Noisia

Harmful workload generator for PostgreSQL.

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Supported workloads:

• idle transactions - active transactions on hot-write tables that do nothing during their lifetime.
• rollbacks - fake invalid queries that generate errors and increase rollbacks counter.
• waiting transactions - transactions that lock hot-write tables and then idle, leading to other transactions getting stuck
• deadlocks - simultaneous transactions where each holds locks that the other transactions want.
• temporary files - queries that produce on-disk temporary files due to lack of work_mem.
• terminate backends - terminate random backends (or queries) using pg_terminate_backend(), pg_cancel_backend().
• failed connections - exhaust all available connections (other clients unable to connect to Postgres).
• fork connections - execute single, short query in a dedicated connection (lead to excessive forking of Postgres backends).
• backend-killer - single session leaks prepared statements (plan-cache growth) inflating its backend's memory until OOM-kill restarts the whole instance; a very large --backend-killer.plan-size makes each PREPARE heavy/slow.
• slot-bloat - a single un-consumed physical replication slot pins WAL so pg_wal grows without bound → disk full → instance PANIC; the data never grows and checkpoints keep running, yet the disk still fills.
• wal-flood - many parallel UPDATE-churn workers (--jobs) flood WAL on the primary by raw write rate, driving replication lag and — when recycle/archiving can't keep up — pg_wal growth toward disk-full; the visible-activity counterpart of slot-bloat (disk-full here is environment-dependent, not guaranteed).
• ...see built-in help for more runtime options.

Disclaimer

ATTENTION: USE ONLY FOR TESTING PURPOSES, DO NOT EXECUTE NOISIA WITHOUT COMPLETE UNDERSTANDING WHAT YOU REALLY DO, RECKLESS USAGE WILL CAUSE PROBLEMS.

DISCLAIMER: THIS SOFTWARE PROVIDED AS-IS WITH NO CARES AND GUARANTEES RELATED TO YOUR DATABASES. USE AT YOUR OWN RISK.

Installation and usage

Check out releases page.

Using Docker

docker pull lesovsky/noisia:latest
docker run --rm -ti lesovsky/noisia:latest noisia --help

Using in your own code

You can import noisia and use necessary workloads in your code. Always use contexts to avoid infinite run. See tiny example below:

package main

import (
	"context"
	"fmt"
	"github.com/lesovsky/noisia/waitxacts"
	"github.com/rs/zerolog"
	"log"
	"os"
	"time"
)

func main() {
	config := waitxacts.Config{
		Conninfo:    "host=127.0.0.1",
		Jobs:        2,
		LocktimeMin: 5*time.Second,
		LocktimeMax: 20*time.Second,
	}

	logger := zerolog.New(zerolog.ConsoleWriter{Out: os.Stdout, TimeFormat: time.RFC3339}).Level(zerolog.InfoLevel).With().Timestamp().Logger()

	ctx, cancel := context.WithTimeout(context.Background(), 4*time.Second)
	defer cancel()

	w, err := waitxacts.NewWorkload(config, logger)
	if err != nil {
		log.Panicln(err)
	}
	
	err = w.Run(ctx)
	if err != nil {
		fmt.Println(err)
	}
}

Workload impact

Running workloads could impact already running workloads produced by other applications. This impact might be expressed as performance degradation, transactions getting stuck, cancelled queries, disconnected clients, etc.

Workload	Impact?
backendkiller	Yes: a single session grows backend RSS until OOM-kill and full instance restart; a very large plan-size makes each PREPARE heavy/slow
deadlocks	No
failconns	Yes: exhaust max_connections limit; this leads to other clients are unable to connect to Postgres
forkconns	Yes: excessive creation of Postgres child processes; potentially might lead to max_connections exhaustion
idlexacts	Yes: might lead to tables and indexes bloat
rollbacks	No
slotbloat	Yes: an un-consumed replication slot pins WAL; pg_wal grows until the filesystem is full, the instance can no longer write and PANICs
tempfiles	Yes: might increase storage utilization and degrade storage performance
terminate	Yes: already established database connections could be terminated accidentally
waitxacts	Yes: locks heavy-write tables; this leads to blocking concurrently executed queries
walflood	Yes: high-rate WAL generation drives replication lag and IO pressure; in a constrained environment pg_wal grows toward disk-full and the instance PANICs

Demo & tuning guides

The escalating workloads each have a dedicated demo and tuning guide covering how to build a reliable stand, tune the pressure, read the self-report, and recover afterwards:

• docs/workloads/backend-killer.md — drive a single backend to OOM and an instance restart; cap memory, disable swap, and turn up the plan pressure.
• docs/workloads/slot-bloat.md — fill pg_wal with one forgotten replication slot until the disk is full; CLI flags, two stand recipes, and slot-crash recovery.
• docs/workloads/wal-flood.md — flood WAL with parallel UPDATE-churn workers; the honest env-dependent contract, the disk-full conditions, demo parameters, and how it differs from slot-bloat.

Contribution

• PR's are welcome.
• Ideas could be proposed here
• About grammar issues or typos let me know here.

License

BSD-3. See LICENSE for more details.