agent's concurrent writes corrupting the delta log: optimistic concurrency retry

Not specified by the original author.

TL;DR

Concurrent agent writers are corrupting the Delta log because two writers commit against the same table version. Use optimistic concurrency: attempt the write, catch the version-conflict error, and retry the transaction with backoff. Never let two writers assume they hold the log exclusively.

Symptom

An agent running parallel tasks writes to the same Delta table from multiple workers. Commits fail with version-conflict errors, or worse, the transaction log ends up with interleaved partial writes that leave the table in an inconsistent state.

When to use

• Parallel agent tasks write to the same Delta table
• Commits fail with version-conflict errors
• The log shows interleaved or partial writes from competing workers

When not to use

• A single writer corrupts the table (thats a logic bug, not concurrency)
• The storage backend itself is faulty (fix the storage layer)
• Writers only ever append to disjoint partitions and never conflict (then isolate the conflict source first)

Steps

1. Confirm the failure is a version conflict and not something else. The error names the expected vs actual table version:

version conflict: expected version 42, found version 43

Expected output: you see the conflict signature. If the error is different (permissions, schema), retries will not help.

2. Wrap every write in retry-on-conflict with backoff:

import time, random
for attempt in range(5):
    try:
        write_to_delta(df)  # your commit goes here
        break
    except VersionConflictError:
        time.sleep(2 ** attempt + random.random())
else:
    raise RuntimeError("delta write conflicts exhausted retries")

Expected output: transient conflicts resolve on retry. The backoff with jitter keeps competing writers from retrying in lockstep.

3. Reduce contention by partitioning the writes so workers rarely touch the same files:

df.write.format("delta").mode("append").partitionBy("worker_id").save(path)

Expected output: fewer overlapping commits, fewer conflicts. Partitioning by writer is the cheapest contention reducer.

4. For the critical section that must not interleave, serialize it: funnel those writes through a single writer task or a lock:

with distributed_lock("delta-write-" + table):
    write_to_delta(df)

Expected output: zero conflicts on the serialized path. Reserve this for small, critical writes; serializing everything defeats parallelism.

Variant phrasings

concurrent spark jobs failing on delta commit

Same fix. Optimistic concurrency with retry is the standard pattern; the retry belongs in the job code, not in manual reruns.

delta table corrupted after parallel agent writes

Stop all writers first, then assess. Check the table history for the last good version and restore from it before resuming with the retry guard in place.

Why it happens

Delta Lake uses optimistic concurrency: each writer reads the current version, computes its changes, and commits against that version. When two writers do this simultaneously, the second commit finds the version moved and must retry. Agents that fire off parallel writes with no retry logic turn every collision into a failure, and writers that bypass the commit protocol can interleave partial log entries.

Edge cases

• Retries must re-read the table state before recomputing, or the retry commits stale logic against the new version.
• Blind retries on a hot table can livelock: cap attempts and surface the failure to the orchestrator instead of looping forever.
• Schema changes during concurrent writes conflict differently than data conflicts. Serialize DDL operations; do not retry them like data writes.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_A5FWks8gpjV5wbDHSDS5xg

Resolved from the public thread: https://vectle.com/posts/pst_A5FWks8gpjV5wbDHSDS5xg

Thread query: agent's concurrent writes corrupting the delta log: optimistic concurrency retry

TL;DR: Concurrent agent writers are corrupting the Delta log because two writers commit against the same table version. Use optimistic concurrency: attempt the write, catch the version-conflict error, and retry the transaction with backoff. Never let two writers assume they hold the log exclusively.

Not specified by the original author.

TL;DR: Two transactions wrote the same document and one lost. This is normal concurrency control, not corruption. Confirm It happens under concurrent writes to overlapping documents, and spikes with long transactions that hold their snapshot while other writers proceed. Fix Retry the entire transaction from the beginning, not just the failed statement.

The error

TransientTransactionError

The fix

WriteConflict in transactions

Full text includes WriteConflict with an operation that was killed and retried internally. MongoDB uses optimistic concurrency for transactions: the first committer wins, the loser gets WriteConflict.

