GraphBit's Memory Efficiency Techniques: Code-Backed Strategies for Optimization

Below is a concise, code‑verified summary of GraphBit’s memory efficiency techniques, with short excerpts and file paths.

1) Memory‑optimized executor profile

• Purpose: run in constrained environments with reduced footprint by lowering concurrency and disabling extras.
• Rust executor profile:

/// Create a workflow executor optimized for memory usage

pub fn new_memory_optimized() -> Self {

let concurrency_config = ConcurrencyConfig::memory_optimized();

let concurrency_manager = Arc::new(ConcurrencyManager::new(concurrency_config));

Self { /* smaller pre-allocs, conservative settings */ }

}

• Python binding (lower concurrency, disable metrics):

fn new_memory_optimized(..) -> PyResult<Self> {

let mut config = ExecutionConfig {

   `mode: ExecutionMode::MemoryOptimized,`

   `max_concurrency: Some(2),   // Lower concurrency to save memory`

   `enable_metrics: false,      // Disable metrics to save memory`

   `..Default::default()`

};

// ...

}

2) Memory‑lean concurrency configuration

• Purpose: cap simultaneous work per node type to control peak memory.

pub fn memory_optimized() -> Self {

let mut node_type_limits = HashMap::with_capacity(8);

node_type_limits.insert("agent".to_string(), 5);

node_type_limits.insert("transform".to_string(), 12);

// ...

Self { global_max_concurrency: 32, node_type_limits }

}

3) Benchmark memory measurement (validation)

• Purpose: track actual memory usage with tracemalloc + RSS deltas.

tracemalloc.start()

self.start_memory = self.process.memory_info().rss / 1024 / 1024

# ...

current_memory, peak_memory = tracemalloc.get_traced_memory()

memory_usage_mb = end_memory - self.start_memory

4) Pre‑allocation to avoid reallocs and fragmentation

• Purpose: reduce allocations and peak usage in hot structures.

Self {

node_map: HashMap::with_capacity(16),

nodes: HashMap::with_capacity(16),

edges: Vec::with_capacity(16),

// ...

}

5) Graph‑level caching (less repeated allocation/work)

• Purpose: cache dependency lookups to avoid rebuilding vectors repeatedly.

// Cached adjacency information for performance

if let Some(deps) = self.dependencies_cache.get(node_id) {

return deps.clone();

}

6) Provider‑level caching to avoid redundant setup

• Purpose: skip repeated model verification (keeps memory churn low).

// Fast path: check cache first to avoid repeated API calls

let verified = self.model_verified.read().await;

if *verified { return Ok(()); }

// ... set *verified = true after first check

7) Memory health checks in Node.js bindings

• Purpose: basic guardrails to surface low‑memory conditions.

let memory_healthy =

available_memory_mb.map_or(true, |mem| mem > 100); // At least 100MB

if !memory_healthy { warn!("Low available memory: {} MB", available); }

8) jemalloc on Unix (allocator choice)

• Purpose: improved allocation behavior and memory efficiency on Unix; disabled where problematic.

#[cfg(all(not(feature = "python"), unix))]

#[global_allocator]

static GLOBAL: jemallocator::Jemalloc = jemallocator::Jemalloc;