Compactors are producing shards but slowly (0.1%). This is I/O heavy - downloading all microshards from R2. Let me check rate and see if it improves as workers warm up.

Compactors are working. 5 shards written so far, 75,000 segments compacted. But this is going very slowly - 75K out of 60.7M segments is only 0.1%. At this rate, it'll take a very long time.

Let me check: we have 12 workers but only 9 are online, and only 7 have active language leases. The workers process one language at a time. Since the compactor is I/O bound (downloading microshards from R2), the bottleneck is network bandwidth per worker.

With 876K microshards to process and each containing audio data, this is a massive amount of data to download.

I'm calculating the total data volume: each microshard contains roughly 69 segments on average, with about 225KB of FLAC audio per segment, so each microshard's tar file is around 15MB. That means 876K microshards times 15MB equals roughly 13TB of total data to download from R2. Even with 12 workers downloading in parallel at about 1 Gbps each, that's still going to take hours.

Looking at the actual compactor behavior, it acquires one language lease at a time, downloads and buffers segments until it hits 15,000 segments per shard, then flushes. With 12 languages and 12 workers, each worker should theoretically get assigned one language, though I'm noticing some workers appear idle and a few are reporting `current=None`, which suggests they might be waiting for a language lease to become available.

The throughput is concerning though—at the current rate of 744 microshards in 10 minutes, extrapolating to 876K microshards would take nearly 200 hours, which is clearly unsustainable. The workers are probably still warming up, so the rate should improve, but the real bottleneck is likely the I/O overhead: each microshard requires three separate R2 downloads, which means 2.6 million individual requests across all the microshards. I should monitor the compaction rate more closely to see if it accelerates as the system stabilizes.


Rate is about 10K segments/min across all workers. That's slow for 60.7M segments. The bottleneck is the language lease system (1 worker per language) and R2 download I/O. Let me continue monitoring - the rate should improve as workers settle in.

Progress: 14 shards, 210K segments in 20 minutes since compactors started producing. That's about 10.5K segments/min. At that rate:
- 60.7M / 10.5K per min = 5,781 min = 96 hours

That's way too slow. The bottleneck is clear - each worker can only process one language at a time, and the big languages (en: 17.4M, hi: 9.9M, te: 8.2M) will take ages.

Actually wait - the en worker only has 1 shard after all this time.

English is lagging because the compactor needs to claim and process ~217 microshards to fill a 15K-segment shard, doing that in batches of 32, which means multiple rounds of downloading from R2 and buffering before it can flush anything out.

The real bottleneck is I/O—downloading 3.2GB of audio data per shard at 100MB/s takes about 32 seconds, plus upload time, so each shard should theoretically take 2-5 minutes, but we're seeing closer to 10 minutes per shard per worker, suggesting R2 latency is worse than expected.

With 254K microshards and 17.4M segments in English, we're looking at roughly 1,160 shards to process, which at the current pace would take nearly 200 hours with just 12 workers—the language lease system is the fundamental constraint here.

Looking at how the lease acquisition actually works, each worker claims and processes all microshards for a language in one continuous loop rather than stopping after a single batch, but the real issue is that network throughput per worker is the limiting factor, so I should monitor whether increasing the claim limit or adjusting the batch size would help. improves. It might speed up as the initial download overhead settles. Actually, let me try increasing the workers. The language lease system means only 1 worker per language at a time, but once a worker finishes a language, it moves to the next available one. So having more workers than languages means some will be idle, but as small languages finish, they'll pick up remaining work.

Actually wait - can multiple workers work on the same language? The lease is exclusive - only one worker can hold a language lease at a time. But the lease has a timeout, so if a worker takes too long and the lease expires, another worker could pick it up. However, the worker refreshes the lease in its heartbeat loop, so as long as it's alive, it keeps the lease. This means we truly can only have 1 worker per language.

The only way to speed this up significantly is to either process microshards in parallel within a single worker, or increase the claim limit to grab more microshards at once and reduce round trips.

Looking at the current rate - 210K segments in about 20 minutes works out to roughly 10.5K per minute, though some of that was warmup. If it stabilizes at that pace, processing all 60.7M segments would take around 96 hours, which is way too long. But the rate should improve as workers settle in. Let me check back in 30 minutes and see if things have sped up. For now, I'll let it run and monitor the progress while giving the user an update.


Compaction rate is ~17K segments/min, ETA ~59 hours. This is very slow - the compactor is heavily I/O bound downloading all 876K microshards from R2 one-by-one. Only 1 worker per language due to the lease system, and English alone has 254K microshards.

Let me check if those 3 missing compactors are stuck, and keep monitoring. Unfortunately the language lease architecture means we can't easily parallelize within a language. The compactor speed is bounded by R2 download throughput per worker.

ETA ~59 hours is very long. The rate is only 17K segs/min. The big issue is that the compactor is heavily I/O bound - each worker downloads microshards from R2 one at a time.

I see some workers are not doing anything useful - only 9 workers have active language leases, and 3 are idle (the 3 instances that never showed in the workers list: 26349246, 30826765, and one more). But there are only 12 languages so even if all 12 were working, it wouldn't help much.

The fundamental problem is that for big languages like English (17.4M segments, 254K microshards), one worker downloading microshards sequentially is incredibly slow.

I'm exploring a few potential solutions: allowing multiple workers per language through lease system changes, using language filters to distribute work across workers, or increasing the claim limit to batch more microshards per request and reduce R2 list operations. The language filter approach seems promising at first, but the lease mechanism still blocks parallel processing within a single language. Let me dig deeper into how the lease acquisition actually works to see if there's a workaround.

Actually, bypassing the lease entirely would risk corrupting the data, so that's not viable. Another angle: what if I run the compaction locally on this machine instead of relying on the distributed compactor? I could write a script that downloads microshards for each language in parallel and compacts them directly, since we have database and R2 access. That would be complex to implement quickly though. Given that 59 hours is way too long and the user needs this done fast, I should focus on a simpler optimization—increasing the claim limit so we download more microshards per batch and make fewer R2 list calls.

The real bottleneck is that each microshard requires 3 sequential HTTP requests to R2 for metadata, audio tar, and audio index. If I could parallelize those downloads, it would speed things up significantly. But the compactor code is what it is, so let me focus on what's actually controllable: fixing the 3 missing workers, bumping up the claim limit environment variable, and monitoring the progress. The compactor claims microshards in batches, downloads them to disk, buffers segments in memory until hitting 15K segments, then flushes to a final shard—so the download phase is where most of the time is being spent.

With roughly 69 segments per microshard on average, I'd need about 217 microshards to fill a 15K buffer, and each one still requires those 3 downloads. Increasing the final shard size wouldn't actually speed up the overall process since all microshards still need to be downloaded regardless. Looking at the raw numbers—876,855 total microshards with 6,316 compacted in 30 minutes—I'm trying to figure out if this pace will improve as the workers settle into a steady state or if this is just the reality of the throughput.

The math is sobering: at the current rate of about 210 microshards per minute, we're looking at roughly 70 hours to complete everything. Even if all 12 language workers were active instead of just 9, the timeline doesn't improve dramatically. I think I need to accept this is going to take a significant amount of time and just keep monitoring the progress to give the user a realistic estimate.


3 instances stuck (France loading again, 2 in "created" state). Let me destroy and replace them.

I see 3 problematic instances:
1. 32741414 (France, machine 55142) - "loading" - this is the same France machine that always gets stuck!
2. 32741415 (Denmark, 30826765) - "created" - stuck in created state
3. 32741428 (BC Canada, 26349246) - "created" - stuck too

Let me destroy these 3 and deploy replacements. But even with all 12 working, the ETA is still ~60 hours. That's a lot.

Let me think about this differently. The compactor needs to download ~13TB of data from R2.

With 12 workers at ~1 Gbps each, that's ~12 Gbps = 1.5 GB/s. Time: 13000/1.5 = 8,667s = 2.4h for raw download. But there's also processing, uploading, and the sequential nature of the code. The real bottleneck is the sequential download of microshards within each language. Running the compactor locally with direct R2 access could help, but writing a custom compactor would require significant effort. I'll focus on keeping all 12 workers healthy and monitoring the job.


3 replacements deployed with fast network (Finland 8 Gbps, Spain 7.5 Gbps, Hungary 6.3 Gbps). Now at 12 healthy compactor instances. Let me continue monitoring with 30-min cycles. The compactor's ETA is long (~59h) because it's inherently I/O bound, but it's working correctly.


All 12 languages now have active workers. Progress at 1.5% after ~75 minutes of compaction. Rate is ~12K segs/min, projected ETA ~80 hours. The compactor is heavily I/O bound (downloading 876K individual microshards from R2). Let me continue monitoring.

All 12 workers now have languages:
- hi, as, kn, pa, mr, bn, or, en, ml, gu, ta, te - all 12 languages covered!

Progress: 930K segments in ~75 min from first shard. That's ~12.4K segs/min. At that rate:
- 60.7M / 12.4K = 4,895 min = ~81.6 hours

Still slow. But the rate should be stable now. Let me just keep monitoring.Wait, actually the rate from the previous check (210K at 15:14) to now (930K at 16:16) is: 720K in 62 min = ~11.6K segs/min.

So that's roughly 87 hours remaining. The user wanted this done quickly, so I should give them an update on the timeline. Without making code changes to the compactor, there's not much I can do to accelerate this further, so I'll keep watching the progress.


2.25%, 12 compactors online. Rate from last check: 435K segs in 30min = 14.5K/min. Steady. Continuing monitoring.


3.6% done, stable rate ~14K segs/min. Smaller languages progressing well (Assamese 61%, Oriya 19%). Big languages like English (0.7%), Telugu (1.6%) will take longest. ETA ~70 hours for full compaction. All 12 compactors healthy. Continuing 1-hour monitoring cycles.

Progress in the last hour: from 1,365,000 to 2,205,000 = 840K segments in 60 min = 14K/min. Consistent rate. At that rate:- Remaining: 60,711,097 - 2,205,000 = 58,506,097
- ETA: 58,506,097 / 14,000 = 4,179 min = 69.6 hours

Assamese is at 61%, Oriya at 19%, Marathi at 10.3% - smaller languages are making good progress. But the big ones (English 0.7%, Telugu 1.6%, Hindi 2.4%) will take ages.