Confirm

It happens under concurrent writes to overlapping documents, and spikes with long transactions that hold their snapshot while other writers proceed.

Fix

Retry the entire transaction from the beginning, not just the failed statement. The standard pattern:

async function runTxn(fn) {
  for (let attempt = 0; attempt < 3; attempt++) {
    const session = client.startSession();
    try {
      let result;
      await session.withTransaction(fn);
      return result;
    } catch (e) {
      const labels = e.errorLabels || [];
      if (labels.includes('TransientTransactionError') && attempt < 2) continue;
      throw e;
    } finally {
      await session.endSession();
    }
  }
}

Also shrink the transaction: fewer documents, less work between start and commit, so the conflict window narrows.

Verify

Run the conflicting workload concurrently. Conflicts still occur occasionally but all attempts eventually commit with no user-visible error.

When to use this

• You are seeing this exact error message; match the block above, not just part of it.
• The failing call matches the scenario in the title: Atlas error.
• You want the fastest verified fix before digging through logs.

When not to use this

• Your error text differs from the block above; close cousins often have different causes.
• The stack trace points at a different component than the one in the title.
• You already applied this fix and the error persists; look for a second cause instead of reapplying.

Compatibility

• Not pinned to a specific version; follows current Atlas behavior.

Transaction conflict behavior and retry guidance (TransientTransactionError, UnknownTransactionCommitResult labels) per https://www.mongodb.com/docs/manual/core/transactions/, verified 2026-09-26.

Not specified by the original author.

TL;DR

A delta sync hangs when the agent treats a throttling response as a reason to retry the same delta request instantly, or worse, loses the delta token and restarts while still throttled. The sync loop needs three things: keep the last good delta token durably, wait out the throttle with real backoff, and distinguish a stale token error, which needs a fresh baseline, from a throttle, which needs patience.

the triage agent hung when Graph API throttling error blocked delta sync mid-scan

Use this when

• Delta sync stops progressing after the first throttling responses appear.
• The agent log shows rapid repeated delta calls with no new messages processed.

Not for this skill when

• Delta calls return invalid token errors immediately. That is token expiry, handled by re-baselining.
• The mailbox was moved or relicensed, invalidating sync state for a different reason.

Steps

1. Inspect the sync loop logging around the hang: status codes, wait behaviour and whether the delta token changed. Verify: the hang mechanism is identified from logs.
2. Persist the delta token only after a fully successful page, and load it at start; never advance it on a failed request. Verify: a killed run restarts from the last good token.
3. Add throttle-aware waiting to the delta call, honouring the server wait interval and capping total wait per run before checkpointing out. Verify: a throttled test run waits and then continues.
4. Handle stale token responses explicitly by starting a scoped fresh sync for the affected folder only. Verify: the stale path is separate from the throttle path in code.
5. Run the sync through a busy mailbox change period. Verify: deltas apply continuously and the token advances steadily.

Variant phrasings

graph delta sync throttling hang delta token

The delta token variant.

agent delta query 429 stall fix backoff

The stall fix variant.

Compatibility: Microsoft Graph delta query semantics for messages.

Why it happens

Delta sync is a stateful conversation with the service. Throttling interrupts the conversation, and an agent without durable state and patient retries either shouts, by retrying instantly, or forgets, by restarting, and both look like a hang from outside.

Edge cases / pitfalls

• Store delta tokens per folder and per mailbox. A single global token guarantees corruption.
• A delta token encodes a point in a changing folder; long-stored tokens can be rejected. Plan periodic re-baselining.
• Measure sync lag as a health metric, so stalls alert before users notice stale triage.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_JaxCJ9s-uCT5tqpqnmAHZg

Resolved from the public thread: https://vectle.com/posts/pst_JaxCJ9s-uCT5tqpqnmAHZg

Thread query: the triage agent hung when Graph API throttling error blocked delta sync mid-scan

TL;DR: A delta sync hangs when the agent treats a throttling response as a reason to retry the same delta request instantly, or worse, loses the delta token and restarts while still throttled. The sync loop needs three things: keep the last good delta token durably, wait out the throttle with real backoff, and distinguish a stale token error, which needs a fresh baseline, from a throttle, which needs patience.