# sign_pdfs_optimized — Walkthrough

> Production-refined fork of `sign_pdfs`. Applies 8 improvements at once; meant to be compared side-by-side with the original in the local tester.

The original `sign_pdfs` is the "before" snapshot — it is left untouched. This file (`sign_pdfs_optimized/handler.py`) is the "after": same contract, same output shape, all the rough edges from the original fixed.

---

## Block 1 — Imports and sentinel

```python
import asyncio, json, os, time, uuid
import aioboto3
import aiofiles

_DONE = object()
```

Nothing new at the import level. `_DONE` is a sentinel object used to signal consumers that the producer has finished. Using a unique object (rather than `None` or a string) means no PDF key will ever accidentally match it.

**Original issue fixed here:** the original put a single `_DONE` on the queue regardless of how many consumers were running. With N consumers, one of them would consume the sentinel and exit, but the others would block forever waiting for a key that never arrives. The fix is one sentinel per consumer — that happens in the producer's `finally` block (Block 4).

---

## Block 2 — Config from event (`_cfg`)

```python
def _cfg(event: dict) -> dict:
    return {
        "bucket":      event.get("bucket")       or os.environ.get("BUCKET_NAME", "my-company-reports-bucket"),
        "prefix":      event.get("prefix")        or os.environ.get("PREFIX", "2026/04/"),
        "expiry":      int(event.get("expiry_seconds") or os.environ.get("URL_EXPIRY_SECONDS", "900")),
        "endpoint":    os.environ.get("S3_ENDPOINT_URL") or None,
        "page_size":   int(event.get("page_size")  or 1000),
        "concurrency": int(event.get("concurrency") or 4),
        "queue_max":   int(os.environ.get("QUEUE_MAX", "2000")),
    }
```

**Improvement 1 — event-driven config.** The original hard-coded `bucket`, `prefix`, and `expiry` as module-level constants read once from env vars. That means one function deployment = one bucket/prefix combination. Here the event is the primary source; env vars are fallbacks. The same deployed function can serve `2026/04/`, `2026/05/`, or an entirely different bucket by changing only the event payload — no redeployment.

`endpoint` is env-only because it is infrastructure (the MinIO URL in local dev, absent in production) — callers should never need to specify it per-request.

`queue_max` bounds the in-memory queue so the producer can't buffer unbounded keys before consumers have a chance to drain it. At 1000 keys/page × 2000 queue slots, the producer is at most 2 pages ahead of the consumers.

---

## Block 3 — Structured logging (`_log`) and EMF metrics (`_emit_emf`)

```python
def _log(event_type: str, **fields):
    print(json.dumps({"event": event_type, **fields}))

def _emit_emf(metrics: dict, **dims):
    print(json.dumps({
        "_aws": {
            "Timestamp": int(time.time() * 1000),
            "CloudWatchMetrics": [{
                "Namespace": "eth/sign_pdfs_optimized",
                "Dimensions": [list(dims.keys())],
                "Metrics": [
                    {"Name": k, "Unit": "Bytes" if k.endswith("Bytes") else "Count"}
                    for k in metrics
                ],
            }],
        },
        **dims, **metrics,
    }))
```

**Improvement 2 — structured JSON logging.** The original printed nothing. `_log` emits one JSON object per line to stdout. CloudWatch Logs Insights can then query fields directly:

```
filter event = "complete" | stats avg(duration_ms) by bin(5m)
```

The local runner's `_extract_json_logs` picks up these lines and surfaces them in the tester's "Structured logs" panel.

**Improvement 3 — EMF metrics.** `_emit_emf` writes a CloudWatch Embedded Metrics Format block to stdout. The Lambda runtime (and locally, the runner's `_extract_emf_metrics`) parses this and publishes real CloudWatch metrics — no `PutMetricData` API call, no extra latency, no per-metric cost. The unit is inferred from the metric name: anything ending in `Bytes` → `"Bytes"`, everything else → `"Count"`.

EMF is emitted once at the end of the invocation, not once per item. Putting it inside a loop would flood CloudWatch with thousands of data points per invocation.

---

## Block 4 — Producer with `try/finally`

```python
async def producer():
    nonlocal pages
    try:
        paginator = s3.get_paginator("list_objects_v2")
        async for page in paginator.paginate(
            Bucket=cfg["bucket"], Prefix=cfg["prefix"],
            PaginationConfig={"PageSize": cfg["page_size"]},
        ):
            pages += 1
            for obj in page.get("Contents", []) or []:
                key = obj["Key"]
                if key.lower().endswith(".pdf"):
                    await queue.put(key)
    finally:
        for _ in range(cfg["concurrency"]):
            await queue.put(_DONE)
```

**Improvement 4 — guaranteed consumer exit.** The `finally` block runs whether the producer succeeds, raises a `ClientError`, or is cancelled. Each of the N consumers gets its own `_DONE` sentinel. Without `finally`, an exception in the producer leaves all consumers blocked on `queue.get()` forever — the handler hangs and eventually times out at 15 minutes.

**Improvement 7 — `PageSize=1000`.** S3's `ListObjectsV2` returns at most 1000 keys per page by default, but specifying it explicitly avoids the paginator defaulting to a smaller value on some SDK versions. Fewer pages = fewer round-trip API calls for large prefixes.

