foundationdb-transactions

name

foundationdb-transactions

description

How to correctly run transactions with the FoundationDB .NET client (FoundationDB.Client / SnowBank) — the ReadAsync/WriteAsync/ReadWriteAsync retry loop, idempotency, the 5-second limit, size limits, atomic mutations, snapshot reads, conflict ranges, and watches. Use whenever code opens a transaction, calls db.ReadAsync/WriteAsync/ReadWriteAsync, handles fdb retry/conflict errors, designs a read-modify-write, or worries about transaction conflicts and consistency. Pairs with the foundationdb-keys-and-layers skill.

FoundationDB .NET — Transactions & the Retry Loop

FoundationDB gives you serializable, ACID transactions over the whole keyspace. The catch: a transaction may conflict and need to be retried, and it has hard limits (time and size). The .NET client handles retries for you via a retry loop — but only if you use it correctly. The single biggest source of bugs is writing a transaction handler that is not safe to run more than once.

If you are encoding keys/values inside the transaction, read the foundationdb-keys-and-layers skill too.

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1. Always use the retry loop

Don't manually BeginTransaction / CommitAsync in application code. Use the retryable methods on IFdbDatabase (or IFdbDatabaseProvider). Pick the narrowest one:

Method	Transaction type	Use for
db.ReadAsync(handler, ct)	IFdbReadOnlyTransaction	reads only; returns a result
db.WriteAsync(handler, ct)	IFdbTransaction	mutations that return nothing (the handler may still read — its transaction is a full read/write one)
db.ReadWriteAsync(handler, ct)	IFdbTransaction	mutations that must return a value out of the transaction

The split between WriteAsync and ReadWriteAsync is about the return value, not about whether you read. Both hand you a full IFdbTransaction. ReadWriteAsync has no "returns nothing" overload — if your handler returns no value, use WriteAsync.

// READ
Book? book = await db.ReadAsync(async tr =>
{
    var bytes = await tr.GetAsync(subspace.Key("D", id));
    return bytes.IsNull ? null : CrystalJson.Deserialize<Book>(bytes);
}, ct);

// WRITE (no reads, nothing to return)
await db.WriteAsync(tr =>
{
    tr.Set(subspace.Key("D", book.Id), FdbValue.ToJson(book));
}, ct);

// READ-MODIFY-WRITE (need a result and/or read before write)
long newBalance = await db.ReadWriteAsync(async tr =>
{
    long current = (await tr.GetAsync(accountKey)).ToInt64();
    long updated = current + amount;
    tr.Set(accountKey, FdbValue.ToFixed64LittleEndian(updated));
    return updated;
}, ct);

The retry loop commits for you (you never call CommitAsync inside the handler) and re-runs the handler on retryable errors until it succeeds, the CancellationToken fires, or a non-retryable error is thrown.

There is a state overload (db.ReadAsync(state, (tr, state) => …, ct)) that lets you pass captured data without allocating a closure — prefer it in hot paths.

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2. THE rule: your handler must be idempotent

The handler lambda can and will run multiple times. Treat it as a pure function of the database state.

❌ Never mutate external/global state inside the handler. No incrementing in-memory counters, no adding to caches/lists, no logging "done", no sending messages, no static field writes. On a retry, those side effects happen again — but the earlier attempt's database writes were discarded.

✅ Do all such work after the loop returns successfully:

// WRONG — _cache is mutated even on attempts that never commit
await db.WriteAsync(tr => { tr.Set(k, v); _cache[id] = book; }, ct);

// RIGHT — only touch external state after the transaction has committed
await db.WriteAsync(tr => tr.Set(k, v), ct);
_cache[id] = book;

The handler may read whatever it needs from the transaction; it just must not affect anything outside it. (See also the success callback overloads, which run once after a successful commit.)

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3. Hard limits you must design around

Limit	Value	Consequence
Transaction lifetime	5 seconds	Long reads/range scans fail with past_version (error 1007). Don't iterate huge ranges in one tx.
Value size	100,000 bytes	Split large blobs across keys (see FdbBlob).
Key size	10,000 bytes	Keep tuple keys reasonable.
Total writes per tx	10,000,000 bytes	Batch large imports across many transactions.

For bulk operations that exceed these, use the Fdb.Bulk.* helpers (import/export/batch) instead of one giant transaction, and the FdbKey.Batched(...) helpers to split index ranges into chunks.

A range scan that might be large should be paged across transactions (resume from the last key's Successor()), not run as one 5-second read.

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4. Conflicts & how to avoid them

A read-write transaction conflicts if another transaction commits a write to a key this transaction read, between this transaction's read version and commit. The retry loop hides the retry, but conflicts cost latency. To reduce them:

• Use atomic mutations instead of read-modify-write where possible — they don't create read conflicts:

  tr.AtomicAdd64(counterKey, +1);          // value stored as fixed little-endian 64-bit
  tr.AtomicIncrement64(counterKey);
  tr.AtomicDecrement64(counterKey, clearIfZero: true);
  tr.AtomicMax(key, v); tr.AtomicMin(key, v);
  tr.AtomicAnd/Or/Xor(key, mask);
  

  (Counters stored for atomic add must be fixed-width little-endian: FdbValue.ToFixed64LittleEndian / Slice.FromFixed64.)