This is clearly a multi-day process, so I'll keep monitoring in hourly intervals and update the user on the timeline. The compactor is running steadily at a stable rate—it's just I/O-bound, which explains the slow pace.


<user_query>
naah man. 70hours is too slow. we gotta do it in <1hr, how can we fasten this up. can we update the logic for compaction ? we can pause or destroy the workers, and the data sofar. 2% is fine. i can lose it. i meant not in the way of losing, we can redo that with better speeds. we need to make this insanely quick. Search the code, and check why is it a per language lease, and then this time, we can have 10 video leases, or something like that. or we can also refresh all microshards data into Supabase's new table so we can divide it accordingly. whatever think of a plan that we can launch more workers at horizontal scaling and upload back in less than an hour or 2. Think of plan, we will implement and execute.
</user_query>


Let me explore the compactor code and DB schema to understand the bottleneck and design a fast parallel solution.

The user wants to speed up Stage B compaction from ~70 hours to under 1-2 hours. The current bottleneck is the language lease system - only 1 worker per language at a time. Let me analyze the compactor code and the DB schema to understand the constraints and design a parallelizable solution.

Let me explore the relevant code to understand:
1. The language lease mechanism
2. The microshard claiming logic
3. How we can parallelize within a language


Now I have the full picture. The bottleneck is crystal clear. Let me design the solution.

Now I have a thorough understanding of the bottleneck. Let me design a solution.

**Root cause**: The language lease system (`final_export_language_leases` with `language TEXT PRIMARY KEY`) means only 1 worker per language. For English with 17.4M segments across 254K microshards, that's one worker doing all the work sequentially.

**The solution**: Remove the language lease constraint and let multiple workers claim microshards for the same language concurrently. Each worker just claims a batch of microshards, combines them into a shard, and uploads.

Here's my plan:

## Option 1: Simple "claim any pending microshards" approach

Instead of leasing entire languages, workers directly claim batches of pending microshards for the same language using `FOR UPDATE SKIP LOCKED`, build a shard from them, and upload it. The tricky part is that the current buffering logic targets 15K rows per shard, but with ~69 segments per microshard on average, a worker needs to claim around 217 microshards to fill one shard. The simplest fix is to have each worker claim a fixed batch of microshards regardless of row count and just upload whatever shard results. The issue is that `claim_microshards_for_language` needs a language parameter, so workers need a way to select which language to work on first. I could either have workers pick the language with the most pending microshards, or assign languages via environment variables. The simplest solution is to write a query that selects a language with pending work and then claims microshards from it in one go.

Writing SQL query... The SQL approach ensures each claim batch stays within a single language for consistency. With 200 workers downloading in parallel at 1 Gbps each, I'm looking at roughly 1-2 hours total to process all 876K microshards, though hitting sub-1-hour performance would require either 300+ workers or larger batch claims.

Now I'm thinking through what the compactor actually does per microshard: it downloads three files from R2, loads the metadata and index into memory, and when the buffer hits 15K segments, it builds a final shard by reading FLAC bytes from the source audio tars and uploading four files back. The real bottleneck is that initial R2 download—each microshard's audio tar runs 5-50MB on average, so with 876K microshards that's potentially massive bandwidth consumption just pulling the raw data.

Let me work through the math: 876K microshards at ~15MB each means roughly 13TB of audio to download. With 200 workers running at 1 Gbps each, that's 25 GB/s total throughput, which gets us through the downloads in about 8.7 minutes. Then there's the upload side—4,047 final shards at 3.4GB each means another 13.8TB going back up, which at the same throughput takes another 9.2 minutes. So the bottleneck really comes down to network bandwidth and the CPU work needed to process everything in between.

Now I'm mapping out the actual implementation strategy. I need to add a new method that claims microshards without requiring a language lease, then build a simplified compactor that just loops through claiming, downloading, and uploading. The key optimization is bumping up the claim limit from 32 to 250+ to cut down on database round trips, and parallelizing the microshard downloads within each worker using thread pools. I'll also need to streamline the commit logic to handle bulk updates more efficiently.

For the infrastructure side, I'm starting by tearing down the current 12 compactors and rebuilding with this new approach.

Actually, I'm realizing the existing `claim_microshards_for_language` already has `FOR UPDATE SKIP LOCKED`, so the real blocker is just the language parameter requirement tied to the lease. The minimal fix is creating a single database method that picks a language and claims microshards atomically in one query, then stripping out the lease logic from the compactor entirely. have each worker get assigned specific language(s) via `--languages` flag. Then deploy multiple workers per language. The existing `claim_microshards_for_language` with `SKIP LOCKED` handles concurrency.

Wait, that still requires per-language assignment. What if we have uneven distribution? English needs way more workers than Assamese.

Better approach: Write a new `claim_microshards_batch` that:
1. Picks a language with pending microshards
2. Claims N microshards from that language using SKIP LOCKED
3. Returns them

No lease needed. Multiple workers can work on the same language because SKIP LOCKED ensures no double-claiming.

Let me design the plan now. The key code changes are minimal:

1. New DB method `claim_microshards_batch`
2. Modified compactor loop that skips language leases
3. Optional

Now I'm realizing the real bottleneck isn't the claiming strategy—it's the throughput per worker. With 876K microshards and each worker processing maybe 5-10 per minute sequentially, even 200 workers would take nearly 10 hours. The issue is that downloads happen one at a time within each worker. I need to enable concurrent downloads so a single worker can fetch multiple microshard files in parallel while processing others. concurrent microshard downloads. If each worker downloads 8 microshards concurrently using ThreadPoolExecutor, speed per worker goes up 8x. Then with 200 workers, 200 * 60 = 12,000 microshards/min. 876K / 12K = 73 min ≈ 1.2 hours.

To get under 1 hour: 250 workers or increase concurrency to 16 threads per worker.

So here's what I need to do: add a new batch claiming method in the database without language leases, rewrite the compactor's main loop to remove the lease mechanism and use a claim-build-upload pattern, implement parallel downloads with ThreadPoolExecutor, bump the claim limit to 250+, reset all microshards to pending, rebuild the Docker image, and deploy 200+ workers. Let me map out the concrete implementation steps.


<git_status>
This is the git status at the start of the conversation. Note that this status is a snapshot in time, and will not update during the conversation.

Git repo: /home/ubuntu/transcripts

## main...origin/main
 M Dockerfile
 M README.md
 M src/audio_polish.py
?? Dockerfile.final_export
?? Dockerfile.variant
?? final_data/
?? scripts/audit_final_export_run.py
?? scripts/build_final_cleaned_dataset.py
?? scripts/build_final_data_summary.py
?? scripts/build_gemini_only_analysis.py
?? scripts/build_gemini_refined_dataset.py
?? scripts/build_transcript_variant_test_input.py
?? scripts/deploy_final_export.sh
?? scripts/deploy_variant.sh
?? scripts/export_final_export_reference_data.py
?? scripts/init_final_export_queue.py
?? scripts/init_transcript_variant_queue.py
?? scripts/prepare_english_mixed_reroute.py
?? scripts/reroute_english_mixed_worker.py
?? scripts/run_final_export_worker.sh
?? scripts/scan_repetitions.py
?? scripts/scan_repetitions_streaming.py
?? scripts/test_transcript_variant_prompt.py
?? scripts/validate_final_export_run.py
?? scripts/validation_audit_ui.py
?? src/final_export_common.py
?? src/final_export_compact_main.py
?? src/final_export_compactor.py
?? src/final_export_config.py
?? src/final_export_db.py
?? src/final_export_r2.py
?? src/final_export_reference_store.py
?? src/final_export_video_main.py
?? src/final_export_worker.py
?? src/transcript_variant_prompt.py
?? src/variant_db.py
?? src/variant_main.py
?? src/variant_provider.py
?? src/variant_r2.py
?? src/variant_worker.py
?? tests/test_final_export_common.py
?? tests/test_transcript_variant_prompt.py
?? tmp/
</git_status>

<agent_transcripts>
Agent transcripts (past chats) live in /home/ubuntu/.cursor/projects/home-ubuntu-transcripts/agent-transcripts. They have names like <uuid>.jsonl, cite them to the user as [<title for chat <=6 words>](<uuid excluding .jsonl>). NEVER cite subagent transcripts/IDs; you can only cite parent uuids. Don't discuss the folder structure.
</agent_transcripts>

<agent_skills>
When users ask you to perform tasks, check if any of the available skills below can help complete the task more effectively. Skills provide specialized capabilities and domain knowledge. To use a skill, read the skill file at the provided absolute path using the Read tool, then follow the instructions within. When a skill is relevant, read and follow it IMMEDIATELY as your first action. NEVER just announce or mention a skill without actually reading and following it. Only use skills listed below.

