docs: bench methodology + tooling under benchmark/

benchmark/HARDWARE.md

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+# Hardware — VM sizing and microbench baselines
+
+## VM: AWS i4i.2xlarge (us-east-2)
+
+| Spec | Value |
+|---|---|
+| vCPU | 8 (Intel Ice Lake Xeon 8375C, 2.9 GHz base / 3.5 GHz turbo) |
+| RAM | 64 GiB |
+| NVMe instance store | 1 × 1.75 TiB (physical attach, NVMe) |
+| Network | Up to 12 Gbps |
+| EBS (root) | 8 GiB gp3 (Ubuntu 24.04 AMI default) |
+| Spot price (us-east-2, 2026-04) | ~$0.20–0.30 / hour |
+| On-demand price | $0.686 / hour |
+
+See [OPS_GOTCHAS.md §1](OPS_GOTCHAS.md) — the NVMe instance store is **not** auto-mounted on Ubuntu 24.04 boot.
+
+## Expected microbench baselines
+
+Run via [tooling/microbench.sh](tooling/microbench.sh). Expected order-of-magnitude numbers:
+
+| Probe | Expected |
+|---|---|
+| sysbench cpu (1-thread events/sec) | ~25 k |
+| sysbench memory bandwidth (1-thread) | ~15 GiB/sec |
+| fio 4 k randwrite, QD=32, direct=1, on NVMe | ~300 k IOPS |
+| fio 4 k randwrite, bandwidth | ~1.2 GiB/sec |
+| fio 4 k randwrite, 99 p latency | ~100 µs |
+| fio 4 k randread, QD=32, on NVMe | ~400 k IOPS |
+
+*Actual numbers to be filled in after running [tooling/microbench.sh](tooling/microbench.sh) on a fresh VM.*
+
+## Postgres tuning (shared across all 7 VMs)
+
+Applied by each VM's bootstrap (see `install/install_*.sh`):
+
+```
+shared_preload_libraries = 'pg_stat_statements,pg_cron'
+cron.database_name = 'bench'
+
+shared_buffers = 4GB
+effective_cache_size = 12GB
+
+synchronous_commit = off
+wal_level = minimal
+wal_compression = lz4
+max_wal_size = 16GB
+checkpoint_completion_target = 0.9
+
+bgwriter_delay = 50ms
+bgwriter_lru_maxpages = 400
+bgwriter_lru_multiplier = 4.0
+
+random_page_cost = 1.1
+effective_io_concurrency = 200
+max_connections = 200
+
+max_wal_senders = 0
+
+autovacuum_vacuum_scale_factor  = 0.01
+autovacuum_analyze_scale_factor = 0.01
+autovacuum_vacuum_cost_delay    = 2ms
+
+jit = off
+listen_addresses = 'localhost'
+```
+
+`synchronous_commit=off` is deliberate — queue workloads are almost always idempotent at the application layer, and the WAL-flush path is the dominant cost for low-latency producers. It's the only PG knob we touch that materially changes safety posture.
+
+`jit=off` because 5-s JIT warmups on `DO` blocks dominated first-transaction latency in early bench runs.
+
+`autovacuum_*_scale_factor=0.01` is aggressive on purpose — we want autovacuum attempting to clean every 1 % dead-tuple ratio so the held-xmin phase of the bench exposes the *inability* to vacuum, not a lazy autovacuum schedule.

benchmark/METHODOLOGY.md

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+# Bench methodology — the definitive reference
+
+This document is the single source of truth for how the PostgreSQL-queue bench is structured, what it measures, and every script/config that makes it work. It is written so a reviewer can reproduce the whole thing end-to-end.
+
+Cross-links:
+
+- Upstream fix: [pgque PR #62](https://github.com/NikolayS/pgque/pull/62) · issue [NikolayS/pgque#61](https://github.com/NikolayS/pgque/issues/61)
+
+---
+
+## 1. Goals and pathology
+
+We reproduce Brandur Leach's "Postgres queues" death spiral (see [Brandur's 2015 post](https://brandur.org/postgres-queues)): a DELETE-based work-queue becomes unvacuumably bloated the moment an unrelated backend holds `xmin` in the past (long transactions, logical-replication slots, stuck standby feedback). Dead tuples accumulate; bitmap / index scans must traverse them; latency explodes; throughput collapses even after the holder releases.