• Snapshot reads (tr.Snapshot.GetAsync(...), tr.Snapshot.GetRange(...)) read without creating a read-conflict on those keys. Use them when a stale read is acceptable (e.g. counting shards, statistics). Don't use snapshot reads for values you then use to compute a write that needs consistency.

• Sharding for write-hot keys: a single frequently-incremented key serializes all writers. Spread writes across random sub-keys and sum on read — exactly what FdbHighContentionCounter does.

• You can add explicit conflict ranges with tr.AddConflictRange(begin, end, FdbConflictRangeType.Read|Write) when you need conflict behavior that differs from what your reads/writes imply (advanced).

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5. Watches — reacting to changes

tr.Watch(key, ct) returns an FdbWatch that completes when the key's value changes after the transaction commits. Use it for change notification without polling. Create the watch inside a transaction (the handler is async; there is no synchronous return overload):

FdbWatch watch = await db.ReadWriteAsync(
    async tr => tr.Watch(signalKey, ct),   // optionally read/set first, then return the watch
    ct);

await watch;   // resolves when signalKey's value changes after this tx commits

• ⚠️ Pass an application/outer CancellationToken to Watch — not the transaction's own tr.Cancellation. The watch outlives the transaction, so binding it to the transaction's token is rejected.
• A watch only notifies that the key changed — it does not deliver the new value. When it fires you must re-read.
• Watches are limited in number per database and should be used for low-frequency signals, not high-throughput streaming.

The signal-key + watch pattern (producer/consumer)

This is how real layers (e.g. a pub/sub firehose) push work between nodes without polling:

• Producer, in the same transaction that writes the data, bumps a single "signal" key the consumer watches: tr.AtomicIncrement32(subscriber.Key("WATCH")). AtomicIncrement guarantees the value changes (so the watch always fires) and never conflicts with other producers.
• Consumer loops: read a batch; if empty, return a watch on the signal key, await it outside the transaction, then loop and re-read.

while (!ct.IsCancellationRequested)
{
    var (batch, watch) = await db.ReadWriteAsync(async tr =>
    {
        var sub = await location.Resolve(tr);
        // snapshot read: scanning the queue shouldn't conflict with producers
        var msgs = await tr.Snapshot.GetRangeAsync(sub.Key("INBOX").ToRange(), FdbRangeOptions.WantAll.WithLimit(100));
        if (msgs.Count == 0)
            return ((FdbRangeChunk?) null, (FdbWatch?) tr.Watch(sub.Key("WATCH"), ct));  // outer token!
        tr.ClearRange(msgs.First, FdbKey.Successor(msgs.Last));   // consume exactly what we read
        return (msgs, (FdbWatch?) null);
    }, ct);

    if (watch != null) { await watch; continue; }   // notified -> loop, re-read
    // dispatch batch...
}

Order messages with commit-time VersionStamps so they sort in publish order across all producers: var stamp = tr.CreateVersionStamp(i); tr.SetVersionStampedKey(inbox.Key(stamp), payload); (see the keys/layers skill).

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6. Layers inside transactions

A Layer resolves its per-transaction State inside the handler and uses it there (see foundationdb-keys-and-layers). Two equivalent styles:

// (a) Layer helper methods resolve State for you:
await store.WriteAsync(db, (tr, state) => state.Insert(tr, book), ct);

// (b) Resolve manually to compose several layers atomically in one tx:
await db.WriteAsync(async tr =>
{
    var books = await bookStore.Resolve(tr);
    var index = await authorIndex.Resolve(tr);
    books.Insert(tr, book);
    index.Add(tr, book.Author, book.Id);
}, ct);

Composing layers in one transaction is the core advantage: all writes commit together or not at all.

The resolved State is valid only for the transaction that produced it. Because the handler may retry, never hoist Resolve(tr) out of the loop or stash the State in a field.

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7. Errors

• FdbException carries an FdbError code. Retryable codes (conflicts, past_version, etc.) are handled by the retry loop automatically — don't catch and swallow them inside the handler.
• A not_committed (1020) conflict is normal under contention; it's retried for you.
• Throwing your own exception out of the handler aborts the transaction and propagates (no commit, no retry). Use this for genuine application errors.
• Don't catch FdbException inside the handler just to retry manually — that fights the loop.

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8. Quick self-check before committing transaction code

• Am I using db.ReadAsync/WriteAsync/ReadWriteAsync (not manual begin/commit)?
• Is the handler free of external side effects (caches, counters, logging, messaging)? Side effects after the loop only.
• Did I avoid await-ing unrelated long work inside the handler (5-second budget)?
• For counters/aggregates, am I using atomic ops (and fixed-LE values) instead of read-modify-write?
• Are large ranges paged across transactions, and large values chunked?
• Are resolved layer States confined to the handler?