# How the Hallelujah Input Method Manages Memory and Loads Large Dictionary Files Efficiently

> Discover how the Hallelujah Input Method efficiently loads large dictionaries, minimizes memory usage, and keeps your UI responsive using lazy loading, streaming, and memory-mapped Marisa-Tries.

- Repository: [dongyuwei/hallelujahim](https://github.com/dongyuwei/hallelujahim)
- Tags: internals
- Published: 2026-02-28

---

**The Hallelujah input method minimizes memory footprint by combining lazy background loading, streaming JSON deserialization via NSInputStream, and memory-mapped binary Marisa-Tries to handle multi-megabyte dictionaries without blocking the UI thread.**

The Hallelujah input method is a macOS IMK extension designed to maintain responsiveness while processing extensive linguistic datasets. According to the dongyuwei/hallelujahim source code, the engine employs coordinated memory management techniques to handle dictionaries exceeding 20MB and 227,000 entries without expanding the entire dataset into Objective-C objects.

## Lazy Background Loading on First Initialization

The input method delays all heavy data operations until the first request for the shared `ConversionEngine` singleton. In `src/ConversionEngine.mm`, the `+sharedEngine` method triggers `loadPreparedData`, which dispatches dictionary loading to a background queue.

This approach prevents the input method from blocking the UI thread during initialization. The implementation uses `dispatch_async` with `DISPATCH_QUEUE_PRIORITY_BACKGROUND` to spread allocation costs over time while returning the singleton immediately to the caller.

```objc
- (void)loadPreparedData {
    // Run everything on a low‑priority background queue.
    dispatch_async(dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_BACKGROUND, 0), ^{
        [self loadTrie];                                  // binary trie
        self.wordsWithFrequencyAndTranslation = [self getWordsWithFrequencyAndTranslation];
        self.substitutions = [self getUserDefinedSubstitutions];
        self.pinyinDict = [self getPinyinData];
        self.phonexEncoded = [self getPhonexEncodedWords];
        self.phonexEncoder = [self getPhonexEncoder];
    });
}

```

## Streaming JSON Deserialization for Large Text Files

Instead of reading entire JSON files into `NSString` objects before parsing, the engine streams data directly from disk. The `deserializeJSON` function in `src/ConversionEngine.mm` opens an `NSInputStream` on the file path and passes it to `NSJSONSerialization`, keeping only a small buffer in RAM while processing the rest incrementally.

This technique handles [`words_with_frequency_and_translation_and_ipa.json`](https://github.com/dongyuwei/hallelujahim/blob/main/words_with_frequency_and_translation_and_ipa.json) (approximately 20MB), [`cedict.json`](https://github.com/dongyuwei/hallelujahim/blob/main/cedict.json), and [`phonex_encoded_words.json`](https://github.com/dongyuwei/hallelujahim/blob/main/phonex_encoded_words.json) without loading their full contents into memory at once.

```objc
// Helper that reads a JSON file directly from disk without loading it all at once.
NSDictionary *deserializeJSON(NSString *path) {
    NSInputStream *stream = [[NSInputStream alloc] initWithFileAtPath:path];
    [stream open];
    NSDictionary *dict = [NSJSONSerialization JSONObjectWithStream:stream
                                                          options:0
                                                            error:nil];
    [stream close];
    return dict;               // Returns a fully populated NSDictionary.
}

```

## Compact Binary Trie for O(1) Prefix Lookups

The main word list containing approximately 227,000 entries is stored as a binary Marisa-Trie in `dictionary/google_227800_words.bin`. The `loadTrie` method in `src/ConversionEngine.mm` loads this file via the native C++ library, which maps the compressed binary structure directly into memory.

This provides **O(1) prefix lookups** through the `wordsStartsWith:` method without expanding the entire word list into individual Objective-C objects. The memory-mapped approach keeps the footprint to a few megabytes while enabling ultra-fast predictive search.

```objc
- (void)loadTrie {
    NSString *path = [[NSBundle mainBundle] pathForResource:@"google_227800_words"
                                                     ofType:@"bin"];
    const char *cPath = [path cStringUsingEncoding:[NSString defaultCStringEncoding]];
    trie.load(cPath);   // Marisa‑Trie loads the binary file into a compressed structure.
}

```

## On-Demand User Data Loading

Optional user-defined shortcuts stored in `~/.you_expand_me.json` are loaded only when explicitly requested via `getUserDefinedSubstitutions`. This on-demand pattern ensures that missing or empty configuration files do not consume resources unnecessarily.

The method uses the same streaming JSON helper for consistency, maintaining the memory-efficient pattern across all dictionary loading operations.

## Summary

- **Lazy initialization** via `+sharedEngine` and `loadPreparedData` prevents UI blocking by dispatching heavy operations to a background queue.
- **Streaming JSON parsing** through `NSInputStream` allows processing of 20MB+ dictionary files without loading them entirely into RAM.
- **Binary Marisa-Trie** storage enables O(1) prefix searches on 227,000 entries while maintaining a minimal memory footprint through memory mapping.
- **On-demand loading** of user configuration files ensures resources are allocated only when custom substitutions are actually present.

## Frequently Asked Questions

### How does Hallelujah IM prevent UI freezing during dictionary loading?

The input method uses `dispatch_async` with `DISPATCH_QUEUE_PRIORITY_BACKGROUND` in the `loadPreparedData` method to load all dictionaries asynchronously. The `ConversionEngine` singleton returns immediately to the UI thread while data population occurs on a separate queue, ensuring the input method remains responsive during initialization.

### What data structure enables fast prefix searches without high memory usage?

The engine employs a **Marisa-Trie** stored as a binary file (`google_227800_words.bin`). The `loadTrie` method maps this compressed structure into memory, allowing the `wordsStartsWith:` method to perform O(1) prefix lookups without expanding the 227,000-entry word list into individual Objective-C objects.

### How are large JSON dictionary files parsed without loading them entirely into RAM?

The `deserializeJSON` function in `src/ConversionEngine.mm` creates an `NSInputStream` directly from the file path and passes it to `NSJSONSerialization`. This streams the JSON data incrementally rather than reading it into an intermediate `NSString`, keeping only a small buffer in memory while processing files like [`words_with_frequency_and_translation_and_ipa.json`](https://github.com/dongyuwei/hallelujahim/blob/main/words_with_frequency_and_translation_and_ipa.json).

### Can users customize the dictionary loading behavior?

Users can define custom shortcuts in `~/.you_expand_me.json`, which the engine loads via `getUserDefinedSubstitutions` only when needed. However, the core dictionary loading strategy—including the binary trie and streaming JSON parsers—is hardcoded in `ConversionEngine.mm` to ensure consistent memory efficiency across all installations.