sairam0424 Skill — Streaming Architecture
When this skill activates
Any task involving real-time data streaming, SSE, chunked transfer encoding, stream processing pipelines, backpressure, partitioning, or windowing strategies.
Mandatory actions when this skill is active
Before writing any code
- Choose transport (SSE vs WebSocket vs long-polling) using the decision matrix.
- Define stream data format (NDJSON, chunked binary, protobuf frames).
- Identify backpressure requirements and partition strategy.
During implementation
- Implement backpressure handling at every pipeline stage.
- Use chunked transfer encoding for HTTP streaming responses.
- Apply appropriate windowing strategy for aggregation needs.
- Partition by key for ordering, round-robin for throughput.
After implementation
- Load test under sustained high-throughput conditions.
- Verify consumer groups scale horizontally without message loss.
- Document partition strategy and windowing semantics.
Transport Decision Matrix
| Transport | Direction | Use For | Limitation |
|---|---|---|---|
| SSE | Server→Client | Notifications, feeds, progress, logs | Text-only, unidirectional |
| WebSocket | Bidirectional | Chat, collaboration, gaming | Proxy complexity, reconnection logic |
| Long-Polling | Client→Server→Client | Legacy envs, infrequent updates | High latency, resource overhead |
- SSE: auto-reconnect via Last-Event-ID, works through load balancers.
- WebSocket: lower per-message overhead after handshake, requires connection management.
- Long-Polling: universally compatible, highest resource cost at scale.
Streaming Response Patterns
- Chunked Transfer:
Transfer-Encoding: chunked— each chunk is a parseable unit. - NDJSON: one JSON object per
\n-separated line, parse incrementally. - Use NDJSON for LLM token streaming, batch results, log streams.
Stream Processing Windows
- Tumbling: fixed-size, non-overlapping. Use for per-minute aggregations.
- Sliding: fixed-size, overlapping by step. Use for moving averages.
- Session: dynamic size, closes after inactivity gap. Use for user sessions.
Backpressure Strategies
- Buffer and Batch: bounded buffer, process in batches at threshold or timer.
- Drop Oldest (lossy): discard stale messages when buffer full. Never for transactions.
- Signal Producer (reactive): consumer signals demand, producer throttles emission.
Partition Strategies
- Key-Based: same key → same partition. Guarantees per-key ordering. Risk: hot partitions.
- Round-Robin: even distribution, max throughput, no ordering guarantees.
Consumer Groups
- Multiple consumers share partitions (one partition per consumer max).
- Scale up to partition count (more consumers = idle).
- Rebalancing on consumer join/leave. Track offsets for resume-from-failure.
Self-check before task completion
- Is the transport correct for the use case (SSE/WebSocket/long-polling)?
- Is backpressure handled at every pipeline stage?
- Are streaming responses chunked with parseable units?
- Is windowing strategy appropriate for aggregation needs?
- Are partitions designed for the right ordering vs throughput trade-off?
- Can consumers scale horizontally without message loss?
- Is reconnection logic implemented for client-side streams?