---

## Block 5 — Consumer with per-item error handling

```python
async def consumer():
    nonlocal errors
    local = 0
    while True:
        item = await queue.get()
        if item is _DONE:
            return local
        try:
            url = await s3.generate_presigned_url(
                "get_object",
                Params={"Bucket": cfg["bucket"], "Key": item},
                ExpiresIn=cfg["expiry"],
            )
            line = json.dumps({"key": item, "url": url}) + "\n"
            async with write_lock:
                await f.write(line)
            local += 1
        except Exception as exc:
            errors += 1
            _log("presign_error", request_id=request_id, key=item, error=str(exc))
```

**Improvement 5 — per-item error isolation.** The original let a single `generate_presigned_url` failure propagate out of the consumer and crash the whole batch. Here, each item is wrapped in its own `try/except`. A bad key increments `errors` and logs the details; the consumer continues with the next key. The caller receives `{"count": N, "errors": M}` and can decide whether to retry the M failures.

`local` counts successful presigns per consumer. The N return values are summed after `asyncio.gather` to get the total count.

The `write_lock` serialises writes to the shared JSONL file — without it, concurrent `await f.write(line)` calls would interleave partial lines.

---

## Block 6 — N concurrent consumers (`asyncio.gather`)

```python
prod_task = asyncio.create_task(producer())
counts = await asyncio.gather(*(consumer() for _ in range(cfg["concurrency"])))
await prod_task
```

**Improvement 8 — concurrent consumers.** The original ran one consumer. Here `asyncio.gather` starts N consumer coroutines simultaneously, all reading from the same queue. Since `generate_presigned_url` is an async S3 API call, each consumer yields while waiting for the response, letting the others proceed. With N=4 and 1000 PDFs, peak in-flight presign requests is 4 instead of 1 — throughput roughly scales with `concurrency` up to the point where the producer can't keep the queue full.

`asyncio.create_task(producer())` schedules the producer to run concurrently with the consumers rather than sequentially before them. The `await prod_task` after gather ensures any producer exception is re-raised (not silently swallowed as it would be in a fire-and-forget task).

---

## Block 7 — Streaming manifest upload

```python
manifest_bytes = os.path.getsize(manifest_path)
manifest_key = f"manifests/{uuid.uuid4()}.jsonl"

with open(manifest_path, "rb") as f:
    await s3.put_object(
        Bucket=cfg["bucket"], Key=manifest_key, Body=f,
        ContentType="application/x-ndjson",
    )
```

**Improvement 6 — streaming upload.** The original read the entire manifest into memory with `await f.read()` before uploading. For a bucket with 100k PDFs, the JSONL manifest could be tens of MB — a full `read()` doubles peak RAM usage. Passing a sync file handle as `Body` lets aiobotocore read the file in chunks internally. Memory usage stays flat regardless of manifest size.

`os.path.getsize` is called before opening the file for upload so the size is available for the EMF block even after the file is deleted.

---

## Block 8 — Cleanup, metrics, and response

```python
os.unlink(manifest_path)

result = {
    "count": count,
    "errors": errors,
    "manifest_key": manifest_key,
    "manifest_url": manifest_url,
}
response_bytes = len(json.dumps(result))
duration_ms = (time.monotonic() - t0) * 1000

_log("complete", request_id=request_id, count=count, errors=errors,
     pages=pages, duration_ms=round(duration_ms, 2))
_emit_emf({
    "PDFsProcessed": count, "S3ListPages": pages,
    "PresignCount": count, "ManifestBytes": manifest_bytes,
    "ResponseBytes": response_bytes,
}, Function="sign_pdfs_optimized")

return result
```

`os.unlink` removes the temp file after upload. `/tmp` is shared across warm invocations — not cleaning up leaks storage toward the 512 MB (default) or 10 GB (max) `/tmp` cap.

`response_bytes = len(json.dumps(result))` captures how large the sync response would be. Lambda's synchronous response limit is 6 MB. This function returns a URL, not a body, so `response_bytes` is typically under 300 bytes — but measuring it makes the headroom concrete.

The final `_log("complete", ...)` and `_emit_emf(...)` emit together at the end so all counts are final. In the tester, the Structured logs panel shows the `complete` event and the EMF panel shows the five metric values.

---

## Block 9 — Entrypoint

```python
def handler(event, context):
    request_id = getattr(context, "aws_request_id", str(uuid.uuid4()))
    result = asyncio.run(_run(event, request_id))
    return {"statusCode": 200, "body": json.dumps(result)}
```

`asyncio.run` creates a fresh event loop for each invocation. Lambda's Python runtime is synchronous at the handler boundary; `asyncio.run` is the correct bridge between the sync entrypoint and the async internals. A module-level `loop = asyncio.get_event_loop()` would work on the first invocation but is deprecated and unreliable across warm invocations.

`getattr(context, "aws_request_id", ...)` falls back to a generated UUID when the context object doesn't have the attribute — which is the case in the local tester, where the runner passes a minimal stub.

---

## Summary of improvements

| # | What changed | Why it matters |
|---|---|---|
| 1 | Config from event, env as fallback | One function serves many prefixes |
| 2 | Structured JSON logging (`_log`) | Queryable in Logs Insights; visible in tester |
| 3 | EMF metrics (`_emit_emf`) | Free CloudWatch metrics, no API call overhead |
| 4 | Producer `try/finally` with N sentinels | Consumers always exit; handler never hangs |
| 5 | Per-item `try/except` in consumer | One bad key doesn't crash the batch |
| 6 | Sync file handle as `Body` | Flat memory for any manifest size |
| 7 | `PageSize=1000` | Fewer S3 round-trips on large prefixes |
| 8 | N concurrent consumers via `asyncio.gather` | Presign throughput scales with `concurrency` |