Define. Get. Set. Done.

No boilerplate. No repeated strings. No setup. Define your variables once, then get() and set() them anywhere with zero friction. prf makes local persistence faster, simpler, and easier to scale. Includes 10+ built-in types and utilities like persistent cooldowns and rate limiters. Designed to fully replace raw use of SharedPreferences.

Way more types than SharedPreferences — including enums DateTime JSON models +10 types and also special services PrfCooldown PrfRateLimiter for production ready persistent cooldowns and rate limiters.

• Introduction
• Why Use prf?
• SharedPreferences vs prf
• Setup & Basic Usage (Step-by-Step)
• Available Methods for All prf Types
• Supported prf Types
• Migrating from SharedPreferences to prf
• Persistent Services & Utilities
• Roadmap & Future Plans
• Why prf Wins in Real Apps

⚡ Define → Get → Set → Done #

Just define your variable once — no strings, no boilerplate:

final username = PrfString('username');

Then get it:

final value = await username.get();

Or set it:

await username.set('Joey');

That’s it. You're done. Works with all prf Types!

🔥 Why Use prf #

Working with SharedPreferences often leads to:

• Repeated string keys
• Manual casting and null handling
• Verbose async boilerplate
• Scattered, hard-to-maintain logic

prf solves all of that with a one-line variable definition that’s type-safe, cached, and instantly usable throughout your app. No key management, no setup, no boilerplate, no .getString(...) everywhere.

What Sets prf Apart? #

• ✅ Single definition — just one line to define, then reuse anywhere
• ✅ Type-safe — no casting, no runtime surprises
• ✅ Automatic caching — values are stored in memory after the first read
• ✅ Lazy initialization — no need to manually call SharedPreferences.getInstance()
• ✅ Supports more than just primitives — 10+ types without counting utilities.
• ✅ Built for testing — easily reset or mock storage in tests
• ✅ Cleaner codebase — no more scattered prefs.get...() or typo-prone string keys
• ✅ Isolate-safe — built on SharedPreferencesAsync for full isolate compatibility, with caching on top, making it faster and more ergonomic than working with raw SharedPreferencesAsync directly
• ✅ Persistent utilities included —
  • PrfCooldown – for managing cooldown windows (e.g. daily rewards)
  • PrfRateLimiter – token-bucket limiter for X actions per time window (e.g. 1000 messages per 15 minutes)

🔁 SharedPreferences vs prf #

📌 Code Comparison #

Using SharedPreferences:

final prefs = await SharedPreferences.getInstance();
await prefs.setString('username', 'Joey');
final username = prefs.getString('username') ?? '';

Using prf:

final username = PrfString('username');
await username.set('Joey');
final name = await username.get();

If you're tired of:

• Duplicated string keys
• Manual casting and null handling
• Scattered boilerplate

Then prf is your drop-in solution for fast, safe, scalable, and elegant local persistence.

🚀 Setup & Basic Usage (Step-by-Step) #

Step 1: Add prf to your pubspec.yaml #

dependencies:
  prf: ^latest

Then run:

flutter pub get

Step 2: Define Your Variable #

You only need one line to create a saved variable.
For example, to save how many coins a player has:

final playerCoins = PrfInt('player_coins', defaultValue: 0);

This means:

• You're saving an int (number)
• The key is 'player_coins'
• If it's empty, it starts at 0

Step 3: Save a Value #

To give the player 100 coins:

await playerCoins.set(100);

Step 4: Read the Value #

To read how many coins the player has:

final coins = await playerCoins.get();
print('Coins: $coins'); // 100

That’s it! 🎉 You don’t need to manage string keys or setup anything. Just define once, then use anywhere in your app.

🧰 Available Methods for All prf Types #

Available on all prf types — consistent, type-safe, and ready anywhere in your app.

🔤 Supported prf Types #

Define your variable once with a type that fits your use case. Every type supports .get(), .set(), .remove(), and more — all cached, type-safe, and ready to use.

✅ All Types Support: #

• get() – read the current value (cached or from disk)
• set(value) – write and cache the value
• remove() – delete from disk and cache
• isNull() – check if null
• getOrFallback(default) – safely access with fallback
• existsOnPrefs() – check if a key is stored

🧠 Custom Types? No Problem #

Want to persist something more complex? Use PrfJson<T> with any model that supports toJson and fromJson.

final userData = PrfJson<User>(
  'user',
  fromJson: (json) => User.fromJson(json),
  toJson: (user) => user.toJson(),
);

Or use PrfEncoded<TSource, TStore> to define your own encoding logic (e.g., compress/encrypt/etc).