+
+Seven systems, each on its own VM:
+
+| System | Version | Pattern |
+|---|---|---|
+| **pgque** | patched via PR #62 | batch ticker + rotating event tables (TRUNCATE) |
+| pgq | v3.5.1 PL-only | same model as pgque; upstream baseline |
+| pgmq | v1.11.0 | single queue table, VISIBILITY + DELETE |
+| pgmq-partitioned | v1.11.0 + pg_partman | range-partitioned queue table |
+| river | v0.34 | SQL-level SKIP LOCKED + DELETE (consumer emulated) |
+| que | v2.4 | SKIP LOCKED + DELETE (consumer emulated) |
+| pg-boss | v12.15 | SKIP LOCKED + DELETE on partitioned `pgboss.job` |
+
+The Go/Ruby/Node workers are installed end-to-end so the schema is authentic, but the actual consumer load is driven via pgbench running each system's *SQL claim pattern*. This isolates the DB-side behaviour from runtime/GC artefacts.
+
+Workload shape (all runs):
+
+- Producer: 1 client, pgbench `-R 1000` rate-cap (full runs use `-R 5000`)
+- Consumer: 1 client for pgque/pgq, 4 clients for everything else
+- Three 30-minute phases back-to-back = **1.5 h per run**:
+  1. **Clean baseline** — no held xmin
+  2. **Held xmin** — `idle_in_tx.py` holds `REPEATABLE READ` open
+  3. **Clean recovery** — holder killed, observe regrowth / catchup
+
+---
+
+## 2. Infrastructure
+
+- AWS us-east-2, **i4i.2xlarge** (8 vCPU, 64 GiB, NVMe instance store)
+- Spot where available; on-demand only as last resort (see Section 10)
+- Ubuntu 24.04, **PG18 from PGDG**, `pg_cron`, `pg_stat_statements`, pg_ash, pgfr
+- Data dir moved to NVMe: `/mnt/pgdata/postgresql/18/main` symlink (see [runners/fix_nvme_mount.sh](runners/fix_nvme_mount.sh) and [OPS_GOTCHAS.md §1](OPS_GOTCHAS.md))
+- One VM per system so any tuning / runtime / GC behaviour is contained
+- SSH key: `<your-ssh-key>` (us-east-2)
+
+VM IPs use placeholder form: `<pgque-ip>`, `<pgq-ip>`, `<pgmq-ip>`, `<river-ip>`, `<pgboss-ip>`, `<que-ip>`, `<pgmq-partitioned-ip>`.
+
+---
+
+## 3. Observability stack
+
+Seven parallel streams run during every bench. All CSVs land in `/tmp/bench/` and are rsynced to the local `/tmp/bench/<system>/` tree every 30 min.
+
+### (a) bloat_sampler.py — pg_stat_user_tables every 30 s
+
+Per-system filter covers each queue's event/metadata tables (including pg-boss partitions and pgmq-partitioned's partman partitions). Writes `bloat.csv`.
+
+Source: [tooling/bloat_sampler.py](tooling/bloat_sampler.py).
+
+### (b) pg_ash — 1 Hz wait-event sampling
+
+pg_cron runs ash sampling at 1 Hz throughout; at end of bench we export:
+
+```sql
+COPY (SELECT sample_time, database_name, active_backends, wait_event, query_id
+      FROM ash.samples(p_interval => '2 hour'::interval, p_limit => 2000000))
+TO '/tmp/bench/ash.csv' CSV HEADER;
+```
+
+### (c) pgfr (pg-flight-recorder) — snapshot observability
+
+pgfr writes snapshots on a pg_cron schedule into `pgfr_record.*`. At end of run we export:
+
+```sql
+COPY (SELECT * FROM pgfr_record.snapshots)           TO '/tmp/bench/pgfr_snapshots.csv'           CSV HEADER;
+COPY (SELECT * FROM pgfr_record.table_snapshots)     TO '/tmp/bench/pgfr_table_snapshots.csv'     CSV HEADER;
+COPY (SELECT * FROM pgfr_record.statement_snapshots) TO '/tmp/bench/pgfr_statement_snapshots.csv' CSV HEADER;
+```
+
+Full pgfr_record schema on every VM includes: `snapshots`/`snapshots_v2` (partitioned), `table_snapshots(_v2)`, `statement_snapshots(_v2)`, `index_snapshots(_v2)`, `replication_snapshots(_v2)`, `vacuum_progress_snapshots(_v2)`, `activity_samples(_archive_v2)`, `lock_samples(_archive_v2)`, `config_snapshots`, `db_role_config_snapshots`.
+
+### (d) sys_metrics_sampler.py — CPU / mem / disk every 10 s
+
+Reads `/proc/stat`, `/proc/meminfo`, `/proc/diskstats` directly (psutil-optional). NVMe device is `nvme1n1` (the instance store, which is what `/mnt/pgdata` sits on). v2 adds per-device IOPS and latency columns.
