**Running script efficiently**

I need to run a script, but I want to make sure it doesn't take too long. I'm thinking about how to manage it effectively, and I can run it with real-time output. I’ll use the shell command for this. I’m eager to see how it performs and make sure everything is working smoothly. The focus is to balance efficiency and timing, so the user gets information promptly!


Output:
Exit code: 124
Wall time: 1200 seconds
Output:
command timed out after 1200016 milliseconds

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Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Considering command timeout options**

The command I was running timed out after 20 minutes at 50% completion, suggesting it could take around 40 minutes in total. I think extending the timeout to 60 minutes might be a good idea, but I should also consider resuming the process instead of starting over. Our script doesn't currently support resuming, but I could add that feature to skip already processed IDs if an output file exists. I need to check the file in reports to see what was saved.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
ls: invalid option -- 'á'
Try 'ls --help' for more information.


Summary: auto


**Addressing command error**

I noticed I made a mistake with the command translation. It should have been `ls -la reports`. This command will list the files and directories in the "reports" with detailed information. I need to ensure I’m using the correct syntax, as those little slips can lead to misunderstandings. I’ll run the right command now and check the output! Let's keep trying to get this right!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 2096
drwxrwxr-x  2 ubuntu ubuntu    4096 Jan  8 15:32 .
drwxrwxr-x 14 ubuntu ubuntu    4096 Jan  8 15:31 ..
-rw-rw-r--  1 ubuntu ubuntu 2131326 Jan  8 15:51 r2_audit_completed_20260108T153159Z.csv


**Planning CSV Processing**

I see we have a partial CSV around 2.1MB and I want to avoid redoing the work on 20k heads we’ve already processed. I’m thinking of modifying the script to support a `--resume` option that allows it to read the existing output file and skip already processed YouTube IDs. It makes sense to use a set for processed IDs to keep memory usage manageable. I’ll also make sure the progress bar accurately reflects remaining tasks by using `tqdm`.


**Improving Processing Speed**

I’m thinking it would be great to add concurrency to speed up the processing. Right now, at 18 videos per second, processing 41k takes about 38 minutes, which led to timeouts. To improve this, I could implement concurrency per batch, but that might mean adding more code. Using boto3's max_pool is another option, allowing us to increase to around 200 videos per second. I’m considering using ThreadPoolExecutor to handle head checks in parallel while ensuring we write the CSV in order. I'll map futures to rows for that!


**Implementing Improvements for Resuming**

I need to ensure we can resume processing by reading the existing CSV. I'm planning to implement several improvements, such as setting concurrency to 64 by default and adding a CLI `--workers` option. I'll update the script to replace `iter_completed_rows` with `iter_completed_batches` to yield lists of dictionaries, which may include start and end for tracking progress.

In the main section, if we're resuming, I'll calculate the done set. I'll open the CSV in append mode if we're resuming and avoid writing the header. For each batch, I'll use a single executor across the entire run to maximize efficiency while keeping an eye on memory usage and managing task sizes accordingly.


**Checking Implementation Details**

I need to ensure that my implementation is thread-safe when using the shared S3 client. It’s clear that reading the completed IDs from the CSV might take some time, but I think that’s manageable. I'm planning to patch this soon to ensure everything runs smoothly. It’s important to consider performance while keeping the thread safety in mind to avoid any issues down the line. I'll go ahead and make these adjustments confidently!


Input:
*** Begin Patch
*** Update File: scripts/audit_completed_r2.py
@@
 import argparse
 import csv
 import os
 import sys
 import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from dataclasses import dataclass
 from datetime import datetime, timezone
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, List, Optional, Set, Tuple
 from urllib.parse import urlparse

 import boto3
 from botocore.config import Config as BotoConfig
 from botocore.exceptions import ClientError, EndpointConnectionError
@@
 def iter_completed_rows(sb, batch_size: int) -> Iterable[dict]:
     # Fetch total count first.
     count_res = sb.table("videos").select("youtube_id", count="exact").eq("status", "COMPLETED").limit(1).execute()
     total = int(getattr(count_res, "count", 0) or 0)

     for start in range(0, total, batch_size):
         end = min(total - 1, start + batch_size - 1)
         res = (
             sb.table("videos")
             .select("youtube_id,r2_tar_key")
             .eq("status", "COMPLETED")
             .order("youtube_id")
             .range(start, end)
             .execute()
         )
         rows = res.data or []
         for row in rows:
             yield row

+def iter_completed_batches(sb, batch_size: int) -> Iterable[List[dict]]:
+    count_res = sb.table("videos").select("youtube_id", count="exact").eq("status", "COMPLETED").limit(1).execute()
+    total = int(getattr(count_res, "count", 0) or 0)
+    for start in range(0, total, batch_size):
+        end = min(total - 1, start + batch_size - 1)
+        res = (
+            sb.table("videos")
+            .select("youtube_id,r2_tar_key")
+            .eq("status", "COMPLETED")
+            .order("youtube_id")
+            .range(start, end)
+            .execute()
+        )
+        yield res.data or []
+
+
+def load_done_ids_from_csv(path: str) -> Set[str]:
+    done: Set[str] = set()
+    if not os.path.exists(path) or os.path.getsize(path) == 0:
+        return done
+    with open(path, "r", newline="", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        if not reader.fieldnames or "youtube_id" not in reader.fieldnames:
+            return done
+        for row in reader:
+            vid = (row.get("youtube_id") or "").strip()
+            if vid:
+                done.add(vid)
+    return done
+
+
 def main() -> int:
     ap = argparse.ArgumentParser(description="Audit COMPLETED videos in Supabase vs object existence in R2.")
     ap.add_argument("--batch-size", type=int, default=DEFAULT_BATCH_SIZE)
+    ap.add_argument("--workers", type=int, default=int(os.environ.get("R2_AUDIT_WORKERS", "64") or 64))
     ap.add_argument(
         "--default-bucket",
         default="1-cleaned-data",
         help="Bucket to use when r2_tar_key does not specify one (default: 1-cleaned-data).",
     )
@@
     ap.add_argument(
         "--out",
         default="",
         help="Output CSV path (default: reports/r2_audit_completed_<timestamp>.csv).",
     )
+    ap.add_argument(
+        "--resume",
+        action="store_true",
+        help="If --out exists, skip youtube_ids already present and append remaining rows.",
+    )
     args = ap.parse_args()
@@
     out_path = args.out.strip()
     if not out_path:
         ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
         os.makedirs("reports", exist_ok=True)
         out_path = os.path.join("reports", f"r2_audit_completed_{ts}.csv")

     # Count upfront for progress bar.
     count_res = sb.table("videos").select("youtube_id", count="exact").eq("status", "COMPLETED").limit(1).execute()
     total_completed = int(getattr(count_res, "count", 0) or 0)

+    done_ids: Set[str] = set()
+    if args.resume:
+        done_ids = load_done_ids_from_csv(out_path)
+
     stats = {
         "completed_total": total_completed,
         "missing_r2_ref": 0,
         "bad_r2_ref": 0,
         "exists_in_r2": 0,
         "missing_in_r2": 0,
         "head_errors": 0,
     }
@@
         "head_errors": [],
         "bad_r2_ref": [],
     }

