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Caching and OS Interaction

Caching and OS Interaction

Section titled “Caching and OS Interaction”

We compare mmapped vs buffered vs async IO and how page cache, read-ahead, and prefetching affect SSTable reads. Historical key/row caches are contrasted with current realities.

In this chapter you will learn

Section titled “In this chapter you will learn”
  • Differences among mmapped, buffered, and async IO
  • Page cache behavior and read-ahead implications
  • Practical defaults for most workloads

IO Modes

Section titled “IO Modes”
  • Memory-mapped (mmap): lowest syscall overhead; relies on page cache; tricky for backpressure
  • Buffered (read): explicit IO, easier to bound; OS page cache still in play
  • Async (AIO/epoll/tokio): concurrency-friendly; hides latency with futures

For a concrete cache/IO implementation walkthrough, see Appendix C.

Practical Defaults

Section titled “Practical Defaults”
  • Cassandra 5.0 defaults to mmap_index_only: index files are mmap’d; Data.db uses buffered I/O. This is the recommended baseline — see Ch. 3 for IOOptions.java wiring details.
  • For workloads saturating Data.db sequentially, disk_access_mode: mmap re-enables full mmap; be aware of JVM address-space pressure on large datasets.
  • Buffered async I/O suits mixed/random workloads and bounded-memory Data.db reads.
  • Keep Bloom enabled; summary sampling reduces seeks
  • Tune prefetch window to match chunk sizes (see Ch. 9)

Memory Pressure Handling

Section titled “Memory Pressure Handling”
  • Implement cache admission/eviction with size caps; evict oldest blocks first (LRU) or LFU for hot workloads.
  • Under pressure, prefer dropping OS page cache (mmap) mappings over user-space caches to avoid double caching.
  • Expose backpressure: throttle prefetch when cache hit rate drops below a target threshold.

Direct IO for Large Scans

Section titled “Direct IO for Large Scans”
  • For long sequential scans on saturated systems, direct IO can reduce page cache churn. Pair with larger read buffers (e.g., multiples of chunk length) and explicit readahead.
  • Fall back to buffered IO when decompression or random access breaks large request alignment.

Buffer Pool Sizing

Section titled “Buffer Pool Sizing”
  • The BufferPool backing RandomAccessReader allocations has a hard ceiling of 64 KiB per buffer (DiskOptimizationStrategy.java:32: MAX_BUFFER_SIZE = 1 << 16).
  • Start with pool size ≈ (concurrency × average request size × 2). Bound by memory budget and adjust to keep allocator overhead <5% CPU.
  • Align buffers to chunk size boundaries to minimize partial-chunk decompression and copies.

Key Takeaways

Section titled “Key Takeaways”
  • Page cache dominates; pick strategies that align with workload
  • Mmap is simple and fast for scans; buffered/async helps control memory and concurrency
  • Prefetch and block caches mitigate random-read penalties

References

Section titled “References”

For implementation details, see Appendix C.