High-Throughput Context Store Design

Throughput Requirements

High-traffic AI applications may require millions of context operations per second. This requires careful architectural design from storage to network layers.

Storage Layer Design

Partitioning Strategy

// Hash-based partitioning
function getPartition(contextId: string, numPartitions: number): number {
  return murmurhash3(contextId) % numPartitions;
}

// Partition configuration
const partitionConfig = {
  numPartitions: 256,
  replicationFactor: 3,
  partitionAssignment: 'consistent_hash'
};

Write Optimization

// Write-ahead log for durability
class WALWriter {
  async write(operation: Operation): Promise<void> {
    await this.wal.append(operation);
    // Batch fsync for throughput
    if (this.pendingWrites >= BATCH_SIZE) {
      await this.wal.sync();
    }
  }
}

// LSM tree for write-optimized storage
// Memtable -> Immutable Memtable -> SSTable

Read Optimization

Tiered Caching

class TieredCache {
  private l1: LocalCache;    // In-process, sub-ms
  private l2: DistributedCache;  // Redis, 1-5ms
  private storage: ContextStore; // Persistent, 5-20ms
  
  async get(id: string): Promise<Context | null> {
    // Check L1
    let ctx = this.l1.get(id);
    if (ctx) return ctx;
    
    // Check L2
    ctx = await this.l2.get(id);
    if (ctx) {
      this.l1.set(id, ctx);
      return ctx;
    }
    
    // Fetch from storage
    ctx = await this.storage.get(id);
    if (ctx) {
      await this.l2.set(id, ctx);
      this.l1.set(id, ctx);
    }
    return ctx;
  }
}

Concurrency Design

Lock-Free Structures

// Lock-free read path
class LockFreeIndex {
  private current: AtomicReference<IndexSnapshot>;
  
  get(key: string): Context | null {
    const snapshot = this.current.get();
    return snapshot.lookup(key);
  }
  
  // Copy-on-write for updates
  update(key: string, value: Context): void {
    let current: IndexSnapshot;
    let updated: IndexSnapshot;
    do {
      current = this.current.get();
      updated = current.with(key, value);
    } while (!this.current.compareAndSet(current, updated));
  }
}

Network Design

Request Routing

// Direct routing to partition owner
class Router {
  route(contextId: string): Node {
    const partition = getPartition(contextId);
    return this.partitionMap.getLeader(partition);
  }
}

// Avoid unnecessary hops
// Client -> Correct node directly

Benchmarking

Throughput Measurement

// Measure at various concurrency levels
const results = [];
for (const concurrency of [10, 100, 500, 1000]) {
  const ops = await benchmark({
    operation: () => store.get(randomId()),
    duration: 60_000,
    concurrency
  });
  results.push({
    concurrency,
    opsPerSecond: ops / 60,
    p99Latency: measureP99()
  });
}

Conclusion

High-throughput context stores require careful design of partitioning, caching, concurrency control, and network routing. Benchmark continuously and optimize bottlenecks systematically.

> High-Throughput Context Store Design