Concurrency and Backpressure Sizing

Concurrency and backpressure settings are the two levers that determine how well the pipeline can keep doing useful work while waiting for I/O.

How to size pipeline.max-concurrency

pipeline.max-concurrency limits live work admitted by a step or live connector segment. For brokered ONE_TO_ONE await over a stream, it also acts as the pending-interaction window: the parser can dispatch up to that many await interactions, then advances as completions are durably recorded and accepted by downstream demand.

1. Start from CPU cores and I/O profile:
   • CPU-bound steps: set concurrency near the number of vCPUs (for example 4 cores → 4–8).
   • I/O-bound steps: you can go higher (for example 4 cores → 32–128), but validate with metrics.
2. Watch tpf.step.inflight:
   • If it stays far below the limit, the step cannot use the concurrency (increase only if you see queueing).
   • If it is pinned at the limit and tpf.step.buffer.queued is growing, you need more concurrency or a faster downstream.

How to size pipeline.defaults.backpressure-buffer-capacity

1. Size for burst absorption:
   • Buffer capacity should cover a burst window you are willing to absorb, not the entire dataset.
2. Estimate memory impact:
   • buffer capacity × average item size = memory footprint per step.
   • Example: 4-core pod, 1 KB average item size, buffer 4,000 → ~4 MB per step (plus overhead).
3. Use the buffer metrics:
   • tpf.step.buffer.queued should spike and drain.
   • Flat, high values indicate backpressure is not propagating or downstream is too slow.

Practical starting point (example)

For a 4-core Graviton pod running I/O-heavy steps:

• pipeline.max-concurrency: start at 32–64 for I/O-heavy steps, 4–8 for CPU-heavy steps.
• pipeline.defaults.backpressure-buffer-capacity: start at 2,048–8,192 for bursty streams and tune downward.

Always validate in your environment using the in-flight and buffer metrics and adjust per step when needed.

Quick rule-of-thumb table

Workload profile	pipeline.max-concurrency (per step)	Buffer capacity guidance
CPU-bound	1–2 × vCPU cores	Small (128–1,024)
Mixed	4–16 × vCPU cores	Medium (1,024–4,096)
I/O-bound	8–32 × vCPU cores	Larger (2,048–8,192)

Tune downward if the buffer stays high or GC increases, and upward only when tpf.step.inflight is consistently below the limit while tpf.step.buffer.queued spikes.

Durable boundaries

Backpressure propagates through live reactive segments. A brokered await can participate in that live flow while the same queue-async transition still owns a live await session: source dispatch is bounded by pipeline.max-concurrency, completions are recorded durably, and downstream demand decides when accepted completions move into the next step.

Durability takes over when the live session is unavailable. A request may complete later through Kafka, SQS, a webhook, a human/API completion, or a restarted worker. In that path, TPF uses await unit state, execution state, and queue admission rather than a single in-memory demand signal.

For await-heavy pipelines, size the system around two kinds of pressure:

• live segment pressure: step inflight counts, step buffers, pending live await interactions, terminal publish write latency, and source admission;
• durable boundary pressure: pending await interactions, completions waiting for durable fallback continuation, work-queue depth, provider permits, broker lag, retry rate, and DLQ events.

In connector-first CSV Payments, Object Ingest controls source-object admission, the CSV parser advances by reactive demand and the live await in-flight window, and Object Publish accepts terminal chunks through a target session. The old CSV reader demand pacer is a legacy fallback for the deprecated file-step path; it is not the main backpressure mechanism for the connector-owned path.

Retry amplification example (real-world)

When an upstream source can admit work faster than a downstream step can call a slow third-party (avg 250 ms per item), it is easy to misconfigure concurrency and trigger retry amplification.

Observed pattern:

• Retries climb while tpf.step.inflight on the third-party step grows steadily (for example +1,000 every 5 minutes).
• The input step buffer utilization stays below 80%.
• Success rate oscillates around 50% as retries saturate the step.

Mitigations:

• Lower per-step concurrency on the third-party step.
• Increase retry wait/backoff to reduce retry pressure.
• Align source admission, per-step concurrency, and downstream throughput.
• Consider enabling the retry amplification guard in log-only mode first, then switch to fail-fast once you have stable thresholds for inflight slope and retry rate.

Deployment

• Package services as independent deployable units
• Use containerization for consistent deployment
• Configure health checks and readiness probes