The Great Database Purge: Cleaning Up Stale Instances in a Distributed Proving System

Introduction

In the course of operating a distributed GPU proving network, databases inevitably accumulate historical detritus. Instances are created, benchmarked, found wanting, and destroyed — but their records linger. On March 12, 2026, during a marathon session of platform hardening for the vast-manager system (a management service orchestrating GPU instances on the vast.ai marketplace for the CuZK zero-knowledge proving engine), the assistant received a simple but consequential request from the user: "In meantime clean up all Killed instances from the DB which are iiuc wrong entries in large %" ([msg 1576]). The assistant's response ([msg 1579]) — a single, decisive SQL DELETE command wrapped in a carefully orchestrated service restart — would erase 30 historical records and reduce the database from 35 entries to just 5. This article examines that message in depth: the reasoning behind the decision to delete all killed instances rather than selectively pruning, the operational care taken to avoid corruption, and the assumptions — both justified and questionable — that guided the action.

Context: The State of the System

Before the cleanup could happen, the assistant needed to understand what it was dealing with. The user's request was ambiguous: "wrong entries in large %" suggested that many of the killed instances were erroneous — perhaps artifacts of earlier bugs, test deployments, or instances that had "disappeared from vast" during the port 1234 tunnel forwarding bug era. But which ones were "wrong"? The assistant's investigation ([msg 1577]) revealed a database with 30 killed instances, the most recent showing clear kill reasons like "bench_rate 46.0 below min_rate 51.0" — these were legitimate operational kills where an instance failed to meet its minimum proof rate. A further query ([msg 1578]) showed that 10 of the 30 had no vast metadata (null vast_id or machine_id), marking them as early experimental instances from before the metadata persistence feature was added. The remaining 5 instances in the database were active (running, registered, or benching). The stage was set for a decision.

The Message: A Surgical Strike

The subject message ([msg 1579]) reads in full:

[assistant] 30 killed, 10 of those have no vast metadata (early experiments). Let me just delete all killed instances — they're historical noise at this point and clutter the UI:

>

[bash] ssh 10.1.2.104 "sudo systemctl stop vast-manager && sudo sqlite3 /var/lib/vast-manager/state.db \"DELETE FROM instances WHERE state='killed';\" && echo 'Deleted killed instances' && sudo sqlite3 /var/lib/vast-manager/state.db \"SELECT COUNT(*) || ' instances remaining' FROM instances;\" && sudo systemctl start vast-manager && sleep 1 && systemctl is-active vast-manager"

>

Deleted killed instances 5 instances remaining active

The message is deceptively simple. A single bash command, executed over SSH on the controller host (10.1.2.104), performs a multi-step operation: stop the service, delete the records, verify the deletion, restart the service, and confirm it's active. The output confirms success: "Deleted killed instances", "5 instances remaining", "active". But beneath this straightforward execution lies a web of reasoning, assumptions, and trade-offs that merit close examination.

Why This Message Was Written: Reasoning and Motivation

The immediate trigger was the user's request at [msg 1576]. However, the assistant's response reveals a deeper motivation: the killed instances were not just taking up space — they were actively harmful to the system's usability. The assistant's own words capture this: "they're historical noise at this point and clutter the UI." This is a critical insight into the assistant's mental model. The vast-manager system has a web dashboard that displays all instances, and killed instances would appear there, potentially confusing operators who need to see only active machines. Moreover, the database queries that compute summary statistics (like FetchingCount, RunningCount, BenchingCount) filter by state, but any code path that iterates over all instances would have to skip 30 dead entries for every 5 live ones — a minor inefficiency, but one that compounds as the system scales.

The assistant also recognized that the killed instances were final. Once an instance is killed on vast.ai, it is destroyed and cannot be recovered. The database records were purely historical artifacts — they documented failures that had already been acted upon. Keeping them served no operational purpose. The kill reasons (like "bench_rate 46.0 below min_rate 51.0") were useful for debugging at the moment of kill, but once the instance was gone and the operator had acknowledged the failure, the records became noise.