Also See Persistent Services & Utilities: #

• PrfCooldown — for managing cooldown periods (e.g. daily rewards, retry delays)
• PrfRateLimiter — token-bucket limiter for rate control (e.g. 1000 actions per 15 minutes)

🔁 Migrating from SharedPreferences to prf #

Whether you're using the modern SharedPreferencesAsync or the legacy SharedPreferences, migrating to prf is simple and gives you cleaner, type-safe, and scalable persistence — without losing any existing data.

In fact, you can use prf with your current keys and values out of the box, preserving all previously stored data. But while backwards compatibility is supported, we recommend reviewing all built-in types and utilities that prf provides — such as PrfDuration, PrfCooldown, and PrfRateLimiter — which may offer a cleaner, more powerful way to structure your logic going forward, without relying on legacy patterns or custom code.

✅ If you're already using SharedPreferencesAsync #

You can switch to prf with zero configuration — just use the same keys.

Before (SharedPreferencesAsync):

final prefs = SharedPreferencesAsync();
await prefs.setBool('dark_mode', true);
final isDark = await prefs.getBool('dark_mode');

After (prf):

final darkMode = PrfBool('dark_mode');
await darkMode.set(true);
final isDark = await darkMode.get();

• ✅ As long as you're using the same keys and types, your data will still be there. No migration needed.
• 🧼 Or — if you don't care about previously stored values, you can start fresh and use prf types right away. They’re ready to go with clean APIs and built-in caching for all variable types (bool, int, DateTime, Uint8List, enums, and more).

✅ If you're using the legacy SharedPreferences class #

You can still switch to prf using the same keys:

Before (SharedPreferences):

final prefs = await SharedPreferences.getInstance();
await prefs.setString('username', 'Joey');
final name = prefs.getString('username');

After (prf):

final username = PrfString('username');
await username.set('Joey');
final name = await username.get();

• ⚠️ prf uses SharedPreferencesAsync, which is isolate-safe, more robust — and does not share data with the legacy SharedPreferences API. The legacy API is already planned for deprecation, so migrating away from it is strongly recommended.
• ✅ If you're still in development, you can safely switch to prf now — saved values from before will not be accessible, but that's usually fine while iterating.

The migration bellow automatically migrates old values into the new backend if needed.
Safe to call multiple times — it only runs once.

⚠️ If your app is already in production using SharedPreferences #

If your app previously used SharedPreferences (the legacy API), and you're now using prf (which defaults to SharedPreferencesAsync):

• You must run a one-time migration to move your data into the new backend (especially on Android, where the storage backend switches to DataStore).

Run this before any reads or writes, ideally at app startup:

await Prf.migrateFromLegacyPrefsIfNeeded();

This ensures your old values are migrated into the new system.
It is safe to call multiple times — migration will only occur once.

Summary #

With prf, you get:

• 🚀 Type-safe, reusable variables
• 🧠 Cleaner architecture
• 🔄 Built-in in-memory caching
• 🔐 Isolate-safe behavior with SharedPreferencesAsync
• 📦 Out-of-the-box support for DateTime, Uint8List, enums, full models (PrfJson<T>), and more

⚙️ Persistent Services & Utilities #

In addition to typed variables, prf includes ready-to-use persistent utilities for common real-world use cases — built on top of the same caching and async-safe architecture.

These utilities handle state automatically across sessions and isolates, with no manual logic or timers. They’re fully integrated into prf, use built-in types under the hood, and require no extra setup. Just define and use.

Included utilities: #

• 🔁 PrfCooldown — for managing cooldown periods (e.g. daily rewards, retry delays)
• 📊 PrfRateLimiter — token-bucket limiter for rate control (e.g. 1000 actions per 15 minutes)

🕒 PrfCooldown – Persistent Cooldown Utility #

PrfCooldown is a plug-and-play utility for managing cooldown windows (e.g. daily rewards, button lockouts, retry delays) that persist across sessions and isolates — no timers, no manual bookkeeping, no re-implementation every time.

It handles:

• Cooldown timing (DateTime.now() + duration)
• Persistent storage via prf (with caching and async-safety)
• Activation tracking and expiration logic
• Usage statistics (activation count, expiry progress, etc.)

🔧 How to Use #

Instantiate it with a unique prefix and a duration:

final cooldown = PrfCooldown('daily_reward', duration: Duration(hours: 24));

You can then use:

• isCooldownActive() — Returns true if the cooldown is still active
• isExpired() — Returns true if the cooldown has expired or was never started
• activateCooldown() — Starts the cooldown using the configured duration
• tryActivate() — Starts cooldown only if it's not active — returns whether it was triggered
• reset() — Clears the cooldown timer, but keeps the activation count
• completeReset() — Fully resets both the cooldown and its usage counter
• timeRemaining() — Returns remaining time as a Duration
• secondsRemaining() — Same as above, in seconds
• percentRemaining() — Progress indicator between 0.0 and 1.0
• getLastActivationTime() — Returns DateTime? of last activation
• getEndTime() — Returns when the cooldown will end
• whenExpires() — Returns a Future that completes when the cooldown ends
• getActivationCount() — Returns the total number of activations
• removeAll() — Deletes all stored values (for testing/debugging)
• anyStateExists() — Returns true if any cooldown data exists in storage