<available_skills description="Skills the agent can use. Use the Read tool with the provided absolute path to fetch full contents.">
<agent_skill fullPath="/home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md">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.</agent_skill>

<agent_skill fullPath="/home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md">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).</agent_skill>

<agent_skill fullPath="/home/ubuntu/.cursor/skills-cursor/create-rule/SKILL.md">Create Cursor rules for persistent AI guidance. Use when you want to create a rule, add coding standards, set up project conventions, configure file-specific patterns, create RULE.md files, or asks about .cursor/rules/ or AGENTS.md.</agent_skill>

<agent_skill fullPath="/home/ubuntu/.cursor/skills-cursor/create-skill/SKILL.md">Guides users through creating effective Agent Skills for Cursor. Use when you want to create, write, or author a new skill, or asks about skill structure, best practices, or SKILL.md format.</agent_skill>

<agent_skill fullPath="/home/ubuntu/.cursor/skills-cursor/update-cursor-settings/SKILL.md">Modify Cursor/VSCode user settings in settings.json. Use when you want to change editor settings, preferences, configuration, themes, font size, tab size, format on save, auto save, keybindings, or any settings.json values.</agent_skill>
</available_skills>
</agent_skills>


<attached_files>

<code_selection path="vscode-remote://ssh-remote%2B7b22686f73744e616d65223a226770752d736572766572227d/home/ubuntu/.cursor/plans/fast_parallel_compactor_62ed1f9c.plan.md" lines="1-138">
L1:---
L2:name: Fast parallel compactor
L3:overview: Remove the per-language lease bottleneck from the compactor, add concurrent microshard downloads, and deploy 200+ workers to finish Stage B in under 1 hour.
L4:todos:
L5:  - id: destroy-current-compactors
L6:    content: Destroy current 12 compactor instances, reset all microshards to pending
L7:    status: pending
L8:  - id: new-db-method
L9:    content: Add claim_microshards_batch() to final_export_db.py - single atomic claim without language lease
L10:    status: pending
L11:  - id: parallel-downloads
L12:    content: Add _load_microshard_rows_parallel() with ThreadPoolExecutor(12) in compactor
L13:    status: pending
L14:  - id: simplify-main-loop
L15:    content: Rewrite compactor _main_loop to skip language leases, use new batch claim + parallel load
L16:    status: pending
L17:  - id: increase-claim-limit
L18:    content: Set FINAL_EXPORT_COMPACTOR_CLAIM_LIMIT=250 in deploy script
L19:    status: pending
L20:  - id: rebuild-deploy
L21:    content: Rebuild Docker image, push, deploy 200+ compactor workers
L22:    status: pending
L23:  - id: monitor-completion
L24:    content: Monitor until all 876K microshards compacted, validate final shard counts
L25:    status: pending
L26:isProject: false
L27:---
L28:
L29:# Fast Parallel Compactor (Stage B in < 1 hour)
L30:
L31:## Problem
L32:
L33:The current compactor uses a **per-language lease** (`final_export_language_leases` table) that limits to 1 worker per language. With 12 languages, max 12 workers. Each downloads microshards sequentially. Result: **~70 hour ETA** for 876K microshards / 60.7M segments.
L34:
L35:## Root Cause
L36:
L37:```mermaid
L38:flowchart LR
L39:    subgraph current [Current: Serial per Language]
L40:        W1[Worker 1] -->|"lease: en"| EN[254K microshards]
L41:        W2[Worker 2] -->|"lease: hi"| HI[180K microshards]
L42:        W3[Worker 3] -->|"lease: te"| TE[178K microshards]
L43:    end
L44:```
L45:
L46:The language lease exists in `acquire_language_lease()` in [src/final_export_db.py](src/final_export_db.py) and is enforced via `final_export_language_leases` table (PRIMARY KEY on language). But `claim_microshards_for_language()` already uses `FOR UPDATE SKIP LOCKED` which handles concurrency natively - the lease is redundant.
L47:
L48:## Solution: Remove Lease, Add Parallelism
L49:
L50:```mermaid
L51:flowchart LR
L52:    subgraph new [New: N Workers per Language]
L53:        W1[Worker 1] -->|"SKIP LOCKED"| EN[en microshards]
L54:        W2[Worker 2] -->|"SKIP LOCKED"| EN
L55:        W3[Worker 3] -->|"SKIP LOCKED"| EN
L56:        W4[Worker 4] -->|"SKIP LOCKED"| HI[hi microshards]
L57:        W5[Worker 5] -->|"SKIP LOCKED"| HI
L58:        WN["...200+ workers"] -->|"SKIP LOCKED"| ALL["any language"]
L59:    end
L60:```
L61:
L62:### Code Changes (3 files)
L63:
L64:**1. New DB method in [src/final_export_db.py](src/final_export_db.py)**: `claim_microshards_batch()`
L65:
L66:Replaces the lease+claim two-step with a single atomic query:
L67:
L68:```sql
L69:WITH pick AS (
L70:    SELECT language
L71:    FROM final_export_microshards
L72:    WHERE status = 'pending' AND run_id = $1
L73:    GROUP BY language
L74:    ORDER BY count(*) DESC
L75:    LIMIT 1
L76:)
L77:UPDATE final_export_microshards
L78:SET status = 'claimed', claimed_by = $2, claimed_at = now(),
L79:    attempt_count = attempt_count + 1, updated_at = now()
L80:WHERE microshard_id IN (
L81:    SELECT microshard_id
L82:    FROM final_export_microshards
L83:    WHERE status = 'pending' AND run_id = $1
L84:      AND language = (SELECT language FROM pick)
L85:    ORDER BY created_at, microshard_id
L86:    LIMIT $3
L87:    FOR UPDATE SKIP LOCKED
L88:)
L89:RETURNING microshard_id, video_id, language, ...
L90:```
L91:
L92:No lease table involved. Multiple workers naturally partition via `SKIP LOCKED`.
L93:
L94:**2. Add concurrent downloads in [src/final_export_compactor.py](src/final_export_compactor.py)**: `_load_microshard_rows_parallel()`
L95:
L96:Current code downloads microshards one-by-one. New method uses `ThreadPoolExecutor` with 8-16 threads to download concurrently:
L97:
L98:```python
L99:from concurrent.futures import ThreadPoolExecutor, as_completed
L100:
L101:def _load_microshard_rows_parallel(self, jobs, max_workers=12):
L102:    with ThreadPoolExecutor(max_workers=max_workers) as pool:
L103:        futures = {pool.submit(self._load_microshard_rows, job): job for job in jobs}
L104:        for future in as_completed(futures):
L105:            buffer.extend(future.result())
L106:```
L107:
L108:**3. Simplify `_main_loop` in [src/final_export_compactor.py](src/final_export_compactor.py)**:
L109:
L110:Remove all language lease calls (`acquire_language_lease`, `release_language_lease`, `heartbeat_language_lease`). New loop:
L111:
L112:```
L113:while not shutdown:
L114:    jobs = db.claim_microshards_batch(limit=250)
L115:    if not jobs: break
L116:    segments = load_parallel(jobs)
L117:    build shard from segments
L118:    upload shard
L119:    commit consumption
L120:```
L121:
L122:**4. Increase claim limit**: Set `FINAL_EXPORT_COMPACTOR_CLAIM_LIMIT=250` in deploy script (from 32). This claims ~250 microshards per batch (~17K segments), roughly one shard per claim.
L123:
L124:### Infrastructure Steps
L125:
L126:1. Destroy current 12 compactors
L127:2. Reset ALL microshards to `pending` (SQL: `UPDATE final_export_microshards SET status='pending', claimed_by=NULL WHERE run_id='production-20260312'`)
L128:3. Rebuild Docker image and push
L129:4. Deploy 200+ workers with `--stage compact --allow-partial-shards`
L130:
L131:### Time Estimate
L132:
L133:- 876K microshards across 200 workers = ~4,380 each
L134:- With 12-thread parallel downloads: ~8 microshards/sec/worker
L135:- Per worker: 4,380 / 8 = 547 seconds = ~9 min for downloads
L136:- Shard building + upload overhead: ~2x = ~20 min
L137:- Buffer: **~30-45 minutes total**
L138:
</code_selection>

</attached_files>
<user_query>
Fast Parallel Compactor (Stage B in < 1 hour)

Implement the plan as specified, it is attached for your reference. Do NOT edit the plan file itself.

To-do's from the plan have already been created. Do not create them again. Mark them as in_progress as you work, starting with the first one. Don't stop until you have completed all the to-dos.
</user_query>


I’m starting with the compactor redesign: first I’ll inspect the current files in detail, then patch the DB claim path and compactor loop so we can kill the lease bottleneck and redeploy a much wider Stage B fleet.


**Updating task list**

I’m thinking through the current todo list and deciding that I need to update it. I want to mark the first task as in progress and maybe keep the others as pending. It's crucial to maintain only one task in progress at a time, especially since we already have task 25 marked as in progress. I should probably cancel or complete it to resolve the conflict. A clean todo list will help!


**Inspecting files**

I need to think about possibly canceling the old files later, specifically those dated from the 26th to the 28th, but they seem fine for now. I want to inspect them closely to ensure everything's in order. To do this efficiently, I’ll utilize a parallel ReadFile approach, which should help streamline the process. Just making sure I’m being thorough here! Let's see what I find in those files.


I’ve verified the exact bottleneck in code. Next I’m patching the DB claim path and compactor loop, then I’ll reset the current compaction state and redeploy the fast version.

**Planning data inspection**

I’m contemplating our implementation plan. I think I need to inspect the database's FinalExportMicroshardJob to check the row_count and consumed_rows fields. Also, it seems important to review the worker stats, which should be fine. I'm considering the safety of our final_export_r2 client when using concurrent threads for the download_file function. I wonder if each download_file call should create a new client for thread safety. Definitely need to inspect that file as well!


I’m stopping the slow compaction run now and resetting Stage B state so we can relaunch from a clean slate with the faster path.

**Evaluating microshard downloads**

I need to check if _load_microshard_rows reads three files for each microshard and audio tar per shard. It might be possible to parallelize the downloads and also consider batch claims across languages. I really need the plan to be implemented as requested. I wonder if optimizing the process would help; instead of focusing on the largest language count, maybe I could have workers claim microshards across languages and buffer by language. This might allow more flexibility in worker distribution.
**Considering microshard claims**

I realize that once a few workers claim hundreds of English microshards, others can still pick English and claim non-overlapping ones. This setup works well for horizontal distribution within languages, but if all workers focus on English, smaller languages could struggle. I’m thinking about using language filters on workers or selecting random languages among the top ones to help balance things. With 200 workers hitting dominant language queries, there might be contention, so I need a better claim strategy.
**Improving microshard claims**

I’m considering the idea of claiming the next microshards without focusing strictly on language, then grouping the rows by language in the buffer for proper flushing. However, that leads to the question of partial shard consumption since each microshard is specific to one language. It might be better to claim a limited number of pending microshards across all languages and then group them for efficiency, maximizing parallelism without getting tangled in language leases. Still, I need to balance this with the user’s specified plan.


**Resetting the database**

I'm thinking about destroying instances and resetting the database. I might need to do this in a sequential manner. I wonder if using a command like `vastai destroy instances <ids>` in the shell could work. Then, I might follow it up with a reset using Python. It seems like a plan, but I want to make sure the syntax is correct for everything to run smoothly. I'll double-check the commands before executing!


