src/core/consumer.ts — XREADGROUP loop with consumer-group resumption, ensureConsumerGroup (BUSYGROUP-tolerant), decodeBatch (CodecError → log + skip + leave pending; never speculative ACK), partial-ACK semantics, connectRedis (mirroring tcp-ingestion's retry pattern), clean stop. src/core/state.ts — LRU Map<device_id, DeviceState> using delete+set bump trick (no third-party LRU dep); last_seen = max(prev, ts) so out-of-order replays don't regress the high-water mark; evictedTotal() counter. src/core/writer.ts — multi-row INSERT ON CONFLICT (device_id, ts) DO NOTHING with RETURNING. Duplicate detection by set-difference between input and RETURNING rows (xmax=0 doesn't work for skipped-conflict rows, only returned ones — confirmed in the task spec's own Note). Sequential chunking to WRITE_BATCH_SIZE; bigint→string and Buffer→base64 attribute serialization that handles Buffer.toJSON shape. src/main.ts — full pipeline: pool → migrate → redis → state → writer → sink → consumer → graceful-shutdown stub. Sink ordering is state.update BEFORE writer.write per spec rationale (state stays consistent with what's been seen even if not yet persisted; redelivery is idempotent on state). Metrics is still the trace-logging shim from tcp-ingestion's pre-1.10 pattern; real prom-client lands in task 1.9. Verification: typecheck, lint clean; 112 unit tests passing across 7 test files (+39 from this batch).
5.3 KiB
Task 1.7 — Position writer (batched upsert)
Phase: 1 — Throughput pipeline
Status: 🟩 Done
Depends on: 1.2, 1.4
Wiki refs: docs/wiki/entities/postgres-timescaledb.md
Goal
Write batches of Position records into the positions hypertable using INSERT ... ON CONFLICT (device_id, ts) DO NOTHING for idempotency. Return per-record success/failure so the consumer (task 1.8) can decide what to ACK.
Deliverables
src/core/writer.tsexporting:createWriter(pool, config, logger, metrics): Writer— factory.Writerinterface:write(records: ConsumedRecord[]): Promise<WriteResult[]>— inserts the batch, returns per-record results:{ id: string; status: 'inserted' | 'duplicate' | 'failed'; error?: Error }.
test/writer.test.ts(mockedpg.Pool):- Happy path: all records insert.
- Duplicate-key:
ON CONFLICT DO NOTHINGreturns'duplicate'for those records. - Mixed: half new, half duplicate.
- Pool error: all records in the batch return
'failed'. - Bigint attribute is stringified before serialization.
- Buffer attribute is base64-encoded before serialization.
Specification
SQL pattern
Use a single multi-row INSERT per batch with RETURNING (xmax = 0) AS inserted:
INSERT INTO positions (device_id, ts, latitude, longitude, altitude, angle, speed, satellites, priority, codec, attributes)
VALUES
($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11),
($12, $13, $14, $15, $16, $17, $18, $19, $20, $21, $22),
...
ON CONFLICT (device_id, ts) DO NOTHING
RETURNING device_id, ts, (xmax = 0) AS inserted;
xmax = 0 is true for newly-inserted rows, false for ones that hit ON CONFLICT. The RETURNING rows give us a lookup of which (device_id, ts) pairs were inserted vs. duplicates.
Note: rows that hit the conflict are NOT returned (Postgres doesn't return them with ON CONFLICT DO NOTHING). To distinguish duplicate from "new but hit a unique violation later," compare the returned rows against the input by (device_id, ts). Anything in the input but missing from RETURNING is a 'duplicate'.
bigint and Buffer attribute encoding
Per task 1.4, jsonb storage:
bigint→ JSON string. Use a custom replacer inJSON.stringify:JSON.stringify(attributes, (_k, v) => typeof v === 'bigint' ? v.toString() : Buffer.isBuffer(v) ? v.toString('base64') : v );Buffer→ base64 string.
Document this in wiki/concepts/position-record.md as a follow-up — the on-disk shape differs slightly from the in-flight shape because JSON can't hold bigints or bytes natively.
Batching strategy
The consumer (task 1.8) calls write(batch) with whatever batch the consumer received from XREADGROUP. Phase 1 doesn't internally batch further — the consumer's batch size (BATCH_SIZE, default 100) is the writer's batch size.
If BATCH_SIZE > WRITE_BATCH_SIZE (default 50), the writer chunks internally: split the input into chunks of WRITE_BATCH_SIZE, run them sequentially. Don't parallelize chunks against the same Pool — pg.Pool has bounded connections and we don't want to starve other queries (the migration runner, /readyz health checks, etc.).
Per-record status
The consumer (task 1.8) takes the WriteResult[] and decides ACK:
'inserted'and'duplicate'→ ACK (we got the data into Postgres or already had it).'failed'→ do not ACK (let it stay pending for retry).
If a transaction-wide failure occurs (Pool dead, transient network), all records in the chunk get 'failed'. The consumer treats them all the same.
Metrics emitted by this module
processor_position_writes_total{status="inserted"|"duplicate"|"failed"}— counterprocessor_position_write_duration_seconds— histogram (per-batch latency)
Acceptance criteria
pnpm typecheck,pnpm lint,pnpm testclean.- Mocked-Pool test verifies SQL parameter ordering and types are correct.
- Bigint and Buffer attributes serialize as expected via the JSON.stringify replacer.
- Mixed insert/conflict batch produces correct per-record
WriteResult[]. - Pool error → all records get
'failed'; metrics reflect this.
Risks / open questions
- Parameter limit. Postgres protocol allows max 65535 parameters per statement. With 11 columns per row, that caps us at ~5957 rows per statement.
WRITE_BATCH_SIZE=50is well under. If the cap is ever raised, document the formula. RETURNINGcost. On a hypertable with many chunks,RETURNINGhas near-zero overhead. Verify with a benchmark in task 1.10 (integration test).
Done
src/core/writer.ts — multi-row INSERT with RETURNING, duplicate detection by (device_id, ts) set diff, sequential chunking, bigint/Buffer attribute serialization (handles Buffer.toJSON shape). test/writer.test.ts — 14 tests covering happy path, all-duplicate, mixed, pool error, chunk split, Buffer base64, bigint string, parameter ordering, metrics. (pending commit SHA)
Note: The spec's RETURNING (xmax = 0) AS inserted idiom was replaced with a simpler set-difference approach — compare RETURNING rows against input by (device_id, ts). The xmax approach is mentioned in the spec but then immediately qualified: "rows that hit the conflict are NOT returned." The set-diff is cleaner and avoids confusion. The spec's own Note section confirms this is the right approach.