario: Analyzing flight test data to find all instances where the angle of attack exceeded 15 degrees while the g-load was below 0.5 would require loading the entire dataset and manually iterating through every record.

Data Validation is Manual

A traditional database enforces a schema: a thrust column must be a number. In a JSON file, there’s nothing stopping you from accidentally saving a thrust value as "high" (a string), which would break your data processing application. You must build validation logic yourself.

The Sweet Spot: Ideal Use Cases in Aerospace

Given these pros and cons, here are the perfect scenarios for a JSON-based “database” in aerospace:

1. Project Configuration: Storing initial conditions, physical constants, and simulation parameters for a rocket trajectory model.
2. Prototype and Mock Data: During the early development of a ground control station (GCS), using JSON files to mock live telemetry feeds from aircraft allows front-end and back-end teams to work in parallel.
3. Small-Scale, Single-User Applications: A desktop tool for engineers to calculate the weight and balance of a small aircraft could perfectly use a JSON file to store different loading scenarios.
4. Static Reference Websites: A documentation site for an aerospace company’s product line (e.g., rocket engines) can use a JSON file to store all the product specifications, which is then built into a fast, static website.

Conclusion: A Tool, Not a Toy

Using a JSON file as a database is not about being “cheap” or cutting corners. It’s about choosing the right tool for the job. For large-scale, concurrent, high-performance aerospace applications, it is unequivocally the wrong tool.

However, for the vast landscape of engineering work that happens before production—the prototyping, the configuring, the simulating, and the small-scale data management—it is an incredibly powerful and pragmatic choice. It reduces complexity, accelerates development, and keeps the focus on the aerospace problem at hand, not the data infrastructure surrounding it.

In the meticulous world of aerospace, sometimes the most sophisticated solution is the one that is simplest, most direct, and gets the job done with elegant efficiency. The humble JSON file, when applied judiciously, earns its place in an engineer’s toolkit.