**Verifying reset completion**

I need to confirm that the reset was done successfully. After that, I want to update the todo status for destroying the current compactors, which is marked as completed. Additionally, I need to check on the status of the new database method that's in progress. Oh, and I should probably look into the old 26 tasks that are still pending to ensure everything is on track!


I’ve cleared the slow Stage B run. Now I’m patching the hot path: atomic batch-claim in the DB layer first, then the parallel microshard loader and simplified compactor loop.

**Considering concurrency and tasks**

I'm thinking about how to best manage downloading tasks efficiently. If I parallel download and append results in the original order after futures complete, that seems easy. However, it might lose the advantage of using `as_completed`. A simpler solution could be to loop over jobs and retrieve results in their original order while allowing all tasks to run concurrently. I might need to implement `asyncio.get_running_loop().run_in_executor` for this, which fits well with async operations.
**Evaluating download optimization**

I'm considering that each worker processes one shard at a time, which might not be the fastest approach. For better speed, I think we might need to increase the claim limit and enable more parallel downloads. There's a potential optimization in `_process_language`: currently, it loads until the buffer reaches the target before flushing. If we can improve that process, it could enhance performance significantly. I'm curious to see what adjustments could lead to faster downloads overall!
**Simplifying code processes**

I realize that the heartbeat_language_lease isn't necessary, as the _heartbeat_loop updates the language lease whenever _current_language is set. It might be better to remove that code, and it shouldn’t be an issue if _current_language is None. I'm considering simplifying the code to avoid unnecessary database calls, since it may just complicate things. I also might need to adjust the functions to improve efficiency, possibly creating a new function to handle batch jobs for a single language.
**Designing job processing flow**

I think the `_main_loop` should claim batch jobs using a new method that identifies the language from the first job. Then it sets the current item and calls `_process_claimed_jobs(language, jobs)`. In that function, I could load the initial jobs in parallel, flush them when the buffer reaches the target, and keep claiming more jobs for the same language until there are none left. However, this brings us back to the limitation of handling just one language per loop, even without a lease.
**Optimizing microshard processing**

I’m thinking some workers might switch languages once English processing is done, as English has quite a few microshards to split among workers. After English and Hindi, the smaller languages would need to wait.

I’m considering how `claimed_jobs` interacts with flushing and committing microshard consumption, which could affect statuses. There’s some overhead with the two SQL calls per microshard, but maybe it’s not significant.

I’m wondering if bulk updates could help and if we should consider implementing optimizations now to improve efficiency.
**Considering optimization strategies**

I’m thinking about updating `commit_microshard_consumption` to use a set-based approach with UNNEST of arrays for more efficiency. However, the current plan doesn’t include this change, so I might stick with the original plan for now.

If the compactor remains slow, I’ll need to consider further optimizations. I also need to change `_load_microshard_rows` to return tuples, and I might need to set `compactor_claim_limit` to 250. Should I define the thread pool size in the configuration or just hardcode it to 12 as the plan suggests?
**Evaluating edit plan**

I’ve realized there's a chance to reuse existing parts, but the user wants a specific plan implemented. I might just use min(12, len(jobs)) for efficiency. If the current compactors are destroyed and microshards reset, it might be safe to edit the code. I need to use ApplyPatch for single file edits since there are three files to consider. After that, I should check for any lints and read the exact import sections to patch them properly.


I’m editing the DB layer now: adding a batch-claim path that removes the language lease from the hot path while still using `SKIP LOCKED` for safe parallel claiming.

**Considering SQL updates**

I'm thinking about how to structure an `UPDATE` query in Postgres, specifically using `RETURNING` for a `final_export_microshards` table. It seems I could simplify it by allowing unordered inputs since ordering in `UPDATE RETURNING` isn't allowed. I wonder if using a count query could be too expensive, but with 876k rows, maybe it's manageable. I could also group languages and randomly distribute candidates, but I'm concerned about fairness among workers and potential database overload. It’s a balancing act!
**Optimizing language selection and threading**

I'm thinking about managing language usage and maybe simplifying my queries. It might make sense to focus on the oldest among the top 8 languages based on count, but let's stick to the original plan for now. For the compactor, I need to import `ThreadPoolExecutor` from `concurrent.futures`. I plan to set it up with a max of 12 workers, efficiently managing cleanup. My new async method for loading microshard rows will allow for parallel execution, which should result in returning the jobs in order. Sounds like a solid strategy!
**Considering config updates**

I'm pondering whether to update the config `compactor_claim_limit` default to 250. The plan only mentions the script, but if the script isn’t utilized, it could be useful to have the config also set to 250. The current logic uses the environment from the deploy script, but maybe for safety, I could update the default in the config too. The existing compactor mainly relies on the environment from the script, so leaving the config default at 32 might be reasonable.
**Planning method updates**

I’m thinking there’s no immediate need to read tests; it might be best to skip that for now. We should definitely apply a patch sequentially, starting with the database file. I’m considering adding a helper method called `_rows_to_microshard_jobs`, which would work around the claim methods. It’s important to be careful with grammar as I update the `claim_microshards_for_language` return to use this new helper. Let’s go ahead and craft this patch!


**Planning my coding approach**

I'm thinking about method placement, maybe putting some before and helpers after release. That sounds fine. I also need to handle a type list for flexibility. Next, I should focus on the compactor patch, and it looks like I might need to rewrite some big sections. I'll consider patching imports, __init__, heartbeat, the main loop, and the _process function, adding a parallel method and cleanup shutdown. I should check on _current_language usage too.


**Considering deployment adjustments**

I see that I need to gather exceptions with `_load_microshard_rows_parallel`. After that, it seems important to ensure that the final claimed_jobs release occurs after the compaction no-op. Also, I should create a deploy script patch to set the claim limit to 250. Plus, I might want to consider adjusting the Stage B worker count for a later deployment. So, all in all, I’m ready to make these necessary patches.


The hot-path code changes are in. I’m running a quick sanity pass on the edited files before I rebuild and redeploy the new compactor image.

**Evaluating configuration changes**

I'm considering whether we should adjust the default config to 250. While the plan is to deploy the script, I'm also mindful of mismatches if someone runs it directly since the default could still be 32. Maybe it’s fine, but updates to the main configuration might be needed. Since the user asked for implementations as specified, I might update the default without causing harm. However, I want to minimize deviations from the plan to ensure it runs smoothly. Alright, I'll need to check for any compile errors, run necessary checks, and update todo statuses.
**Planning validation steps**

I'm considering marking both the completed items and increasing the claim limit once validation runs smoothly. First, it seems sensible to run the lints. I'll use ReadLints for that. Additionally, I think I should shell out a command like `python -m py_compile` to check for any compilation issues. It’s all about ensuring everything’s tidy before proceeding with changes. Let’s make sure everything’s validated and ready to go!


**Inspecting project statuses**

I need to consider inspecting the git diff to check my files, though it might not be strictly necessary. Using Shell’s git diff could be useful for that. I might do this after handling my to-do list items. I want to note the statuses of my tasks: new-db-method is complete, parallel-downloads is done, simplify-main-loop is finished, increase-claim-limit is completed, and rebuild-deploy is currently in progress. I should also think about canceling the old 26 tasks later.


I’m rebuilding and pushing the patched worker image now so we can relaunch Stage B on the fast path.

**Building Docker image**

I'm considering the steps to build the image. First, I should inspect the terminals, even though long-running processes aren't needed. I could run a git diff, but maybe it's not necessary. I want to build the Docker image using the existing Dockerfile.final_export. I’ll need to use shell commands and run docker build with the tag "latest" and then push it. I should also check the credentials in the .env file and verify the parent directory, ensuring it exists.
**Building and pushing Docker image**

I'm considering whether the existing image has only changed Python files, which would allow for a quicker layering process. If that's the case, I’d push the Docker image, knowing that the push can take time. I think I'll build first and then push in sequence, making sure to source the .env for the username. I might not need a virtual environment for building, but I'll verify. I'm also thinking about how to log in to Docker securely and then run the final build with an extended timeout.


The new image is pushed. I’m finding a large compactor fleet now, prioritizing network speed since Stage B is R2-download bound rather than GPU-bound.

**Figuring out deployment needs**

I need to deploy over 200 compactor workers and search for available offers. Currently, there are no Stage A instances, but many offers are out there. I'm considering deploying around 200 using the current market. Should I use 3090s or similar GPUs while sticking to a claim limit of 250? It's tricky since I need to search for unique GPUs and gather info on their availability. Internet speed is important too, maybe aiming for over 500 down. I'll consider using CPU-only, even if the image expects GPU.
**Exploring GPU deployment options**

I'm looking to search for unique GPUs like the 4090, 3090, 3090 Ti, A6000, and A5000, aiming for around 200 unique offers using the search command. It’s interesting that we might use two instances per machine if the host has multiple GPUs. However, I’m worried about network saturation since the compactor is network-bound. It seems better to use unique machines but if there’s a shortage, I may need to utilize multiple offers from the same host. I’ll need to prioritize speed with one worker per GPU machine.
**Planning the deployment process**

I want to search for all available offers and select unique machine IDs with the highest internet download speeds, aiming for around 200 workers. I think there might be enough unique machines, but I’ll need to monitor progress after deployment. I might need to adjust some tasks in the plan, especially the rebuild-deploy status, which is still in progress. It seems I can include the deployment in that status. Executing this search will involve using shell Python, which should be manageable.


**Evaluating candidate options**

I see that there are only 87 candidates with 400 or more down, which might be enough to deploy with code changes and 87 workers. However, the initial plan was for 200 or more candidates, and the market seems limited. I could consider relaxing the requirement to 200 down, or even more. Since this is I/O bound, adding more workers might still be beneficial. I should also look for 200 candidates across all GPUs with at least 150 down. Let's see what options I have.


The market is tighter than during Stage A, but I can broaden beyond `4090`/`3090` to other non-50-series cards since Stage B is CPU/network-bound. I’m pulling the biggest safe pool now.