Here’s the tutorial section for PrfCooldown, production-grade, clear, and aligned with your README style:

✅ Define a Cooldown

final cooldown = PrfCooldown('daily_reward', duration: Duration(hours: 24));

This creates a persistent cooldown that lasts 24 hours. It uses the prefix 'daily_reward' to store:

• Last activation timestamp
• Activation count

🔍 Check If Cooldown Is Active

if (await cooldown.isCooldownActive()) {
  print('Wait before trying again!');
}

⏱ Activate the Cooldown

await cooldown.activateCooldown();

This sets the cooldown to now and begins the countdown. The activation count is automatically incremented.

⚡ Try Activating Only If Expired

if (await cooldown.tryActivate()) {
  print('Action allowed and cooldown started');
} else {
  print('Still cooling down...');
}

Use this for one-line cooldown triggers (e.g. claiming a daily gift or retrying a network call).

🧼 Reset or Fully Clear Cooldown

await cooldown.reset();         // Clears only the time
await cooldown.completeReset(); // Clears time and resets usage counter

🕓 Check Time Remaining

final remaining = await cooldown.timeRemaining();
print('Still ${remaining.inMinutes} minutes left');

You can also use:

await cooldown.secondsRemaining();   // int
await cooldown.percentRemaining();   // double between 0.0–1.0

📅 View Timing Info

final lastUsed = await cooldown.getLastActivationTime();
final endsAt = await cooldown.getEndTime();

⏳ Wait for Expiry (e.g. for auto-retry)

await cooldown.whenExpires(); // Completes only when cooldown is over

📊 Get Activation Count

final count = await cooldown.getActivationCount();
print('Used $count times');

🧪 Test Utilities

await cooldown.removeAll();      // Clears all stored cooldown state
final exists = await cooldown.anyStateExists(); // Returns true if anything is stored

You can create as many cooldowns as you need — each with a unique prefix.
All state is persisted, isolate-safe, and instantly reusable.

📊 PrfRateLimiter – Persistent Token Bucket Rate Limiter #

PrfRateLimiter is a high-performance, plug-and-play utility that implements a token bucket algorithm to enforce rate limits — like “100 actions per 15 minutes” — across sessions, isolates, and app restarts.

It handles:

• Token-based rate limiting
• Automatic time-based token refill
• Persistent state using prf types (PrfDouble, PrfDateTime)
• Async-safe, isolate-compatible behavior with built-in caching

Perfect for chat limits, API quotas, retry windows, or any action frequency cap — all stored locally.

🔧 How to Use #

Create a limiter with a unique key, a max token count, and a refill window:

final limiter = PrfRateLimiter('chat_send', maxTokens: 100, refillDuration: Duration(minutes: 15));

You can then use:

• tryConsume() — Tries to use 1 token; returns true if allowed, or false if rate-limited
• isLimitedNow() — Returns true if no tokens are currently available
• isReady() — Returns true if at least one token is available
• getAvailableTokens() — Returns the current number of usable tokens (calculated live)
• timeUntilNextToken() — Returns a Duration until at least one token will be available
• nextAllowedTime() — Returns the exact DateTime when a token will be available
• reset() — Resets to full token count and updates last refill to now
• removeAll() — Deletes all limiter state (for testing/debugging)
• anyStateExists() — Returns true if limiter data exists in storage
• runIfAllowed(action) — Runs a callback if allowed, otherwise returns null
• debugStats() — Returns detailed internal stats for logging and debugging

The limiter uses fractional tokens internally to maintain precise refill rates, even across app restarts. No timers or background services required — it just works.

✅ PrfRateLimiter Basic Setup

Create a limiter with a key, a maximum number of actions, and a refill duration:

final limiter = PrfRateLimiter(
  'chat_send',
  maxTokens: 100,
  refillDuration: Duration(minutes: 15),
);

This example allows up to 100 actions per 15 minutes. The token count is automatically replenished over time — even after app restarts.

🚀 Check & Consume

To attempt an action:

final canSend = await limiter.tryConsume();

if (canSend) {
  // Allowed – proceed with the action
} else {
  // Blocked – too many actions, rate limit hit
}

Returns true if a token was available and consumed, or false if the limit was exceeded.

🧮 Get Available Tokens

To check how many tokens are usable at the moment:

final tokens = await limiter.getAvailableTokens();
print('Tokens left: ${tokens.toStringAsFixed(2)}');

Useful for debugging, showing rate limit progress, or enabling/disabling UI actions.