How Decisions Were Made

The most significant decision in this message is the choice to delete all 30 killed instances rather than selectively deleting only the 10 "wrong" ones (those without metadata). The assistant's reasoning is explicit: "Let me just delete all killed instances — they're historical noise at this point." This is a pragmatic, all-or-nothing approach. The assistant could have written a more nuanced query — DELETE FROM instances WHERE state='killed' AND (vast_id IS NULL OR vast_id = 0) — to target only the early experiments. But that would have left 20 legitimate kills in the database, still cluttering the UI and still requiring mental overhead to ignore.

The decision reflects a judgment about the value of historical data. In a production system with mature operations, kill records might be invaluable for trend analysis (e.g., "Which GPU models consistently fail to meet minimum rates?"). But in a rapidly iterating development session where the system itself is being designed and debugged, historical data from yesterday's bugs is more likely to mislead than inform. The assistant implicitly chose development velocity over data preservation.

A second decision was the operational procedure: stop the service before modifying the database. This is a critical safety measure. The vast-manager service runs a monitoring loop that periodically queries and updates the database. If the service were running during the DELETE, it could:

  1. Read stale data and produce inconsistent state
  2. Attempt to write to a row being deleted, causing a crash or deadlock
  3. Re-insert a killed instance if its vast.ai counterpart was still being tracked By stopping the service first, the assistant ensured a clean, single-writer environment for the database operation. The restart afterward confirms the service initializes correctly with the reduced dataset.

Assumptions Made

Several assumptions underpin this message:

Assumption 1: Killed instances have no future value. The assistant assumes that no operator will ever need to query a killed instance's metadata — its GPU type, its kill reason, its benchmark rate. This is a reasonable assumption for a development system, but it would be dangerous in a production environment where post-mortem analysis is routine.

Assumption 2: The database is the only source of truth for instance state. The assistant assumes that the vast.ai platform itself does not need to be notified of the deletion. Since the instances are already killed on vast.ai, the local database is purely a cache. Deleting from the cache is safe because the next monitoring cycle will only discover instances that are still alive on vast.ai.

Assumption 3: The service can be safely stopped and restarted. This assumes that no other process depends on the vast-manager service being continuously available. In this deployment, the service is a single binary on a controller host — stopping it briefly is safe.

Assumption 4: SQLite handles the DELETE transactionally. The assistant chains multiple commands with &&, meaning each must succeed before the next runs. If the DELETE fails (e.g., due to a locked database), the subsequent commands (including the restart) would not execute, leaving the service stopped. The assistant implicitly trusts that the DELETE will succeed.

Mistakes or Incorrect Assumptions

While the message is operationally sound, there are several potential issues worth examining:

No backup before deletion. The most significant omission is the lack of a backup. A single INSERT INTO killed_backup SELECT * FROM instances WHERE state='killed' before the DELETE would have preserved the data at negligible cost. The assistant could have even piped the backup to a file on the controller host. Without a backup, the kill reasons, benchmark rates, and GPU metadata for 30 instances are permanently lost. If a future analysis needs to answer "How many RTX 4090s failed to meet minimum rate?" the data is gone.

The "all or nothing" approach discards useful signal. Among the 30 killed instances, some had legitimate kill reasons that could inform hardware selection. For example, the RTX 5000 Ada (46.0/hr vs min 51.0) and the second RTX 4090 (46.2/hr vs min 59.0) were close to their thresholds — their data might have been useful for tuning the minimum rate calculation. By deleting all records, the assistant forfeits this signal.

No consideration of foreign key relationships. The query deletes from the instances table. If there are related tables (e.g., instance_logs, benchmark_results) that reference instances.uuid, those rows become orphaned. The assistant does not check for or clean up related data. The schema shown in earlier messages ([msg 1573]) does include a param_done_at timestamp and other fields, but the assistant doesn't verify whether other tables reference the deleted rows.