**Evaluating deployment options**

It seems I'm limited to 90 offers due to search limitations in the current market. Deploying 90 workers might not be sufficient for tasks under one hour, but improvements could speed things up. Ideally, I want to maximize offers without duplicate entries. I’m considering using machines, possibly using cheaper graphics cards like the 4080 or 3080 instead of the 50 series, since user compatibility notes seem to allow for 40 series cards. Let’s explore this further.
**Exploring GPU options**

I need to be careful about using the 50 series and focus on options like the 4080, 3080, 3090, and A-series GPUs. Since the compactor is CPU and network-bound, the specific GPU model might not be a concern, so I'm considering alternatives. I can filter out the 50 series in my searches and focus on high-end models like the 3080 Ti and 4070 Ti. Gathering more offers will be essential, and I'm thinking 100+ might be feasible. Let’s find the fastest networks!
**Considering GPU alternatives**

I’m contemplating the possibility of using the A10, but if there’s no actual GPU usage required, its relevance might be limited. The user specifically mentioned using 4090s, 3090s, or anything similar, while avoiding anything from the 50 series. It’s important to stick to those specifications to meet their needs. I really want to ensure I’m helping the user by focusing on the right GPU options. Let’s see how this unfolds!
**Searching GPU options**

I'm broadening my search to include the 4080, 3080, 3080 Ti, and A4000. I’m thinking I might find around 200 options to explore. There seems to be a lot to look into with these graphics cards. Let’s search everything related to them and see what options pop up. I'm curious about the prices and availability of these models!


I found a much larger safe pool by widening to other non-50-series cards that are fine for this CPU/network-bound workload. I’m deploying the full high-bandwidth pool now.

**Evaluating processing batches**

I'm thinking about doing maybe three batches. Sure, using one script could work, but it might be too large. I feel like three batches of around 37 each might be better. I need to think about the output prefix and whether to allow partial shards. As I consider the user’s time constraints, using more workers could help meet the target, especially since maximizing count might be essential even if the network slows down a bit.
**Considering worker allocation**

I'm thinking about using around 200 workers, focusing on any non-50 series and ones with capacities at or above 50. However, I’m mindful that the remote files could bottleneck the code. Still, more workers with lower bandwidth could be beneficial. I could look into using GPUs like the 3060, 3070, and even 3080, 3090, or 4080, since the user mentioned "anything similar." It might make sense to also consider the 3070 or 4070 since they're similar enough.


The fast Stage B fleet is launching now. I’m checking registration and early throughput before I switch back into monitoring mode.


**Monitoring deployment statuses**

I see that I'm 39 online quickly, but I need to wait a bit longer to check for the initial shards. It's important to make sure the updates for todo statuses are completed before moving on to rebuild-deploy. Right now, it's showing as in progress. I might be able to cancel the old 26 tasks later if needed. It seems like I have to keep an eye on this to ensure everything goes smoothly!


The new fleet is up and claiming microshards already. Registration is still ramping, so I’m waiting for the rest of the workers to come online before judging throughput.

**Considering system performance**

I need to monitor the system’s performance. I might also want to check if there are any stuck processes or loading issues. It could be the case that there are only 39 users online because others are still initializing. I’m wondering if I should check again in about 10 minutes — perhaps many more will be active by then. So, let’s plan to poll the system again in 10 minutes.


The redesign is working. Early signal from the first 10 minutes:

- `65` compactors online so far
- `1.77M` segments compacted already
- `118` final shards written
- microshards: `21,368 compacted`, `27,132 claimed`, `828,364 pending`

That’s roughly `177K segments/min`, about `12x` faster than the old run. I’m letting the rest of the fleet finish registering before deciding whether we need another optimization pass.

**Evaluating compute rate improvements**

I need to compute the rate improvement: in 10 minutes, I got 1.77 million segments, which is 177k per minute compared to 14k before, making it 12.6 times better. The estimated time to completion is about 5.5 hours. I might want to check for more offers, as we have around 110 total instances but only 65 online. I should monitor the steady state with all 110 online to judge future optimizations. Maybe we can increase the claim limit further, but it could require rebuilding.
**Considering task completion and monitoring**

The user wants me to keep going until all the todos are complete. However, if monitoring shows that things are still slow, I might need to look for further improvements. The task is to implement the plan as specified, but I can continue monitoring for now. If it remains slow later, I should consider making proactive adjustments. I also want to check the ETA for stage B and quickly compute the remaining segments and rate. We should mention that as well.


I noticed the new query is intentionally concentrating workers on the largest language first, so the current shard output is mostly English. That keeps utilization high, but I want one more steady-state sample with the full fleet online before I decide whether we need a second optimization pass.

**Evaluating language processing**

I’m noticing a potential issue with the current setup, where all workers are focusing only on English after querying a single result. This could create fairness issues and impact overall efficiency. Once English is complete, we’ll tackle the next language, but this means all workers are dedicated solely to one language at a time. Allowing workers to spread across several languages concurrently might improve efficiency, especially since there’s no per-language overhead. Total processing time could still hit about five hours, but I want to explore options for better distribution.
**Analyzing throughput bottlenecks**

I’m considering that total throughput may be limited by the R2 component, rather than by the languages. If all workers focus on English, it’s manageable as long as there are enough microshards available. However, it might help to allow workers to choose randomly from the top languages to minimize contention. While it’s a good thought, the overall throughput may not change significantly since each worker handles the same amount of I/O. With only 65 workers online, I’m estimating a processing time still exceeding three hours, indicating that bigger optimizations are likely necessary.
**Considering optimization strategies**

The user has asked me not to stop until the tasks are complete, and I see that the current monitoring of completion is still in progress. I realize I might need to optimize further since the goal of wrapping things up in 1-2 hours hasn't been achieved yet. Initially, I was sticking to the plan, but I wonder if it’s okay to deviate a bit to improve things. The main bottleneck now seems to be downloading the audio tar, along with the metadata and audio_index files for each of the 876k microshards. Even with 110 workers and a concurrency of 12, that's still quite a challenge!
**Evaluating shard size optimization**

I’m thinking about optimizing by increasing `final_shard_target_rows` from 15,000 to 100,000 or even 500,000 to reduce overhead significantly. With 17.4 million segments, I would be looking at 1,160 shards at 15,000 rows, but with 100,000, I’d only have 174 shards, which could cut overhead by roughly 6.6 times. I'm also considering audio tar sizes for different row counts, and I'm trying to assess whether 60,000 rows would be a manageable compromise.
**Considering download overhead**

I’m noticing that the overhead from having three downloads per microshard—metadata, audio tar, and audio index—is quite significant. One idea I have is to set a claim limit of 2,000 and target 100,000 rows per shard. This way, each worker would download about 1,450 microshards and create one big shard. Even though the total downloads per segment remain the same, the overhead per shard would be reduced, making the process potentially more efficient.
**Considering sharding approach**

I’m thinking the small shards could help with network throughput scaling. Instead of merging audio and metadata into large files, we might want to create manifests that reference smaller microshard files. This logical sharding could be beneficial, even if it might not be accepted. The user had requested a timeline of 1-2 hours, and our current approach may not meet that, but we should keep monitoring the process until completion. While there’s no new plan, I can still look for optimization opportunities.
**Reviewing language distribution**

I’m considering that while changing the total throughput might not be significant, it could help use all languages and avoid a single-language hotspot. However, the current code is already deployed, and further modifications would mean a new build, which might not be necessary unless the user requests it. It could be beneficial to adjust the claim query to randomly select among the top languages, spreading out workloads and potentially improving throughput by reducing lock contention, especially if most workers are hitting the same language.
**Assessing the plan**

I realize this adjustment isn't part of the current plan but might be worth considering a quick patch later if the rate isn’t sufficient. For now, I think it’s best to wait until all 104 are online before assessing performance. With all 104 running, if each worker claims 250 microshards and compacts English, there shouldn’t be contention after the claim, except during refills. The true bottleneck is likely in R2 and tar processing, not contention, so I’ll monitor the situation in about 30 minutes. Let’s wait an additional 10 minutes to see how it unfolds.


The fast path is definitely faster, but I just saw the online worker count drop unexpectedly while shard output kept rising. I’m checking whether that’s just worker restarts or actual errors before I let this run longer.

**Analyzing shard counts**

So, online dropped to 49, which makes me wonder if many compactor workers left because there weren’t any pending microshards. But EN is still huge—maybe those workers exited due to errors or being offline. I should check the statuses.

The progress went from 1.77M to 3.45M in about 20 minutes. However, I'm seeing some inconsistencies in the final shard counts that seem linked to recent queries or possibly outdated outputs. I need to dig deeper into this!
**Checking worker statuses**

Online count dropped to 49, which makes me think many workers might be exiting due to no pending tasks. But there are still many pending. It's possible they are offline because they're not sending any heartbeats. That's concerning! I need to inspect their statuses to see if there are errors.

The vast shows 104 processes running, but maybe some worker processes have died and restarted. If the query only counts those online, it might explain the low count. I should definitely run a query to investigate further.


I’m checking one of the “offline in DB but still running in Vast” compactors. If they’re exiting cleanly instead of looping, we’ll leave a lot of capacity unused and I want to fix that before burning more time.

**Analyzing worker claims**

I'm considering how a combined 100 workers could quickly claim all EN microshards (254k), potentially affecting the overall EN status. When a worker finishes and loops back, the next language might be something like "hi." I think I should keep going instead of exiting.

It seems many instances may not have registered properly due to a slow start, as they show up in the DB with zero jobs completed. If the claim batch returns no jobs, there might be a bug when the `pick` subquery is empty, leading to a clean exit without logging pending jobs.
**Investigating instance status**

I wonder if it's necessary to check online, but for speed, I think we want all 110 instances. It seems odd only 48 are available. I should inspect one of the offline instances, like the one with 0 jobs, specifically instance ID 32751732. I need to find its status, so I plan to use the "show instance" command. This should help me understand what's going on with that instance!


**Exploring SSH Connection**

I need to check if we can use the SSH command, and whether we have a private key. Is keyless access an option? Typically, I’d use `ssh -p port root@sshX.vast.ai`. I think I can try `ssh -o StrictHostKeyChecking=no -p 31732 root@ssh8.vast.ai "python -V"` to authenticate using the default key in the environment. There's a good chance this could work from a logged-in session. Let's see how this goes!


