Lightweight Collections: Simplifying Data Storage for Small Tasks and Rapid Prototyping

1. Introduction to Lightweight Data Storage

With modern APIs, developers frequently need to quickly prototype solutions or automate small tasks. While traditional databases like MongoDB or Firebase excel in large-scale storage and management, they can feel needlessly heavy-handed and complex for simple operations or single-document collections.

Consider scenarios like quick API exploration or automated tasks within environments such as Google Apps Script: you typically deal with small data sets. You might not want to spin up a database, configure connections, and administer security rules. You need a small, quick utility for data collection that leverages whatever storage mechanism you have at hand — like AppScript’s built-in properties, sheets, or caches — and gives you maximum flexibility at a minimum cost.

This article introduces a streamlined JavaScript-based collection manager, expressly designed for lightweight, single-document collection storage — providing specialized facilities for validation, duplication-prevention, and storage limits, entirely configurable and agnostic to your underlying storage medium.

2. Technical Breakdown: How It Works

Your collections script relies on three main configurable operations: reading, writing, and deleting data. To minimize complexity — and to ensure compatibility with virtually anything from Google Spreadsheet to MongoDB — you define simple callbacks for these operations:

function get_set_on_properties_() {
  const store = PropertiesService.getScriptProperties();
  const mem = new Collector_({
    read: id => JSON.parse(store.getProperty(id)),
    write: (id, obj) => store.setProperty(id, JSON.stringify(obj)),
    remove: id => store.deleteProperty(id)
  });
  return mem;
}

Example usage

Here’s a basic scenario:

• Store Initialization

const mem = get_set_on_properties_();

• Adding Items

const result = mem.addItem(
  'Tasks', 
  { task_id: 123, description: 'Check email' });

• Validation and Duplication Prevention

mem.options.isDupe(itemA, itemB) = (a, b) => a.task_id === b.task_id;
mem.options.isValid = item => item.task_id && typeof item.description === 'string';

• Storage Limit

You can easily set limits per collection:

mem.options.limit = 10; // Maximum of 10 items stored.

Test Example: Adding Valid and Invalid Items

function looping_insertion_results() {
  const items = [
    { me: "One" }, 
    { me: "Two" }, 
    { me: "Do not let me in" }, 
    { me: "Two" }];  

  mem.deleteCollection(KEY1);
  
  const insertionResults = mem.addItems(KEY1, items, {
    isValid: item => item.me !== "Do not let me in",
    isDupe: (a, b) => a.me === b.me
  });  

 insertionResults.forEach((result, i) => {
    const item = items[i];
    const statusMessage = result.is_added ? "Added " : "Not added ";
    const reason = result.is_added ? "" : ": " + result.reason;
    console.log(`${i + 1}. ${statusMessage} '${item.me}' ${reason}`);
  });
}
/* Output:
1. Added 'One' 
2. Added 'Two' 
3. Not added 'Do not let me in': not valid
4. Not added 'Two': duplicate
*/

Such flexibility lets you thoroughly control how your data is stored, validated, and deduplicated, all at the script/client level.

3. Use cases: Where is This Approach Particularly Useful?

Small App Script utilities:

• Automation scripts (e.g., maintain a list of emails already sent)
• Rapid prototyping and discovery with APIs or services
• Temporary state-keeping or session management in script-based solutions

Flexible Data Storage:

• Temporarily store multiple data points in MongoDB or Firebase as a single-document array for faster retrieval and simpler data management.

4. Limitations and Best Practices

Like every simplified tool, there are compromises and constraints.

Limitations:

• Performance & Scalability: While sufficient for small tasks, single-document storage won’t handle extensive collections efficiently.
• Atomicity: Concurrent edits to the store might overwrite changes unless specifically managed.
• Complex queries and indexing: Complex queries generally require more robust database solutions.

Mitigation strategies:

• Use it for small, controlled datasets (dozens or hundreds, not thousands or millions).
• Implement simple locking mechanisms within App Scripts if concurrency arises.
• Regularly prune collections to stay within manageable size constraints.

Best practices:

• Validate and check duplicates at the insertion point.
• Apply reasonable limits to each collection to maintain performance.
• Use clear, simple identifiers and keys for your collections.

5. Comparison with Traditional Databases

When to Choose What:

Choose Lightweight when:

• Rapid prototyping is required.
• You want minimal maintenance and set-up overhead.
• Your data remains small to moderate-sized (<1000 items).

Choose Traditional DB when:

• Complex, indexed querying is essential.
• High volumes of structured data are anticipated.
• Strong consistency and transactional guarantees are non-negotiable.

Conclusion

A lightweight, single-document collection store — such as the script demonstrated in this article — offers remarkable simplicity and developer flexibility for small tasks and rapid prototyping. For the correct problems, it provides speed, low overhead, and direct control over validation and duplication logic without reliance on heavy external dependencies or configurations.

While not a universal solution, it complements traditional storage mechanisms, fitting perfectly into the toolbelt of developers who frequently build quick solutions, explore APIs, or perform frequent scripted tasks without the complexity or overhead of robust traditional databases.

Sample code

Apps Script | Google for Developers

See you, friends!