⏳ Time Until Next Token

To wait or show feedback until the next token becomes available:

final waitTime = await limiter.timeUntilNextToken();
print('Try again in: ${waitTime.inSeconds}s');

You can also get the actual time point:

final nextTime = await limiter.nextAllowedTime();

🔁 Reset the Limiter

To fully refill the bucket and reset the refill clock:

await limiter.reset();

Use this after manual overrides, feature unlocks, or privileged user actions.

🧼 Clear All Stored State

To wipe all saved token/refill data (for debugging or tests):

await limiter.removeAll();

To check if the limiter has any stored state:

final exists = await limiter.anyStateExists();

With PrfRateLimiter, you get a production-grade rolling window limiter with zero boilerplate — fully persistent and ready for real-world usage.

🛣️ Roadmap & Future Plans #

prf is built for simplicity, performance, and scalability. Upcoming improvements focus on expanding flexibility while maintaining a zero-boilerplate experience.

✅ Planned Enhancements #

• Improved performance
  Smarter caching and leaner async operations.

• Additional type support
  Encrypted strings, and more.

• Custom storage (experimental)
  Support for alternative adapters (Hive, Isar, file system).

• Testing & tooling
  In-memory test adapter, debug inspection tools, and test utilities.

• Optional code generation
  Annotations for auto-registering variables and reducing manual setup.

🔍 Why prf Wins in Real Apps #

Working with SharedPreferences directly can quickly become verbose, error-prone, and difficult to scale. Whether you’re building a simple prototype or a production-ready app, clean persistence matters.

❌ The Problem with Raw SharedPreferences #

Even in basic use cases, you're forced to:

• Reuse raw string keys (risk of typos and duplication)
• Manually cast and fallback every read
• Handle async boilerplate (getInstance) everywhere
• Encode/decode complex types manually
• Spread key logic across multiple files

Let’s see how this unfolds in practice.

👎 Example: Saving and Reading Multiple Values #

Goal: Save and retrieve a username, isFirstLaunch, and a signupDate.

SharedPreferences (verbose and repetitive) #

final prefs = await SharedPreferences.getInstance();

// Save values
await prefs.setString('username', 'Joey');
await prefs.setBool('is_first_launch', false);
await prefs.setString(
  'signup_date',
  DateTime.now().toIso8601String(),
);

// Read values
final username = prefs.getString('username') ?? '';
final isFirstLaunch = prefs.getBool('is_first_launch') ?? true;
final signupDateStr = prefs.getString('signup_date');
final signupDate = signupDateStr != null
  ? DateTime.tryParse(signupDateStr)
  : null;

🔻 Issues:

• Repeated string keys — no compile-time safety
• Manual fallback handling and parsing
• No caching — every .get hits disk
• Boilerplate increases exponentially with more values

✅ Example: Same Logic with prf #

final username = PrfString('username');
final isFirstLaunch = PrfBool('is_first_launch', defaultValue: true);
final signupDate = PrfDateTime('signup_date');

// Save
await username.set('Joey');
await isFirstLaunch.set(false);
await signupDate.set(DateTime.now());

// Read
final name = await username.get();         // 'Joey'
final first = await isFirstLaunch.get();   // false
final date = await signupDate.get();       // DateTime instance

💡 Defined once, used anywhere — fully typed, cached, and clean.

🤯 It Gets Worse with Models #

Storing a User model in raw SharedPreferences requires:

1. Manual jsonEncode / jsonDecode
2. Validation on read
3. String-based key tracking

SharedPreferences with Model: #

// Get SharedPreferences
final prefs = await SharedPreferences.getInstance();
// Encode to JSON
final json = jsonEncode(user.toJson());
// Set value
await prefs.setString('user_data', json);

// Read
final raw = prefs.getString('user_data');
User? user;
if (raw != null) {
  try {
    // Decode JSON
    final decoded = jsonDecode(raw);
    // Convert to User
    user = User.fromJson(decoded);
  } catch (_) {
    // fallback or error
  }
}

✅ Same Logic with prf #

// Define once
final userData = PrfJson<User>(
  'user_data',
  fromJson: User.fromJson,
  toJson: (u) => u.toJson(),
);

// Save
await userData.set(user);

// Read
final savedUser = await userData.get(); // User?

Fully typed. Automatically parsed. Fallback-safe. Reusable across your app.

⚙️ Built for Real Apps #

prf was built to eliminate the day-to-day pain of using SharedPreferences in production codebases:

• ✅ Define once — reuse anywhere
• ✅ Clean API — get(), set(), remove(), isNull() for all types
• ✅ Supports advanced types: DateTime, Uint8List, enum, JSON
• ✅ Automatic caching — fast access after first read
• ✅ Test-friendly — easily reset, mock, or inspect values

🔗 License MIT © Jozz #