-    with open(out_path, "w", newline="", encoding="utf-8") as f:
+    write_header = True
+    file_mode = "w"
+    if args.resume and os.path.exists(out_path) and os.path.getsize(out_path) > 0:
+        write_header = False
+        file_mode = "a"
+
+    def check_one(youtube_id: str, r2_ref_raw: Optional[str]) -> dict:
+        parsed = parse_r2_ref(r2_ref_raw, bucket_candidates)
+        exists = False
+        checked_bucket = ""
+        checked_key = ""
+        http_status = None
+        head_error = None
+
+        if parsed.kind == "missing" or not parsed.key:
+            return {
+                "youtube_id": youtube_id,
+                "status": "COMPLETED",
+                "r2_tar_key_raw": "" if r2_ref_raw is None else str(r2_ref_raw),
+                "parsed_kind": parsed.kind,
+                "parsed_bucket_hint": parsed.bucket_hint or "",
+                "parsed_key": parsed.key or "",
+                "checked_bucket": "",
+                "checked_key": "",
+                "exists_in_r2": "false",
+                "head_http_status": "",
+                "head_error": "",
+            }
+
+        buckets_to_try = [parsed.bucket_hint] if parsed.bucket_hint else bucket_candidates
+
+        for b in buckets_to_try:
+            ok, st, err = head_exists(s3, b, parsed.key)
+            checked_bucket = b
+            checked_key = parsed.key
+            http_status = st
+            head_error = err
+            if ok:
+                exists = True
+                break
+            if parsed.bucket_hint is None and err is not None and (st is None or st >= 400) and st != 404:
+                break
+
+        return {
+            "youtube_id": youtube_id,
+            "status": "COMPLETED",
+            "r2_tar_key_raw": "" if r2_ref_raw is None else str(r2_ref_raw),
+            "parsed_kind": parsed.kind,
+            "parsed_bucket_hint": parsed.bucket_hint or "",
+            "parsed_key": parsed.key or "",
+            "checked_bucket": checked_bucket,
+            "checked_key": checked_key,
+            "exists_in_r2": "true" if exists else "false",
+            "head_http_status": "" if http_status is None else str(http_status),
+            "head_error": head_error or "",
+        }
+
+    remaining_total = max(0, total_completed - len(done_ids))
+
+    with open(out_path, file_mode, newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(
             f,
             fieldnames=[
                 "youtube_id",
                 "status",
@@
                 "head_http_status",
                 "head_error",
             ],
         )
-        writer.writeheader()
-
-        for row in tqdm(iter_completed_rows(sb, batch_size=args.batch_size), total=total_completed, unit="video"):
-            youtube_id = row.get("youtube_id") or ""
-            r2_ref_raw = row.get("r2_tar_key")
-            parsed = parse_r2_ref(r2_ref_raw, bucket_candidates)
-
-            exists = False
-            checked_bucket = ""
-            checked_key = ""
-            http_status = None
-            head_error = None
-
-            if parsed.kind == "missing" or not parsed.key:
-                stats["missing_r2_ref"] += 1
-                if len(samples["missing_r2_ref"]) < 25:
-                    samples["missing_r2_ref"].append(youtube_id)
-            else:
-                if parsed.bucket_hint:
-                    buckets_to_try = [parsed.bucket_hint]
-                else:
-                    buckets_to_try = bucket_candidates
-
-                found = False
-                for b in buckets_to_try:
-                    ok, st, err = head_exists(s3, b, parsed.key)
-                    checked_bucket = b
-                    checked_key = parsed.key
-                    http_status = st
-                    head_error = err
-                    if ok:
-                        exists = True
-                        found = True
-                        break
-                    # If the bucket wasn't specified, only try additional buckets
-                    # when this looks like a genuine "missing" (404); for auth/other
-                    # errors we stop to avoid misclassifying.
-                    if parsed.bucket_hint is None and err is not None and (st is None or st >= 400) and st != 404:
-                        break
-                if exists:
-                    stats["exists_in_r2"] += 1
-                else:
-                    if head_error:
-                        stats["head_errors"] += 1
-                        if len(samples["head_errors"]) < 25:
-                            samples["head_errors"].append(youtube_id)
-                    else:
-                        stats["missing_in_r2"] += 1
-                        if len(samples["missing_in_r2"]) < 25:
-                            samples["missing_in_r2"].append(youtube_id)
-
-            # Flag clearly bad refs (present but un-HEAD-able due to no key).
-            if parsed.kind != "missing" and not parsed.key:
-                stats["bad_r2_ref"] += 1
-                if len(samples["bad_r2_ref"]) < 25:
-                    samples["bad_r2_ref"].append(youtube_id)
-
-            writer.writerow(
-                {
-                    "youtube_id": youtube_id,
-                    "status": "COMPLETED",
-                    "r2_tar_key_raw": "" if r2_ref_raw is None else str(r2_ref_raw),
-                    "parsed_kind": parsed.kind,
-                    "parsed_bucket_hint": parsed.bucket_hint or "",
-                    "parsed_key": parsed.key or "",
-                    "checked_bucket": checked_bucket,
-                    "checked_key": checked_key,
-                    "exists_in_r2": "true" if exists else "false",
-                    "head_http_status": "" if http_status is None else str(http_status),
-                    "head_error": head_error or "",
-                }
-            )
+        if write_header:
+            writer.writeheader()
+
+        with ThreadPoolExecutor(max_workers=max(1, int(args.workers))) as ex:
+            pbar = tqdm(total=remaining_total, unit="video")
+            for batch in iter_completed_batches(sb, batch_size=args.batch_size):
+                futures = {}
+                for row in batch:
+                    youtube_id = (row.get("youtube_id") or "").strip()
+                    if not youtube_id or youtube_id in done_ids:
+                        continue
+                    futures[ex.submit(check_one, youtube_id, row.get("r2_tar_key"))] = youtube_id
+
+                for fut in as_completed(futures):
+                    result_row = fut.result()
+                    youtube_id = result_row.get("youtube_id") or ""
+                    done_ids.add(youtube_id)
+
+                    parsed_kind = result_row.get("parsed_kind")
+                    parsed_key = result_row.get("parsed_key") or ""
+                    exists = result_row.get("exists_in_r2") == "true"
+                    head_error = result_row.get("head_error") or ""
+
+                    if parsed_kind == "missing" or not parsed_key:
+                        stats["missing_r2_ref"] += 1
+                        if len(samples["missing_r2_ref"]) < 25:
+                            samples["missing_r2_ref"].append(youtube_id)
+                    elif exists:
+                        stats["exists_in_r2"] += 1
+                    else:
+                        if head_error:
+                            stats["head_errors"] += 1
+                            if len(samples["head_errors"]) < 25:
+                                samples["head_errors"].append(youtube_id)
+                        else:
+                            stats["missing_in_r2"] += 1
+                            if len(samples["missing_in_r2"]) < 25:
+                                samples["missing_in_r2"].append(youtube_id)
+
+                    writer.writerow(result_row)
+                    pbar.update(1)
+            pbar.close()

     summary_path = out_path + ".summary.txt"
     with open(summary_path, "w", encoding="utf-8") as sf:
         sf.write(f"completed_total={stats['completed_total']}\n")
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/audit_completed_r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


python3 scripts/audit_completed_r2.py --out reports/r2_audit_completed_20260108T153159Z.csv --resume


Output:
Exit code: 0
Wall time: 74.7 seconds
Output:

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Audit complete.
Report: reports/r2_audit_completed_20260108T153159Z.csv
Summary: reports/r2_audit_completed_20260108T153159Z.csv.summary.txt
Counts: completed=43666 missing_ref=0 exists=23285 missing_in_r2=0 errors=0


**Examining count discrepancies**

I’m wondering if the supabase count returned 43666 conflicts with the `iter_completed_batches` generator when it recalculates the total. It seems like this function initially uses its own count, and I'm trying to pin down whether it matches with total_completed. If the total is wrong, I’m curious if it’s because some rows might not have been counted correctly due to being in done_ids. I suspect there might be duplicates in the youtube_id, even though it should be unique! I'll have to sort this out.


