# arriven/db1000n

Repository: https://github.com/arriven/db1000n
Canonical: https://ross.abutalabs.com/products/db1000n
Language: Go
License: MIT
License Family: permissive
Last push: 2024-02-24T16:48:25+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1649, "days_push": 921, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1148, forks 192 (observed 2026-08-28T04:03:46.093011+00:00)

## What it is
db1000n is a simple distributed load generation tool written in Go that fetches a JSON config from a local or remote location and runs the described load generation jobs in parallel. It was created as an open-source alternative to existing load generation tools.

## Use cases
- generate load against a target for load testing
- run distributed load generation jobs from a JSON config
- stress test my own web service
- fetch remote config and launch parallel load jobs
- open source alternative to commercial load generation tools

## When to choose
- you need a simple, config-driven distributed load generator
- you want a lightweight open-source Go tool for load testing
- you need to run many parallel load jobs from a shared remote config

## When to avoid
- you need detailed metrics, reporting, or realistic user simulation from mature load testing tools like k6 or Locust
- you cannot legally or ethically direct load at the target system
- you need a GUI or managed cloud load testing service

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: load-testing, http-client, cli
- domain: developer-tools, testing, networking
- platform: windows, go, cli
- tags: load-generation, distributed-load-testing, stress-testing, config-driven, linux, macos, docker

## Member repositories
- arriven/db1000n (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.093011+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:33:48.955151+00:00, confidence not recorded.
  - readme: https://github.com/arriven/db1000n (fetched 2026-08-28T04:03:46.093011+00:00, sha f24f766b5821)
- Data as of 2026-08-30T08:39:29.467469+00:00.