+
+Source: [tooling/sys_metrics_sampler.py](tooling/sys_metrics_sampler.py).
+
+### (e) pg_stat_statements_snapshot.py — pgss time-series every 10 s
+
+Polls pgss with a regex filter over our queue-related query shapes, diffs consecutive snapshots downstream. Used as a cross-check for NOTICE-based ev/s (important for pgque/pgq/pgmq where DO-block wrappers hide per-statement rows).
+
+Source: [tooling/pg_stat_statements_snapshot.py](tooling/pg_stat_statements_snapshot.py).
+
+### (f) pgbench `--aggregate-interval=10 --log`
+
+Both producer and consumer run with per-10 s aggregate logs (min/max/sum/sumsq latency). Files: `producer_agg.<pid>` and `consumer_agg.<pid>.<worker>` under `/tmp/bench/`.
+
+### (g) Consumer NOTICE instrumentation
+
+Each instrumented `consumer.sql` is wrapped in a DO block that emits exactly one `RAISE NOTICE 'ev ts=<epoch_s> n=<events>'` per call. This gives us an authoritative per-call consumed-events stream that is immune to the pgss DO-wrapper opacity problem.
+
+The seven instrumented consumers are in [consumers/](consumers/). The parser for `consumer.log` NOTICE lines is [tooling/parse_events_consumed.py](tooling/parse_events_consumed.py).
+
+### (h) idle_in_tx.py — the death-spiral inducer
+
+Opens a `REPEATABLE READ` transaction, pins xmin, sleeps forever. Kill with SIGTERM to release the horizon. Source: [tooling/idle_in_tx.py](tooling/idle_in_tx.py).
+
+### (i) pgq_ticker_daemon.py — tight ticker loop for pgq
+
+pgq upstream has no built-in ticker daemon (the C one was always external). pgque runs inline; for a fair comparison we run a tight Python loop on the pgq VM that calls `pgq.ticker()` at 1 Hz and `pgq.maint_operations()` every 5 s. Source: [tooling/pgq_ticker_daemon.py](tooling/pgq_ticker_daemon.py).
+
+---
+
+## 4. Bench runner
+
+Orchestrates the phase scheduler. Forks producer, consumer (both pgbench), bloat_sampler, sys_metrics_sampler, pgss snapshotter; at t=1800 s opens `idle_in_tx`; runs `VACUUM VERBOSE` at phase boundaries; at t=3600 s kills the holder; another `VACUUM VERBOSE`; at t=5400 s harvests ash/pgfr/pgss.
+
+Source: [runners/run_r7.sh](runners/run_r7.sh).
+
+---
+
+## 5. Clean-slate reset
+
+Before every run we kill stragglers, DROP the system's schema/extensions, unschedule lingering `pg_cron` jobs, re-run `install.sh`, and reset all stats. The companion `full_reset.sql` calls `pg_stat_statements_reset()`, `pg_stat_reset()`, `pg_stat_reset_shared()` for bgwriter/checkpointer/wal/io, `TRUNCATE ash.sample`, and `DELETE FROM cron.job_run_details`.
+
+Source: [runners/clean_reinstall.sh](runners/clean_reinstall.sh). See also [OPS_GOTCHAS.md §4, §5, §7](OPS_GOTCHAS.md) for adjacent-schema pitfalls.
+
+---
+
+## 6. Per-system install scripts
+
+Each VM has its own `/tmp/install.sh` (mirrored into [install/](install/)). Pattern differs by system:
+
+- **pgque** — `git clone` the PR #62 branch, `make USE_PGXS=1 install`, run the SQL installer, `SELECT pgque.create_queue('bench_queue')`, schedule `pgque.ticker()` via `pg_cron` (also called inline in `consumer.sql`). Driven from AMI user-data; see [install/README.md](install/README.md) and [install/bootstrap.sh](install/bootstrap.sh).
+- **pgq** — PGDG `postgresql-18-pgq3` or `git clone --branch v3.5.1`, then `CREATE EXTENSION pgq` and immediately apply `switch_plonly.sql` to replace the C `insert_event_raw` with PL/pgSQL (pg_proc lang check `= 'plpgsql'` as a gate). See [install/install_pgq.sh](install/install_pgq.sh).
+- **pgmq** — PGDG `postgresql-18-pgmq` + `CREATE EXTENSION pgmq` + `pgmq.create('bench_queue')`. See [install/install_pgmq.sh](install/install_pgmq.sh).