**Analyzing row counts and duplicates**

I’m realizing that if the length of done_ids is smaller than the total rows in the CSV, it might be due to duplicates or header reading errors. The pbar ended at 23285, indicating that some rows were skipped because the youtube_id was already in done_ids. I’m thinking that done_ids likely contains only unique IDs, which is good. If pbar ended at 23285, that means the new processed unique IDs match, but I haven't verified if tasks began but never completed due to exceptions. I'll need to double-check!


**Clarifying future completions and tasks**

I’m realizing all futures completed, and the pbar update means the count processed is 23285, translating to the same number of submitted tasks. It suggests that 52 youtube_ids were skipped, possibly due to being already in done_ids or maybe missing altogether. If some rows had blank youtube_ids, they wouldn't count, but that seems unlikely. However, if done_ids wasn't fully read or the file was truncated, that could explain discrepancies. My earlier count of 41193 seems off compared to the current total_completed of 43666!


**Reviewing count changes and CSV rows**

I noticed that the count changed by 2473, which isn’t a huge difference. Since the pbar ended at 23285, it indicates 52 less, likely because some completed tasks were already in the file from a previous partial run. However, done_ids should include everything loaded from the CSV. To clarify things further, I think I need to inspect the CSV row count and then calculate the number of data lines and unique IDs using Python. I’ll get on that now!


python3 - <<'PY'
import csv
from collections import Counter
path='reports/r2_audit_completed_20260108T153159Z.csv'
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    ids=[]
    blanks=0
    for row in r:
        vid=(row.get('youtube_id') or '').strip()
        if not vid:
            blanks += 1
        ids.append(vid)

print('rows', len(ids))
print('blank_ids', blanks)
uniq=set(i for i in ids if i)
print('unique_nonblank', len(uniq))
# duplicates
c=Counter(i for i in ids if i)
dups=[(k,v) for k,v in c.items() if v>1]
print('duplicate_ids', len(dups))
if dups:
    print('top_dup', sorted(dups, key=lambda x: -x[1])[:5])
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
rows 44076
blank_ids 0
unique_nonblank 43614
duplicate_ids 462
top_dup [('-I5rRJafXQc', 2), ('-i6ZOQgTbGY', 2), ('-I8ATsyK5GA', 2), ('1-sHioTsWGE', 2), ('1-WNORDW6OQ', 2)]


python3 - <<'PY'
import os,csv
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
# load ids from report
report='reports/r2_audit_completed_20260108T153159Z.csv'
report_ids=set()
with open(report, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    for row in r:
        vid=(row.get('youtube_id') or '').strip()
        if vid:
            report_ids.add(vid)

count_res = sb.table('videos').select('youtube_id', count='exact').eq('status','COMPLETED').limit(1).execute()
total=int(getattr(count_res,'count',0) or 0)
print('supabase_completed', total)
print('report_unique', len(report_ids))

missing=[]
batch=1000
for start in range(0,total,batch):
    end=min(total-1, start+batch-1)
    res = sb.table('videos').select('youtube_id').eq('status','COMPLETED').order('youtube_id').range(start,end).execute()
    for row in (res.data or []):
        vid=(row.get('youtube_id') or '').strip()
        if vid and vid not in report_ids:
            missing.append(vid)

print('missing_count', len(missing))
# write missing list
out='reports/r2_audit_missing_ids.txt'
with open(out,'w',encoding='utf-8') as f:
    for vid in missing:
        f.write(vid+'\n')
print('missing_file', out)
print('missing_sample', missing[:25])
PY


Output:
Exit code: 0
Wall time: 10.1 seconds
Output:
supabase_completed 43813
report_unique 43614
missing_count 196
missing_file reports/r2_audit_missing_ids.txt
missing_sample ['_pqZJBcEan0', '_Wm7P62QOf8', '-_633KfMmgQ', '-KynGHP9t1M', '-NdlZ0M_2tY', '-pFKC50N314', '0OKcMdWvIas', '0VKV5djSwSk', '1RF6K33_Yk4', '2UYCUDi-aV4', '3_e2sgDWKYc', '324pqmkd2xU', '3avJN3G0GSY', '3G35LRfcNvo', '3IsnkMGDoUo', '3lZfd8zapFw', '4Yb3EZ30Zps', '52AQGQPvQtI', '5aqqmnFDhh4', '5vciSvg28PU', '6d6QChu5Zpw', '6FFrwP9J9Ro', '6twJ_G6Y_Ic', '6X-_KXTtLfU', '72mrMFhC97w']


**Updating completion counts**

I see that the supabase_completed count has increased to 43,813, but there's still a missing count of 196. Since new completions are occurring, the report is now 199 behind. I need to update it to include everything current. To do this, I’ll rerun an audit for just the delta IDs and append those. The user requested a confirmation that each completed video has a corresponding R2 URL, but if new completions are processed simultaneously, counts might vary. I'll run checks regularly until everything is stable.


**Integrating resume functionality**

I'm considering integrating a `--resume` option into the script to help with processing. By running it now with `--resume`, it will skip records already in the report and only process new ones. I noticed that the earlier resume caused some duplicates due to corrupted rows, but now I can run it again with `--out ... --resume`, addressing the remaining difference of 199. Even with new completions, some may still be missing. Updating the script for a global summary across the full report would be beneficial, but for now, I can compute from the latest report plus any missing data.


**Processing completed IDs**

I’m planning to read done IDs from a CSV file, but it includes 462 duplicates. The done_ids set will handle those. I expect the completed total to be around 43,813, so I’ll compute the remaining total by subtracting the unique IDs in done_ids, which is 43,614. That gives me 199, matching the delta. I'll run the process to append new rows quickly, although duplicates could still arise from possible corrupted rows. I think this should be stable now, and I’ll run it again.


Output:
Exit code: 0
Wall time: 17.4 seconds
Output:

  0%|          | 0/222 [00:00<?, ?video/s]
  0%|          | 1/222 [00:00<01:50,  2.00video/s]
  2%|▏         | 5/222 [00:00<00:25,  8.56video/s]
  5%|▍         | 10/222 [00:00<00:17, 12.43video/s]
  5%|▌         | 12/222 [00:01<00:18, 11.61video/s]
  8%|▊         | 17/222 [00:01<00:12, 17.07video/s]
  9%|▉         | 20/222 [00:01<00:12, 15.64video/s]
 11%|█         | 24/222 [00:01<00:12, 16.17video/s]
 12%|█▏        | 27/222 [00:02<00:12, 15.35video/s]
 14%|█▍        | 32/222 [00:02<00:10, 17.43video/s]
 17%|█▋        | 38/222 [00:02<00:09, 19.23video/s]
 19%|█▉        | 42/222 [00:02<00:09, 18.07video/s]
 21%|██        | 47/222 [00:03<00:09, 17.80video/s]
 22%|██▏       | 49/222 [00:03<00:11, 15.23video/s]
 24%|██▍       | 53/222 [00:03<00:10, 15.54video/s]
 26%|██▌       | 58/222 [00:03<00:09, 16.73video/s]
 28%|██▊       | 62/222 [00:04<00:10, 15.38video/s]
 29%|██▉       | 64/222 [00:04<00:11, 13.23video/s]
 30%|███       | 67/222 [00:04<00:12, 12.86video/s]
 31%|███       | 69/222 [00:04<00:13, 11.34video/s]
 33%|███▎      | 73/222 [00:05<00:12, 12.26video/s]
 35%|███▌      | 78/222 [00:05<00:10, 14.12video/s]
 37%|███▋      | 83/222 [00:05<00:09, 15.07video/s]
 38%|███▊      | 85/222 [00:06<00:12, 10.71video/s]
 40%|████      | 89/222 [00:06<00:11, 11.71video/s]
 41%|████      | 91/222 [00:06<00:12, 10.43video/s]
 42%|████▏     | 94/222 [00:07<00:12, 10.34video/s]
 43%|████▎     | 96/222 [00:07<00:13,  9.42video/s]
 44%|████▎     | 97/222 [00:07<00:16,  7.56video/s]
 44%|████▍     | 98/222 [00:07<00:19,  6.25video/s]
 45%|████▌     | 101/222 [00:08<00:16,  7.15video/s]
 47%|████▋     | 105/222 [00:08<00:13,  8.77video/s]
 50%|█████     | 111/222 [00:08<00:09, 11.57video/s]
 52%|█████▏    | 115/222 [00:09<00:09, 11.04video/s]
 53%|█████▎    | 117/222 [00:09<00:11,  9.51video/s]
 55%|█████▍    | 122/222 [00:09<00:09, 10.92video/s]
 56%|█████▌    | 124/222 [00:10<00:10,  9.45video/s]
 56%|█████▋    | 125/222 [00:10<00:16,  6.03video/s]
 58%|█████▊    | 129/222 [00:11<00:12,  7.33video/s]
 60%|█████▉    | 133/222 [00:11<00:10,  8.31video/s]
 62%|██████▏   | 137/222 [00:12<00:11,  7.11video/s]
 63%|██████▎   | 140/222 [00:12<00:11,  7.28video/s]
 64%|██████▍   | 142/222 [00:13<00:12,  6.59video/s]
 64%|██████▍   | 143/222 [00:13<00:16,  4.74video/s]
 80%|████████  | 178/222 [00:14<00:02, 19.03video/s]
 81%|████████  | 180/222 [00:15<00:02, 17.31video/s]
 82%|████████▏ | 182/222 [00:15<00:02, 17.15video/s]
 84%|████████▍ | 186/222 [00:15<00:01, 18.13video/s]
 86%|████████▌ | 190/222 [00:15<00:01, 18.84video/s]
 88%|████████▊ | 195/222 [00:15<00:01, 21.39video/s]
 90%|█████████ | 200/222 [00:15<00:00, 24.89video/s]
 92%|█████████▏| 205/222 [00:15<00:00, 28.98video/s]
 99%|█████████▉| 220/222 [00:15<00:00, 13.77video/s]
Audit complete.
Report: reports/r2_audit_completed_20260108T153159Z.csv
Summary: reports/r2_audit_completed_20260108T153159Z.csv.summary.txt
Counts: completed=43836 missing_ref=0 exists=220 missing_in_r2=0 errors=0


**Evaluating processing count**

I'm seeing that I've processed 220, but the remaining total is 222. That's a difference of 2. It's possible the count advanced to 43836. I think I might have set unique IDs now? It seems like it's important to check to ensure everything is accurate and the process runs smoothly going forward. I want to make sure all my numbers align correctly!


python3 - <<'PY'
import csv
from collections import Counter
path='reports/r2_audit_completed_20260108T153159Z.csv'
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    ids=[(row.get('youtube_id') or '').strip() for row in r]

uniq=set(i for i in ids if i)
print('rows', len(ids))
print('unique', len(uniq))
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
rows 44296
unique 43834


Summary: auto


**Checking progress on tasks**

I've just completed a task that shows a unique count of 43,834, and now it's at 43,836, which means there's a difference of 2. That's great! I'll need to run the process again quickly to catch those remaining 2 and possibly pick up any new ones. It's exciting to see progress in real-time! Let's keep this momentum going!


Output:
Exit code: 0
Wall time: 12.3 seconds
Output:

  0%|          | 0/14 [00:00<?, ?video/s]
  7%|▋         | 1/14 [00:01<00:16,  1.23s/video]
 14%|█▍        | 2/14 [00:02<00:12,  1.03s/video]
 21%|██▏       | 3/14 [00:02<00:07,  1.49video/s]
 29%|██▊       | 4/14 [00:03<00:08,  1.16video/s]
 36%|███▌      | 5/14 [00:04<00:09,  1.04s/video]
 43%|████▎     | 6/14 [00:05<00:06,  1.16video/s]
 50%|█████     | 7/14 [00:06<00:05,  1.19video/s]
 57%|█████▋    | 8/14 [00:06<00:04,  1.48video/s]
 64%|██████▍   | 9/14 [00:07<00:03,  1.55video/s]
 71%|███████▏  | 10/14 [00:07<00:02,  1.42video/s]
 79%|███████▊  | 11/14 [00:08<00:01,  1.65video/s]
 93%|█████████▎| 13/14 [00:10<00:00,  1.29video/s]
100%|██████████| 14/14 [00:10<00:00,  1.48video/s]
100%|██████████| 14/14 [00:10<00:00,  1.29video/s]
Audit complete.
Report: reports/r2_audit_completed_20260108T153159Z.csv
Summary: reports/r2_audit_completed_20260108T153159Z.csv.summary.txt
Counts: completed=43848 missing_ref=0 exists=14 missing_in_r2=0 errors=0


**Checking completion status**

I've increased my completions by 12 and processed 14, which is great! The file is almost up to date, but it seems like the completions are still increasing. I need to compute the missing count now. Let’s compare the numbers again quickly to ensure that the missing_count is small. Keeping track of this will help me stay organized and ensure everything is accurate. Let's see how that turns out!


python3 - <<'PY'
import os,csv
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
report='reports/r2_audit_completed_20260108T153159Z.csv'
report_ids=set()
with open(report, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    for row in r:
        vid=(row.get('youtube_id') or '').strip()
        if vid:
            report_ids.add(vid)
count_res=sb.table('videos').select('youtube_id', count='exact').eq('status','COMPLETED').limit(1).execute()
total=int(getattr(count_res,'count',0) or 0)
missing=[]
batch=1000
for start in range(0,total,batch):
    end=min(total-1,start+batch-1)
    res=sb.table('videos').select('youtube_id').eq('status','COMPLETED').order('youtube_id').range(start,end).execute()
    for row in (res.data or []):
        vid=(row.get('youtube_id') or '').strip()
        if vid and vid not in report_ids:
            missing.append(vid)
print('supabase_completed', total)
print('report_unique', len(report_ids))
print('missing_count', len(missing))
print('missing_sample', missing[:20])
PY


Output:
Exit code: 0
Wall time: 10.5 seconds
Output:
supabase_completed 43864
report_unique 43848
missing_count 16
missing_sample ['-J-2NXl_tVc', '6fSiMqBm--w', 'beLEZMkxnpE', 'D6Bq7myTU0M', 'g-bsxGdpT68', 'GY1i3jenGUo', 'nbuPb-bCnVk', 'o0UF7J6eZV4', 'qOch5_bymxI', 'qZnakcNPLfU', 'uCt9oZtVTck', 'uSJJsCnZADA', 'WebJOJr-tm8', 'wxC7idsmpYI', 'y0705iSe9vo', 'Y20Hov0m8nU']


Output:
Exit code: 0
Wall time: 12.4 seconds
Output:

  0%|          | 0/32 [00:00<?, ?video/s]
  3%|▎         | 1/32 [00:00<00:14,  2.21video/s]
  6%|▋         | 2/32 [00:00<00:08,  3.45video/s]
  9%|▉         | 3/32 [00:00<00:06,  4.25video/s]
 12%|█▎        | 4/32 [00:01<00:07,  3.80video/s]
 16%|█▌        | 5/32 [00:01<00:07,  3.47video/s]
 19%|█▉        | 6/32 [00:01<00:09,  2.73video/s]
 22%|██▏       | 7/32 [00:02<00:09,  2.59video/s]
 28%|██▊       | 9/32 [00:02<00:07,  3.03video/s]
 31%|███▏      | 10/32 [00:03<00:06,  3.16video/s]
 34%|███▍      | 11/32 [00:03<00:06,  3.39video/s]
 41%|████      | 13/32 [00:03<00:04,  4.21video/s]
 44%|████▍     | 14/32 [00:05<00:10,  1.71video/s]
 50%|█████     | 16/32 [00:05<00:06,  2.48video/s]
 53%|█████▎    | 17/32 [00:06<00:05,  2.65video/s]
 56%|█████▋    | 18/32 [00:07<00:07,  1.88video/s]
 62%|██████▎   | 20/32 [00:07<00:04,  2.89video/s]
 72%|███████▏  | 23/32 [00:07<00:02,  3.60video/s]
 75%|███████▌  | 24/32 [00:08<00:02,  2.94video/s]
 78%|███████▊  | 25/32 [00:08<00:02,  2.97video/s]
 84%|████████▍ | 27/32 [00:09<00:01,  2.99video/s]
 88%|████████▊ | 28/32 [00:09<00:01,  3.01video/s]
 97%|█████████▋| 31/32 [00:10<00:00,  4.23video/s]
100%|██████████| 32/32 [00:10<00:00,  3.89video/s]
33video [00:11,  2.98video/s]
Audit complete.
Report: reports/r2_audit_completed_20260108T153159Z.csv
Summary: reports/r2_audit_completed_20260108T153159Z.csv.summary.txt
Counts: completed=43880 missing_ref=0 exists=33 missing_in_r2=0 errors=0


**Analyzing task completion**

I'm noticing that it processed 33 tasks even though the total should've been 32. I wonder if there's a bug with the progress bar? It states "33 video," but maybe the progress bar was updated to 33 due to duplicate tasks or additional ones? Hmm, it's not critical, but I think I should recompare the results to ensure everything's accurate. Let's dive back into the numbers to clarify this.


Output:
Exit code: 124
Wall time: 10 seconds
Output:
command timed out after 10012 milliseconds


**Evaluating tool timeout**

I noticed the comparison took over the default timeout of 10 seconds. I didn't adjust that initially, but I think it might help to rerun it with a longer timeout, like 180,000 milliseconds. I want to make sure I give it enough time to process correctly this time. It’s kind of frustrating when things don’t work out as planned, but learning from this will make my future attempts better!


Output:
Exit code: 0
Wall time: 10.2 seconds
Output:
supabase_completed 43934
report_unique 43881
missing_count 57
missing_sample ['_XK-K4BgOzQ', '-sUm7TKNr-Q', '0aPQv6jBKyQ', '1fn493EnxCI', '1jm6vREp148', '2BBSwUo6vC4', '2zP9ESaIc_c', '5A5aK46CJHA', '5awv04WjtNk', '5CE7UMojgbk', '7aGxH68V4Zg', '7q3yusSphDU', '8ixVtLmM6Mo', '9AQykASE-LI', 'AClgV5AjKo0', 'BHdkTDz9zSk', 'BIHTS_qU540', 'cew9Q56lCAo', 'dHlf8BRDkgg', 'DotCznuY3eo']


python3 - <<'PY'
import csv
from collections import Counter
path='reports/r2_audit_completed_20260108T153159Z.csv'
counts=Counter()
missing=[]
missing_ref=[]
errors=[]
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    for row in r:
        exists=(row.get('exists_in_r2') or '').lower()=='true'
        kind=row.get('parsed_kind') or ''
        head_err=row.get('head_error') or ''
        raw=row.get('r2_tar_key_raw') or ''
        vid=row.get('youtube_id') or ''
        if kind=='missing' or not raw.strip():
            counts['missing_ref'] += 1
            if len(missing_ref)<10:
                missing_ref.append(vid)
        elif exists:
            counts['exists'] += 1
        else:
            counts['missing_in_r2'] += 1
            if len(missing)<10:
                missing.append(vid)
        if head_err:
            counts['head_errors'] += 1
            if len(errors)<10:
                errors.append((vid, head_err, row.get('head_http_status')))
        counts['rows'] += 1

print('rows', counts['rows'])
print('exists', counts['exists'])
print('missing_ref', counts['missing_ref'])
print('missing_in_r2', counts['missing_in_r2'])
print('head_errors', counts['head_errors'])
print('missing_ref_sample', missing_ref)
print('missing_in_r2_sample', missing)
print('head_errors_sample', errors)
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
rows 44343
exists 44343
missing_ref 0
missing_in_r2 0
head_errors 0
missing_ref_sample []
missing_in_r2_sample []
head_errors_sample []


**Verifying report findings**

I've confirmed that all rows in the report exist in R2 with non-missing references, but there are duplicates. I need to maintain the current count and provide an accurate answer. The user's last count was 40,676, but we're currently at 43,934. I can mention that as of now, Supabase has N completed entries, and we've verified M of them, which confirm their existence in R2. I’ll also note there's K newly completed since the audit started to keep them informed.


**Auditing R2 entries**

The report shows that each row has an r2_tar_key and exists in R2, but there are still 57 entries missing in the current Supabase that haven't been audited yet. I wonder if we should run an audit on those too. We can run the script again to catch more, but since the pipeline continues running, we'll only get a snapshot unless we take a break. I'll approach this by auditing all completed entries, ignoring deltas, and using improved concurrency. Given our experience, it might take around 2.5 minutes to audit about 44,000 entries efficiently.


**Creating a new snapshot**

I'm planning to create a new snapshot file right now and run a full audit to capture all completed entries at the start, which should bring the missing count close to zero. This means I'll ignore any new completions that occur after the start time. I’ll run the script with a new output file for all completed entries, estimated to take around two minutes. Once it's done, I can inform the user that total completed counts are up to date, with no missing references or errors. I'll set the appropriate parameters for the script, including a timeout of 10 minutes and optimal concurrency. Let's get this running!


python3 scripts/audit_completed_r2.py --batch-size 1000 --workers 64


Output:
Exit code: 0
Wall time: 121 seconds
Output:

  0%|          | 0/44024 [00:00<?, ?video/s]
  0%|          | 1/44024 [00:01<22:00:36,  1.80s/video]
  0%|          | 3/44024 [00:01<6:15:21,  1.95video/s]
  0%|          | 6/44024 [00:02<2:46:13,  4.41video/s]
  0%|          | 11/44024 [00:02<1:17:31,  9.46video/s]
  0%|          | 17/44024 [00:02<45:14, 16.21video/s]
  0%|          | 26/44024 [00:02<26:08, 28.04video/s]
  0%|          | 35/44024 [00:02<18:30, 39.62video/s]
  0%|          | 42/44024 [00:02<16:04, 45.58video/s]
  0%|          | 57/44024 [00:02<10:50, 67.61video/s]
  0%|          | 75/44024 [00:02<07:44, 94.61video/s]
  0%|          | 96/44024 [00:02<05:53, 124.17video/s]
  0%|          | 117/44024 [00:03<04:59, 146.77video/s]
  0%|          | 142/44024 [00:03<04:14, 172.19video/s]
  0%|          | 161/44024 [00:03<04:09, 176.09video/s]
  0%|          | 189/44024 [00:03<03:38, 200.77video/s]
  0%|          | 215/44024 [00:03<03:41, 197.89video/s]
  1%|          | 236/44024 [00:03<03:42, 196.37video/s]
  1%|          | 270/44024 [00:03<03:24, 213.90video/s]
  1%|          | 303/44024 [00:03<03:13, 225.49video/s]
  1%|          | 347/44024 [00:04<03:17, 220.70video/s]
  1%|          | 397/44024 [00:04<02:48, 258.27video/s]
  1%|          | 460/44024 [00:04<02:39, 273.71video/s]
  1%|          | 545/44024 [00:04<02:02, 353.64video/s]
  1%|▏         | 609/44024 [00:04<01:53, 381.98video/s]
  1%|▏         | 648/44024 [00:04<02:28, 292.74video/s]
  2%|▏         | 714/44024 [00:05<02:12, 328.03video/s]
  2%|▏         | 794/44024 [00:05<01:42, 420.16video/s]
  2%|▏         | 861/44024 [00:05<01:43, 418.59video/s]
  2%|▏         | 920/44024 [00:05<01:46, 403.48video/s]
  2%|▏         | 1001/44024 [00:05<01:56, 370.15video/s]
  2%|▏         | 1042/44024 [00:05<01:59, 360.19video/s]
  2%|▏         | 1080/44024 [00:06<02:09, 330.40video/s]
  3%|▎         | 1126/44024 [00:06<02:02, 350.41video/s]
  3%|▎         | 1163/44024 [00:06<02:07, 335.90video/s]
  3%|▎         | 1198/44024 [00:06<02:32, 281.45video/s]
  3%|▎         | 1269/44024 [00:06<02:22, 300.13video/s]
  3%|▎         | 1322/44024 [00:06<02:28, 287.91video/s]
  3%|▎         | 1413/44024 [00:06<01:44, 407.07video/s]
  3%|▎         | 1472/44024 [00:07<02:00, 353.92video/s]
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Audit complete.
Report: reports/r2_audit_completed_20260108T155753Z.csv
Summary: reports/r2_audit_completed_20260108T155753Z.csv.summary.txt
Counts: completed=44024 missing_ref=0 exists=43943 missing_in_r2=0 errors=0


python3 - <<'PY'
import os
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
# count COMPLETED with youtube_id empty string
r1=sb.table('videos').select('id', count='exact').eq('status','COMPLETED').eq('youtube_id','').limit(1).execute()
print('empty_string_count', getattr(r1,'count',None))
# count COMPLETED with youtube_id is null
try:
    r2=sb.table('videos').select('id', count='exact').eq('status','COMPLETED').is_('youtube_id','null').limit(1).execute()
    print('null_count', getattr(r2,'count',None))
except Exception as e:
    print('null_count_query_failed', type(e).__name__, str(e)[:200])
PY


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:
empty_string_count 0
null_count 0


**Evaluating table uniqueness**

I'm considering whether the table can actually enforce uniqueness. It seems likely that youtube_id might not be unique since they could appear across different languages or contexts. While the upsert operation uses youtube_id as a conflict reference, suggesting it should be unique, older data might lack constraints. To clarify this, I could query Supabase to count distinct youtube_ids against total counts, but distinct counts aren't easily accessible through PostgREST. Instead, I’ll fetch all youtube_ids and compute duplicates in Python. This seems manageable for 44k entries.


python3 - <<'PY'
import os
from collections import Counter
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
count_res=sb.table('videos').select('youtube_id', count='exact').eq('status','COMPLETED').limit(1).execute()
total=int(getattr(count_res,'count',0) or 0)
ids=[]
batch=1000
for start in range(0,total,batch):
    end=min(total-1,start+batch-1)
    res=sb.table('videos').select('youtube_id').eq('status','COMPLETED').order('youtube_id').range(start,end).execute()
    ids.extend([(r.get('youtube_id') or '').strip() for r in (res.data or [])])

c=Counter(i for i in ids if i)
dups=[(k,v) for k,v in c.items() if v>1]
print('completed_rows', total)
print('unique_youtube_ids', len(c))
print('dup_ids', len(dups))
print('dup_rows_overhead', sum(v-1 for _,v in dups))
print('top5', sorted(dups, key=lambda x:-x[1])[:5])
PY


Output:
Exit code: 0
Wall time: 10 seconds
Output:
completed_rows 44232
unique_youtube_ids 44221
dup_ids 11
dup_rows_overhead 11
top5 [('e1v48CTmkq0', 2), ('fJTL6v6vUjE', 2), ('O9tNP8qwtY8', 2), ('qFfWPuxRPrA', 2), ('R1rPc5wBJIA', 2)]


**Fixing code logic**

I should consider that the count may need to be smaller in the generator because of eventual consistency or row changes. If the count changes between calls and the generator reduces, I need to fix the script. We should only call the count once and pass the total into the iterator to ensure a fixed snapshot. The iterator should take a `total` argument, and I should also look into the progress bar update which might be off by one due to skipped tasks.


**Patching report script**

I noticed that I can use the previous report file with validated 44,343 rows for the user. However, I'd like to create a final snapshot with a consistent count. It would also be better to avoid duplicates in the report, particularly due to early termination, although it's not critical. For a cleaner new snapshot, I'll make sure to patch the script accordingly. It’s important to keep everything tidy and accurate for the user!


Input:
*** Begin Patch
*** Update File: scripts/audit_completed_r2.py
@@
-def iter_completed_batches(sb, batch_size: int) -> Iterable[List[dict]]:
-    count_res = sb.table("videos").select("youtube_id", count="exact").eq("status", "COMPLETED").limit(1).execute()
-    total = int(getattr(count_res, "count", 0) or 0)
-    for start in range(0, total, batch_size):
+def iter_completed_batches(sb, batch_size: int, total: int) -> Iterable[List[dict]]:
+    for start in range(0, total, batch_size):
         end = min(total - 1, start + batch_size - 1)
         res = (
             sb.table("videos")
             .select("youtube_id,r2_tar_key")
@@
-            for batch in iter_completed_batches(sb, batch_size=args.batch_size):
+            for batch in iter_completed_batches(sb, batch_size=args.batch_size, total=total_completed):
                 futures = {}
                 for row in batch:
                     youtube_id = (row.get("youtube_id") or "").strip()
                     if not youtube_id or youtube_id in done_ids:
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/audit_completed_r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Running audit report again**

I need to rerun the snapshot to ensure our numbers are consistent, although we already have one. I'll execute a new script to generate a report with a fixed count, which will create a file named `reports/r2_audit_completed_<ts>.csv`. It’s probably a good idea to wait a minute or two for it to complete, but I think everything should be fine. I'm eager to see the updated results!


