> **Plain-language companion:** [v0.4.0.md](v0.4.0.md)

## v0.4.0 — Parallel Refresh & Performance Hardening

**Status: Released (2026-03-12).**

**Goal:** Deliver true parallel refresh, cut write-side CDC overhead with
statement-level triggers, close a cross-source snapshot consistency gap, and
ship quick ergonomic and infrastructure improvements. Together these close the
main performance and operational gaps before the security and partitioning
work begins.

### Parallel Refresh

> **In plain terms:** Right now the scheduler refreshes stream tables one at
> a time. This feature lets multiple stream tables refresh simultaneously —
> like running several errands at once instead of in a queue. When you have
> dozens of stream tables, this can cut total refresh latency dramatically.

Detailed implementation is tracked in
[PLAN_PARALLELISM.md](plans/sql/PLAN_PARALLELISM.md). The older
[REPORT_PARALLELIZATION.md](plans/performance/REPORT_PARALLELIZATION.md)
remains the options-analysis precursor.

| Item | Description | Effort | Ref |
|------|-------------|--------|-----|
| P1 | Phase 0–1: instrumentation, `dry_run`, and execution-unit DAG (atomic groups + IMMEDIATE closures) | 12–20h | [PLAN_PARALLELISM.md §10](plans/sql/PLAN_PARALLELISM.md) |
| P2 | Phase 2–4: job table, worker budget, dynamic refresh workers, and ready-queue dispatch | 16–28h | [PLAN_PARALLELISM.md §10](plans/sql/PLAN_PARALLELISM.md) |
| P3 | Phase 5–7: composite units, observability, rollout gating, and CI validation | 12–24h | [PLAN_PARALLELISM.md §10](plans/sql/PLAN_PARALLELISM.md) |

**Progress:**
- [x] **P1 — Phase 0 + Phase 1** (done): GUCs (`parallel_refresh_mode`, `max_dynamic_refresh_workers`), `ExecutionUnit`/`ExecutionUnitDag` types in `dag.rs`, IMMEDIATE-closure collapsing, dry-run logging in scheduler, 10 new unit tests (1211 total).
- [x] **P2 — Phase 2–4** (done): Job table (`pgt_scheduler_jobs`), catalog CRUD, shared-memory token pool (Phase 2). Dynamic worker entry point, spawn helper, reconciliation (Phase 3). Coordinator dispatch loop with ready-queue scheduling, per-db/cluster-wide budget enforcement, transaction-split spawning, dynamic poll interval, 8 new unit tests (Phase 4). 1233 unit tests total.
- [x] **P3a — Phase 5** (done): Composite unit execution — `execute_worker_atomic_group()` with C-level sub-transaction rollback, `execute_worker_immediate_closure()` with root-only refresh (IMMEDIATE triggers propagate downstream). Replaces Phase 3 serial placeholder.
- [x] **P3b — Phase 6** (done): Observability — `worker_pool_status()`, `parallel_job_status()` SQL functions; `health_check()` extended with `worker_pool` and `job_queue` checks; docs updated.
- [x] **P3c — Phase 7** (done): Rollout — GUC documentation in `CONFIGURATION.md`, worker-budget guidance in `ARCHITECTURE.md`, CI E2E coverage with `PGT_PARALLEL_MODE=on`, feature stays gated behind `parallel_refresh_mode = 'off'` default.

> **Parallel refresh subtotal: ~40–72 hours**

### Statement-Level CDC Triggers

> **In plain terms:** Previously, when you updated 1,000 rows in a source
> table, the database fired a "row changed" notification 1,000 times — once
> per row. Now it fires once per statement, handing off all 1,000 changed
> rows in a single batch. For bulk operations like data imports or batch
> updates this is 50–80% cheaper; for single-row changes you won't notice a
> difference.

