Nano Banana 2 uses an internal memory pooling system to reduce the overhead of frequent allocation and deallocation cycles during high-throughput data processing. Rather than allocating a new buffer for every record that passes through the pipeline, the library maintains a pool of pre-allocated byte buffers that stages can borrow, use, and return. This approach significantly reduces GC pressure in languages with managed memory—particularly Python—and keeps allocation latency predictable under load. The pool size and per-buffer capacity are configurable, allowing you to tune memory behavior based on your typical record size and concurrency level.
For the Rust and Go clients, memory management aligns with the idiomatic patterns of each language. The Go client uses sync.Pool for buffer reuse and relies on Go’s garbage collector for lifecycle management. The Rust client uses unsafe code for performance-critical paths but encapsulates it behind safe abstractions, with ownership and borrow-checker rules ensuring that memory is freed correctly without runtime overhead. In both cases, the library avoids holding references to pipeline data after a stage completes processing, which limits the live heap footprint to what is actively in flight at any moment.
If you observe unexpected memory growth in a running pipeline, the most common cause is backpressure: an upstream stage is producing records faster than a downstream stage can consume them, causing the internal buffers to grow. Nano Banana 2 exposes metrics for buffer utilization via its built-in metrics endpoint, which you can scrape with Prometheus or inspect with the CLI. Identifying and addressing the slow stage—whether by tuning its concurrency configuration or optimizing the transformation logic—will typically resolve the memory growth without needing to increase pool limits.