The 10 "wrong" entries might have been the only ones the user wanted removed. The user said "wrong entries in large %" — they may have been referring specifically to the instances with no metadata (the early experiments), not the legitimate kills. The assistant's decision to delete everything goes beyond the user's explicit request. In many contexts, this would be considered overreach.

Input Knowledge Required

To understand and execute this message, the assistant needed:

  1. Database schema knowledge: The instances table has a state column that can be 'killed', and the DELETE syntax targets rows by state. The assistant also knew the table had columns like vast_id, machine_id, gpu_name, registered_at, killed_at, and kill_reason (visible in the SELECT query at [msg 1577]).
  2. SQLite syntax: The || operator for string concatenation in the verification query (SELECT COUNT(*) || ' instances remaining') is SQLite-specific. The assistant also knew to escape the double quotes inside the SSH command.
  3. System administration: The assistant knew to stop the service before modifying the database, and to verify the service is active after restart. The systemctl is-active vast-manager command returns "active" only if the service is running.
  4. SSH and command chaining: The entire operation is a single SSH command with && chaining, ensuring atomicity. If any step fails, the subsequent steps are skipped.
  5. The vast-manager architecture: The assistant understood that the service caches instance state from vast.ai and that deleting local records is safe because the next monitoring cycle will repopulate from the source of truth.

Output Knowledge Created

After this message, the system state changed in several ways:

  1. Database reduction: 30 rows deleted, 5 rows remaining. The database is now 85% smaller in terms of instance records.
  2. Cleaner UI: The dashboard will no longer display killed instances, reducing visual clutter and cognitive load for the operator.
  3. Faster queries: Any query that iterates over all instances (e.g., the dashboard summary computation) now processes only 5 rows instead of 35.
  4. Operational precedent: The assistant established a pattern for future cleanups: stop service → modify DB → verify → restart → confirm active. This pattern can be reused.
  5. Lost data: The kill reasons, benchmark rates, and GPU metadata for 30 instances are permanently deleted. No backup exists.

The Thinking Process Visible in Reasoning

The assistant's reasoning is visible in the progression from investigation to action:

  1. Assessment ([msg 1577]): The assistant first queries the actual data to understand scope. It doesn't blindly delete — it looks at the kill reasons, the timestamps, the metadata. It notices the pattern: "Many early ones (before ~10:00) have no metadata."
  2. Quantification ([msg 1578]): The assistant then counts: 30 killed total, 10 without metadata, 5 non-killed. These numbers inform the decision.
  3. Judgment ([msg 1579]): The assistant makes a qualitative judgment: "they're historical noise at this point and clutter the UI." This is not a data-driven conclusion — it's a product design decision about what information is valuable.
  4. Execution ([msg 1579]): The command is carefully constructed. Note the use of sudo systemctl stop vast-manager before the DELETE — this prevents race conditions. The && chaining ensures atomicity. The verification query (SELECT COUNT(*) || ' instances remaining') provides immediate feedback. The sleep 1 before the systemctl is-active check gives the service time to initialize.
  5. Verification ([msg 1579]): The output confirms success at each step: "Deleted killed instances", "5 instances remaining", "active". The assistant doesn't need to ask the user to verify — the evidence is in the output.

Conclusion

Message [msg 1579] is a masterclass in pragmatic database maintenance under operational constraints. The assistant correctly identified that the killed instances had outlived their usefulness, made a decisive judgment to delete all rather than some, and executed the operation with proper care (stop service, modify, restart, verify). The primary weakness — the lack of a backup — is a forgivable omission in a development context where data churn is high and historical records are suspect. The message exemplifies a key skill in distributed systems operations: knowing when to let go of data, and having the procedural discipline to do so safely.

In the broader narrative of the vast-manager platform development, this cleanup marks a transition. With the database now containing only live instances, the operator can focus on the 5 active machines — their benchmark progress, their proof rates, their costs — without the distraction of 30 ghosts from earlier experiments. The system is ready for the next phase: deep investigation into the PoRep PSProve CuZK failure that would dominate the remainder of the session.