Output:
Exit code: 0
Wall time: 124.2 seconds
Output:

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Audit complete.
Report: reports/r2_audit_completed_20260108T160051Z.csv
Summary: reports/r2_audit_completed_20260108T160051Z.csv.summary.txt
Counts: completed=44307 missing_ref=0 exists=44227 missing_in_r2=0 errors=0


Summary: auto


python3 - <<'PY'
import os
from collections import Counter
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
count_res=sb.table('videos').select('youtube_id', count='exact').eq('status','COMPLETED').limit(1).execute()
total=int(getattr(count_res,'count',0) or 0)
ids=[]
batch=1000
for start in range(0,total,batch):
    end=min(total-1,start+batch-1)
    res=sb.table('videos').select('youtube_id').eq('status','COMPLETED').order('youtube_id').range(start,end).execute()
    ids.extend([(r.get('youtube_id') or '').strip() for r in (res.data or [])])

c=Counter(i for i in ids if i)
dups=[(k,v) for k,v in c.items() if v>1]
over=sum(v-1 for _,v in dups)
print('completed_rows', total)
print('unique_youtube_ids', len(c))
print('dup_ids', len(dups))
print('dup_rows_overhead', over)
print('top5', sorted(dups, key=lambda x:-x[1])[:5])
PY


Output:
Exit code: 0
Wall time: 10.6 seconds
Output:
completed_rows 44490
unique_youtube_ids 44481
dup_ids 9
dup_rows_overhead 9
top5 [('iZPrbhfTUzw', 2), ('La6hfqkh4bs', 2), ('LA7zMbYQn6w', 2), ('MmsoTI1kq7I', 2), ('neKOhh-F8ps', 2)]


**Evaluating pagination approaches**

I'm realizing that if the completed set grows while paginating, some rows may change indices, leading to potential skips—like those 80 missing items. It turns out we can’t depend on offset pagination if the table is changing. So, I'll consider keyset pagination instead, using `youtube_id > last_youtube_id` for a more stable snapshot. However, since `youtube_id` is random, there's still the chance of missing newer rows with lower IDs. Maybe paginating by `created_at` or `processing_completed_at` could work? But then, those might change too... hmm.


**Stabilizing dataset for query**

The user wants to verify the current completed tasks, but if new completions are coming in, I could temporarily freeze the dataset. I would query for completed videos with a `processing_completed_at` timestamp earlier than or equal to my snapshot time, which I’d capture at the start. This would stabilize it and allow for offset pagination based on `processing_completed_at` and `youtube_id`. However, I have to consider that `processing_completed_at` might be null. So, I need to make sure it isn’t before the snapshot and that I capture `now_utc`. Then I could use `gte.{snapshot}` for the query.


**Refining the audit process**

I’m looking into how PostgREST handles multiple order clauses, especially with supabase-py. It seems I can use `.order('processing_completed_at').order('youtube_id')`, which appends. For keyset pagination, I’d filter using `or=(processing_completed_at.gt.last_ts, and(processing_completed_at.eq.last_ts,youtube_id.gt.last_id)`, but that's complicated. I suppose I could accept slight mismatches and use a resume feature to cover new entries later.

If the user asked for 40,000 already completed tasks, I can report on the audited rows, where all have `r2_tar_key`. For new entries, I could suggest rerunning the script. Making the audit script more robust by including a `--freeze-at` snapshot option for `processing_completed_at` could really help stabilize retrieval!


**Implementing pagination modifications**

Step 3 of my plan is still in progress, but I'm moving to summarize Step 4. I want to implement modifications by using keyset pagination based on the primary key `id` (UUID), which could offer stability. While ordering by `id` can be stable, I realize offset pagination is still shaky with new inserts. However, if I filter for completed rows with `processing_completed_at` less than or equal to the snapshot, that should stabilize the set.

In Supabase, I’ll check the `processing_completed_at` column, capturing a snapshot time as the current UTC ISO string. Applying the filter with `.lte('processing_completed_at', snapshot_time)` and filtering out nulls should work. Since null comparisons yield null in SQL, they will be excluded. I might also filter `updated_at` for additional stability, but I think I’ll stick with `processing_completed_at` for counting completed tasks by snapshot.


**Finalizing the count query**

I’m looking to finalize the count query with the conditions: selecting `youtube_id` and doing an exact count where the status is `COMPLETED` and `processing_completed_at` is less than or equal to the snapshot time. This will ensure that pagination remains stable and the offset is safe. I need to patch the script to support the `--snapshot-completed-before` argument by setting the snapshot to either the provided argument or the current time in UTC.

When I use this snapshot in the count and batch queries, I should avoid recalculating bucket candidates multiple times. The output summary must mention the snapshot used, and any count mismatches should directly relate to this count. Also, during resume mode when rerunning, I should decide if the snapshot time needs to be updated or can remain the same; I think we'll default to the current time for consistency.


Input:
*** Begin Patch
*** Update File: scripts/audit_completed_r2.py
@@
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from typing import Iterable, List, Optional, Set, Tuple
 from urllib.parse import urlparse

@@
 def main() -> int:
     ap = argparse.ArgumentParser(description="Audit COMPLETED videos in Supabase vs object existence in R2.")
     ap.add_argument("--batch-size", type=int, default=DEFAULT_BATCH_SIZE)
     ap.add_argument("--workers", type=int, default=int(os.environ.get("R2_AUDIT_WORKERS", "64") or 64))
+    ap.add_argument(
+        "--snapshot-completed-before",
+        default="",
+        help=(
+            "Optional ISO timestamp to freeze the COMPLETED set (filters `processing_completed_at` <= timestamp). "
+            "Default: now (UTC)."
+        ),
+    )
     ap.add_argument(
         "--default-bucket",
         default="1-cleaned-data",
         help="Bucket to use when r2_tar_key does not specify one (default: 1-cleaned-data).",
     )
@@
     sb = create_client(os.environ["URL"], os.environ["SUPABASE_ADMIN"])
     s3 = build_s3_client()

+    snapshot_ts = (args.snapshot_completed_before or "").strip()
+    if not snapshot_ts:
+        snapshot_ts = datetime.now(timezone.utc).isoformat()
+
     default_bucket = (args.default_bucket or "").strip()
     env_bucket = (os.environ.get("R2_BUCKET") or "").strip()
     fallback = [b.strip() for b in (args.fallback_buckets or "").split(",") if b.strip()]
     # Reasonable bucket search order when the row doesn't specify bucket.
@@
         out_path = os.path.join("reports", f"r2_audit_completed_{ts}.csv")

     # Count upfront for progress bar.
-    count_res = sb.table("videos").select("youtube_id", count="exact").eq("status", "COMPLETED").limit(1).execute()
+    count_res = (
+        sb.table("videos")
+        .select("youtube_id", count="exact")
+        .eq("status", "COMPLETED")
+        .lte("processing_completed_at", snapshot_ts)
+        .limit(1)
+        .execute()
+    )
     total_completed = int(getattr(count_res, "count", 0) or 0)
@@
     stats = {
         "completed_total": total_completed,
+        "snapshot_completed_before": snapshot_ts,
         "missing_r2_ref": 0,
         "bad_r2_ref": 0,
         "exists_in_r2": 0,
         "missing_in_r2": 0,
         "head_errors": 0,
@@

     remaining_total = max(0, total_completed - len(done_ids))

     with open(out_path, file_mode, newline="", encoding="utf-8") as f:
@@
         with ThreadPoolExecutor(max_workers=max(1, int(args.workers))) as ex:
             pbar = tqdm(total=remaining_total, unit="video")
-            for batch in iter_completed_batches(sb, batch_size=args.batch_size, total=total_completed):
+            for batch in iter_completed_batches(sb, batch_size=args.batch_size, total=total_completed):
                 futures = {}
                 for row in batch:
                     youtube_id = (row.get("youtube_id") or "").strip()
                     if not youtube_id or youtube_id in done_ids:
                         continue
-                    futures[ex.submit(check_one, youtube_id, row.get("r2_tar_key"))] = youtube_id
+                    futures[ex.submit(check_one, youtube_id, row.get("r2_tar_key"))] = youtube_id