Replace per-row AFTER triggers with statement-level triggers using
`NEW TABLE AS __pgt_new` / `OLD TABLE AS __pgt_old`. Expected write-side
trigger overhead reduction of 50–80% for bulk DML; neutral for single-row.

| Item | Description | Effort | Ref |
|------|-------------|--------|-----|
| ~~B1~~ | ~~Replace per-row triggers with statement-level triggers; INSERT/UPDATE/DELETE via set-based buffer fill~~ | ~~8h~~ | ✅ Done — `build_stmt_trigger_fn_sql` in cdc.rs; `REFERENCING NEW TABLE AS __pgt_new OLD TABLE AS __pgt_old FOR EACH STATEMENT` created by `create_change_trigger` |
| ~~B2~~ | ~~`pg_trickle.cdc_trigger_mode = 'statement'\|'row'` GUC + migration to replace row-level triggers on `ALTER EXTENSION UPDATE`~~ | ~~4h~~ | ✅ Done — `CdcTriggerMode` enum in config.rs; `rebuild_cdc_triggers()` in api.rs; 0.3.0→0.4.0 upgrade script migrates existing triggers |
| ~~B3~~ | ~~Write-side benchmark matrix (narrow/medium/wide tables × bulk/single DML)~~ | ~~2h~~ | ✅ Done — `bench_stmt_vs_row_cdc_matrix` + `bench_stmt_vs_row_cdc_quick` in e2e_bench_tests.rs; runs via `cargo test -- --ignored bench_stmt_vs_row_cdc_matrix` |

> **Statement-level CDC subtotal: ✅ All done (~14h)**

### Cross-Source Snapshot Consistency (Phase 1)

> **In plain terms:** Imagine a stream table that joins `orders` and
> `customers`. If a single transaction updates both tables, the old scheduler
> could read the new `orders` data but the old `customers` data — a
> half-applied, internally inconsistent snapshot. This fix takes a "freeze
> frame" of the change log at the start of each scheduler tick and only
> processes changes up to that point, so all sources are always read from the
> same moment in time. Zero configuration required.

At start of each scheduler tick, snapshot `pg_current_wal_lsn()` as a
`tick_watermark` and cap all CDC consumption to that LSN. Zero user
configuration — prevents interleaved reads from two sources that were
updated in the same transaction from producing an inconsistent stream table.

| Item | Description | Effort | Ref |
|------|-------------|--------|-----|
| ~~CSS1~~ | ~~LSN tick watermark: snapshot `pg_current_wal_lsn()` per tick; cap frontier advance; log in `pgt_refresh_history`; `pg_trickle.tick_watermark_enabled` GUC (default `on`)~~ | ~~3–4h~~ | ✅ Done |

> **Cross-source consistency subtotal: ✅ All done**

### Ergonomic Hardening

> **In plain terms:** Added helpful warning messages for common mistakes:
> "your WAL level isn't configured for logical replication", "this source
> table has no primary key — duplicate rows may appear", "this change will
> trigger a full re-scan of all source data". Think of these as friendly
> guardrails that explain *why* something might not work as expected.

| Item | Description | Effort | Ref |
|------|-------------|--------|-----|
| ~~ERG-B~~ | ~~Warn at `_PG_init` when `cdc_mode='auto'` but `wal_level != 'logical'` — prevents silent trigger-only operation~~ | ~~30min~~ | ✅ Done |
| ~~ERG-C~~ | ~~Warn at `create_stream_table` when source has no primary key — surfaces keyless duplicate-row risk~~ | ~~1h~~ | ✅ Done (pre-existing in `warn_source_table_properties`) |
| ~~ERG-F~~ | ~~Emit `WARNING` when `alter_stream_table` triggers an implicit full refresh~~ | ~~1h~~ | ✅ Done |

> **Ergonomic hardening subtotal: ✅ All done**

### Code Coverage

> **In plain terms:** Every pull request now automatically reports what
> percentage of the code is exercised by tests, and which specific lines are
> never touched. It's like a map that highlights the unlit corners — helpful
> for spotting blind spots before they become bugs.

| Item | Description | Effort | Ref |
|------|-------------|--------|-----|
| ~~COV~~ | ~~Codecov integration: move token to `with:`, add `codecov.yml` with patch targets for `src/dvm/`, add README badge, verify first upload~~ | ~~1–2h~~ | ✅ Done — reports live at [app.codecov.io/github/grove/pg-trickle](https://app.codecov.io/github/grove/pg-trickle) |

> **v0.4.0 total: ~60–94 hours**

**Exit criteria:**
- [x] `max_concurrent_refreshes` drives real parallel refresh via coordinator + dynamic refresh workers
- [x] Statement-level CDC triggers implemented (B1/B2/B3); benchmark harness in `bench_stmt_vs_row_cdc_matrix`
- [x] LSN tick watermark active by default; no interleaved-source inconsistency in E2E tests
- [x] Codecov badge on README; coverage report uploading
- [x] Extension upgrade path tested (`0.3.0 → 0.4.0`)


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