Bun's file I/O operations are faster than Node.js primarily due to its innovative approach to buffer management, lower-level optimizations in its runtime, and architectural differences in how it handles asynchronous operations and memory usage.
One of the most critical differences that contribute to Bun's performance is Bun's use of a single, shared 250 KB buffer for all streaming file reads. In practice, this means that no matter how many files are being streamed concurrently, Bun reuses the same buffer for every file read operation. This results in a constant, O(1) memory usage pattern for file streaming, which does not increase with the number of concurrent file streams. In contrast, Node.js typically allocates a separate buffer for each file stream. This means memory usage grows linearly (O(N)) with the number of concurrent files being read or written. As memory allocations increase, Node.js experiences more frequent garbage collection, cache misses, and potentially even memory exhaustion under high concurrency, which negatively impacts performance.
Bun's fixed buffer size of 250 KB dictates the chunk size read from the disk in a single I/O operation. Even large files are read in manageable 250 KB chunks, reusing the same buffer. While this implies more read operations compared to using larger or per-stream buffers, the performance trade-off is negligible. The gain from drastically reduced memory pressure, limited garbage collection overhead, and predictable memory usage outweighs the cost of more frequent reads. This makes Bun highly efficient and stable under heavy loads with many simultaneous file operations.
Node.js, on the other hand, uses a design where each file stream gets its own buffer, often sized around 64 KB or larger. This approach reduces system read calls when reading large files by handling more data per operation but increases memory usage as the number of simultaneous streams grows. This can lead to less efficient memory management and overall system slowdowns when serving many files concurrently.
Beyond buffer reuse, Bun's file I/O is implemented using the Zig programming language, which enables fine-grained control over system-level details and minimal abstraction overhead. The Zig-based runtime optimizes asynchronous operations more aggressively than Node.js, which relies on older C++ codebases combined with the V8 JavaScript engine. This results in faster asynchronous file reads/writes in Bun due to more efficient system call handling and less runtime overhead.
In terms of API, Bun offers APIs for file I/O operations that are largely compatible with Node.js but are tailored to leverage Bun's runtime efficiencies. For example, Bun.file() can load files lazily and support multiple content formats, performing reads up to 10 times faster than Node.js counterparts. Meanwhile, Bun.write() offers versatile writing capabilities that can perform up to 3 times faster than Node.js's fs.writeFile method. These APIs internally benefit from the optimized buffer management, lower-level system call integration, and reduced copying of data.
Another aspect of performance in Bun comes from better CPU cache locality and reduced memory fragmentation. The reuse of a single buffer and the optimized handling of concurrent I/O operations keep most data accesses localized in memory, improving cache performance and throughput. This contrasts with Node.js's scattered buffers for each stream, which can lead to cache thrashing and less efficient CPU usage during heavy I/O workloads.
Bun's HTTP server and WebSocket capabilities also demonstrate complementary improvements in concurrency and throughput, which benefit from the same low-level optimizations that boost file I/O speed. This integrated approach means Bun isn't just faster in file reading/writing itself, but also in serving those files over network requests, which is a common real-world scenario.
In summary, Bun's speed advantage in file I/O over Node.js can be attributed to:
- Single shared 250 KB buffer reused for all file streaming operations, resulting in constant low memory usage irrespective of concurrency.
- Use of the Zig programming language for runtime and asynchronous I/O optimizations, reducing system call overhead.
- Reduced garbage collection pressure and memory fragmentation due to efficient buffer reuse.
- APIs optimized to leverage these runtime improvements, providing faster file read/write operations than Node.js.
- Improved CPU cache locality and memory use patterns for better throughput under load.
- A more modern, integrated runtime architecture designed for high concurrency, supporting both file I/O and network operations efficiently.