Using a shared memory segment to update high-volume market data from a separate process is a well-established low-latency architecture pattern, especially in trading systems, real-time analytics, or high-frequency environments. Let’s walk through the pros, cons, and challenges of this design.

Pros

• Low Latency - shared memory enables zero-copy communication. Unlike sockets or IPC mechanisms, there's no need to serialize/deserialize or move data between kernel and user space.

• High Throughput - ideal for high-volume data because you're avoiding per-message system calls and kernel overhead.

• Cross-Process Isolation - separating the market data feed handler from consumers improves fault isolation and can reduce the risk of a consumer bug impacting data ingestion.

• Multiprocess Scalability - multiple consumers can access the same memory segment concurrently (with careful synchronization), enabling horizontal scale across processes or even containers in some setups.

• Determinism - you can manage memory layout precisely (e.g., using ring buffers or structs), giving fine control over cache locality and memory access patterns.

Cons

• Complexity of Synchronization - yYou have to implement your own locking, sequencing, or memory fences to avoid race conditions or stale reads. This is low-level and error-prone.

• Debugging Difficulty - bugs like memory corruption, stale reads, or visibility issues are harder to diagnose than message-based communication errors.

• Portability and Maintainability - shared memory setups are OS-specific and often non-trivial to port across platforms. APIs like POSIX (shm_open, mmap) vs. Windows shared memory are quite different.

• Memory Management - manual memory allocation and layout is more prone to leaks and fragmentation unless managed carefully.

• Security Risks - if not configured properly, a rogue process could corrupt memory. You also need to consider access control (especially across users or containers).

Challenges

• Cache Coherency and False Sharing - multiple processes (or threads) accessing the same cache lines can cause contention, leading to performance degradation due to cache invalidation traffic.

• Atomicity and Lock-Free Design - for ultra-low latency use cases, you may want to avoid locks and use lock-free data structures (like ring buffers à la Disruptor pattern). These are notoriously hard to get right.

• Startup and Coordination - setting up the shared memory region and ensuring producers and consumers agree on structure/versioning/layout is non-trivial.

• Consistency and Recovery - if the producer crashes, consumers may be reading a half-updated structure. You need guardrails like sequence numbers or CRCs to detect inconsistent reads.

• Instrumentation and Observability - it’s difficult to inspect what’s going on inside shared memory during runtime unless you add explicit logging, debug views, or attach debuggers with shared memory awareness.

When It Works Well

• High-frequency trading (HFT) systems
• Market data tick processors
• Systems with extreme latency sensitivity and low message size
• Environments where you control both producer and consumers tightly

Alternatives

• Memory-mapped files (for persistence or inter-process sharing across reboots)
• Unix domain sockets (slower but simpler and safer)
• RDMA (for cross-host shared memory)
• Message buses (e.g., Aeron) for high-performance but structured data flow