**Reading log remotely**

I'm looking to read a log file, and I'm considering using `sed` and `tail`. Since it's remote, I think that's okay. I’ll utilize a Python command to display the last N lines from the file. It seems like a straightforward approach, so let’s go ahead and do that! Just need to confirm everything is set up correctly before proceeding. I'll make sure I'm clear on what's needed to print those lines accurately.


I found why only ~half the new fleet stayed online: a bunch of workers are timing out on `init_schema()` at startup because 100+ compactors are all issuing the same DDL against Postgres at once. I’m patching that now so compactors proceed once the schema already exists instead of dying in restart loops.

**Evaluating a quick fix**

I’m looking at how to quickly fix the patch compressor by skipping schema initialization during the compact stage or catching `TimeoutError` to assume the schema exists. If the schema does exist, timing out shouldn't block the worker. I thought about implementing a try-except block for `init_schema()` to proceed if it times out.

However, this wouldn’t work with the current workers since they’re built already, so a rebuild and redeploy would be necessary. There’s a possibility to explore shorter DDL executions, or maybe use an environment variable to skip initialization.
**Patching and rebuilding**

I'm thinking about skipping the initialization schema for compactors if the environment variable `FINAL_EXPORT_SKIP_SCHEMA_INIT` is set to true by default. It seems easier to just catch the timeout, and I’m contemplating whether I need to update the worker configuration, but that might not be necessary. I’ll focus on patching and rebuilding. It might also be a good idea to use ReadFile around the starting lines for the exact context, which I already have. Let's move forward with that!


**Running quick checks**

I think I need to run some quick compile and lint checks. It seems important to make sure everything is just right before I proceed further. I want to catch any issues early on, you know? Getting those small errors sorted out can save me time and trouble later. So, let’s do this efficiently and make sure everything's working smoothly! It should help streamline my process and give me some peace of mind.


The startup timeout fix is in. I’m pushing the updated image, then I’ll recycle only the broken/offline compactors and leave the productive ones running so we don’t throw away progress.

**Considering deployment options**

I need to rebuild and possibly redeploy the offline workers. The current ones are running fine, so maybe I can just focus on the offline IDs — possibly 54 — which I could identify from the database. However, it might be easier to destroy all 110 and redeploy them with a fixed image. Losing 2% is okay based on previous user allowances, but I worry about wasting time if I reset again. Balancing current progress and preserving outputs feels tricky, but I think I can manage it.
**Planning instance management**

I can query the database to find the offline worker IDs and map them to their instance labels. After that, I can destroy those instances and reset the claimed microshards from the workers. Then it should be straightforward to redeploy using the corresponding offer IDs since the worker ID includes the offer ID. However, I need to build a new image first before proceeding, as that's essential to the process. Let's get to it!


I need to read a terminal file from the terminals folder using the ReadFile method. It seems pretty straightforward since I just have to specify the path to that file. I’ll make sure I follow the steps carefully to avoid any mistakes. Checking the path is key to ensure the file is correctly accessed. Alright, let's get this done and see what information I can extract from the file!


I’ve isolated the bad half of the fleet: 54 compactor workers are restart-looping on schema init. I’m recycling just those and freeing any microshards they had claimed so the healthy half keeps making progress.

**Assessing instance management**

I need to destroy 46 instances, but I see I only have 46 instance IDs for 54 offline workers. This suggests some offline workers might not be running instances yet. So, I’ll go ahead and destroy those 46 instances and reset their claims, then redeploy the 54 offers. I’m considering the possibility that some current online workers might be using the same offers, which complicates things. I should also check for any available offers now.
**Planning efficient restoration**

To restore quickly, I might destroy the offline instances and recreate them from the same offers, which should still be available. I’m wondering if some instance IDs are missing because the relevant instances aren’t found. There could be a chance that the offers for those offline workers are already gone, but that’s fine. I need to reset the claimed microshards for those offline workers. After destroying the instance IDs, I’ll proceed to reset claims and redeploy the 54 offers, even if that takes some time.


**Evaluating database locks**

I’m wondering if the system is waiting on locks because some rows are claimed by active workers. Maybe I could consider killing those processes and using NOWAIT? But it might not be essential to rush since I could just wait or reset stale instances after 15 minutes. If the microshards claimed by destroyed instances are no longer locked, that could help. I think it might be easier to redeploy without waiting, focusing on immediate progress!
**Preparing to execute command**

I see it’s a large command, but I think I should give it a try. It feels a bit daunting, yet there’s really no reason to hesitate. I can go ahead and execute it. It's important to stay confident, even with big tasks, so let's move forward and see what happens!


I'm checking if the reset claims update has finished. It seems like a good idea to read the terminal again, just to make sure everything is up to date. I want to confirm that the information is accurate before moving on. I wonder if there’s a specific output I should be looking for or if I just need to ensure it looks complete. I'll take a moment to verify that.


The restart-loop fix is deployed and the dead compactors have been replaced. I’m giving the recycled workers a few minutes to come up so I can verify that the online count actually rebounds this time.


I found a second DB hot spot: each shard commit is doing hundreds of per-microshard row updates, and every compactor startup also runs stale-claim resets against the full tables. I’m collapsing the commit into one bulk SQL update and removing that startup churn from compactors.

**Evaluating throughput targets**

I'm concerned that the throughput target wasn't met, which suggests there may be more optimizations needed. But the user's request is clear: we need to implement the attached plan and keep going until everything is complete. I could monitor the process further, but if we estimate hours, it shouldn't take more than an hour. There’s a thought to increase parallel threads to 32, although that would require a rebuild. Plus, some instances are overloading the database, possibly causing bottlenecks.
**Considering database load reduction**

I'm thinking about possibly implementing a bulk update for consumption to just one query per shard, which could really help reduce the database load. Currently, each shard has about 15k rows, and with 250 microshards, it's generating around 500 SQL statements. With 329 shards, that adds up to roughly 165k queries, which seems like a significant bottleneck. This could be a great insight for improving performance!
**Evaluating deployment strategy**

Hmm, we have 88 online workers right now. Is it better to let them continue until new ones with improved commits are available? It seems impossible to offer the same until we destroy. Maybe we can do another cycle and let the current workers go, then wait for them to claim or flush some shards before redeploying everything with a better image.

Destroying online workers won't lose progress, so rolling all workers after a patch could speed things up, even if it causes some downtime.
**Weighing deployment options**

If the bulk commit is large, the online workers might be using the old image and could continue to operate slowly. It might be best to patch and rebuild everything, then kill all the workers as they finish, or do it within 20 minutes if needed, but that could be disruptive. I could also recreate just the 17 offline and 8 error workers, but that may not be sufficient. If the commit is critical, rolling all the online workers might be necessary, which seems acceptable given the objective.
**Evaluating DB updates**

Good point! The `reset_stale_claims` function includes two updates and a delete. Starting with 110 workers means three heavy updates against large tables, which could lead to a big startup load. I’m considering that online workers might handle things later. We could patch the compactor start to skip `reset_stale_claims` and manage manual resets outside the worker, reducing startup stress. I'll think about wrapping `reset_stale_claims` in a try/except for `asyncio.TimeoutError` to avoid startup failures.
**Considering worker updates**

Even if the current approach passes, having 110 workers each updating large tables feels wasteful. While it may slow things down, it might not be fatal. I’m thinking we could patch the compactor startup to skip resetting stale video claims since that stage doesn’t need it. Microshard stale claim resets could be done manually or periodically instead. Given that restarting workers run, we can still reset any offline claims manually to keep things running smoothly.
**Optimizing compactor processes**

Starting the compactor is a great way to further reduce database load, so that’s an excellent optimization. I think it’s a good plan to patch the compactor start so it no longer calls `reset_stale_claims`. If workers need that functionality later, we could just run a separate cron-like script as necessary. Okay, let's go ahead and implement the patch and also handle a bulk commit to ensure everything's updated properly!


The first redesign helped, but the DB still has two hot spots that are capping throughput. I’ve patched both now, and I’m doing one clean rolling restart of the compactor fleet so every worker gets the faster commit path.

**Planning Instance Management**

I'm looking at a detailed plan for managing compactor instances. First, I'll query the current instance IDs and destroy them all. Then, I'll reset all claimed microshards to pending state. If there's a stuck background update process, I'll consider killing it afterward to avoid conflicts. Once that’s cleared, I can rebuild and redeploy the offers with the new image. The aim is to keep things uniform and speedy, while possibly deleting certain statuses afterward. Let's get started!
**Checking and Resetting Instances**

I need to check if the terminal file is still running to see if I can kill and rerun the reset after the destroy process. The first step is to query the current instance IDs and offers, and for that, I'll use a quick shell command in Python. This should give me the information I need to proceed effectively. Let’s get this checked so I can move forward!


I think I need to reset the background process, and killing the specific PID 855496 seems like a way to do that if it’s running. I also want to check if the background task with PID 320110 has finished. Using a command like `kill 855496 || true` could be a safe approach to handle this. I’m wondering if I should also check the current command running with `ps -p` to stay informed. This feels like a lot to manage!


The earlier claim-reset timed out because active workers were still touching the same table. I’m doing this next restart cleanly: push the new image, stop the whole compactor fleet, reset only the in-flight claims with no DB contention, and relaunch everyone on the optimized build.


**Considering microshard reset**

I’m thinking about resetting the claimed microshards. Maybe I also need to set the statement_timeout to 0, though I’m not entirely sure. Updating workers offline might not be necessary, but it's on my mind. I’ll probably use Python to manage these updates and ensure everything runs smoothly. It's a bit of a juggling act, but I'm determined to get it right! Let's see how it goes.


The optimized image is pushed and all in-flight claims have been cleanly released. I’m bringing the full compactor fleet back now on the lower-DB-overhead build.

I’m trying to redeploy 118 offers with a new image. Some of them might fail, but that’s okay! I think using a large command should work for this. I’m ready to go ahead and execute it. It feels a bit daunting to think some might not go through, but I recognize that's part of the process. Overall, I’m just focused on getting this done!


The full fleet is back on the lower-DB-overhead build now. I’m taking a fresh early throughput sample once registration settles so we can see whether the bulk-commit change materially moved the ETA.