                 for fut in as_completed(futures):
                     result_row = fut.result()
@@
                     writer.writerow(result_row)
                     pbar.update(1)
             pbar.close()
@@
     with open(summary_path, "w", encoding="utf-8") as sf:
         sf.write(f"completed_total={stats['completed_total']}\n")
+        sf.write(f"snapshot_completed_before={stats['snapshot_completed_before']}\n")
         sf.write(f"missing_r2_ref={stats['missing_r2_ref']}\n")
         sf.write(f"bad_r2_ref={stats['bad_r2_ref']}\n")
         sf.write(f"exists_in_r2={stats['exists_in_r2']}\n")
         sf.write(f"missing_in_r2={stats['missing_in_r2']}\n")
         sf.write(f"head_errors={stats['head_errors']}\n")
@@
     print(f"Report: {out_path}")
     print(f"Summary: {summary_path}")
     print(
         "Counts: "
         f"completed={stats['completed_total']} "
         f"missing_ref={stats['missing_r2_ref']} "
         f"exists={stats['exists_in_r2']} "
         f"missing_in_r2={stats['missing_in_r2']} "
         f"errors={stats['head_errors']}"
     )
     return 0
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/audit_completed_r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Update File: scripts/audit_completed_r2.py
@@
-def iter_completed_batches(sb, batch_size: int, total: int) -> Iterable[List[dict]]:
+def iter_completed_batches(sb, batch_size: int, total: int, snapshot_ts: str) -> Iterable[List[dict]]:
     for start in range(0, total, batch_size):
         end = min(total - 1, start + batch_size - 1)
         res = (
             sb.table("videos")
             .select("youtube_id,r2_tar_key")
             .eq("status", "COMPLETED")
+            .lte("processing_completed_at", snapshot_ts)
             .order("youtube_id")
             .range(start, end)
             .execute()
         )
         yield res.data or []
@@
-            for batch in iter_completed_batches(sb, batch_size=args.batch_size, total=total_completed):
+            for batch in iter_completed_batches(sb, batch_size=args.batch_size, total=total_completed, snapshot_ts=snapshot_ts):
                 futures = {}
                 for row in batch:
                     youtube_id = (row.get("youtube_id") or "").strip()
                     if not youtube_id or youtube_id in done_ids:
                         continue
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/audit_completed_r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Output:
Exit code: 0
Wall time: 126.8 seconds
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 93%|█████████▎| 41359/44686 [01:56<00:10, 303.64video/s]
 93%|█████████▎| 41485/44686 [01:56<00:07, 448.43video/s]
 93%|█████████▎| 41535/44686 [01:56<00:07, 433.88video/s]
 93%|█████████▎| 41596/44686 [01:56<00:07, 424.66video/s]
 93%|█████████▎| 41656/44686 [01:56<00:06, 432.86video/s]
 93%|█████████▎| 41701/44686 [01:56<00:07, 392.35video/s]
 93%|█████████▎| 41755/44686 [01:56<00:06, 425.25video/s]
 94%|█████████▎| 41812/44686 [01:57<00:07, 405.08video/s]
 94%|█████████▎| 41876/44686 [01:57<00:06, 445.57video/s]
 94%|█████████▍| 41923/44686 [01:57<00:07, 385.80video/s]
 94%|█████████▍| 42001/44686 [01:57<00:09, 274.22video/s]
 94%|█████████▍| 42036/44686 [01:57<00:09, 282.74video/s]
 94%|█████████▍| 42082/44686 [01:58<00:09, 287.17video/s]
 94%|█████████▍| 42115/44686 [01:58<00:09, 276.82video/s]
 94%|█████████▍| 42177/44686 [01:58<00:07, 331.19video/s]
 94%|█████████▍| 42214/44686 [01:58<00:07, 320.60video/s]
 95%|█████████▍| 42287/44686 [01:58<00:06, 375.50video/s]
 95%|█████████▍| 42348/44686 [01:58<00:06, 363.86video/s]
 95%|█████████▍| 42388/44686 [01:58<00:06, 363.65video/s]
 95%|█████████▍| 42443/44686 [01:59<00:07, 297.19video/s]
 95%|█████████▌| 42476/44686 [01:59<00:07, 277.31video/s]
 95%|█████████▌| 42593/44686 [01:59<00:05, 374.34video/s]
 96%|█████████▌| 42693/44686 [01:59<00:05, 396.29video/s]
 96%|█████████▌| 42733/44686 [01:59<00:05, 341.61video/s]
 96%|█████████▌| 42885/44686 [02:00<00:03, 526.41video/s]
 96%|█████████▌| 42956/44686 [02:00<00:03, 557.61video/s]
 96%|█████████▋| 43018/44686 [02:00<00:05, 295.89video/s]
 96%|█████████▋| 43065/44686 [02:00<00:05, 306.10video/s]
 96%|█████████▋| 43109/44686 [02:00<00:04, 320.61video/s]
 97%|█████████▋| 43151/44686 [02:01<00:04, 332.11video/s]
 97%|█████████▋| 43192/44686 [02:01<00:04, 328.89video/s]
 97%|█████████▋| 43231/44686 [02:01<00:04, 306.53video/s]
 97%|█████████▋| 43312/44686 [02:01<00:03, 354.82video/s]
 97%|█████████▋| 43350/44686 [02:01<00:04, 317.80video/s]
 97%|█████████▋| 43432/44686 [02:01<00:02, 421.28video/s]
 97%|█████████▋| 43483/44686 [02:01<00:03, 388.07video/s]
 97%|█████████▋| 43537/44686 [02:02<00:02, 398.87video/s]
 98%|█████████▊| 43602/44686 [02:02<00:03, 357.48video/s]
 98%|█████████▊| 43691/44686 [02:02<00:02, 433.86video/s]
 98%|█████████▊| 43738/44686 [02:02<00:02, 405.34video/s]
 98%|█████████▊| 43794/44686 [02:02<00:02, 429.53video/s]
 98%|█████████▊| 43847/44686 [02:02<00:02, 402.86video/s]
 98%|█████████▊| 43892/44686 [02:02<00:02, 382.97video/s]
 98%|█████████▊| 44000/44686 [02:03<00:01, 352.64video/s]
 99%|█████████▊| 44038/44686 [02:03<00:03, 185.83video/s]
 99%|█████████▊| 44075/44686 [02:04<00:02, 206.38video/s]
 99%|█████████▊| 44111/44686 [02:04<00:02, 224.69video/s]
 99%|█████████▉| 44142/44686 [02:04<00:02, 229.41video/s]
 99%|█████████▉| 44210/44686 [02:04<00:01, 307.64video/s]
 99%|█████████▉| 44249/44686 [02:04<00:01, 308.29video/s]
 99%|█████████▉| 44288/44686 [02:04<00:01, 310.13video/s]
 99%|█████████▉| 44332/44686 [02:04<00:01, 303.16video/s]
 99%|█████████▉| 44402/44686 [02:05<00:00, 303.74video/s]
100%|█████████▉| 44483/44686 [02:05<00:00, 403.39video/s]
100%|█████████▉| 44551/44686 [02:05<00:00, 441.42video/s]
100%|█████████▉| 44601/44686 [02:05<00:00, 432.70video/s]
100%|██████████| 44686/44686 [02:05<00:00, 356.30video/s]
Audit complete.
Report: reports/r2_audit_completed_20260108T160504Z.csv
Summary: reports/r2_audit_completed_20260108T160504Z.csv.summary.txt
Counts: completed=44686 missing_ref=0 exists=44686 missing_in_r2=0 errors=0


**Analyzing bucket usage**

I think it's important to determine which buckets were used. Are they all consolidated in a single bucket? I’ll go back to the report and analyze the data by counting the checked buckets. That way, I can quickly figure out the distribution and see if further action is necessary. Let’s keep this analysis moving!