+- **pgmq-partitioned** — pgmq + `pg_partman` + `pgmq.create_partitioned('bench_queue', 'id', '10000')`. Reuses [install/install_pgmq.sh](install/install_pgmq.sh) plus [install/pgmq-partitioned_setup_5min.sql](install/pgmq-partitioned_setup_5min.sql) for the partman schedule.
+- **river** — `go install github.com/riverqueue/river/cmd/river@v0.34.x` and `river migrate-up` (sets schema), then the consumer SQL emulates the SKIP-LOCKED claim pattern. See [install/install_river.sh](install/install_river.sh).
+- **que** — Ruby + `gem install que -v 2.4.x`, `bundle exec que:install` creates `que_jobs`; consumer SQL emulates the Ruby Que SELECT/DELETE. Driven from AMI user-data — see [install/README.md](install/README.md) and [OPS_GOTCHAS.md §7](OPS_GOTCHAS.md).
+- **pg-boss** — local `npm install pg-boss@12.15`, run `new PgBoss(DSN).start()` once to migrate schema; consumer SQL emulates the `SKIP LOCKED` claim on `pgboss.job`. See [install/install_pgboss.sh](install/install_pgboss.sh).
+
+---
+
+## 7. Per-system consumer.sql patterns
+
+All seven instrumented consumers are in [consumers/](consumers/):
+
+- [consumer_pgque.sql](consumers/consumer_pgque.sql) — ticker + `next_batch` + `get_batch_events` + `finish_batch`
+- [consumer_pgq.sql](consumers/consumer_pgq.sql) — identical, schema swap pgque → pgq
+- [consumer_pgmq.sql](consumers/consumer_pgmq.sql) — `pgmq.read(50)` + `pgmq.delete`
+- [consumer_pgmq-partitioned.sql](consumers/consumer_pgmq-partitioned.sql) — identical SQL to pgmq (schema hides partitioning)
+- [consumer_river.sql](consumers/consumer_river.sql) — SKIP LOCKED + DELETE on `river_job`
+- [consumer_que.sql](consumers/consumer_que.sql) — SKIP LOCKED + DELETE on `que_jobs`
+- [consumer_pgboss.sql](consumers/consumer_pgboss.sql) — SKIP LOCKED + DELETE on `pgboss.job`
+
+All producers are in [producers/](producers/).
+
+---
+
+## 8. Analysis and chart generation
+
+- [charts/r5_analyze.py](charts/r5_analyze.py) — full 2-panel chart (dead tuples + consumer latency, linear scale, no symlog).
+- [charts/r6_smoke_chart.py](charts/r6_smoke_chart.py) — smoke Solarized-Dark 2-panel chart (events/s + pgque per-table dead tuples).
+- [gifs/r4_gif_v17_solarized.py](gifs/r4_gif_v17_solarized.py) — dead-tuples animated GIF (7 systems, Solarized-Dark).
+- [gifs/r4_gif_tps_solarized.py](gifs/r4_gif_tps_solarized.py) — TPS/latency animated GIF.
+
+---
+
+## 9. Cost discipline
+
+- us-east-2 spot preferred (~$0.22/h); when spot is exhausted we hop to another region before going on-demand.
+- On-demand for the primary subject under test when data integrity matters most.
+- **~$15 per 1.5 h bench run** (7 VMs).
+- Rsync every 30 min pulls `/tmp/bench/` to local, so a spot reclaim loses at most a partial phase.
+
+See [OPS_GOTCHAS.md §12](OPS_GOTCHAS.md) for spot-reclaim mitigations.
+
+---
+
+## Reproducing from scratch
+
+1. Launch 7 × i4i.2xlarge in us-east-2 (spot first) with `bench_userdata.sh` (symlinks `/mnt/pgdata`, installs PGDG PG18, pg_cron, pg_ash, pgfr, pg_stat_statements). See [runners/fix_nvme_mount.sh](runners/fix_nvme_mount.sh) for the NVMe side if the userdata path breaks.
+2. `scp` per-system `install.sh`, `producer.sql`, instrumented `consumer.sql`, plus the samplers in [tooling/](tooling/).
+3. `bash clean_reinstall.sh <sys>` on each VM.
+4. `bash run_r7.sh <sys>` — 1.5 h, writes `/tmp/bench/*.csv` + `*.log`.
+5. Rsync everything back to `/tmp/bench/<sys>/`.
+6. Run [charts/r5_analyze.py](charts/r5_analyze.py) for the verdict table + 2-panel PNG.
+
+That is the whole rig.