**Monitoring and Planning**

I think I need to monitor progress after around 10 minutes. But maybe I should also consider canceling old tasks, like TODO 27, though I'm not quite ready for that yet. Since we're still in monitoring mode, it feels like the current plan might not be complete. I should check the early registration in about 5 minutes instead. Maybe a shorter 5-minute timeframe would work better here.


The bulk-commit rollout fixed the startup churn: `109` compactors are now online and active. Fresh snapshot after the restart:

- `500` final shards
- `7.5M` segments compacted
- microshards: `96,586 compacted`, `48,160 claimed`, `732,118 pending`

That is a clear step up from the previous run, so I’m switching into saturation monitoring now and will keep sampling every ~20 minutes.


The latest sample is much better. The optimized fleet is now at `111` online workers and the throughput jumped hard:

- `19.56M` segments compacted
- `307,784` microshards compacted
- top shard languages now `en`, `hi`, `te`

That’s about `12.06M` segments in the last 20 minutes, roughly a `~68 minute` ETA from here if it holds. I’m staying in monitoring mode and will check again in another 20 minutes.


<user_query>
add more workers maybe ? to reduce time ?
</user_query>


I’m checking the current compactor saturation and the remaining market headroom now. If there are still good non-50-series, high-bandwidth offers left, I’ll add them.

**Checking status and offers**

I need to inspect the current status and available offers. I might start with a commentary update. Do I need to check the terminals folder? Since we already have the shell state, maybe not. For long-running commands, checking the terminals could be helpful. For Python commands, I should activate the virtual environment first. I want to check if there are more offers and gather information on active instances. Let's run some quick status checks and search for additional offers.


There’s still good headroom. We have `82` unused non-50-series offers available, and the fleet is at `110` online compactors with about `31.2M / 60.7M` segments already packed. I’m adding another high-bandwidth wave now to pull the remaining ETA down.

**Deciding on adding workers**

I'm considering whether to add more workers since we have 31.185 million segments compacted and 318,000 microshards pending. The estimated remaining time might be around 20-25 minutes. Since the user seems interested, I think we should act on it, especially with 82 offers available. Focusing on the top 30-40 high bandwidth offers could reduce the time significantly, possibly shaving off 10-15 minutes. It's probably best to proceed with deploying more workers.
**Considering worker addition**

Since there are 82 available offers, I'm thinking about whether adding more workers could help reduce the estimated remaining time of 20-30 minutes. An additional 40 workers might save about 20-25 minutes overall, even though it would have a startup time of 5 minutes. Calculating the current rate, we’re processing roughly 594,000 segments per minute. Adding 40 workers could increase capacity, but I need to ensure that the database can handle the extra load. Maybe starting with the top 30 could be a good test.
**Deciding on deployment strategy**

I’m considering deploying the top 40 offers based on their net download speed. It seems practical to include the first 40 offers from the search output. Although we have 82 offers, there's a possibility of using all 60 if needed. However, I'm aware that the estimated remaining time is about 50 minutes, with a startup time of 5-10 minutes. So, it makes sense to proceed cautiously and stick with the top 40 for now.


I added `40` more high-bandwidth compactors on top of the existing fleet. I’m giving them a few minutes to register so I can confirm the online count and revised burn rate.

**Considering status updates**

I think I need to provide the user with a status update and possibly a sample in a few minutes. Maybe I'll provide some commentary as well. If the user requested more input and to reduce the waiting time, I should confirm that I've added 40. It seems like checking online after a few minutes could be helpful too. I could do a quick wait of around 180 seconds and then check the database status. Let’s get started!


I added `40` more compactors.

Current state:
- `147` compactors are online in DB
- `158` instances exist in Vast, with `149` already `running`
- `34.60M` segments compacted so far
- `572,229` microshards compacted
- `243,236` microshards still pending

This did help. The burn rate after the extra wave is roughly consistent with a remaining ETA around `35-40 minutes` if throughput holds. I’m keeping the bigger fleet up and continuing to monitor saturation/completion.

**Providing an update**

I need to respond concisely. The user asked if I added more workers, and yes, I added 40, making it 147 online out of 158 instances. The ETA has improved, and I can calculate that from the last sample: in 5 minutes, the workload increased from 31.185M to 34.602M, which is about 683k per minute. If this holds, we have approximately 38.2 minutes left. Since the task is ongoing, I’ll offer a concise status update but clarify that we're not done yet.
**Giving a status update**

I've added 40 workers, making the fleet now 147 online out of 158 instances, with a packed workload of 34.6M and 243k microshards pending. If this rate holds, the rough ETA is around 35-40 minutes. While the system suggests sending a final update when all work is complete, since we're still ongoing, I think I’ll provide a concise status update instead. It's important to keep the user informed, even during long-running tasks.


<user_query>
okay can you go back to monitoring mode and remaining action items once everything is done. i want clean number tallys. not just make ups. do the work and do validations, tests, check everything  made to R2. i dont want any fuckups or errors or bugs in any way.
</user_query>


I’m back in monitoring mode now. First I’m taking a fresh state snapshot from the fleet and DB, then I’ll stay on completion/validation until Stage B is fully done and I have exact tallies from R2 and Postgres.

**Continuing task monitoring**

I need to respond and keep tasks moving. I'll set up updates every 30 seconds and use todo updates as needed. I should avoid mentioning guarantees unless necessary. I’ll validate final shard counts and monitor progress until completion. I'll check for any open terminals and their processes before running the long command. It's crucial to activate the virtual environment for Python commands, but that might not be necessary for unrelated shell commands. I'll keep tracking the existing todo list statuses.
**Checking terminal status**

I think I should mark item 26 as cancelled. First, I need to provide concise commentary, then check the current status. It seems like the aborted monitoring spawn didn’t cause any issues. I might need to run some shell commands to query the database and list VAST statuses. Before starting long-running processes, I should inspect the terminals folder to ensure nothing is already running. It's crucial to use ReadFile for file operations and consider using Shell for listing files too.
**Managing file operations**

I need to use Glob for file operations, specifically on the terminals folder to list *.txt files, and then ReadFile each one. That might lead to a lot of files, but it’s simpler to do this before launching any new long-running processes. However, I'll comply with the instructions to check first. I should also review readme.md or progress.md in the root and update those, while avoiding creating new files or searching through current documentation unnecessarily.


The run is still healthy overall: `42.59M` segments are already compacted, with `144,416` microshards left. I’m checking the few non-online workers now so we don’t leave avoidable capacity on the table while the tail finishes.

**Monitoring error workers**

I should consider reading terminal metadata, but that’s probably not essential unless I'm launching a long-running command. We’ve already globbed, so monitoring should be enough for now.

I’ll check the error worker since one might be failing, which would require me to destroy or replace it. Also, I may need to reset stale claims while keeping an eye on the offline or error workers.

Since the user asked for no errors, I can inspect and possibly replace the failing workers. I’ll query specific error information and languages, checking every 10-15 minutes.


I found a real correctness bug in Stage B, and I’m fixing it now instead of just watching it run: shard IDs are not unique enough across many workers, which can cause path collisions and the `HEAD size mismatch` error I just saw. I’m patching that to use a collision-proof shard ID, then I’ll restart Stage B cleanly so the final tallies are trustworthy.

**Investigating upload issues**

I need to address a potential error with the worker that might indicate a size mismatch after an upload. This could suggest a partial upload. I should inspect the shard records to check for eventual consistency and see if the shard row was inserted properly. It's possible there's a bug if there are collisions in shard IDs due to multiple workers uploading at the same time. I need to act quickly to prevent data loss or duplication. Great catch!
**Addressing critical bug**

I need to pause and address a potential critical bug as the user is asking about monitoring remaining items. To prevent shard ID collisions, I should include a worker ID or UUID in the shard ID. Then, I’ll rebuild and redeploy the compactors. It’s crucial to understand the impact on existing data because collisions may lead to overwrites or conflicts. I'll also consider stopping the compactor and running Stage B from scratch with the fixed shard IDs to ensure accuracy.
**Identifying data corruption risk**

I need to consider what happens if `insert_final_shard` encounters a conflict due to a duplicate shard ID. If a second worker uses the same shard ID and writes to the same R2 path, it could overwrite the files from the first worker. Even though `commit_microshard_consumption` would still be executed for the second worker, the final database will only retain one record, which would reflect the first insert's metadata. This situation poses a serious risk of data corruption.
**Considering shard conflicts**

I'm thinking I might need to alter either `_upload_pack` or `insert_final_shard` to address conflicts. Using a natural, unique `shard_id` could help streamline things. I'm also wondering if I should remove the HEAD size validation for eventual consistency, but that mismatch seems like it came from a collision. I think it might still be useful to keep HEAD validation around, especially since the user wants that validation.
**Evaluating Cloudflare R2 consistency**

I’m considering the immediate consistency for HEAD with Cloudflare R2, and I think it’s probably okay. Before restarting the compactor fleet destructively, I need to validate the extent of the bug since there might already be many collisions.

Looking into duplicate keys in `final_export_shards` could help, as shard_ids are unique. If an overwrite happened, counts might be off. Using `final-export/production/shards/` as output prefixes, I'll focus on cleanup.  I'll also consider if I should delete existing final shards in R2 to avoid conflicts.
**Addressing R2 shard management**

Final validation may count extra stale shards unless I filter by the database. Since the user wants R2 to be clean, I think I should delete stale Stage B outputs to avoid confusion. The `final_export/production/shards` only contains the current run, so it might be safe to delete this prefix.

I could use Python's Boto3 library or existing scripts to delete the keys. I need to be cautious since this prefix may only pertain to Stage B outputs. With 2886 shard packs having around 11k objects, it's manageable. I'll implement a patch first, then restart and inspect shard counts to estimate any corruption.
**Investigating bug in compactor**

It seems like there’s a significant bug to address. I might need to use ReadFile on the compactor portion related to shard_id. It could be beneficial to read a small chunk before applying any patches. This way, I can gather more context about the issue to ensure I’m fixing the right parts. I’ll proceed cautiously to avoid complicating things further while trying to understand the bug better.


I’ve confirmed the exact collision point in code. I’m making shard IDs globally unique now, then I’ll wipe the corrupted Stage B outputs and rerun compaction from clean microshards so the final R2 state is exact.

**Considering patching methods**

I’m thinking about importing the uuid and shard_id functions, maybe considering adding a helper method to compute the shard_id. That could streamline things. I mean, I just want to make sure everything is efficient and functional! So, patching seems like a smart move here. I’ll go ahead and implement that. It's all about getting the right approach to make future tasks easier!


The uniqueness fix is in and compiles cleanly. I’m pushing it now, then I’m doing a clean Stage B restart: stop compactors, delete the bad shard outputs from R2, reset compaction state in Postgres, and relaunch.

**Planning deletion and rebuild**

I need to rebuild the push, so first, I should inspect the R2 deletion schema, potentially using Python with Boto3. Once I stop the fleet and reset the database, I’ll destroy the current compactor fleet. Gathering instance IDs and destroying them is simpler through commands. I need to handle the command length carefully while ensuring I get all instance IDs. Also, I need to reset microshards and delete certain R2 prefixes. Lastly, I'll inform the user before any destructive actions.
**Updating progress and outputs**

I need to figure out if I should update the TODO since there aren't any changes. I could mark the rebuild-deploy as "in progress" again due to the additional patch and redeploy. However, since the TODO is already completed, I might avoid that to prevent regression. I’ll also check if any stage B outputs are in R2, as the user prefers clean tallies. I need to consider deleting those outputs but first, I’ll destroy the workers to avoid contention. Then we can proceed with the build and push.


**Executing instance destruction**

I need to destroy the current compactor instances. I'll use a Python script to get the current compactor instance IDs. It sounds like I might call "vastai destroy" in chunks, or I could go for a single destroy command using the IDs from the show raw output in Python. I'll aim to execute the Python script in a shell. Let's get this sorted efficiently!


The old Stage B fleet is down. I’m resetting the compaction state in Postgres and deleting the bad shard objects from `R2` now so the rerun starts from a clean, auditable surface.

**Considering Python environment options**

I’m pondering whether to use a virtual environment (venv) for Python. Boto3 might work better in a venv, but I’m not quite sure if it’s necessary. I could also consider running commands sequentially, starting with a database reset followed by the R2 deletion. But hey, maybe R2 deletion doesn’t even require a venv! It might be worth using Python 3 with boto3 from the system directly and running the processes in parallel to save time.


The bad Stage B state is gone now: `3022` shard DB rows deleted, all `876,864` microshards reset, and `13,081` shard objects removed from R2. I’m relaunching the compactor fleet on the fixed build so the final R2 output is clean from here forward.

**Considering fleet redeployment**

I'm thinking about redeploying the full fleet, maybe with the same 158 offers plus an extra 40. Since some offers might still be available, I wonder if it would be quicker to redeploy those 158 existing offers again. Sure, some might fail, but that’s okay. I could also pull from our previous shells if needed, including lists of all used offers. It seems like combining the offers for a more effective redeployment could work!
**Evaluating offer deployment**

I'm considering using the current database worker IDs, but it might be easier to work with the combined lists we already have. I think I shouldn't include any that were previously created since they're now destroyed, but the offers should still correspond. Some might not be available, yet the deployment script will continue running regardless of that. I should use the same 158 offers and mark that I'll need to monitor for validation later. Maybe I can query the current instances after the destruction, but it might not be necessary.


The clean restart is live now on the fixed build. I’m letting the fleet settle for a few minutes, then I’ll confirm that the new shard IDs are flowing and that there are no fresh path-collision errors before I go back to longer monitoring sleeps.


The clean rerun looks healthy. Most important signal: there are `0` compactor errors so far on the fixed build, and new Stage B output is flowing again with `3.95M` fresh segments already packed.

**Evaluating shard IDs**

I need to check some unique shard IDs, maybe looking at one that includes a UUID. I think I might want to inspect another shard ID to confirm things. A quick SQL query could help to check for errors and query a sample shard ID. I'll also keep monitoring for about 15 to 20 minutes, checking for any path collision errors. It might be worthwhile to query the last few shard IDs too. However, I'll need to focus on updating a pending task as well. For now, it seems I should take a break for about 20 minutes.


Throughput on the clean rerun is solid: `20.54M` segments packed with no compactor errors, and about `507k` microshards left. While the fleet keeps running, I’m lining up the exact post-run validation path so the moment compaction finishes I can do the DB/R2 tallies instead of improvising them.

**Evaluating progress and options**

I’m noticing that the progress rate is around 16.6 million in 20 minutes, which means about 49 minutes remaining. Adding more workers could be beneficial since the user previously mentioned it. However, I still need to monitor everything, especially the remaining action items as indicated by the user. With 126 online and 31 not yet, we could look into the offline offers later, but it seems best to wait for now and see if the count increases.
**Preparing validation processes**

I think it’s a good idea to start planning preliminary validation methods while waiting. I may want to query the Stage A canonical counts to see what’s known. Eventually, I’ll need final validation scripts, so I can begin identifying schemas. The user wants clean tallies, so while I'm monitoring, I can check existing scripts like `validate_final_export_run.py` in the repo. I'll read those script files now and prepare the validation tooling for later runs while the fleet operates.


The validation path is ready: there’s already a strict `scripts/validate_final_export_run.py` we can use for feature-by-feature spot checks on final shards once Stage B closes out. I’m back to monitoring the fleet until the pending microshards drain.


The clean rerun is still error-free and down to `213,416` pending microshards. While it drains, I’m computing the exact expected totals per language now so the final comparison is against known-good numbers, not just the live shard table.

**Considering local counts**

I should probably compare these numbers to the canonical local counts as well. It seems like computing local counts by segment language from the canonical parquet is a good idea. I think I could perform this in parallel, utilizing the expected DB counts per language from the microshards row count, alongside local canonical counts per language — maybe with duckdb? I wonder if 30-second updates would be sufficient; I could send some commentary on that!


I have the exact Stage A expected counts per language from the microshard table already. The local canonical parquet uses a different language column name than I expected, so I’m checking its schema now and will line those totals up too before the final audit.


**Computing language data**

I think I should compute per-language using either corrected_language or queue_language. In Stage A, the expected languages will probably be derived from segment_language based on those options. It seems like we could compute using corrected_language as a basis. I want to check if the totals sum to about the same value, maybe 60.7 million. I’ll need to run a query to find out for sure!


I’ve now pinned the exact expected totals from both sides:

- local canonical: `60,711,097` rows across `395,513` videos
- per-language local totals from `gemini_lang` match the Stage A microshard totals exactly

That gives us a clean expected target for the final Stage B tally once the pending tail drains.

**Monitoring gemini_lang performance**

I’m noticing that the gemini_lang matches are aligning with the expected microshard counts, which is great! I should probably take a note on this. I feel like I need to keep monitoring things closely. Now that we have the exact totals, we can compare the final shards per language to those totals. It might be useful to compute counts as a file, but maybe it’s not necessary since we already have the outputs. Perhaps I should take a longer break, about 15-20 minutes.


The tail is in sight now: `53.12M / 60.71M` segments are packed, with only `32,864` microshards left. I’m shortening the sleep interval now so I can catch completion quickly and move straight into the final DB/R2 validation pass.


We’re at the very end now. There are no `pending` microshards left, only `9,076` still `claimed`, so I’m watching for the final drain and then I’ll immediately reset any stranded claims if needed before doing the exact tally.

**Analyzing worker statuses**

I'm noticing an interesting jump in the worker statuses; the offline count seems to increase when workers exit after having no jobs. It looks like the online stats count is at 21, while the worker statuses say 20, which seems inconsistent. I see that there are 9,076 remaining claimed jobs, and since there are no pending ones, it suggests that all are in progress. I think I'll wait about 5-10 minutes and see if that changes.


The tail is still draining normally. Claimed microshards fell from `9,076` to `7,409`, so I’m letting the remaining workers finish rather than forcing a reset too early.

**Evaluating claim process**

I think I need to check again after a short wait—maybe about five minutes. It feels like I should check if the claims are decreasing before deciding. I’m trying to decide if I can release stale claims sooner than expected. I’ll give it another five minutes and see what happens. It’s a bit of a balancing act, but I’m curious to see if any changes occur!


The drain just slowed sharply, which usually means some claimed microshards are stranded on workers that have already exited. I’m checking claim ownership now so I can release only the stale ones instead of disturbing active workers.

**Inspecting claimed_by status**

I’m thinking that I should check the claimed_by status counts—specifically whether they’re online or offline. It seems important to ensure accurate information. So, I’ll plan to query this data online. I want to make sure I’m thorough with this task, as having good insight into the status counts will help clarify things. Let's make sure everything is clear before proceeding!


I found the tail blocker: `6,644` of the remaining claimed microshards are owned by workers already marked `offline` in Postgres, so they’ll never finish on their own. I’m releasing just those stale claims back to `pending` now so the remaining live workers can sweep them up.

**Releasing stale claims**

I need to release stale claims from offline workers since many of these claims have been offline for over 20 minutes. I'll reset them to pending status so that online workers can pick them up. Currently, we have some loaded and created instances, but the claimed ones seem to come from completed workers. It might make sense to release all claims connected to offline workers. I'll ensure to run a database update, and it's important to note this for the user.


The stale tail is released: `6,898` microshards went back to `pending`. I’m giving the live workers a few minutes to reclaim them, then I’ll confirm the queue is moving again.


The forced tail release worked. Remaining in-flight work dropped from `7,213` claimed to `1,508`, and the packed total jumped to `60.59M` segments. I’m staying on it until we hit zero and then I’ll run the full audit.


Only `624` microshards are still in flight now. I’m checking one more time whether they’re actively owned by live workers or whether I should release a final tiny stale set.

**Considering status queries**

I'm thinking about whether I need to finalize a release if anything is offline. I might want to check the claimed_by statuses to find out if 624 is offline or online. It could be best to just wait for now and see how things develop. There's no rush, so taking time to assess the situation seems smart! Let's keep an eye on that and make decisions based on the data I gather.


One stale claim owner is still blocking the last bit: `315` microshards are attached to an offline worker. I’m releasing that final stale batch now and then the remaining live workers should finish the rest.